diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 00000000..656c0044
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Run tests
+
+on:
+ push:
+ branches: [main, prod]
+ pull_request:
+ branches: [main, prod]
+
+jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ lfs: true
+
+ - uses: extractions/setup-just@v1
+
+ - name: Set up Python 3.11
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.11
+
+ - name: Install dependencies
+ run: |
+ just install
+
+ - name: Run checks
+ run: |
+ just check
+
+ - name: Run tests
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ run: |
+ just test
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..f6513a5d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,191 @@
+.vscode/
+.venv/
+.hermit/
+runs/
+outputs/
+wandb/
+
+artifacts/
+
+output_audio_processor/
+output_tokenizer/
+
+*.csv
+*.json
+epd_eval/
+.git/
+env_vars.sh
+sync_watch
+clearml.conf
+aml.md
+.DS_Store
+*.safetensors
+*.pt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+.vscode/
+
+.neptune/
+mds_output/
+mlruns/
+output/
+
diff --git a/Justfile b/Justfile
new file mode 100644
index 00000000..4e1b3a07
--- /dev/null
+++ b/Justfile
@@ -0,0 +1,67 @@
+export WANDB_PROJECT:="ultravox"
+export WANDB_LOG_MODEL:="checkpoint"
+export PROJECT_DIR:="ultravox"
+export VENV_NAME:="venv"
+export MCLOUD_CLUSTER:="r7z22"
+export MCLOUD_INSTANCE:="oci.bm.gpu.b4.8"
+
+default: format check test
+
+create-venv:
+ pip install --upgrade virtualenv # older virtualenv had some issues in Debian
+ python -m venv ${VENV_NAME}
+ just install
+
+install:
+ # Install torch 2.2.1 if needed, not present in requirements.txt
+ just python -c \"import torch\" 2>/dev/null || just pip install torch==2.2.1
+ just pip install -r requirements.txt
+ just pip install -r requirements-dev.txt
+ just python -m pip install types-requests
+
+format:
+ . ./activate ${VENV_NAME} && autoflake ${PROJECT_DIR} --remove-all-unused-imports --quiet --in-place -r --exclude third_party --exclude ultravox/model/gazelle
+ . ./activate ${VENV_NAME} && isort ${PROJECT_DIR} --force-single-line-imports
+ . ./activate ${VENV_NAME} && black ${PROJECT_DIR}
+
+check:
+ . ./activate ${VENV_NAME} && black ${PROJECT_DIR} --check
+ . ./activate ${VENV_NAME} && isort ${PROJECT_DIR} --check --force-single-line-imports
+ . ./activate ${VENV_NAME} && autoflake ${PROJECT_DIR} --check --quiet --remove-all-unused-imports -r --exclude third_party --exclude ultravox/model/gazelle
+ . ./activate ${VENV_NAME} && mypy ${PROJECT_DIR}
+
+test *ARGS=".":
+ . ./activate ${VENV_NAME} && cd ${PROJECT_DIR} && pytest --ignore third_party {{ARGS}}
+
+@python *FLAGS:
+ . ./activate ${VENV_NAME} && python {{FLAGS}}
+
+@pip *FLAGS:
+ . ./activate ${VENV_NAME} && pip {{FLAGS}}
+
+train *FLAGS:
+ just python -m ultravox.training.train {{FLAGS}}
+
+train_asr *FLAGS:
+ just train --config_path ultravox/training/configs/asr_tinyllama.yaml {{FLAGS}}
+
+browse *FLAGS:
+ just python -m ultravox.tools.data_tool {{FLAGS}}
+
+infer *FLAGS:
+ just python -m ultravox.tools.infer_tool {{FLAGS}}
+
+eval *FLAGS:
+ just python -m ultravox.tools.eval_tool {{FLAGS}}
+
+mds *FLAGS:
+ just python -m ultravox.tools.mds_tool {{FLAGS}}
+
+gradio *FLAGS:
+ just python -m ultravox.tools.gradio_demo {{FLAGS}}
+
+run *FLAGS:
+ mcli run -f mcloud.yaml --follow {{FLAGS}}
+
+mcloud *FLAGS:
+ mcli interactive {{FLAGS}} --cluster ${MCLOUD_CLUSTER} --instance ${MCLOUD_INSTANCE} --name `whoami` --command "bash -c \"$(cat setup.sh)\""
diff --git a/README.md b/README.md
new file mode 100644
index 00000000..05c36423
--- /dev/null
+++ b/README.md
@@ -0,0 +1,182 @@
+
+
+
+
+
+
+
+An open, fast, and extensible multimodal LLM
+
+
+# About
+
+Ultravox is a new kind of multimodal LLM that can understand text as well as human speech, without the need for a separate Audio Speech Recognition (ASR) stage. Building on research like [AudioLM](https://arxiv.org/abs/2209.03143), [SeamlessM4T](https://ai.meta.com/blog/seamless-m4t/), [Gazelle](https://tincans.ai/slm), [SpeechGPT](https://github.com/0nutation/SpeechGPT/tree/main/speechgpt), and others, we've extended Meta's [Llama 3 model](https://llama.meta.com/llama3/) with a multimodal projector that converts audio directly into the high-dimensional space used by Llama 3. This direct coupling allows Ultravox to respond much more quickly than systems that combine separate ASR and LLM components. In the future this will also allow Ultravox to natively understand the paralinguistic cues of timing and emotion that are omnipresent in human speech.
+
+The current version of Ultravox (v0.1), when invoked with audio content has a time-to-first-token (TTFT) of approximately 200ms, and a tokens-per-second rate of ~100, all using a Llama 3 8B backbone. While quite fast, we believe there is considerable room for improvement in these numbers.
+
+Ultravox currently takes in audio and emits speech. As we evolve the model, we'll train it to be able to emit a stream of speech tokens that can then be converted directly into raw audio by an appropriate unit vocoder. We're interested in working with interested parties to build this functionality!
+
+### Demo
+
+Coming soon!
+
+### Discord
+
+Join us on our Discord server [here](https://discord.gg/YhX5GjCH).
+
+### Inference Server
+
+You can try out Ultravox using your own audio content (as a WAV file), using the following curl command:
+
+```
+curl -X POST -H "Authorization: Bearer $ULTRAVOX_API_KEY" -d @data.json https://ultravox.api.fixie.ai/v1/chat/completions
+```
+
+where `data.json` contains:
+
+```
+{
+ "model": "fixie-ai/ultravox-v0.1",
+ "content": [
+ {
+ "type": "text",
+ "text": "What’s in <|audio|>?"
+ },
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": f"data:audio/wav;base64,{base64_wav}"
+ }
+ }
+ ],
+ "stream": true
+}
+```
+
+### Model
+
+You can download the latest weights from the [Ultravox Hugging Face page](https://huggingface.co/fixie-ai/ultravox).
+
+### Architecture
+
+https://docs.google.com/presentation/d/1ey81xuuMzrJaBwztb_Rq24Cit37GQokD2aAes_KkGVI/edit
+
+# Contributing
+
+Read on if you're interested in training your own version of Ultravox.
+
+## Environment Setup (Mac)
+
+Install the basic tools:
+
+- [`Homebrew`](https://brew.sh) is a package manager for MacOS that also mostly works for Linux. If you're running Debian or Ubuntu Linux, you can alternatively get by with apt.
+- [`Just`](https://just.systems/man/en/) simplifies our shell workflows. It frequently functions as our interface to all the other tools.
+
+```bash
+/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+brew update
+brew install just
+```
+
+Create a Python virtual environment and install the necessary packages:
+
+```bash
+just create-env
+```
+
+For now we're using venv for Python virtual environments.
+We may switch to `Poetry` in the future.
+
+### Mosaic Environment Setup
+
+You need to setup a few things to run on the Mosaic Platform.
+
+1. Install & login to the Mosaic CLI
+
+```bash
+pip install --upgrade mosaicml-cli
+
+mcli init
+
+mcli set api-key
+```
+
+2. set API keys for tools we use:
+
+```bash
+# Huggging Face token for accessing walled data and models
+mcli create secret env HF_TOKEN=hf_
+
+# WandB token for logging experiments
+mcli create secret env WANDB_PROJECT=ultravox
+mcli create secret env WANDB_API_KEY=
+
+# GCP credentials for accessing data (e.g. BoolQ)
+# Get service_account.json file from Justin/Farzad and put it in the root dir, then
+mcli create secret gcp
+```
+
+## Training
+
+```bash
+just train
+```
+
+For DDP training make sure to use:
+`torchrun --nproc_per_node=8 -m ultravox.training.train`
+
+### Local Training
+
+```bash
+python -m ultravox.training.train --config_path ultravox/training/configs/asr_tinyllama.yaml --data_set 'dummy' --device cpu --batch_size 1 --exp_name
+```
+
+### MosaicML Training
+
+You need to setup your SSH key in the Mosaic Platform: https://docs.mosaicml.com/projects/mcli/en/latest/resources/secrets/ssh.html#page-secrets-ssh
+
+```bash
+## Create a new SSH key and add it to the Mosaic Platform
+# ssh-keygen -f ~/.ssh/mclid_id_rsa
+## add the **public** key to Github
+# mcli create secret ssh ~/.ssh/mclid_id_rsa
+
+mcli run -f mcloud.yaml --follow
+```
+
+Other useful commands:
+
+```bash
+mcli get clusters
+
+mcli util r7z2
+mcli get runs
+mcli get runs --cluster r7z2
+
+mcli run -f mcloud.yaml --follow
+```
+
+For interactive runs, we don't recommend using `--interactive`. Instead set the `command` to be something like
+`sleep 3600` and then connect to it using `mcli connect --tmux`.
+This way your environment (code and packages) will be the same as the training environment.
+The value `3600` (1 hour), is used as an example.
+
+IMPORTANT: Make sure to stop the machine when you're done with any job, specially interactive ones!
+
+### Running evaluations
+
+1. Use `infer_tool.py --json > file` to create a jsonl output from a given model/dataset combo, where each line contains two values: **question** and **answer**.
+2. Use `eval_tool.py -f file` to evaluate the jsonl file, which will produce an average score for the model on the dataset.
+
+## Misc
+
+Useful commands:
+
+```bash
+just update # update dependencies
+just format # run formatting (black, isort, autoflake)
+just python # activate venv and run python
+just pip # install a package in the venv using the right pip
+```
+
+The `legacy` directory contains some initial experiments. We'll pull in the useful parts as we go.
diff --git a/activate b/activate
new file mode 100755
index 00000000..fe47bd14
--- /dev/null
+++ b/activate
@@ -0,0 +1,6 @@
+#!/bin/sh
+# If we are in CI, we want to use the existing venv due to disk space issues
+set +u
+if [ -z "${CI}" ]; then
+ . $1/bin/activate
+fi
diff --git a/docs/assets/Introducing Banner.svg b/docs/assets/Introducing Banner.svg
new file mode 100644
index 00000000..7fd44084
--- /dev/null
+++ b/docs/assets/Introducing Banner.svg
@@ -0,0 +1,1422 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ I n t r o d u c i n g :
+
+
\ No newline at end of file
diff --git a/docs/assets/UV Hero Image (1).png b/docs/assets/UV Hero Image (1).png
new file mode 100644
index 00000000..71a1724d
Binary files /dev/null and b/docs/assets/UV Hero Image (1).png differ
diff --git a/docs/assets/UV logo black.svg b/docs/assets/UV logo black.svg
new file mode 100644
index 00000000..1834a701
--- /dev/null
+++ b/docs/assets/UV logo black.svg
@@ -0,0 +1,26 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV logo color dark.svg b/docs/assets/UV logo color dark.svg
new file mode 100644
index 00000000..5bb5facf
--- /dev/null
+++ b/docs/assets/UV logo color dark.svg
@@ -0,0 +1,57 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV logo color light.svg b/docs/assets/UV logo color light.svg
new file mode 100644
index 00000000..45402924
--- /dev/null
+++ b/docs/assets/UV logo color light.svg
@@ -0,0 +1,47 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV logo white.svg b/docs/assets/UV logo white.svg
new file mode 100644
index 00000000..33d0e339
--- /dev/null
+++ b/docs/assets/UV logo white.svg
@@ -0,0 +1,26 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV stacked Black.svg b/docs/assets/UV stacked Black.svg
new file mode 100644
index 00000000..75a20841
--- /dev/null
+++ b/docs/assets/UV stacked Black.svg
@@ -0,0 +1,26 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV stacked color dark.svg b/docs/assets/UV stacked color dark.svg
new file mode 100644
index 00000000..94e9a6f2
--- /dev/null
+++ b/docs/assets/UV stacked color dark.svg
@@ -0,0 +1,57 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV stacked color light.svg b/docs/assets/UV stacked color light.svg
new file mode 100644
index 00000000..e5166215
--- /dev/null
+++ b/docs/assets/UV stacked color light.svg
@@ -0,0 +1,52 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/UV stacked white.svg b/docs/assets/UV stacked white.svg
new file mode 100644
index 00000000..e119e4da
--- /dev/null
+++ b/docs/assets/UV stacked white.svg
@@ -0,0 +1,26 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/assets/foo.txt b/docs/assets/foo.txt
new file mode 100644
index 00000000..257cc564
--- /dev/null
+++ b/docs/assets/foo.txt
@@ -0,0 +1 @@
+foo
diff --git a/legacy/Justfile b/legacy/Justfile
new file mode 100644
index 00000000..13ebd912
--- /dev/null
+++ b/legacy/Justfile
@@ -0,0 +1,25 @@
+export WANDB_PROJECT:="ultravox"
+export WANDB_LOG_MODEL:="checkpoint"
+export PROJECT_DIR:="ultravox"
+
+poetry *FLAGS:
+ cd ${PROJECT_DIR} && poetry {{FLAGS}}
+
+python *FLAGS:
+ cd ${PROJECT_DIR} && poetry run python {{FLAGS}}
+
+format:
+ cd ${PROJECT_DIR} && poetry run autoflake . --remove-all-unused-imports --quiet --in-place -r --exclude third_party
+ cd ${PROJECT_DIR} && poetry run isort . --force-single-line-imports
+ cd ${PROJECT_DIR} && poetry run black .
+
+check:
+ cd ${PROJECT_DIR} && poetry check
+ cd ${PROJECT_DIR} && poetry run black . --check
+ cd ${PROJECT_DIR} && poetry run isort . --check --force-single-line-imports
+ cd ${PROJECT_DIR} && poetry run autoflake . --check --quiet --remove-all-unused-imports -r --exclude third_party
+ cd ${PROJECT_DIR} && poetry run mypy .
+ cd ${PROJECT_DIR} && poetry run deptry .
+
+test *ARGS="--dist loadgroup -n auto .":
+ cd ${PROJECT_DIR} && poetry run pytest --ignore third_party {{ARGS}}
diff --git a/legacy/README.md b/legacy/README.md
new file mode 100644
index 00000000..4497d3d5
--- /dev/null
+++ b/legacy/README.md
@@ -0,0 +1,94 @@
+# UltraVox
+
+## AzureML
+
+### Installation and Config
+
+```bash
+brew update && brew install azure-cli
+az extension add --name ml --yes
+
+az login
+az account set --subscription 520aa0b2-6a19-4a45-8c03-4c301d1f847a
+az configure --defaults workspace=gpu-supercomput
+```
+
+```bash
+az ml job create -f ./azureml/configs/audiollm.yml --web
+```
+
+## Random Documentation
+
+### LLM + AudioEnc (ours) vs SpeechGPT
+
+```python
+# SpeechGPT adds new tokens to the embedding and then trains them
+nn.Embedding(32000, 2048) + nn.Embedding(4000, 2048) # old text tokens + new audio tokens
+nn.Embedding(36000, 2048)
+
+###
+# In other words:
+###
+
+# SpeechGPT tokenizes audio and text separately, then concatenates the embeddings
+llm(embed(concat(audio_tokenizer(audio), text_token)))
+## ------------------- vs -------------------
+# We create the audio embeddings directly from the audio and skip embedding the audio tokens
+llm(concat(audio_enc(audio) * weight, embed(text_token)))
+# This means we can easily propagate gradients to the audio encoder (i.e. train end to end)
+```
+
+### How does language modeling work with audio?
+
+```python
+# t[n] <- t[1..n-1]
+# The brown fox jumps over the fence
+
+# a1 a2 a3 a4 The brown fox jumps over the fence
+# samples:
+# a1 a2 a3 -> a4
+# a1 a2 a3 a4 -> The
+# a1 a2 a3 a4 The -> brown
+# a1 a2 a3 a4 The brown -> fox
+# a1 a2 a3 a4 The brown fox -> jumps
+# a1 a2 a3 a4 The brown fox jumps -> over
+# a1 a2 a3 a4 The brown fox jumps over -> the
+# a1 a2 a3 a4 The brown fox jumps over the -> fence
+```
+
+## TODO
+
+- [ ] generation metrics (low_pri: added more metrics to cover shifts)
+- [x] more metrics to cover shifts
+- [x] torchrun
+- [ ] shard dataset
+- [ ] datasets.distributed.split_dataset_by_node
+- [ ] cache preprocessed data
+- [ ] tokens (start&end)
+- [ ] loss for audio ending
+
+- [ ] combine multiple datasets (low priority for now)
+
+- [x] half precision
+- [x] LR ramp up
+- [x] loss mask
+- [x] get and and running with Azure
+- [x] bfloat16
+- [x] auth: HF, WANDB, ClearML
+- [x] store models
+- [x] loss: language
+- [x] LoRA adapted model
+ - [x] freeze for rank=0
+- [x] audio model stride: start with ~200ms
+- [x] audio stride: stack instead of skip
+- [x] multi-GPU
+- [ ] fix hyperparams
+- [ ] loss not going down: is it a real issue?
+- [ ] optimizations (Azure specific): deepspeed, nebula, onnx-runtime
+ - [ ] https://huggingface.co/docs/transformers/perf_train_gpu_one
+ - [ ] torch.compile
+ - [ ] (low) n_shards
+ - [ ] increase GPU utilization
+- [x] W2v-BERT-2 [not merged to HF]
+- [ ] is dataloading happening correctly? no same data?
+- [x] WER
diff --git a/legacy/audio_generation_time.md b/legacy/audio_generation_time.md
new file mode 100644
index 00000000..3e5860a7
--- /dev/null
+++ b/legacy/audio_generation_time.md
@@ -0,0 +1,35 @@
+Recognize this speech, this is input: prompt/gs_172_cropped.wav
+
+Input audio: gs_172_cropped.wav (14 seconds long)
+Total generation time (in seconds):
+
+# Whisper on Mac CPU
+
+tiny (40M) 0.36
+large-v2 (1.5B) 10.59
+============
+
+# Whisper on GPU
+
+tiny (40M) 0.23
+medium (0.8B) 1.02
+large-v2 (1.5B) 1.37
+
+---
+
+Cuda v12.3
+medium 0.88
+large-v2 1.17
+
+---
+
+SDP + Flash + Float16
+medium 0.72
+large-v2 0.94
+============
+
+# SpeechGPT (7B) on GPU 2.9
+
+SDP + Flash 2.0
+507 input tokens, 60 output tokens
+============
diff --git a/legacy/azureml/configs/audiollm.yml b/legacy/azureml/configs/audiollm.yml
new file mode 100644
index 00000000..7fdfb218
--- /dev/null
+++ b/legacy/azureml/configs/audiollm.yml
@@ -0,0 +1,29 @@
+code: ../../src/ultravox
+command: >-
+ bash runjob.sh train/config.yaml --output_dir outputs
+# bash runjob.sh train/config.yaml --output_dir ${{outputs.output_folder}}
+inputs:
+ lr: 0.1 # Just a placeholder, this is not used anywhere yet
+# outputs:
+# output_folder:
+# type: custom_model
+environment: azureml://registries/azureml/environments/acpt-pytorch-2.0-cuda11.7/versions/25 # or higher
+# compute: azureml:gpu-brrr # removing this to disable accidental runs
+display_name: audio-llm-test
+experiment_name: audio-llm-test
+description: Train an LLM with an audio encoder to understand human speech
+services:
+ my_vs_code:
+ type: vs_code
+ nodes: all # For distributed jobs, use the `nodes` property to pick which node you want to enable interactive services on. If `nodes` are not selected, by default, interactive applications are only enabled on the head node. Values are "all", or compute node index (for ex. "0", "1" etc.)
+ my_tensor_board:
+ type: tensor_board
+ log_dir: "outputs/logs" # relative path of Tensorboard logs (same as in your training script)
+ nodes: all
+ my_jupyter_lab:
+ type: jupyter_lab
+ nodes: all
+ my_ssh:
+ type: ssh
+ ssh_public_keys: "ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAILgKpvHUEV/TZ6c4P0pWm59q19o/pgecKl17m2D8Eaaj farzad@fixie.ai"
+ nodes: all
diff --git a/legacy/journal.md b/legacy/journal.md
new file mode 100644
index 00000000..7b8612d3
--- /dev/null
+++ b/legacy/journal.md
@@ -0,0 +1,89 @@
+# Training Journal
+
+# Jan - Feb 2024
+
+## W2V-BERT-2
+
+### [LS FT w/ WD 0.05](https://wandb.ai/fixie/ultravox/runs/dvquuhym)
+
+- first time training worked!
+ - 500x64BS + 100x64BS + worked at ~1.2Kx32BS -> ~2.4Kx32BS
+- train loss lags behind val loss (really high reg?)
+
+### [GS FT w/o WD](https://wandb.ai/fixie/ultravox/runs/9yyh0zd4)
+
+- overfitting: may need to add weight decay back
+
+### [GS FT w/ WD 0.01, constant LR 5e-5](https://wandb.ai/fixie/ultravox/runs/zdq723i5)
+
+- idea: find right WD and reduce overfitting
+
+## continuation exps
+
+To test:
+
+- WD
+- LS vs GS
+- speech tag or no?
+- batch size
+- LR
+- \*audio stride
+
+### [WD=0 Test](https://wandb.ai/fixie/ultravox/runs/huypf4ez)
+
+### [BS=16](https://wandb.ai/fixie/ultravox/runs/7g9c93ej)
+
+### [BS=4](https://wandb.ai/fixie/ultravox/runs/x97fpqrf)
+
+- the smaller the better?!
+
+### [GS](https://wandb.ai/fixie/ultravox/runs/bazab1jz)
+
+### [Speech Tag](https://wandb.ai/fixie/ultravox/runs/cia60j23)
+
+- no difference
+
+### [4xLR=2e-4](https://wandb.ai/fixie/ultravox/runs/5b9o6l5w)
+
+- too high
+
+### [.4LR=2e-5](https://wandb.ai/fixie/ultravox/runs/)
+
+- too low
+
+### [BS=2](https://wandb.ai/fixie/ultravox/runs/fy6beoy9)
+
+## Train on the fastest parameters
+
+### [Wav2Vec2Bert](https://wandb.ai/fixie/ultravox/runs/l44100jk)
+
+### [Wav2Vec2](https://wandb.ai/fixie/ultravox/runs/a1niw6mj)
+
+# Mar 18, 2024
+
+## EOU method #1
+
+### exp1: end shift left
+
+### exp2: start/end shift right
+
+### exp3: cropped audio
+
+Q: can I increase bs?
+Q: other ways to make EOU more nuanced:
+
+- padding with more END?
+- noise
+
+TODO: fix WER when no text is provided
+
+## [Bugfix] Freeze Text Embeddings
+
+I finally realized that the text embeddings were not being frozen.
+
+- Params before:
+ - trainable params: 80,234,496 || all params: 1,430,185,600 || trainable%: 5.6%
+- Params after:
+ - trainable params: 14,698,496 || all params: 1,430,185,600 || trainable%: 1.0%
+
+## [AudioTag or not?](https://wandb.ai/fixie/ultravox/runs/coom6x21)
diff --git a/legacy/ltu2_notes.md b/legacy/ltu2_notes.md
new file mode 100644
index 00000000..1c307791
--- /dev/null
+++ b/legacy/ltu2_notes.md
@@ -0,0 +1,9 @@
+# LTU 2 things to check
+
+- torchrun
+- model.print_trainable_parameters()
+- peft whole model
+- padding left to allow batching. huh?
+- pad token to unk: why?
+- ds: explicit train_test_split and shuffle: do we need this?
+- why? `model.is_parallelizable = model.model_parallel = True`
diff --git a/legacy/src/ultravox/.gitignore b/legacy/src/ultravox/.gitignore
new file mode 100644
index 00000000..c49b998a
--- /dev/null
+++ b/legacy/src/ultravox/.gitignore
@@ -0,0 +1,167 @@
+playground/
+runs/
+azure_runs/
+outputs/
+wandb/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+.vscode/
diff --git a/legacy/src/ultravox/__init__.py b/legacy/src/ultravox/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/legacy/src/ultravox/inference/asr_eval.py b/legacy/src/ultravox/inference/asr_eval.py
new file mode 100644
index 00000000..d84bd589
--- /dev/null
+++ b/legacy/src/ultravox/inference/asr_eval.py
@@ -0,0 +1,43 @@
+from dataclasses import dataclass
+
+import datasets
+import pyrallis
+from train import data
+
+from .speechlm_inference import SpeechLMInference
+from .speechlm_inference import SpeechLMInferenceConfig
+
+
+@dataclass
+class InferenceConfig:
+ model_path: str = "runs/tinyllama-hubertL-LS-10Hz-bs1"
+ dataset_streaming: bool = False
+ dataset_path: str = None
+ num_samples: int = 200
+ batch_size: int = 1
+ num_beams: int = 4
+
+
+if __name__ == "__main__":
+ cfg = pyrallis.parse(config_class=InferenceConfig)
+ infer = SpeechLMInference(SpeechLMInferenceConfig(path=cfg.model_path))
+
+ if cfg.dataset_path:
+ ds = datasets.load_dataset("audiofolder", data_dir=cfg.dataset_path)["train"]
+ infer.evaluate(ds, batch_size=cfg.batch_size, num_beams=cfg.num_beams)
+ else:
+ dataset_types = [
+ data.DatasetType.LIBRISPEECH,
+ data.DatasetType.COMMON_VOICE,
+ ]
+ for ds_type in dataset_types:
+ ds = data.get_dataset_split(
+ dataset_name=ds_type,
+ train=False,
+ streaming=cfg.dataset_streaming,
+ shuffle=True,
+ max_num_samples=cfg.num_samples,
+ max_duration_in_seconds=30,
+ )
+ print(f"{ds.info.dataset_name} validation set ({cfg.num_samples} samples):")
+ infer.evaluate(ds, batch_size=cfg.batch_size, num_beams=cfg.num_beams)
diff --git a/legacy/src/ultravox/inference/epd_eval.py b/legacy/src/ultravox/inference/epd_eval.py
new file mode 100644
index 00000000..3f5b430b
--- /dev/null
+++ b/legacy/src/ultravox/inference/epd_eval.py
@@ -0,0 +1,61 @@
+import glob
+from dataclasses import dataclass
+
+import evaluate
+import pyrallis
+import torch
+
+from .speechlm_inference import SpeechLMInference
+from .speechlm_inference import SpeechLMInferenceConfig
+
+
+@dataclass
+class InferenceConfig:
+ model: SpeechLMInferenceConfig
+ data_path: str = "../../../epd_data"
+ threshold: float = None
+ prompt: str = (
+ "Transcribe speech to text and indicate whether user is done talking or they might continue with [END] or [...]: {audio}"
+ )
+
+
+# @torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False)
+@torch.inference_mode
+def main():
+ cfg = pyrallis.parse(config_class=InferenceConfig)
+ infer = SpeechLMInference(cfg.model)
+
+ metrics = [
+ evaluate.load("accuracy"),
+ evaluate.load("confusion_matrix"),
+ ]
+
+ classes = {0: "false", 1: "true"}
+
+ for gt, dirname in classes.items():
+ for audio_path in glob.glob(f"{cfg.data_path}/{dirname}/*.wav"):
+ inputs = infer.prep_audio(audio_path, cfg.prompt)
+ audio_logits = infer.get_audio_logits(inputs)
+
+ if cfg.threshold is None:
+ audio_preds = audio_logits.argmax(dim=-1).cpu()
+ is_eou = audio_preds == infer.data_prep_fn.eou_token_id
+ else:
+ eou_logit = audio_logits[..., infer.data_prep_fn.eou_token_id]
+ mid_logit = audio_logits[..., infer.data_prep_fn.mid_token_id]
+ is_eou = torch.sigmoid(eou_logit - mid_logit)
+ # is_eou = audio_logits.softmax(-1)[..., infer.data_prep_fn.eou_token_id]
+ is_eou = is_eou > cfg.threshold
+ # print(is_eou[-1])
+ epd_pred = is_eou[-1].int().item()
+ for metric in metrics:
+ metric.add(predictions=epd_pred, references=gt)
+
+ print(metrics[0].compute())
+ # normalize (str): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population.
+ print("Confusion matrix:")
+ print(metrics[1].compute(normalize="true")["confusion_matrix"])
+
+
+if __name__ == "__main__":
+ main()
diff --git a/legacy/src/ultravox/inference/epd_eval_results.md b/legacy/src/ultravox/inference/epd_eval_results.md
new file mode 100644
index 00000000..90d4b028
--- /dev/null
+++ b/legacy/src/ultravox/inference/epd_eval_results.md
@@ -0,0 +1,57 @@
+# Evaluations for EPD
+
+## Base
+
+{'accuracy': 0.5769230769230769}
+Confusion matrix:
+[[0.66666667 0.33333333]
+ [0.54545455 0.45454545]]
+
+## 3x END
+
+{'accuracy': 0.46153846153846156}
+Confusion matrix:
+[[0.13333333 0.86666667]
+ [0.09090909 0.90909091]]
+
+## 2 END 8 Random ... (shift left)
+
+{'accuracy': 0.5384615384615384}
+Confusion matrix:
+[[0.2 0.8]
+ [0. 1.]]
+
+## Same but shift right
+
+{'accuracy': 0.7307692307692307}
+Confusion matrix:
+[[0.6 0.4 ]
+ [0.09090909 0.90909091]]
+
+## 8->16 random ... (reverted)
+
+{'accuracy': 0.6923076923076923}
+Confusion matrix:
+[[0.66666667 0.33333333]
+ [0.27272727 0.72727273]]
+
+## 30% crop (not trained fully)
+
+{'accuracy': 0.6153846153846154}
+Confusion matrix:
+[[0.33333333 0.66666667]
+ [0. 1.]]
+
+## + 40% silence
+
+{'accuracy': 0.5769230769230769}
+Confusion matrix:
+[[1. 0.]
+ [1. 0.]]
+
+## + system prompt
+
+{'accuracy': 0.6923076923076923}
+Confusion matrix:
+[[0.8 0.2 ]
+ [0.45454545 0.54545455]]
diff --git a/legacy/src/ultravox/inference/gradio_demo.py b/legacy/src/ultravox/inference/gradio_demo.py
new file mode 100644
index 00000000..d01450df
--- /dev/null
+++ b/legacy/src/ultravox/inference/gradio_demo.py
@@ -0,0 +1,174 @@
+import time
+from dataclasses import dataclass
+
+import gradio as gr
+import pyrallis
+import torch
+from inference import speechlm_inference
+
+
+@dataclass
+class DemoConfig:
+ model: speechlm_inference.SpeechLMInferenceConfig
+ default_prompt: str = (
+ "Transcribe speech to text and indicate whether user is done talking or they might continue with [END] or [...]: {audio}"
+ )
+ default_num_beams: int = 4
+ default_temp: float = 0.1
+
+
+class SpeechLMInferenceWithContinuation(speechlm_inference.SpeechLMInference):
+ def direct_token_equivalent(self, audio_features):
+ # 1 x T x C
+ audio_embed = self.model.forward_audio(audio_features)
+ # 32000 x C -> 32000 x 1 x C
+ text_embedding_map = self.model.embed_tokens.weight.unsqueeze(1)
+
+ closest_token_ids = [
+ torch.nn.functional.cosine_similarity(
+ audio_embed[:, idx : idx + 1],
+ text_embedding_map,
+ dim=-1,
+ )
+ .argmax(dim=0)
+ .item()
+ for idx in range(audio_embed.shape[1])
+ ]
+ return self.tokenizer.decode(closest_token_ids)
+
+ # @torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False)
+ @torch.inference_mode
+ def transcribe(self, audio_path, prompt, num_beams=4, temp=0.7):
+ stats = {}
+ start = time.time()
+
+ audio_only_inputs = self.prep_audio(audio_path, prompt)
+
+ stats["audio_prep_time"] = round(time.time() - start, 2)
+
+ stats["audio_direct_token_equivalent"] = self.direct_token_equivalent(
+ audio_only_inputs["audio_features"]
+ )
+
+ ## pass 0: endpoint detection
+ eou_pred_time = time.time()
+ audio_logits = self.get_audio_logits(audio_only_inputs)
+ audio_preds = audio_logits.argmax(dim=-1).cpu()
+ is_eou = audio_preds == self.data_prep_fn.eou_token_id
+ stats["epd_early"] = is_eou[-1].item()
+ eou_probs = audio_logits.softmax(dim=-1)[..., self.data_prep_fn.eou_token_id]
+ stats["epd_early_last_frames"] = self.tokenizer.decode(audio_preds[-20:])
+ stats["epd_early_last_probs"] = [
+ round(x, 2) for x in eou_probs[-2:].cpu().tolist()
+ ]
+ if is_eou.any():
+ first_eou = is_eou.int().argmax().item()
+ stats["first_epd_early_timestamp"] = (
+ first_eou * self.processor.total_audio_stride / 16_000
+ )
+
+ stats["epd_early_pred_time"] = round(time.time() - eou_pred_time, 2)
+
+ transcript_start = time.time()
+ ## pass 1: get the transcript
+ tokens = self.model.generate(
+ **audio_only_inputs,
+ max_new_tokens=100,
+ num_beams=num_beams,
+ do_sample=True,
+ temperature=temp,
+ top_k=10,
+ top_p=0.95,
+ )
+ text_res = self.tokenizer.decode(tokens.tolist()[0][:-1])
+ transcript = text_res.rsplit("<|assistant|>", 1)[-1]
+ epd_late_end = "[END]" in transcript
+ epd_late_mid = "[...]" in transcript
+ stats["epd_late"] = (
+ True if epd_late_end else False if epd_late_mid else "unknown"
+ )
+ stats["transcription_time"] = round(time.time() - transcript_start, 2)
+ stats["num_transcript_tokens"] = len(tokens[0]) - len(
+ audio_only_inputs["input_ids"][0]
+ )
+ stats["transcript_tps"] = round(
+ stats["num_transcript_tokens"] / stats["transcription_time"]
+ )
+ generation_start = time.time()
+
+ ## pass 2: generate the response by forcing response to be generated
+ text = text_res + " Response:"
+ # text = text_res
+
+ updated_input = self.tokenizer([text], return_tensors="pt")
+ audio_only_inputs = {**audio_only_inputs, **updated_input}
+ audio_only_inputs = {
+ k: v.to(device=self.config.device) for k, v in audio_only_inputs.items()
+ }
+
+ tokens = self.model.generate(
+ **audio_only_inputs,
+ max_new_tokens=100,
+ num_beams=num_beams,
+ do_sample=True,
+ temperature=temp,
+ top_k=10,
+ top_p=0.95,
+ )
+ text = self.tokenizer.decode(tokens.tolist()[0])
+ text = text.rsplit("<|assistant|>", 1)[-1].strip()
+ stats["generation_time"] = round(time.time() - generation_start, 2)
+ stats["num_generation_tokens"] = len(tokens[0]) - len(
+ audio_only_inputs["input_ids"][0]
+ )
+ stats["generation_tps"] = round(
+ stats["num_generation_tokens"] / stats["generation_time"]
+ )
+ return stats, text
+ # return {"stats": {"audio": audio}, "Text": "Hello, world!", "audio output (may be empty)": audio}
+
+
+def main():
+ cfg = pyrallis.parse(config_class=DemoConfig)
+
+ infer = SpeechLMInferenceWithContinuation(cfg.model)
+
+ demo = gr.Interface(
+ infer.transcribe,
+ [
+ gr.Audio(type="filepath", show_download_button=True),
+ gr.Text(
+ label="Prompt",
+ value=cfg.default_prompt,
+ ),
+ gr.Number(
+ label="Num Beams", value=cfg.default_num_beams, minimum=1, maximum=8
+ ),
+ gr.Number(
+ label="temperature",
+ value=cfg.default_temp,
+ minimum=0.1,
+ maximum=2.0,
+ step=0.1,
+ ),
+ ],
+ [
+ gr.JSON(label="stats"),
+ gr.Text(label="Text"),
+ ],
+ title="ASR via LLM",
+ description=f"""This is a demo of the LLM model for ASR. It will transcribe the audio and then generate a response.
+It has not been trained on instruction following, so it's not very good at it.
+
+EPD: endpoint detection\\
+EPD Early: detection right after the last audio token\\
+EPD Late: detection after the transcription\\
+Multi-channel audio: both channels are simply added together, but Gradio cannot trim them correctly.
+
+Model name: {cfg.model.path.replace("runs/", "").replace("/", " ").strip()}""",
+ )
+ demo.launch(share=True)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/legacy/src/ultravox/inference/run_audio_llm.py b/legacy/src/ultravox/inference/run_audio_llm.py
new file mode 100644
index 00000000..91063633
--- /dev/null
+++ b/legacy/src/ultravox/inference/run_audio_llm.py
@@ -0,0 +1,217 @@
+import safetensors.torch
+import torch
+from train import data
+from train.models import multimodal as multimodal_models
+
+# from train.models import text as text_models
+
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+dtype = torch.float16 if device.type == "cuda" else torch.float32
+
+audio_llm_config = multimodal_models.SpeechLMConfig(
+ audio_enc_name="wav2vec2",
+ llm_name="tinyllama",
+ # llm_name="llama2-7b",
+ audio_stride=6,
+ # torch_dtype=dtype,
+ use_cpu=device.type == "cpu",
+)
+
+audio_llm_model = multimodal_models.SpeechLM(audio_llm_config)
+processor = multimodal_models.SpeechLMProcessor.from_config(audio_llm_config)
+
+tokenizer = processor.tokenizer
+audio_proc = processor.audio_processor
+total_audio_stride = processor.total_audio_stride
+
+
+tokenizer.pad_token_id = 0 # unk. we want this to be different from the eos token
+# tokenizer.padding_side = "left"
+
+
+# print(load_result)
+for param in audio_llm_model.parameters():
+ param.requires_grad = False
+
+audio_llm_model.eval()
+audio_llm_model.llm.config.use_cache = True
+
+train_ds, _ = data.get_dataset(
+ dataset_name=data.DatasetType.GIGASPEECH,
+ dev_env=True,
+ # max_duration_in_seconds=config.max_audio_duration_in_seconds,
+)
+
+tokenize_fn = data.AudioTextTokenizer(
+ audio_proc,
+ tokenizer,
+ total_audio_stride,
+ cfg=data.AudioTextTokenizerConfig(inference_mode=True),
+)
+
+data_collator = data.DataCollatorForSeq2SeqWithAudio(
+ tokenizer,
+ pad_to_multiple_of=8,
+ return_tensors="pt",
+ padding=True,
+ audio_dtype=dtype,
+)
+
+
+train_ds = train_ds.map(tokenize_fn)
+
+# For LibriSpeech
+# atrain_ds = train_ds.remove_columns(["file", "speaker_id", "chapter_id", "id"])
+# For GigaSpeech
+# atrain_ds = train_ds.remove_columns(["audio", "text", "id"])
+
+it = iter(train_ds)
+pt_x = next(it)
+pt_x = next(it)
+pt_x = next(it)
+text = pt_x.pop("text")
+audio = pt_x.pop("audio")
+# x = data_collator([pt_x])
+
+
+import torch
+
+# audio_llm_model.can_generate = lambda: True
+# audio_llm_model.generation_config = audio_llm_model.llm.generation_config
+# audio_llm_model.config = argparse.Namespace(is_encoder_decoder=False)
+# audio_llm_model.main_input_name = "input_ids"
+# audio_llm_model.device = device
+
+audio_llm_model.to(device=device)
+
+
+# audio_llm_model = torch.compile(audio_llm_model)
+
+# import pdb
+
+# pdb.set_trace()
+
+
+# tokenize_fn.instructions = ""
+audio_only_input = tokenize_fn({"audio": audio})
+# audio_only_input = tokenize_fn({"audio": audio, "text": " ".join(text.split()[:1])})
+# audio_only_input = tokenize_fn({"audio": audio})
+print(tokenizer.decode(audio_only_input["input_ids"]))
+
+# print(tokenizer.decode(audio_only_input["input_ids"][:20]))
+# audio_only_input["input_ids"] = audio_only_input["input_ids"][:20]
+# audio_only_input["attention_mask"] = audio_only_input["attention_mask"][:-3]
+# audio_only_input["input_ids"] = audio_only_input["input_ids"][:20]
+# audio_only_input["attention_mask"] = audio_only_input["attention_mask"][:20]
+# audio_only_input["audio_features"] = audio_only_input["audio_features"][
+# ..., : 4 * total_audio_stride + 1
+# ]
+# audio_only_input["audio_token_mask"] = audio_only_input["audio_token_mask"][:20]
+audio_only_input.pop("attention_mask")
+# print("Labels:", audio_only_input.pop("labels"))
+audio_only_input.pop("audio")
+print("Full text:", text)
+print("Text provided:", audio_only_input.pop("text", ""))
+audio_only_inputs = data_collator([audio_only_input])
+# audio_only_inputs.pop("audio_features")
+audio_only_inputs = {k: v.to(device=device) for k, v in audio_only_inputs.items()}
+
+# audio_llm_model = combined_model
+with torch.no_grad():
+ outputs = audio_llm_model(**audio_only_inputs)
+
+tokens = outputs["logits"].argmax(dim=-1)
+print("Loss before: ", outputs["loss"])
+print(tokenizer.decode(tokens.tolist()[0]))
+
+
+state_dict = safetensors.torch.load_file(
+ # "runs/audiollm-tinyllamaR0-wav2vec2frozen-GigaSpeech-template-smooth0.1-8bs/model.safetensors",
+ "runs/audiollm-tinyllamaR0-wav2vec2frozen-GigaSpeech-template-smooth0.1-shiftfix-64bs/model.safetensors",
+ # "../../audiollm-tinyllamaR0-wav2vec2frozen-GigaSpeech-template-smooth0.1-model.safetensors",
+ # "../../audiollm-llama7bR0-wav2vec2frozen-GigaSpeech-template-smooth0.1-model.safetensors",
+ device=device.type,
+)
+
+state_dict = {
+ # k.replace("audio_to_embed", "audio_to_embed.1"): v
+ k: v
+ for k, v in state_dict.items()
+ if "llm" not in k
+}
+
+load_result = audio_llm_model.load_state_dict(state_dict, False)
+
+if load_result.unexpected_keys:
+ raise ValueError(f"Unexpected keys: {load_result.unexpected_keys}")
+
+outputs = audio_llm_model(**audio_only_inputs)
+tokens = outputs["logits"].argmax(dim=-1)
+print("Loss after: ", outputs["loss"])
+print(tokenizer.decode(tokens.tolist()[0]))
+
+tokens = audio_llm_model.generate(
+ **audio_only_inputs,
+ max_new_tokens=20,
+ # num_beams=4,
+ do_sample=True,
+ temperature=0.7,
+ top_k=10,
+ top_p=0.95,
+)
+print(tokenizer.decode(tokens.tolist()[0]))
+
+import pdb
+
+pdb.set_trace()
+
+# TODO: stop criterion?
+
+outputs = audio_llm_model(**audio_only_inputs)
+tokens = outputs["logits"].argmax(dim=-1)
+
+# audio_len = outputs["audio_embed"].shape[-2]
+# logits = outputs["logits"][..., audio_len:, :]
+
+# next_logits = outputs["logits"][..., 1:, :]
+# expected = x["labels"][..., :-1]
+
+# ccr = next_logits.argmax(dim=-1) == expected
+# assisted_cer = 1 - ccr.sum() / (expected != -100).sum()
+
+
+predicted = tokenizer.decode(tokens.tolist()[0])
+
+
+# def transcribe():
+# sr, y = audio
+# y = y.astype(np.float32)
+# y /= np.max(np.abs(y))
+# if sr != 16_000:
+# print(f"Got wrong sampling rate {sr}, resampling.")
+# y = librosa.resample(y, orig_sr=sr, target_sr=16_000)
+
+# last_audio = y
+# input_features = processor(
+# y, sampling_rate=16_000, return_tensors="pt"
+# ).input_features
+# predicted_ids = model.generate(input_features)
+# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+# return transcription[0]
+
+
+# # with gr.Blocks() as demo:
+# # with gr.Column():
+# # for _ in range(4):
+# # audio, label = generate_audio()
+# # output = gr.Audio(sources=["microphone"])
+
+# demo = gr.Interface(
+# transcribe,
+# gr.Audio(sources=["microphone"]),
+# "text",
+# )
+# _ = demo.launch(inline=False, inbrowser=True, debug=True)
+
+# demo.launch(debug=True)
diff --git a/legacy/src/ultravox/inference/speechlm_inference.py b/legacy/src/ultravox/inference/speechlm_inference.py
new file mode 100644
index 00000000..4649bc67
--- /dev/null
+++ b/legacy/src/ultravox/inference/speechlm_inference.py
@@ -0,0 +1,231 @@
+import datetime
+import glob
+import json
+import os
+from dataclasses import dataclass
+from dataclasses import field
+
+import datasets
+import evaluate
+import jiwer
+import safetensors.torch
+import torch
+import torch.utils.data
+import torchaudio
+import transformers
+from train import data
+from train.models import multimodal as multimodal_models
+
+
+@dataclass
+class SpeechLMInferenceConfig:
+ path: str
+ device: str = "cuda:0"
+ dtype: torch.dtype = torch.float32
+ add_audio_tag_ratio: float = 1.0
+ freezing_config: multimodal_models.FreezingConfig = field(
+ default_factory=multimodal_models.FreezingConfig
+ )
+
+
+class SpeechLMInference:
+ def __init__(self, config: SpeechLMInferenceConfig):
+ self.config = config
+
+ model_config = multimodal_models.SpeechLMConfig.from_pretrained(config.path)
+ model_config.torch_dtype = config.dtype
+ self.model = multimodal_models.SpeechLM(model_config)
+ self.model.apply_lora_configs(config.freezing_config)
+ processor = multimodal_models.SpeechLMProcessor.from_config(self.model.config)
+ self.processor = processor
+
+ for path in glob.glob(os.path.join(str(config.path), "model*.safetensors")):
+ state_dict = safetensors.torch.load_file(path)
+ mismatch = self.model.load_state_dict(state_dict, strict=False)
+ if mismatch.unexpected_keys:
+ raise ValueError(
+ f"Unexpected keys in state dict: {mismatch.unexpected_keys}"
+ )
+
+ self.model = self.model.to(config.device)
+
+ self.tokenizer = processor.tokenizer
+ audio_proc = processor.audio_processor
+ total_audio_stride = processor.total_audio_stride
+
+ for param in self.model.parameters():
+ param.requires_grad = False
+
+ self.model.eval()
+ self.model.llm.config.use_cache = True
+ processor.tokenizer.pad_token_id = 0
+ processor.tokenizer.padding_side = "left"
+
+ self.data_prep_fn = data.AudioTextTokenizer(
+ audio_proc,
+ self.tokenizer,
+ total_audio_stride,
+ cfg=data.AudioTextTokenizerConfig(
+ inference_mode=True,
+ add_audio_tag_ratio=config.add_audio_tag_ratio,
+ ),
+ )
+
+ self.data_collator = data.DataCollatorForSeq2SeqWithAudio(
+ self.tokenizer,
+ pad_to_multiple_of=8,
+ return_tensors="pt",
+ padding=True,
+ audio_dtype=self.config.dtype,
+ )
+
+ def get_audio_logits(self, batch_of_1):
+ out = self.model.forward(**batch_of_1)
+ start = batch_of_1["audio_token_start_idx"].item()
+ alen = batch_of_1["audio_token_len"].item()
+ audio_logits = out.logits[0, start : start + alen]
+ return audio_logits
+
+ def prep_audio(self, audio_path, prompt):
+ target_sr = 16_000
+ array, sr = torchaudio.load(audio_path, normalize=True)
+ array = torchaudio.functional.resample(array, sr, target_sr).sum(0)
+ audio = {"array": array, "sampling_rate": target_sr}
+ audio_only_input = self.data_prep_fn({"audio": audio, "prompt": prompt})
+ audio_only_input.pop("prompt", None)
+ audio_only_input.pop("labels")
+ audio_only_input.pop("audio", None)
+ audio_only_input.pop("text", "")
+ audio_only_inputs = self.data_collator([audio_only_input])
+ audio_only_inputs = {
+ k: v.to(device=self.config.device) for k, v in audio_only_inputs.items()
+ }
+ return audio_only_inputs
+
+ # @torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False)
+ # @torch.inference_mode
+ # def batch_transcribe(self, sample_audios, num_beams=4, temp=0.7):
+ # # prep and collate, but only the audio, not text/prompt, etc.
+ # audio_only_inputs = self.data_collator(
+ # [self.data_prep_fn({"audio": s}) for s in sample_audios]
+ # )
+
+ # audio_only_inputs = {
+ # k: v.to(device=self.config.device) for k, v in audio_only_inputs.items()
+ # }
+
+ # ## pass 1: get the transcript
+ # tokens = self.model.generate(
+ # **audio_only_inputs,
+ # max_new_tokens=100,
+ # num_beams=num_beams,
+ # do_sample=True,
+ # temperature=temp,
+ # top_k=10,
+ # top_p=0.95,
+ # )
+
+ # texts = [self.tokenizer.decode(t) for t in tokens.tolist()]
+
+ # return texts
+
+ @torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False)
+ @torch.inference_mode
+ def evaluate(self, ds: datasets.Dataset, batch_size=1, num_beams=4):
+ ds = ds.map(self.data_prep_fn)
+
+ trainer = transformers.Seq2SeqTrainer(
+ model=self.model,
+ tokenizer=self.tokenizer,
+ args=transformers.Seq2SeqTrainingArguments(
+ output_dir="runs/eval",
+ predict_with_generate=True,
+ report_to=[],
+ dataloader_num_workers=0,
+ per_device_eval_batch_size=batch_size,
+ ),
+ eval_dataset=ds,
+ data_collator=self.data_collator,
+ compute_metrics=ComputeMetrics(self.tokenizer),
+ )
+
+ # sample = next(iter(ds))
+ # collator = trainer._get_collator_with_removed_columns(self.data_collator)
+ # batch = collator([sample])
+ # batch = {k: v.to(device=self.config.device) for k, v in batch.items()}
+ # out = self.model(**batch)
+
+ print(
+ trainer.evaluate(
+ max_new_tokens=64,
+ num_beams=num_beams,
+ top_k=10,
+ top_p=0.95,
+ do_sample=True,
+ )
+ )
+
+
+@dataclass
+class ComputeMetrics:
+ tokenizer: transformers.LlamaTokenizer
+ preamble: str = "Transcript:"
+
+ def _remove_prefix_suffix(self, text: str):
+ text = text.split(self.preamble, maxsplit=1)[-1]
+ text = text.rsplit(self.tokenizer.eos_token, maxsplit=1)[0]
+ return text.strip()
+
+ def __post_init__(self):
+ self.cer_metric = evaluate.load("cer")
+ self.wer_metric = evaluate.load("wer")
+
+ def __call__(self, eval_preds: transformers.EvalPrediction):
+ targets = eval_preds.label_ids
+ logits = eval_preds.predictions
+
+ ref_strs = []
+ pred_strs = []
+
+ for logit, target in zip(logits, targets):
+ ref_ids = target[target > 0]
+ pred_ids = logit[logit > 0]
+ # CER complains if the string is empty
+ ref_strs.append(self._remove_prefix_suffix(self.tokenizer.decode(ref_ids)))
+ pred_strs.append(
+ self._remove_prefix_suffix(self.tokenizer.decode(pred_ids))
+ )
+
+ base_transforms = jiwer.Compose(
+ [
+ jiwer.RemovePunctuation(),
+ jiwer.ToLowerCase(),
+ jiwer.ExpandCommonEnglishContractions(),
+ jiwer.RemoveKaldiNonWords(),
+ jiwer.RemoveWhiteSpace(replace_by_space=True),
+ ]
+ )
+ pred_strs_clean = base_transforms(pred_strs)
+ ref_strs_clean = base_transforms(ref_strs)
+
+ eval_path = "eval/results/"
+ os.makedirs(eval_path, exist_ok=True)
+
+ file_name = datetime.datetime.now().strftime("%Y-%m-%d--%H-%M-%S") + ".json"
+
+ with open(os.path.join(eval_path, file_name), "w") as res_file:
+ json.dump(
+ [
+ {"ref": r, "pred": p, "ref_trans": rt, "pred_trans": pt}
+ for r, p, rt, pt in zip(
+ ref_strs, pred_strs, ref_strs_clean, pred_strs_clean
+ )
+ ],
+ res_file,
+ indent=2,
+ )
+
+ return {
+ "wer": jiwer.wer(ref_strs_clean, pred_strs_clean),
+ "cer": jiwer.cer(ref_strs_clean, pred_strs_clean),
+ }
diff --git a/legacy/src/ultravox/poetry.lock b/legacy/src/ultravox/poetry.lock
new file mode 100644
index 00000000..eadf951a
--- /dev/null
+++ b/legacy/src/ultravox/poetry.lock
@@ -0,0 +1,6289 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "accelerate"
+version = "0.25.0"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "accelerate-0.25.0-py3-none-any.whl", hash = "sha256:c7bb817eb974bba0ff3ea1ba0f24d55afb86d50e3d4fe98d6922dc69cf2ccff1"},
+ {file = "accelerate-0.25.0.tar.gz", hash = "sha256:ecf55b0ab278a1dac8539dde0d276977aff04683f07ede73eaf02478538576a1"},
+]
+
+[package.dependencies]
+huggingface-hub = "*"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.3.1"
+torch = ">=1.10.0"
+
+[package.extras]
+dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.0.241)", "scikit-learn", "scipy", "timm", "tqdm", "transformers", "urllib3 (<2.0.0)"]
+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.0.241)", "urllib3 (<2.0.0)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
+test-prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
+
+[[package]]
+name = "adal"
+version = "1.2.7"
+description = "Note: This library is already replaced by MSAL Python, available here: https://pypi.org/project/msal/ .ADAL Python remains available here as a legacy. The ADAL for Python library makes it easy for python application to authenticate to Azure Active Directory (AAD) in order to access AAD protected web resources."
+optional = false
+python-versions = "*"
+files = [
+ {file = "adal-1.2.7-py2.py3-none-any.whl", hash = "sha256:2a7451ed7441ddbc57703042204a3e30ef747478eea022c70f789fc7f084bc3d"},
+ {file = "adal-1.2.7.tar.gz", hash = "sha256:d74f45b81317454d96e982fd1c50e6fb5c99ac2223728aea8764433a39f566f1"},
+]
+
+[package.dependencies]
+cryptography = ">=1.1.0"
+PyJWT = ">=1.0.0,<3"
+python-dateutil = ">=2.1.0,<3"
+requests = ">=2.0.0,<3"
+
+[[package]]
+name = "aiofiles"
+version = "23.2.1"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"},
+ {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.9.1"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"},
+ {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"},
+ {file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9b05d5cbe9dafcdc733262c3a99ccf63d2f7ce02543620d2bd8db4d4f7a22f83"},
+ {file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c4fa235d534b3547184831c624c0b7c1e262cd1de847d95085ec94c16fddcd5"},
+ {file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:289ba9ae8e88d0ba16062ecf02dd730b34186ea3b1e7489046fc338bdc3361c4"},
+ {file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bff7e2811814fa2271be95ab6e84c9436d027a0e59665de60edf44e529a42c1f"},
+ {file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81b77f868814346662c96ab36b875d7814ebf82340d3284a31681085c051320f"},
+ {file = "aiohttp-3.9.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3b9c7426923bb7bd66d409da46c41e3fb40f5caf679da624439b9eba92043fa6"},
+ {file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:8d44e7bf06b0c0a70a20f9100af9fcfd7f6d9d3913e37754c12d424179b4e48f"},
+ {file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:22698f01ff5653fe66d16ffb7658f582a0ac084d7da1323e39fd9eab326a1f26"},
+ {file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ca7ca5abfbfe8d39e653870fbe8d7710be7a857f8a8386fc9de1aae2e02ce7e4"},
+ {file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:8d7f98fde213f74561be1d6d3fa353656197f75d4edfbb3d94c9eb9b0fc47f5d"},
+ {file = "aiohttp-3.9.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:5216b6082c624b55cfe79af5d538e499cd5f5b976820eac31951fb4325974501"},
+ {file = "aiohttp-3.9.1-cp310-cp310-win32.whl", hash = "sha256:0e7ba7ff228c0d9a2cd66194e90f2bca6e0abca810b786901a569c0de082f489"},
+ {file = "aiohttp-3.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:c7e939f1ae428a86e4abbb9a7c4732bf4706048818dfd979e5e2839ce0159f23"},
+ {file = "aiohttp-3.9.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:df9cf74b9bc03d586fc53ba470828d7b77ce51b0582d1d0b5b2fb673c0baa32d"},
+ {file = "aiohttp-3.9.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ecca113f19d5e74048c001934045a2b9368d77b0b17691d905af18bd1c21275e"},
+ {file = "aiohttp-3.9.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8cef8710fb849d97c533f259103f09bac167a008d7131d7b2b0e3a33269185c0"},
+ {file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bea94403a21eb94c93386d559bce297381609153e418a3ffc7d6bf772f59cc35"},
+ {file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:91c742ca59045dce7ba76cab6e223e41d2c70d79e82c284a96411f8645e2afff"},
+ {file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6c93b7c2e52061f0925c3382d5cb8980e40f91c989563d3d32ca280069fd6a87"},
+ {file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ee2527134f95e106cc1653e9ac78846f3a2ec1004cf20ef4e02038035a74544d"},
+ {file = "aiohttp-3.9.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:11ff168d752cb41e8492817e10fb4f85828f6a0142b9726a30c27c35a1835f01"},
+ {file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:b8c3a67eb87394386847d188996920f33b01b32155f0a94f36ca0e0c635bf3e3"},
+ {file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c7b5d5d64e2a14e35a9240b33b89389e0035e6de8dbb7ffa50d10d8b65c57449"},
+ {file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:69985d50a2b6f709412d944ffb2e97d0be154ea90600b7a921f95a87d6f108a2"},
+ {file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:c9110c06eaaac7e1f5562caf481f18ccf8f6fdf4c3323feab28a93d34cc646bd"},
+ {file = "aiohttp-3.9.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d737e69d193dac7296365a6dcb73bbbf53bb760ab25a3727716bbd42022e8d7a"},
+ {file = "aiohttp-3.9.1-cp311-cp311-win32.whl", hash = "sha256:4ee8caa925aebc1e64e98432d78ea8de67b2272252b0a931d2ac3bd876ad5544"},
+ {file = "aiohttp-3.9.1-cp311-cp311-win_amd64.whl", hash = "sha256:a34086c5cc285be878622e0a6ab897a986a6e8bf5b67ecb377015f06ed316587"},
+ {file = "aiohttp-3.9.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f800164276eec54e0af5c99feb9494c295118fc10a11b997bbb1348ba1a52065"},
+ {file = "aiohttp-3.9.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:500f1c59906cd142d452074f3811614be04819a38ae2b3239a48b82649c08821"},
+ {file = "aiohttp-3.9.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0b0a6a36ed7e164c6df1e18ee47afbd1990ce47cb428739d6c99aaabfaf1b3af"},
+ {file = "aiohttp-3.9.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69da0f3ed3496808e8cbc5123a866c41c12c15baaaead96d256477edf168eb57"},
+ {file = "aiohttp-3.9.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:176df045597e674fa950bf5ae536be85699e04cea68fa3a616cf75e413737eb5"},
+ {file = "aiohttp-3.9.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b796b44111f0cab6bbf66214186e44734b5baab949cb5fb56154142a92989aeb"},
+ {file = "aiohttp-3.9.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f27fdaadce22f2ef950fc10dcdf8048407c3b42b73779e48a4e76b3c35bca26c"},
+ {file = "aiohttp-3.9.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bcb6532b9814ea7c5a6a3299747c49de30e84472fa72821b07f5a9818bce0f66"},
+ {file = "aiohttp-3.9.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:54631fb69a6e44b2ba522f7c22a6fb2667a02fd97d636048478db2fd8c4e98fe"},
+ {file = "aiohttp-3.9.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:4b4c452d0190c5a820d3f5c0f3cd8a28ace48c54053e24da9d6041bf81113183"},
+ {file = "aiohttp-3.9.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:cae4c0c2ca800c793cae07ef3d40794625471040a87e1ba392039639ad61ab5b"},
+ {file = "aiohttp-3.9.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:565760d6812b8d78d416c3c7cfdf5362fbe0d0d25b82fed75d0d29e18d7fc30f"},
+ {file = "aiohttp-3.9.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:54311eb54f3a0c45efb9ed0d0a8f43d1bc6060d773f6973efd90037a51cd0a3f"},
+ {file = "aiohttp-3.9.1-cp312-cp312-win32.whl", hash = "sha256:85c3e3c9cb1d480e0b9a64c658cd66b3cfb8e721636ab8b0e746e2d79a7a9eed"},
+ {file = "aiohttp-3.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:11cb254e397a82efb1805d12561e80124928e04e9c4483587ce7390b3866d213"},
+ {file = "aiohttp-3.9.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:8a22a34bc594d9d24621091d1b91511001a7eea91d6652ea495ce06e27381f70"},
+ {file = "aiohttp-3.9.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:598db66eaf2e04aa0c8900a63b0101fdc5e6b8a7ddd805c56d86efb54eb66672"},
+ {file = "aiohttp-3.9.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c9376e2b09895c8ca8b95362283365eb5c03bdc8428ade80a864160605715f1"},
+ {file = "aiohttp-3.9.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:41473de252e1797c2d2293804e389a6d6986ef37cbb4a25208de537ae32141dd"},
+ {file = "aiohttp-3.9.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9c5857612c9813796960c00767645cb5da815af16dafb32d70c72a8390bbf690"},
+ {file = "aiohttp-3.9.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ffcd828e37dc219a72c9012ec44ad2e7e3066bec6ff3aaa19e7d435dbf4032ca"},
+ {file = "aiohttp-3.9.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:219a16763dc0294842188ac8a12262b5671817042b35d45e44fd0a697d8c8361"},
+ {file = "aiohttp-3.9.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f694dc8a6a3112059258a725a4ebe9acac5fe62f11c77ac4dcf896edfa78ca28"},
+ {file = "aiohttp-3.9.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:bcc0ea8d5b74a41b621ad4a13d96c36079c81628ccc0b30cfb1603e3dfa3a014"},
+ {file = "aiohttp-3.9.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:90ec72d231169b4b8d6085be13023ece8fa9b1bb495e4398d847e25218e0f431"},
+ {file = "aiohttp-3.9.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:cf2a0ac0615842b849f40c4d7f304986a242f1e68286dbf3bd7a835e4f83acfd"},
+ {file = "aiohttp-3.9.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:0e49b08eafa4f5707ecfb321ab9592717a319e37938e301d462f79b4e860c32a"},
+ {file = "aiohttp-3.9.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:2c59e0076ea31c08553e868cec02d22191c086f00b44610f8ab7363a11a5d9d8"},
+ {file = "aiohttp-3.9.1-cp38-cp38-win32.whl", hash = "sha256:4831df72b053b1eed31eb00a2e1aff6896fb4485301d4ccb208cac264b648db4"},
+ {file = "aiohttp-3.9.1-cp38-cp38-win_amd64.whl", hash = "sha256:3135713c5562731ee18f58d3ad1bf41e1d8883eb68b363f2ffde5b2ea4b84cc7"},
+ {file = "aiohttp-3.9.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:cfeadf42840c1e870dc2042a232a8748e75a36b52d78968cda6736de55582766"},
+ {file = "aiohttp-3.9.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:70907533db712f7aa791effb38efa96f044ce3d4e850e2d7691abd759f4f0ae0"},
+ {file = "aiohttp-3.9.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cdefe289681507187e375a5064c7599f52c40343a8701761c802c1853a504558"},
+ {file = "aiohttp-3.9.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7481f581251bb5558ba9f635db70908819caa221fc79ee52a7f58392778c636"},
+ {file = "aiohttp-3.9.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:49f0c1b3c2842556e5de35f122fc0f0b721334ceb6e78c3719693364d4af8499"},
+ {file = "aiohttp-3.9.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0d406b01a9f5a7e232d1b0d161b40c05275ffbcbd772dc18c1d5a570961a1ca4"},
+ {file = "aiohttp-3.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d8e4450e7fe24d86e86b23cc209e0023177b6d59502e33807b732d2deb6975f"},
+ {file = "aiohttp-3.9.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3c0266cd6f005e99f3f51e583012de2778e65af6b73860038b968a0a8888487a"},
+ {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ab221850108a4a063c5b8a70f00dd7a1975e5a1713f87f4ab26a46e5feac5a0e"},
+ {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:c88a15f272a0ad3d7773cf3a37cc7b7d077cbfc8e331675cf1346e849d97a4e5"},
+ {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:237533179d9747080bcaad4d02083ce295c0d2eab3e9e8ce103411a4312991a0"},
+ {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:02ab6006ec3c3463b528374c4cdce86434e7b89ad355e7bf29e2f16b46c7dd6f"},
+ {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04fa38875e53eb7e354ece1607b1d2fdee2d175ea4e4d745f6ec9f751fe20c7c"},
+ {file = "aiohttp-3.9.1-cp39-cp39-win32.whl", hash = "sha256:82eefaf1a996060602f3cc1112d93ba8b201dbf5d8fd9611227de2003dddb3b7"},
+ {file = "aiohttp-3.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:9b05d33ff8e6b269e30a7957bd3244ffbce2a7a35a81b81c382629b80af1a8bf"},
+ {file = "aiohttp-3.9.1.tar.gz", hash = "sha256:8fc49a87ac269d4529da45871e2ffb6874e87779c3d0e2ccd813c0899221239d"},
+]
+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
+ {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+]
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
+[[package]]
+name = "altair"
+version = "5.2.0"
+description = "Vega-Altair: A declarative statistical visualization library for Python."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "altair-5.2.0-py3-none-any.whl", hash = "sha256:8c4888ad11db7c39f3f17aa7f4ea985775da389d79ac30a6c22856ab238df399"},
+ {file = "altair-5.2.0.tar.gz", hash = "sha256:2ad7f0c8010ebbc46319cc30febfb8e59ccf84969a201541c207bc3a4fa6cf81"},
+]
+
+[package.dependencies]
+jinja2 = "*"
+jsonschema = ">=3.0"
+numpy = "*"
+packaging = "*"
+pandas = ">=0.25"
+toolz = "*"
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+dev = ["anywidget", "geopandas", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pyarrow (>=11)", "pytest", "pytest-cov", "ruff (>=0.1.3)", "types-jsonschema", "types-setuptools", "vega-datasets", "vegafusion[embed] (>=1.4.0)", "vl-convert-python (>=1.1.0)"]
+doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+ {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.8"
+description = "ANTLR 4.8 runtime for Python 3.7"
+optional = false
+python-versions = "*"
+files = [
+ {file = "antlr4-python3-runtime-4.8.tar.gz", hash = "sha256:15793f5d0512a372b4e7d2284058ad32ce7dd27126b105fb0b2245130445db33"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.2.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
+ {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = "*"
+files = [
+ {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+ {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
+
+[[package]]
+name = "argcomplete"
+version = "3.2.1"
+description = "Bash tab completion for argparse"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "argcomplete-3.2.1-py3-none-any.whl", hash = "sha256:30891d87f3c1abe091f2142613c9d33cac84a5e15404489f033b20399b691fec"},
+ {file = "argcomplete-3.2.1.tar.gz", hash = "sha256:437f67fb9b058da5a090df505ef9be0297c4883993f3f56cb186ff087778cfb4"},
+]
+
+[package.extras]
+test = ["coverage", "mypy", "pexpect", "ruff", "wheel"]
+
+[[package]]
+name = "async-timeout"
+version = "4.0.3"
+description = "Timeout context manager for asyncio programs"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
+ {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
+]
+
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+ {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "audioread"
+version = "3.0.1"
+description = "Multi-library, cross-platform audio decoding."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "audioread-3.0.1-py3-none-any.whl", hash = "sha256:4cdce70b8adc0da0a3c9e0d85fb10b3ace30fbdf8d1670fd443929b61d117c33"},
+ {file = "audioread-3.0.1.tar.gz", hash = "sha256:ac5460a5498c48bdf2e8e767402583a4dcd13f4414d286f42ce4379e8b35066d"},
+]
+
+[package.extras]
+test = ["tox"]
+
+[[package]]
+name = "autoflake"
+version = "2.2.1"
+description = "Removes unused imports and unused variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "autoflake-2.2.1-py3-none-any.whl", hash = "sha256:265cde0a43c1f44ecfb4f30d95b0437796759d07be7706a2f70e4719234c0f79"},
+ {file = "autoflake-2.2.1.tar.gz", hash = "sha256:62b7b6449a692c3c9b0c916919bbc21648da7281e8506bcf8d3f8280e431ebc1"},
+]
+
+[package.dependencies]
+pyflakes = ">=3.0.0"
+tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "azure-common"
+version = "1.1.28"
+description = "Microsoft Azure Client Library for Python (Common)"
+optional = false
+python-versions = "*"
+files = [
+ {file = "azure-common-1.1.28.zip", hash = "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3"},
+ {file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
+]
+
+[[package]]
+name = "azure-core"
+version = "1.29.6"
+description = "Microsoft Azure Core Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-core-1.29.6.tar.gz", hash = "sha256:13b485252ecd9384ae624894fe51cfa6220966207264c360beada239f88b738a"},
+ {file = "azure_core-1.29.6-py3-none-any.whl", hash = "sha256:604a005bce6a49ba661bb7b2be84a9b169047e52fcfcd0a4e4770affab4178f7"},
+]
+
+[package.dependencies]
+anyio = ">=3.0,<5.0"
+requests = ">=2.21.0"
+six = ">=1.11.0"
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aio = ["aiohttp (>=3.0)"]
+
+[[package]]
+name = "azure-graphrbac"
+version = "0.61.1"
+description = "Microsoft Azure Graph RBAC Client Library for Python"
+optional = false
+python-versions = "*"
+files = [
+ {file = "azure-graphrbac-0.61.1.zip", hash = "sha256:53e98ae2ca7c19b349e9e9bb1b6a824aeae8dcfcbe17190d20fe69c0f185b2e2"},
+ {file = "azure_graphrbac-0.61.1-py2.py3-none-any.whl", hash = "sha256:7b4e0f05676acc912f2b33c71c328d9fb2e4dc8e70ebadc9d3de8ab08bf0b175"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+msrest = ">=0.5.0"
+msrestazure = ">=0.4.32,<2.0.0"
+
+[[package]]
+name = "azure-identity"
+version = "1.15.0"
+description = "Microsoft Azure Identity Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-identity-1.15.0.tar.gz", hash = "sha256:4c28fc246b7f9265610eb5261d65931183d019a23d4b0e99357facb2e6c227c8"},
+ {file = "azure_identity-1.15.0-py3-none-any.whl", hash = "sha256:a14b1f01c7036f11f148f22cd8c16e05035293d714458d6b44ddf534d93eb912"},
+]
+
+[package.dependencies]
+azure-core = ">=1.23.0,<2.0.0"
+cryptography = ">=2.5"
+msal = ">=1.24.0,<2.0.0"
+msal-extensions = ">=0.3.0,<2.0.0"
+
+[[package]]
+name = "azure-keyvault-secrets"
+version = "4.7.0"
+description = "Microsoft Azure Key Vault Secrets Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-keyvault-secrets-4.7.0.zip", hash = "sha256:77ee2534ba651a1f306c85d7b505bc3ccee8fea77450ebafafc26aec16e5445d"},
+ {file = "azure_keyvault_secrets-4.7.0-py3-none-any.whl", hash = "sha256:a16c7e6dfa9cba68892bb6fcb905bf2e2ec1f2a6dc05522b61df79621e050901"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-core = ">=1.24.0,<2.0.0"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.0.1"
+
+[[package]]
+name = "azure-mgmt-authorization"
+version = "4.0.0"
+description = "Microsoft Azure Authorization Management Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-authorization-4.0.0.zip", hash = "sha256:69b85abc09ae64fc72975bd43431170d8c7eb5d166754b98aac5f3845de57dc4"},
+ {file = "azure_mgmt_authorization-4.0.0-py3-none-any.whl", hash = "sha256:d8feeb3842e6ddf1a370963ca4f61fb6edc124e8997b807dd025bc9b2379cd1a"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azure-mgmt-containerregistry"
+version = "10.3.0"
+description = "Microsoft Azure Container Registry Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-containerregistry-10.3.0.tar.gz", hash = "sha256:ae21651855dfb19c42d91d6b3a965c6c611e23f8bc4bf7138835e652d2f918e3"},
+ {file = "azure_mgmt_containerregistry-10.3.0-py3-none-any.whl", hash = "sha256:851e1c57f9bc4a3589c6b21fb627c11fd6cbb57a0388b7dfccd530ba3160805f"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azure-mgmt-core"
+version = "1.4.0"
+description = "Microsoft Azure Management Core Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-core-1.4.0.zip", hash = "sha256:d195208340094f98e5a6661b781cde6f6a051e79ce317caabd8ff97030a9b3ae"},
+ {file = "azure_mgmt_core-1.4.0-py3-none-any.whl", hash = "sha256:81071675f186a585555ef01816f2774d49c1c9024cb76e5720c3c0f6b337bb7d"},
+]
+
+[package.dependencies]
+azure-core = ">=1.26.2,<2.0.0"
+
+[[package]]
+name = "azure-mgmt-keyvault"
+version = "10.3.0"
+description = "Microsoft Azure Key Vault Management Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-keyvault-10.3.0.tar.gz", hash = "sha256:183b4164cf1868b8ea7efeaa98edad7d2a4e14a9bd977c2818b12b75150cd2a2"},
+ {file = "azure_mgmt_keyvault-10.3.0-py3-none-any.whl", hash = "sha256:3410cf6c703e9570ed3c8e9716e483c02b1804adde6ab437ddc8feac4545acd6"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azure-mgmt-network"
+version = "25.1.0"
+description = "Microsoft Azure Network Management Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-network-25.1.0.tar.gz", hash = "sha256:f939f75bf139e03d2c457a4f07a5ebadb5affdd70aa54be9a0af44b8e52132fc"},
+ {file = "azure_mgmt_network-25.1.0-py3-none-any.whl", hash = "sha256:94191ce8ae243ab6e9c489f21330b3908937e296d443fea4e5100d57d52e14c8"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azure-mgmt-resource"
+version = "23.0.1"
+description = "Microsoft Azure Resource Management Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-resource-23.0.1.zip", hash = "sha256:c2ba6cfd99df95f55f36eadc4245e3dc713257302a1fd0277756d94bd8cb28e0"},
+ {file = "azure_mgmt_resource-23.0.1-py3-none-any.whl", hash = "sha256:f185eec72bbc39f42bcb83ae6f1bad744f0e3f20a12d9b2b3e70d16c74ad9cc0"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azure-mgmt-storage"
+version = "21.1.0"
+description = "Microsoft Azure Storage Management Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "azure-mgmt-storage-21.1.0.tar.gz", hash = "sha256:d6d3c0e917c988bc9ed0472477d3ef3f90886009eb1d97a711944f8375630162"},
+ {file = "azure_mgmt_storage-21.1.0-py3-none-any.whl", hash = "sha256:593f2544fc4f05750c4fe7ca4d83c32ea1e9d266e57899bbf79ce5940124e8cc"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.2,<2.0.0"
+isodate = ">=0.6.1,<1.0.0"
+
+[[package]]
+name = "azureml-core"
+version = "1.54.0.post1"
+description = "Azure Machine Learning core packages, modules, and classes"
+optional = false
+python-versions = ">=3.7,< 4.0"
+files = [
+ {file = "azureml_core-1.54.0.post1-py3-none-any.whl", hash = "sha256:44b199f60e723f2359e033bbdf4884d10c40f62d7598003dc99e92f1b136ffa6"},
+]
+
+[package.dependencies]
+adal = ">=1.2.0,<=1.2.7"
+argcomplete = "<4"
+azure-common = ">=1.1.12,<2.0.0"
+azure-core = "<2.0.0"
+azure-graphrbac = ">=0.40.0,<1.0.0"
+azure-mgmt-authorization = ">=0.40.0,<5"
+azure-mgmt-containerregistry = ">=8.2.0,<11"
+azure-mgmt-keyvault = ">=0.40.0,<11.0.0"
+azure-mgmt-network = "25.1.0"
+azure-mgmt-resource = ">=15.0.0,<=24.0.0"
+azure-mgmt-storage = ">=16.0.0,<=22.0.0"
+"backports.tempfile" = "*"
+contextlib2 = "<22.0.0"
+docker = "<7.0.0"
+humanfriendly = ">=4.7,<11.0"
+jmespath = "<2.0.0"
+jsonpickle = "<4.0.0"
+knack = "<0.12.0"
+msal = ">=1.15.0,<2.0.0"
+msal-extensions = ">=0.3.0,<=1.0.0"
+msrest = ">=0.5.1,<=0.7.1"
+msrestazure = ">=0.4.33,<=0.6.4"
+ndg-httpsclient = "<=0.5.1"
+packaging = ">=20.0,<=24.0"
+paramiko = ">=2.0.8,<4.0.0"
+pathspec = "<1.0.0"
+pkginfo = "*"
+PyJWT = "<3.0.0"
+pyopenssl = "<24.0.0"
+python-dateutil = ">=2.7.3,<3.0.0"
+pytz = "*"
+requests = {version = ">=2.19.1,<3.0.0", extras = ["socks"]}
+SecretStorage = "<4.0.0"
+urllib3 = ">1.26.17,<3.0.0"
+
+[[package]]
+name = "backports-tempfile"
+version = "1.0"
+description = "Backport of new features in Python's tempfile module"
+optional = false
+python-versions = "*"
+files = [
+ {file = "backports.tempfile-1.0-py2.py3-none-any.whl", hash = "sha256:05aa50940946f05759696156a8c39be118169a0e0f94a49d0bb106503891ff54"},
+ {file = "backports.tempfile-1.0.tar.gz", hash = "sha256:1c648c452e8770d759bdc5a5e2431209be70d25484e1be24876cf2168722c762"},
+]
+
+[package.dependencies]
+"backports.weakref" = "*"
+
+[[package]]
+name = "backports-weakref"
+version = "1.0.post1"
+description = "Backport of new features in Python's weakref module"
+optional = false
+python-versions = "*"
+files = [
+ {file = "backports.weakref-1.0.post1-py2.py3-none-any.whl", hash = "sha256:81bc9b51c0abc58edc76aefbbc68c62a787918ffe943a37947e162c3f8e19e82"},
+ {file = "backports.weakref-1.0.post1.tar.gz", hash = "sha256:bc4170a29915f8b22c9e7c4939701859650f2eb84184aee80da329ac0b9825c2"},
+]
+
+[[package]]
+name = "bcrypt"
+version = "4.1.2"
+description = "Modern password hashing for your software and your servers"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "bcrypt-4.1.2-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:ac621c093edb28200728a9cca214d7e838529e557027ef0581685909acd28b5e"},
+ {file = "bcrypt-4.1.2-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ea505c97a5c465ab8c3ba75c0805a102ce526695cd6818c6de3b1a38f6f60da1"},
+ {file = "bcrypt-4.1.2-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:57fa9442758da926ed33a91644649d3e340a71e2d0a5a8de064fb621fd5a3326"},
+ {file = "bcrypt-4.1.2-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:eb3bd3321517916696233b5e0c67fd7d6281f0ef48e66812db35fc963a422a1c"},
+ {file = "bcrypt-4.1.2-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:6cad43d8c63f34b26aef462b6f5e44fdcf9860b723d2453b5d391258c4c8e966"},
+ {file = "bcrypt-4.1.2-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:44290ccc827d3a24604f2c8bcd00d0da349e336e6503656cb8192133e27335e2"},
+ {file = "bcrypt-4.1.2-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:732b3920a08eacf12f93e6b04ea276c489f1c8fb49344f564cca2adb663b3e4c"},
+ {file = "bcrypt-4.1.2-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1c28973decf4e0e69cee78c68e30a523be441972c826703bb93099868a8ff5b5"},
+ {file = "bcrypt-4.1.2-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b8df79979c5bae07f1db22dcc49cc5bccf08a0380ca5c6f391cbb5790355c0b0"},
+ {file = "bcrypt-4.1.2-cp37-abi3-win32.whl", hash = "sha256:fbe188b878313d01b7718390f31528be4010fed1faa798c5a1d0469c9c48c369"},
+ {file = "bcrypt-4.1.2-cp37-abi3-win_amd64.whl", hash = "sha256:9800ae5bd5077b13725e2e3934aa3c9c37e49d3ea3d06318010aa40f54c63551"},
+ {file = "bcrypt-4.1.2-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:71b8be82bc46cedd61a9f4ccb6c1a493211d031415a34adde3669ee1b0afbb63"},
+ {file = "bcrypt-4.1.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68e3c6642077b0c8092580c819c1684161262b2e30c4f45deb000c38947bf483"},
+ {file = "bcrypt-4.1.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:387e7e1af9a4dd636b9505a465032f2f5cb8e61ba1120e79a0e1cd0b512f3dfc"},
+ {file = "bcrypt-4.1.2-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:f70d9c61f9c4ca7d57f3bfe88a5ccf62546ffbadf3681bb1e268d9d2e41c91a7"},
+ {file = "bcrypt-4.1.2-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:2a298db2a8ab20056120b45e86c00a0a5eb50ec4075b6142db35f593b97cb3fb"},
+ {file = "bcrypt-4.1.2-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ba55e40de38a24e2d78d34c2d36d6e864f93e0d79d0b6ce915e4335aa81d01b1"},
+ {file = "bcrypt-4.1.2-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:3566a88234e8de2ccae31968127b0ecccbb4cddb629da744165db72b58d88ca4"},
+ {file = "bcrypt-4.1.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:b90e216dc36864ae7132cb151ffe95155a37a14e0de3a8f64b49655dd959ff9c"},
+ {file = "bcrypt-4.1.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:69057b9fc5093ea1ab00dd24ede891f3e5e65bee040395fb1e66ee196f9c9b4a"},
+ {file = "bcrypt-4.1.2-cp39-abi3-win32.whl", hash = "sha256:02d9ef8915f72dd6daaef40e0baeef8a017ce624369f09754baf32bb32dba25f"},
+ {file = "bcrypt-4.1.2-cp39-abi3-win_amd64.whl", hash = "sha256:be3ab1071662f6065899fe08428e45c16aa36e28bc42921c4901a191fda6ee42"},
+ {file = "bcrypt-4.1.2-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:d75fc8cd0ba23f97bae88a6ec04e9e5351ff3c6ad06f38fe32ba50cbd0d11946"},
+ {file = "bcrypt-4.1.2-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:a97e07e83e3262599434816f631cc4c7ca2aa8e9c072c1b1a7fec2ae809a1d2d"},
+ {file = "bcrypt-4.1.2-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:e51c42750b7585cee7892c2614be0d14107fad9581d1738d954a262556dd1aab"},
+ {file = "bcrypt-4.1.2-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:ba4e4cc26610581a6329b3937e02d319f5ad4b85b074846bf4fef8a8cf51e7bb"},
+ {file = "bcrypt-4.1.2.tar.gz", hash = "sha256:33313a1200a3ae90b75587ceac502b048b840fc69e7f7a0905b5f87fac7a1258"},
+]
+
+[package.extras]
+tests = ["pytest (>=3.2.1,!=3.3.0)"]
+typecheck = ["mypy"]
+
+[[package]]
+name = "bitarray"
+version = "2.9.2"
+description = "efficient arrays of booleans -- C extension"
+optional = false
+python-versions = "*"
+files = [
+ {file = "bitarray-2.9.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:917905de565d9576eb20f53c797c15ba88b9f4f19728acabec8d01eee1d3756a"},
+ {file = "bitarray-2.9.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b35bfcb08b7693ab4bf9059111a6e9f14e07d57ac93cd967c420db58ab9b71e1"},
+ {file = "bitarray-2.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ea1923d2e7880f9e1959e035da661767b5a2e16a45dfd57d6aa831e8b65ee1bf"},
+ {file = "bitarray-2.9.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e0b63a565e8a311cc8348ff1262d5784df0f79d64031d546411afd5dd7ef67d"},
+ {file = "bitarray-2.9.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cf0620da2b81946d28c0b16f3e3704d38e9837d85ee4f0652816e2609aaa4fed"},
+ {file = "bitarray-2.9.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:79a9b8b05f2876c7195a2b698c47528e86a73c61ea203394ff8e7a4434bda5c8"},
+ {file = "bitarray-2.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:345c76b349ff145549652436235c5532e5bfe9db690db6f0a6ad301c62b9ef21"},
+ {file = "bitarray-2.9.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4e2936f090bf3f4d1771f44f9077ebccdbc0415d2b598d51a969afcb519df505"},
+ {file = "bitarray-2.9.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:f9346e98fc2abcef90b942973087e2462af6d3e3710e82938078d3493f7fef52"},
+ {file = "bitarray-2.9.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e6ec283d4741befb86e8c3ea2e9ac1d17416c956d392107e45263e736954b1f7"},
+ {file = "bitarray-2.9.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:962892646599529917ef26266091e4cb3077c88b93c3833a909d68dcc971c4e3"},
+ {file = "bitarray-2.9.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:e8da5355d7d75a52df5b84750989e34e39919ec7e59fafc4c104cc1607ab2d31"},
+ {file = "bitarray-2.9.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:603e7d640e54ad764d2b4da6b61e126259af84f253a20f512dd10689566e5478"},
+ {file = "bitarray-2.9.2-cp310-cp310-win32.whl", hash = "sha256:f00079f8e69d75c2a417de7961a77612bb77ef46c09bc74607d86de4740771ef"},
+ {file = "bitarray-2.9.2-cp310-cp310-win_amd64.whl", hash = "sha256:1bb33673e7f7190a65f0a940c1ef63266abdb391f4a3e544a47542d40a81f536"},
+ {file = "bitarray-2.9.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:fe71fd4b76380c2772f96f1e53a524da7063645d647a4fcd3b651bdd80ca0f2e"},
+ {file = "bitarray-2.9.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d527172919cdea1e13994a66d9708a80c3d33dedcf2f0548e4925e600fef3a3a"},
+ {file = "bitarray-2.9.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:052c5073bdcaa9dd10628d99d37a2f33ec09364b86dd1f6281e2d9f8d3db3060"},
+ {file = "bitarray-2.9.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e064caa55a6ed493aca1eda06f8b3f689778bc780a75e6ad7724642ba5dc62f7"},
+ {file = "bitarray-2.9.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:508069a04f658210fdeee85a7a0ca84db4bcc110cbb1d21f692caa13210f24a7"},
+ {file = "bitarray-2.9.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4da73ebd537d75fa7bccfc2228fcaedea0803f21dd9d0bf0d3b67fef3c4af294"},
+ {file = "bitarray-2.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5cb378eaa65cd43098f11ff5d27e48ee3b956d2c00d2d6b5bfc2a09fe183be47"},
+ {file = "bitarray-2.9.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d14c790b91f6cbcd9b718f88ed737c78939980c69ac8c7f03dd7e60040c12951"},
+ {file = "bitarray-2.9.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:7eea9318293bc0ea6447e9ebfba600a62f3428bea7e9c6d42170ae4f481dbab3"},
+ {file = "bitarray-2.9.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:b76ffec27c7450b8a334f967366a9ebadaea66ee43f5b530c12861b1a991f503"},
+ {file = "bitarray-2.9.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:76b76a07d4ee611405045c6950a1e24c4362b6b44808d4ad6eea75e0dbc59af4"},
+ {file = "bitarray-2.9.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:c7d16beeaaab15b075990cd26963d6b5b22e8c5becd131781514a00b8bdd04bd"},
+ {file = "bitarray-2.9.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60df43e868a615c7e15117a1e1c2e5e11f48f6457280eba6ddf8fbefbec7da99"},
+ {file = "bitarray-2.9.2-cp311-cp311-win32.whl", hash = "sha256:e788608ed7767b7b3bbde6d49058bccdf94df0de9ca75d13aa99020cc7e68095"},
+ {file = "bitarray-2.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:a23397da092ef0a8cfe729571da64c2fc30ac18243caa82ac7c4f965087506ff"},
+ {file = "bitarray-2.9.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:90e3a281ffe3897991091b7c46fca38c2675bfd4399ffe79dfeded6c52715436"},
+ {file = "bitarray-2.9.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bed637b674db5e6c8a97a4a321e3e4d73e72d50b5c6b29950008a93069cc64cd"},
+ {file = "bitarray-2.9.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e49066d251dbbe4e6e3a5c3937d85b589e40e2669ad0eef41a00f82ec17d844b"},
+ {file = "bitarray-2.9.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3c4344e96642e2211fb3a50558feff682c31563a4c64529a931769d40832ca79"},
+ {file = "bitarray-2.9.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aeb60962ec4813c539a59fbd4f383509c7222b62c3fb1faa76b54943a613e33a"},
+ {file = "bitarray-2.9.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ed0f7982f10581bb16553719e5e8f933e003f5b22f7d25a68bdb30fac630a6ff"},
+ {file = "bitarray-2.9.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c71d1cabdeee0cdda4669168618f0e46b7dace207b29da7b63aaa1adc2b54081"},
+ {file = "bitarray-2.9.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b0ef2d0a6f1502d38d911d25609b44c6cc27bee0a4363dd295df78b075041b60"},
+ {file = "bitarray-2.9.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:6f71d92f533770fb027388b35b6e11988ab89242b883f48a6fe7202d238c61f8"},
+ {file = "bitarray-2.9.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:ba0734aa300757c924f3faf8148e1b8c247176a0ac8e16aefdf9c1eb19e868f7"},
+ {file = "bitarray-2.9.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:d91406f413ccbf4af6ab5ae7bc78f772a95609f9ddd14123db36ef8c37116d95"},
+ {file = "bitarray-2.9.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:87abb7f80c0a042f3fe8e5264da1a2756267450bb602110d5327b8eaff7682e7"},
+ {file = "bitarray-2.9.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4b558ce85579b51a2e38703877d1e93b7728a7af664dd45a34e833534f0b755d"},
+ {file = "bitarray-2.9.2-cp312-cp312-win32.whl", hash = "sha256:dac2399ee2889fbdd3472bfc2ede74c34cceb1ccf29a339964281a16eb1d3188"},
+ {file = "bitarray-2.9.2-cp312-cp312-win_amd64.whl", hash = "sha256:48a30d718d1a6dfc22a49547450107abe8f4afdf2abdcbe76eb9ed88edc49498"},
+ {file = "bitarray-2.9.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:2c6be1b651fad8f3adb7a5aa12c65b612cd9b89530969af941844ae680f7d981"},
+ {file = "bitarray-2.9.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5b399ae6ab975257ec359f03b48fc00b1c1cd109471e41903548469b8feae5c"},
+ {file = "bitarray-2.9.2-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0b3543c8a1cb286ad105f11c25d8d0f712f41c5c55f90be39f0e5a1376c7d0b0"},
+ {file = "bitarray-2.9.2-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:03adaacb79e2fb8f483ab3a67665eec53bb3fd0cd5dbd7358741aef124688db3"},
+ {file = "bitarray-2.9.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ae5b0657380d2581e13e46864d147a52c1e2bbac9f59b59c576e42fa7d10cf0"},
+ {file = "bitarray-2.9.2-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7c1f4bf6ea8eb9d7f30808c2e9894237a96650adfecbf5f3643862dc5982f89e"},
+ {file = "bitarray-2.9.2-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:a8873089be2aa15494c0f81af1209f6e1237d762c5065bc4766c1b84321e1b50"},
+ {file = "bitarray-2.9.2-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:677e67f50e2559efc677a4366707070933ad5418b8347a603a49a070890b19bc"},
+ {file = "bitarray-2.9.2-cp36-cp36m-musllinux_1_1_ppc64le.whl", hash = "sha256:a620d8ce4ea2f1c73c6b6b1399e14cb68c6915e2be3fad5808c2998ed55b4acf"},
+ {file = "bitarray-2.9.2-cp36-cp36m-musllinux_1_1_s390x.whl", hash = "sha256:64115ccabbdbe279c24c367b629c6b1d3da9ed36c7420129e27c338a3971bfee"},
+ {file = "bitarray-2.9.2-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:5d6fb422772e75385b76ad1c52f45a68bd4efafd8be8d0061c11877be74c4d43"},
+ {file = "bitarray-2.9.2-cp36-cp36m-win32.whl", hash = "sha256:852e202875dd6dfd6139ce7ec4e98dac2b17d8d25934dc99900831e81c3adaef"},
+ {file = "bitarray-2.9.2-cp36-cp36m-win_amd64.whl", hash = "sha256:7dfefdcb0dc6a3ba9936063cec65a74595571b375beabe18742b3d91d087eefd"},
+ {file = "bitarray-2.9.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b306c4cf66912511422060f7f5e1149c8bdb404f8e00e600561b0749fdd45659"},
+ {file = "bitarray-2.9.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a09c4f81635408e3387348f415521d4b94198c562c23330f560596a6aaa26eaf"},
+ {file = "bitarray-2.9.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5361413fd2ecfdf44dc8f065177dc6aba97fa80a91b815586cb388763acf7f8d"},
+ {file = "bitarray-2.9.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e8a9475d415ef1eaae7942df6f780fa4dcd48fce32825eda591a17abba869299"},
+ {file = "bitarray-2.9.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9b87baa7bfff9a5878fcc1bffe49ecde6e647a72a64b39a69cd8a2992a43a34"},
+ {file = "bitarray-2.9.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bb6b86cfdfc503e92cb71c68766a24565359136961642504a7cc9faf936d9c88"},
+ {file = "bitarray-2.9.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:cd56b8ae87ebc71bcacbd73615098e8a8de952ecbb5785b6b4e2b07da8a06e1f"},
+ {file = "bitarray-2.9.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:3fa909cfd675004aed8b4cc9df352415933656e0155a6209d878b7cb615c787e"},
+ {file = "bitarray-2.9.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b069ca9bf728e0c5c5b60e00a89df9af34cc170c695c3bfa3b372d8f40288efb"},
+ {file = "bitarray-2.9.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:6067f2f07a7121749858c7daa93c8774325c91590b3e81a299621e347740c2ae"},
+ {file = "bitarray-2.9.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:321841cdad1dd0f58fe62e80e9c9c7531f8ebf8be93f047401e930dc47425b1e"},
+ {file = "bitarray-2.9.2-cp37-cp37m-win32.whl", hash = "sha256:54e16e32e60973bb83c315de9975bc1bcfc9bd50bb13001c31da159bc49b0ca1"},
+ {file = "bitarray-2.9.2-cp37-cp37m-win_amd64.whl", hash = "sha256:f4dcadb7b8034aa3491ee8f5a69b3d9ba9d7d1e55c3cc1fc45be313e708277f8"},
+ {file = "bitarray-2.9.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:c8919fdbd3bb596b104388b56ae4b266eb28da1f2f7dff2e1f9334a21840fe96"},
+ {file = "bitarray-2.9.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:eb7a9d8a2e400a1026de341ad48e21670a6261a75b06df162c5c39b0d0e7c8f4"},
+ {file = "bitarray-2.9.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6ec84668dd7b937874a2b2c293cd14ba84f37be0d196dead852e0ada9815d807"},
+ {file = "bitarray-2.9.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2de9a31c34e543ae089fd2a5ced01292f725190e379921384f695e2d7184bd3"},
+ {file = "bitarray-2.9.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9521f49ae121a17c0a41e5112249e6fa7f6a571245b1118de81fb86e7c1bc1ce"},
+ {file = "bitarray-2.9.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6cc6545d6d76542aee3d18c1c9485fb7b9812b8df4ebe52c4535ec42081b48f"},
+ {file = "bitarray-2.9.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:856bbe1616425f71c0df5ef2e8755e878d9504d5a531acba58ab4273c52c117a"},
+ {file = "bitarray-2.9.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4bba8042ea6ab331ade91bc435d81ad72fddb098e49108610b0ce7780c14e68"},
+ {file = "bitarray-2.9.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:a035da89c959d98afc813e3c62f052690d67cfd55a36592f25d734b70de7d4b0"},
+ {file = "bitarray-2.9.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6d70b1579da7fb71be5a841a1f965d19aca0ef27f629cfc07d06b09aafd0a333"},
+ {file = "bitarray-2.9.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:405b83bed28efaae6d86b6ab287c75712ead0adbfab2a1075a1b7ab47dad4d62"},
+ {file = "bitarray-2.9.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:7eb8be687c50da0b397d5e0ab7ca200b5ebb639e79a9f5e285851d1944c94be9"},
+ {file = "bitarray-2.9.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:eceb551dfeaf19c609003a69a0cf8264b0efd7abc3791a11dfabf4788daf0d19"},
+ {file = "bitarray-2.9.2-cp38-cp38-win32.whl", hash = "sha256:bb198c6ed1edbcdaf3d1fa3c9c9d1cdb7e179a5134ef5ee660b53cdec43b34e7"},
+ {file = "bitarray-2.9.2-cp38-cp38-win_amd64.whl", hash = "sha256:648d2f2685590b0103c67a937c2fb9e09bcc8dfb166f0c7c77bd341902a6f5b3"},
+ {file = "bitarray-2.9.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:ea816dc8f8e65841a8bbdd30e921edffeeb6f76efe6a1eb0da147b60d539d1cf"},
+ {file = "bitarray-2.9.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4d0e32530f941c41eddfc77600ec89b65184cb909c549336463a738fab3ed285"},
+ {file = "bitarray-2.9.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4a22266fb416a3b6c258bf7f83c9fe531ba0b755a56986a81ad69dc0f3bcc070"},
+ {file = "bitarray-2.9.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc6d3e80dd8239850f2604833ff3168b28909c8a9357abfed95632cccd17e3e7"},
+ {file = "bitarray-2.9.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f135e804986b12bf14f2cd1eb86674c47dea86c4c5f0fa13c88978876b97ebe6"},
+ {file = "bitarray-2.9.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:87580c7f7d14f7ec401eda7adac1e2a25e95153e9c339872c8ae61b3208819a1"},
+ {file = "bitarray-2.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64b433e26993127732ac7b66a7821b2537c3044355798de7c5fcb0af34b8296f"},
+ {file = "bitarray-2.9.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1e497c535f2a9b68c69d36631bf2dba243e05eb343b00b9c7bbdc8c601c6802d"},
+ {file = "bitarray-2.9.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e40b3cb9fa1edb4e0175d7c06345c49c7925fe93e39ef55ecb0bc40c906b0c09"},
+ {file = "bitarray-2.9.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:f2f8692f95c9e377eb19ca519d30d1f884b02feb7e115f798de47570a359e43f"},
+ {file = "bitarray-2.9.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:f0b84fc50b6dbeced4fa390688c07c10a73222810fb0e08392bd1a1b8259de36"},
+ {file = "bitarray-2.9.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:d656ad38c942e38a470ddbce26b5020e08e1a7ea86b8fd413bb9024b5189993a"},
+ {file = "bitarray-2.9.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6ab0f1dbfe5070db98771a56aa14797595acd45a1af9eadfb193851a270e7996"},
+ {file = "bitarray-2.9.2-cp39-cp39-win32.whl", hash = "sha256:0a99b23ac845a9ea3157782c97465e6ae026fe0c7c4c1ed1d88f759fd6ea52d9"},
+ {file = "bitarray-2.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:9bbcfc7c279e8d74b076e514e669b683f77b4a2a328585b3f16d4c5259c91222"},
+ {file = "bitarray-2.9.2-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:43847799461d8ba71deb4d97b47250c2c2fb66d82cd3cb8b4caf52bb97c03034"},
+ {file = "bitarray-2.9.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4f44381b0a4bdf64416082f4f0e7140377ae962c0ced6f983c6d7bbfc034040"},
+ {file = "bitarray-2.9.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a484061616fb4b158b80789bd3cb511f399d2116525a8b29b6334c68abc2310f"},
+ {file = "bitarray-2.9.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1ff9e38356cc803e06134cf8ae9758e836ccd1b793135ef3db53c7c5d71e93bc"},
+ {file = "bitarray-2.9.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:b44105792fbdcfbda3e26ee88786790fda409da4c71f6c2b73888108cf8f062f"},
+ {file = "bitarray-2.9.2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:7e913098de169c7fc890638ce5e171387363eb812579e637c44261460ac00aa2"},
+ {file = "bitarray-2.9.2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6fe315355cdfe3ed22ef355b8bdc81a805ca4d0949d921576560e5b227a1112"},
+ {file = "bitarray-2.9.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f708e91fdbe443f3bec2df394ed42328fb9b0446dff5cb4199023ac6499e09fd"},
+ {file = "bitarray-2.9.2-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b7b09489b71f9f1f64c0fa0977e250ec24500767dab7383ba9912495849cadf"},
+ {file = "bitarray-2.9.2-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:128cc3488176145b9b137fdcf54c1c201809bbb8dd30b260ee40afe915843b43"},
+ {file = "bitarray-2.9.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:21f21e7f56206be346bdbda2a6bdb2165a5e6a11821f88fd4911c5a6bbbdc7e2"},
+ {file = "bitarray-2.9.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5f4dd3af86dd8a617eb6464622fb64ca86e61ce99b59b5c35d8cd33f9c30603d"},
+ {file = "bitarray-2.9.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6465de861aff7a2559f226b37982007417eab8c3557543879987f58b453519bd"},
+ {file = "bitarray-2.9.2-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dbaf2bb71d6027152d603f1d5f31e0dfd5e50173d06f877bec484e5396d4594b"},
+ {file = "bitarray-2.9.2-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:2f32948c86e0d230a296686db28191b67ed229756f84728847daa0c7ab7406e3"},
+ {file = "bitarray-2.9.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:be94e5a685e60f9d24532af8fe5c268002e9016fa80272a94727f435de3d1003"},
+ {file = "bitarray-2.9.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5cc9381fd54f3c23ae1039f977bfd6d041a5c3c1518104f616643c3a5a73b15"},
+ {file = "bitarray-2.9.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd926e8ae4d1ed1ac4a8f37212a62886292f692bc1739fde98013bf210c2d175"},
+ {file = "bitarray-2.9.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:461a3dafb9d5fda0bb3385dc507d78b1984b49da3fe4c6d56c869a54373b7008"},
+ {file = "bitarray-2.9.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:393cb27fd859af5fd9c16eb26b1c59b17b390ff66b3ae5d0dd258270191baf13"},
+ {file = "bitarray-2.9.2.tar.gz", hash = "sha256:a8f286a51a32323715d77755ed959f94bef13972e9a2fe71b609e40e6d27957e"},
+]
+
+[[package]]
+name = "bitsandbytes"
+version = "0.42.0"
+description = "k-bit optimizers and matrix multiplication routines."
+optional = false
+python-versions = "*"
+files = [
+ {file = "bitsandbytes-0.42.0-py3-none-any.whl", hash = "sha256:63798680912cc63bb77b535a2d0860af024e290a52e157f777ad2a52e2585967"},
+ {file = "bitsandbytes-0.42.0.tar.gz", hash = "sha256:fc1505f184f0d275766f2a6c663f1a43b734c1409b5c5a406f3a6073d9f329fd"},
+]
+
+[package.dependencies]
+scipy = "*"
+
+[[package]]
+name = "black"
+version = "23.12.1"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "black-23.12.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0aaf6041986767a5e0ce663c7a2f0e9eaf21e6ff87a5f95cbf3675bfd4c41d2"},
+ {file = "black-23.12.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c88b3711d12905b74206227109272673edce0cb29f27e1385f33b0163c414bba"},
+ {file = "black-23.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a920b569dc6b3472513ba6ddea21f440d4b4c699494d2e972a1753cdc25df7b0"},
+ {file = "black-23.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:3fa4be75ef2a6b96ea8d92b1587dd8cb3a35c7e3d51f0738ced0781c3aa3a5a3"},
+ {file = "black-23.12.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8d4df77958a622f9b5a4c96edb4b8c0034f8434032ab11077ec6c56ae9f384ba"},
+ {file = "black-23.12.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:602cfb1196dc692424c70b6507593a2b29aac0547c1be9a1d1365f0d964c353b"},
+ {file = "black-23.12.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c4352800f14be5b4864016882cdba10755bd50805c95f728011bcb47a4afd59"},
+ {file = "black-23.12.1-cp311-cp311-win_amd64.whl", hash = "sha256:0808494f2b2df923ffc5723ed3c7b096bd76341f6213989759287611e9837d50"},
+ {file = "black-23.12.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:25e57fd232a6d6ff3f4478a6fd0580838e47c93c83eaf1ccc92d4faf27112c4e"},
+ {file = "black-23.12.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2d9e13db441c509a3763a7a3d9a49ccc1b4e974a47be4e08ade2a228876500ec"},
+ {file = "black-23.12.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d1bd9c210f8b109b1762ec9fd36592fdd528485aadb3f5849b2740ef17e674e"},
+ {file = "black-23.12.1-cp312-cp312-win_amd64.whl", hash = "sha256:ae76c22bde5cbb6bfd211ec343ded2163bba7883c7bc77f6b756a1049436fbb9"},
+ {file = "black-23.12.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1fa88a0f74e50e4487477bc0bb900c6781dbddfdfa32691e780bf854c3b4a47f"},
+ {file = "black-23.12.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a4d6a9668e45ad99d2f8ec70d5c8c04ef4f32f648ef39048d010b0689832ec6d"},
+ {file = "black-23.12.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b18fb2ae6c4bb63eebe5be6bd869ba2f14fd0259bda7d18a46b764d8fb86298a"},
+ {file = "black-23.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:c04b6d9d20e9c13f43eee8ea87d44156b8505ca8a3c878773f68b4e4812a421e"},
+ {file = "black-23.12.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3e1b38b3135fd4c025c28c55ddfc236b05af657828a8a6abe5deec419a0b7055"},
+ {file = "black-23.12.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4f0031eaa7b921db76decd73636ef3a12c942ed367d8c3841a0739412b260a54"},
+ {file = "black-23.12.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97e56155c6b737854e60a9ab1c598ff2533d57e7506d97af5481141671abf3ea"},
+ {file = "black-23.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:dd15245c8b68fe2b6bd0f32c1556509d11bb33aec9b5d0866dd8e2ed3dba09c2"},
+ {file = "black-23.12.1-py3-none-any.whl", hash = "sha256:78baad24af0f033958cad29731e27363183e140962595def56423e626f4bee3e"},
+ {file = "black-23.12.1.tar.gz", hash = "sha256:4ce3ef14ebe8d9509188014d96af1c456a910d5b5cbf434a09fef7e024b3d0d5"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
+[[package]]
+name = "blinker"
+version = "1.7.0"
+description = "Fast, simple object-to-object and broadcast signaling"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "blinker-1.7.0-py3-none-any.whl", hash = "sha256:c3f865d4d54db7abc53758a01601cf343fe55b84c1de4e3fa910e420b438d5b9"},
+ {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
+]
+
+[[package]]
+name = "cachetools"
+version = "5.3.2"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+ {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+]
+
+[[package]]
+name = "certifi"
+version = "2023.11.17"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
+ {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
+]
+
+[[package]]
+name = "cffi"
+version = "1.16.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"},
+ {file = "cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9"},
+ {file = "cffi-1.16.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673"},
+ {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896"},
+ {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684"},
+ {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7"},
+ {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614"},
+ {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743"},
+ {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d"},
+ {file = "cffi-1.16.0-cp310-cp310-win32.whl", hash = "sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a"},
+ {file = "cffi-1.16.0-cp310-cp310-win_amd64.whl", hash = "sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1"},
+ {file = "cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b84834d0cf97e7d27dd5b7f3aca7b6e9263c56308ab9dc8aae9784abb774d404"},
+ {file = "cffi-1.16.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417"},
+ {file = "cffi-1.16.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627"},
+ {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8a9d3ebe49f084ad71f9269834ceccbf398253c9fac910c4fd7053ff1386936"},
+ {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d"},
+ {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56"},
+ {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e"},
+ {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c6a164aa47843fb1b01e941d385aab7215563bb8816d80ff3a363a9f8448a8dc"},
+ {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb"},
+ {file = "cffi-1.16.0-cp311-cp311-win32.whl", hash = "sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab"},
+ {file = "cffi-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:db8e577c19c0fda0beb7e0d4e09e0ba74b1e4c092e0e40bfa12fe05b6f6d75ba"},
+ {file = "cffi-1.16.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956"},
+ {file = "cffi-1.16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e"},
+ {file = "cffi-1.16.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e"},
+ {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2"},
+ {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357"},
+ {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b7be2d771cdba2942e13215c4e340bfd76398e9227ad10402a8767ab1865d2e6"},
+ {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969"},
+ {file = "cffi-1.16.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520"},
+ {file = "cffi-1.16.0-cp312-cp312-win32.whl", hash = "sha256:b2ca4e77f9f47c55c194982e10f058db063937845bb2b7a86c84a6cfe0aefa8b"},
+ {file = "cffi-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235"},
+ {file = "cffi-1.16.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc"},
+ {file = "cffi-1.16.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0"},
+ {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b"},
+ {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c"},
+ {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b"},
+ {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324"},
+ {file = "cffi-1.16.0-cp38-cp38-win32.whl", hash = "sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a"},
+ {file = "cffi-1.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36"},
+ {file = "cffi-1.16.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed"},
+ {file = "cffi-1.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b29ebffcf550f9da55bec9e02ad430c992a87e5f512cd63388abb76f1036d8d2"},
+ {file = "cffi-1.16.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872"},
+ {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8"},
+ {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f"},
+ {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c0f31130ebc2d37cdd8e44605fb5fa7ad59049298b3f745c74fa74c62fbfcfc4"},
+ {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098"},
+ {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000"},
+ {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe"},
+ {file = "cffi-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4"},
+ {file = "cffi-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8"},
+ {file = "cffi-1.16.0.tar.gz", hash = "sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0"},
+]
+
+[package.dependencies]
+pycparser = "*"
+
+[[package]]
+name = "chardet"
+version = "5.2.0"
+description = "Universal encoding detector for Python 3"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
+ {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+ {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
+ {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
+ {file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"},
+ {file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"},
+ {file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-win32.whl", hash = "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25"},
+ {file = "charset_normalizer-3.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"},
+ {file = "charset_normalizer-3.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d"},
+ {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
+]
+
+[[package]]
+name = "clearml"
+version = "1.13.2"
+description = "ClearML - Auto-Magical Experiment Manager, Version Control, and MLOps for AI"
+optional = false
+python-versions = "*"
+files = [
+ {file = "clearml-1.13.2-py2.py3-none-any.whl", hash = "sha256:5e75246ea7eaaea8a9f1d0a3f16f1dd1038994f18e8fcda44404496ce4c259d6"},
+]
+
+[package.dependencies]
+attrs = ">=18.0"
+furl = ">=2.0.0"
+jsonschema = ">=2.6.0"
+numpy = ">=1.10"
+pathlib2 = ">=2.3.0"
+Pillow = ">=4.1.1"
+psutil = ">=3.4.2"
+pyjwt = {version = ">=2.4.0,<2.9.0", markers = "python_version > \"3.5\""}
+pyparsing = ">=2.0.3"
+python-dateutil = ">=2.6.1"
+PyYAML = ">=3.12"
+referencing = {version = "<0.40", markers = "python_version >= \"3.8\""}
+requests = ">=2.20.0"
+six = ">=1.13.0"
+urllib3 = ">=1.21.1"
+
+[package.extras]
+azure = ["azure-storage-blob (>=12.0.0)"]
+gs = ["google-cloud-storage (>=1.13.2)"]
+s3 = ["boto3 (>=1.9)"]
+
+[[package]]
+name = "click"
+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
+ {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+description = "Cross-platform colored terminal text."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+files = [
+ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
+ {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
+]
+
+[[package]]
+name = "contextlib2"
+version = "21.6.0"
+description = "Backports and enhancements for the contextlib module"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "contextlib2-21.6.0-py2.py3-none-any.whl", hash = "sha256:3fbdb64466afd23abaf6c977627b75b6139a5a3e8ce38405c5b413aed7a0471f"},
+ {file = "contextlib2-21.6.0.tar.gz", hash = "sha256:ab1e2bfe1d01d968e1b7e8d9023bc51ef3509bba217bb730cee3827e1ee82869"},
+]
+
+[[package]]
+name = "contourpy"
+version = "1.2.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.9"
+files = [
+ {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"},
+ {file = "contourpy-1.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ab459a1cbbf18e8698399c595a01f6dcc5c138220ca3ea9e7e6126232d102bb4"},
+ {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fdd887f17c2f4572ce548461e4f96396681212d858cae7bd52ba3310bc6f00f"},
+ {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5d16edfc3fc09968e09ddffada434b3bf989bf4911535e04eada58469873e28e"},
+ {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1c203f617abc0dde5792beb586f827021069fb6d403d7f4d5c2b543d87edceb9"},
+ {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b69303ceb2e4d4f146bf82fda78891ef7bcd80c41bf16bfca3d0d7eb545448aa"},
+ {file = "contourpy-1.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:884c3f9d42d7218304bc74a8a7693d172685c84bd7ab2bab1ee567b769696df9"},
+ {file = "contourpy-1.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4a1b1208102be6e851f20066bf0e7a96b7d48a07c9b0cfe6d0d4545c2f6cadab"},
+ {file = "contourpy-1.2.0-cp310-cp310-win32.whl", hash = "sha256:34b9071c040d6fe45d9826cbbe3727d20d83f1b6110d219b83eb0e2a01d79488"},
+ {file = "contourpy-1.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:bd2f1ae63998da104f16a8b788f685e55d65760cd1929518fd94cd682bf03e41"},
+ {file = "contourpy-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:dd10c26b4eadae44783c45ad6655220426f971c61d9b239e6f7b16d5cdaaa727"},
+ {file = "contourpy-1.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5c6b28956b7b232ae801406e529ad7b350d3f09a4fde958dfdf3c0520cdde0dd"},
+ {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ebeac59e9e1eb4b84940d076d9f9a6cec0064e241818bcb6e32124cc5c3e377a"},
+ {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:139d8d2e1c1dd52d78682f505e980f592ba53c9f73bd6be102233e358b401063"},
+ {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1e9dc350fb4c58adc64df3e0703ab076f60aac06e67d48b3848c23647ae4310e"},
+ {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18fc2b4ed8e4a8fe849d18dce4bd3c7ea637758c6343a1f2bae1e9bd4c9f4686"},
+ {file = "contourpy-1.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:16a7380e943a6d52472096cb7ad5264ecee36ed60888e2a3d3814991a0107286"},
+ {file = "contourpy-1.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8d8faf05be5ec8e02a4d86f616fc2a0322ff4a4ce26c0f09d9f7fb5330a35c95"},
+ {file = "contourpy-1.2.0-cp311-cp311-win32.whl", hash = "sha256:67b7f17679fa62ec82b7e3e611c43a016b887bd64fb933b3ae8638583006c6d6"},
+ {file = "contourpy-1.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:99ad97258985328b4f207a5e777c1b44a83bfe7cf1f87b99f9c11d4ee477c4de"},
+ {file = "contourpy-1.2.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:575bcaf957a25d1194903a10bc9f316c136c19f24e0985a2b9b5608bdf5dbfe0"},
+ {file = "contourpy-1.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9e6c93b5b2dbcedad20a2f18ec22cae47da0d705d454308063421a3b290d9ea4"},
+ {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:464b423bc2a009088f19bdf1f232299e8b6917963e2b7e1d277da5041f33a779"},
+ {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:68ce4788b7d93e47f84edd3f1f95acdcd142ae60bc0e5493bfd120683d2d4316"},
+ {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d7d1f8871998cdff5d2ff6a087e5e1780139abe2838e85b0b46b7ae6cc25399"},
+ {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6e739530c662a8d6d42c37c2ed52a6f0932c2d4a3e8c1f90692ad0ce1274abe0"},
+ {file = "contourpy-1.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:247b9d16535acaa766d03037d8e8fb20866d054d3c7fbf6fd1f993f11fc60ca0"},
+ {file = "contourpy-1.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:461e3ae84cd90b30f8d533f07d87c00379644205b1d33a5ea03381edc4b69431"},
+ {file = "contourpy-1.2.0-cp312-cp312-win32.whl", hash = "sha256:1c2559d6cffc94890b0529ea7eeecc20d6fadc1539273aa27faf503eb4656d8f"},
+ {file = "contourpy-1.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:491b1917afdd8638a05b611a56d46587d5a632cabead889a5440f7c638bc6ed9"},
+ {file = "contourpy-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5fd1810973a375ca0e097dee059c407913ba35723b111df75671a1976efa04bc"},
+ {file = "contourpy-1.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:999c71939aad2780f003979b25ac5b8f2df651dac7b38fb8ce6c46ba5abe6ae9"},
+ {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7caf9b241464c404613512d5594a6e2ff0cc9cb5615c9475cc1d9b514218ae8"},
+ {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:266270c6f6608340f6c9836a0fb9b367be61dde0c9a9a18d5ece97774105ff3e"},
+ {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dbd50d0a0539ae2e96e537553aff6d02c10ed165ef40c65b0e27e744a0f10af8"},
+ {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11f8d2554e52f459918f7b8e6aa20ec2a3bce35ce95c1f0ef4ba36fbda306df5"},
+ {file = "contourpy-1.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ce96dd400486e80ac7d195b2d800b03e3e6a787e2a522bfb83755938465a819e"},
+ {file = "contourpy-1.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6d3364b999c62f539cd403f8123ae426da946e142312a514162adb2addd8d808"},
+ {file = "contourpy-1.2.0-cp39-cp39-win32.whl", hash = "sha256:1c88dfb9e0c77612febebb6ac69d44a8d81e3dc60f993215425b62c1161353f4"},
+ {file = "contourpy-1.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:78e6ad33cf2e2e80c5dfaaa0beec3d61face0fb650557100ee36db808bfa6843"},
+ {file = "contourpy-1.2.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:be16975d94c320432657ad2402f6760990cb640c161ae6da1363051805fa8108"},
+ {file = "contourpy-1.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b95a225d4948b26a28c08307a60ac00fb8671b14f2047fc5476613252a129776"},
+ {file = "contourpy-1.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:0d7e03c0f9a4f90dc18d4e77e9ef4ec7b7bbb437f7f675be8e530d65ae6ef956"},
+ {file = "contourpy-1.2.0.tar.gz", hash = "sha256:171f311cb758de7da13fc53af221ae47a5877be5a0843a9fe150818c51ed276a"},
+]
+
+[package.dependencies]
+numpy = ">=1.20,<2.0"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+
+[[package]]
+name = "cryptography"
+version = "41.0.7"
+description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "cryptography-41.0.7-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:3c78451b78313fa81607fa1b3f1ae0a5ddd8014c38a02d9db0616133987b9cdf"},
+ {file = "cryptography-41.0.7-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:928258ba5d6f8ae644e764d0f996d61a8777559f72dfeb2eea7e2fe0ad6e782d"},
+ {file = "cryptography-41.0.7-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a1b41bc97f1ad230a41657d9155113c7521953869ae57ac39ac7f1bb471469a"},
+ {file = "cryptography-41.0.7-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:841df4caa01008bad253bce2a6f7b47f86dc9f08df4b433c404def869f590a15"},
+ {file = "cryptography-41.0.7-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:5429ec739a29df2e29e15d082f1d9ad683701f0ec7709ca479b3ff2708dae65a"},
+ {file = "cryptography-41.0.7-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:43f2552a2378b44869fe8827aa19e69512e3245a219104438692385b0ee119d1"},
+ {file = "cryptography-41.0.7-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:af03b32695b24d85a75d40e1ba39ffe7db7ffcb099fe507b39fd41a565f1b157"},
+ {file = "cryptography-41.0.7-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:49f0805fc0b2ac8d4882dd52f4a3b935b210935d500b6b805f321addc8177406"},
+ {file = "cryptography-41.0.7-cp37-abi3-win32.whl", hash = "sha256:f983596065a18a2183e7f79ab3fd4c475205b839e02cbc0efbbf9666c4b3083d"},
+ {file = "cryptography-41.0.7-cp37-abi3-win_amd64.whl", hash = "sha256:90452ba79b8788fa380dfb587cca692976ef4e757b194b093d845e8d99f612f2"},
+ {file = "cryptography-41.0.7-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:079b85658ea2f59c4f43b70f8119a52414cdb7be34da5d019a77bf96d473b960"},
+ {file = "cryptography-41.0.7-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:b640981bf64a3e978a56167594a0e97db71c89a479da8e175d8bb5be5178c003"},
+ {file = "cryptography-41.0.7-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:e3114da6d7f95d2dee7d3f4eec16dacff819740bbab931aff8648cb13c5ff5e7"},
+ {file = "cryptography-41.0.7-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d5ec85080cce7b0513cfd233914eb8b7bbd0633f1d1703aa28d1dd5a72f678ec"},
+ {file = "cryptography-41.0.7-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:7a698cb1dac82c35fcf8fe3417a3aaba97de16a01ac914b89a0889d364d2f6be"},
+ {file = "cryptography-41.0.7-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:37a138589b12069efb424220bf78eac59ca68b95696fc622b6ccc1c0a197204a"},
+ {file = "cryptography-41.0.7-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:68a2dec79deebc5d26d617bfdf6e8aab065a4f34934b22d3b5010df3ba36612c"},
+ {file = "cryptography-41.0.7-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:09616eeaef406f99046553b8a40fbf8b1e70795a91885ba4c96a70793de5504a"},
+ {file = "cryptography-41.0.7-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:48a0476626da912a44cc078f9893f292f0b3e4c739caf289268168d8f4702a39"},
+ {file = "cryptography-41.0.7-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c7f3201ec47d5207841402594f1d7950879ef890c0c495052fa62f58283fde1a"},
+ {file = "cryptography-41.0.7-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:c5ca78485a255e03c32b513f8c2bc39fedb7f5c5f8535545bdc223a03b24f248"},
+ {file = "cryptography-41.0.7-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:d6c391c021ab1f7a82da5d8d0b3cee2f4b2c455ec86c8aebbc84837a631ff309"},
+ {file = "cryptography-41.0.7.tar.gz", hash = "sha256:13f93ce9bea8016c253b34afc6bd6a75993e5c40672ed5405a9c832f0d4a00bc"},
+]
+
+[package.dependencies]
+cffi = ">=1.12"
+
+[package.extras]
+docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
+docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
+nox = ["nox"]
+pep8test = ["black", "check-sdist", "mypy", "ruff"]
+sdist = ["build"]
+ssh = ["bcrypt (>=3.1.5)"]
+test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test-randomorder = ["pytest-randomly"]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+description = "Composable style cycles"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
+ {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
+]
+
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
+[[package]]
+name = "cython"
+version = "3.0.8"
+description = "The Cython compiler for writing C extensions in the Python language."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+ {file = "Cython-3.0.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a846e0a38e2b24e9a5c5dc74b0e54c6e29420d88d1dafabc99e0fc0f3e338636"},
+ {file = "Cython-3.0.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45523fdc2b78d79b32834cc1cc12dc2ca8967af87e22a3ee1bff20e77c7f5520"},
+ {file = "Cython-3.0.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa0b7f3f841fe087410cab66778e2d3fb20ae2d2078a2be3dffe66c6574be39"},
+ {file = "Cython-3.0.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e87294e33e40c289c77a135f491cd721bd089f193f956f7b8ed5aa2d0b8c558f"},
+ {file = "Cython-3.0.8-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:a1df7a129344b1215c20096d33c00193437df1a8fcca25b71f17c23b1a44f782"},
+ {file = "Cython-3.0.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:13c2a5e57a0358da467d97667297bf820b62a1a87ae47c5f87938b9bb593acbd"},
+ {file = "Cython-3.0.8-cp310-cp310-win32.whl", hash = "sha256:96b028f044f5880e3cb18ecdcfc6c8d3ce9d0af28418d5ab464509f26d8adf12"},
+ {file = "Cython-3.0.8-cp310-cp310-win_amd64.whl", hash = "sha256:8140597a8b5cc4f119a1190f5a2228a84f5ca6d8d9ec386cfce24663f48b2539"},
+ {file = "Cython-3.0.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:aae26f9663e50caf9657148403d9874eea41770ecdd6caf381d177c2b1bb82ba"},
+ {file = "Cython-3.0.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:547eb3cdb2f8c6f48e6865d5a741d9dd051c25b3ce076fbca571727977b28ac3"},
+ {file = "Cython-3.0.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5a567d4b9ba70b26db89d75b243529de9e649a2f56384287533cf91512705bee"},
+ {file = "Cython-3.0.8-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:51d1426263b0e82fb22bda8ea60dc77a428581cc19e97741011b938445d383f1"},
+ {file = "Cython-3.0.8-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c26daaeccda072459b48d211415fd1e5507c06bcd976fa0d5b8b9f1063467d7b"},
+ {file = "Cython-3.0.8-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:289ce7838208211cd166e975865fd73b0649bf118170b6cebaedfbdaf4a37795"},
+ {file = "Cython-3.0.8-cp311-cp311-win32.whl", hash = "sha256:c8aa05f5e17f8042a3be052c24f2edc013fb8af874b0bf76907d16c51b4e7871"},
+ {file = "Cython-3.0.8-cp311-cp311-win_amd64.whl", hash = "sha256:000dc9e135d0eec6ecb2b40a5b02d0868a2f8d2e027a41b0fe16a908a9e6de02"},
+ {file = "Cython-3.0.8-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:90d3fe31db55685d8cb97d43b0ec39ef614fcf660f83c77ed06aa670cb0e164f"},
+ {file = "Cython-3.0.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e24791ddae2324e88e3c902a765595c738f19ae34ee66bfb1a6dac54b1833419"},
+ {file = "Cython-3.0.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f020fa1c0552052e0660790b8153b79e3fc9a15dbd8f1d0b841fe5d204a6ae6"},
+ {file = "Cython-3.0.8-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:18bfa387d7a7f77d7b2526af69a65dbd0b731b8d941aaff5becff8e21f6d7717"},
+ {file = "Cython-3.0.8-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:fe81b339cffd87c0069c6049b4d33e28bdd1874625ee515785bf42c9fdff3658"},
+ {file = "Cython-3.0.8-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:80fd94c076e1e1b1ee40a309be03080b75f413e8997cddcf401a118879863388"},
+ {file = "Cython-3.0.8-cp312-cp312-win32.whl", hash = "sha256:85077915a93e359a9b920280d214dc0cf8a62773e1f3d7d30fab8ea4daed670c"},
+ {file = "Cython-3.0.8-cp312-cp312-win_amd64.whl", hash = "sha256:0cb2dcc565c7851f75d496f724a384a790fab12d1b82461b663e66605bec429a"},
+ {file = "Cython-3.0.8-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:870d2a0a7e3cbd5efa65aecdb38d715ea337a904ea7bb22324036e78fb7068e7"},
+ {file = "Cython-3.0.8-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7e8f2454128974905258d86534f4fd4f91d2f1343605657ecab779d80c9d6d5e"},
+ {file = "Cython-3.0.8-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1949d6aa7bc792554bee2b67a9fe41008acbfe22f4f8df7b6ec7b799613a4b3"},
+ {file = "Cython-3.0.8-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c9f2c6e1b8f3bcd6cb230bac1843f85114780bb8be8614855b1628b36bb510e0"},
+ {file = "Cython-3.0.8-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:05d7eddc668ae7993643f32c7661f25544e791edb745758672ea5b1a82ecffa6"},
+ {file = "Cython-3.0.8-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:bfabe115deef4ada5d23c87bddb11289123336dcc14347011832c07db616dd93"},
+ {file = "Cython-3.0.8-cp36-cp36m-win32.whl", hash = "sha256:0c38c9f0bcce2df0c3347285863621be904ac6b64c5792d871130569d893efd7"},
+ {file = "Cython-3.0.8-cp36-cp36m-win_amd64.whl", hash = "sha256:6c46939c3983217d140999de7c238c3141f56b1ea349e47ca49cae899969aa2c"},
+ {file = "Cython-3.0.8-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:115f0a50f752da6c99941b103b5cb090da63eb206abbc7c2ad33856ffc73f064"},
+ {file = "Cython-3.0.8-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9c0f29246734561c90f36e70ed0506b61aa3d044e4cc4cba559065a2a741fae"},
+ {file = "Cython-3.0.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ab75242869ff71e5665fe5c96f3378e79e792fa3c11762641b6c5afbbbbe026"},
+ {file = "Cython-3.0.8-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6717c06e9cfc6c1df18543cd31a21f5d8e378a40f70c851fa2d34f0597037abc"},
+ {file = "Cython-3.0.8-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:9d3f74388db378a3c6fd06e79a809ed98df3f56484d317b81ee762dbf3c263e0"},
+ {file = "Cython-3.0.8-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ae7ac561fd8253a9ae96311e91d12af5f701383564edc11d6338a7b60b285a6f"},
+ {file = "Cython-3.0.8-cp37-cp37m-win32.whl", hash = "sha256:97b2a45845b993304f1799664fa88da676ee19442b15fdcaa31f9da7e1acc434"},
+ {file = "Cython-3.0.8-cp37-cp37m-win_amd64.whl", hash = "sha256:9e2be2b340fea46fb849d378f9b80d3c08ff2e81e2bfbcdb656e2e3cd8c6b2dc"},
+ {file = "Cython-3.0.8-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2cde23c555470db3f149ede78b518e8274853745289c956a0e06ad8d982e4db9"},
+ {file = "Cython-3.0.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7990ca127e1f1beedaf8fc8bf66541d066ef4723ad7d8d47a7cbf842e0f47580"},
+ {file = "Cython-3.0.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b983c8e6803f016146c26854d9150ddad5662960c804ea7f0c752c9266752f0"},
+ {file = "Cython-3.0.8-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a973268d7ca1a2bdf78575e459a94a78e1a0a9bb62a7db0c50041949a73b02ff"},
+ {file = "Cython-3.0.8-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:61a237bc9dd23c7faef0fcfce88c11c65d0c9bb73c74ccfa408b3a012073c20e"},
+ {file = "Cython-3.0.8-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:3a3d67f079598af49e90ff9655bf85bd358f093d727eb21ca2708f467c489cae"},
+ {file = "Cython-3.0.8-cp38-cp38-win32.whl", hash = "sha256:17a642bb01a693e34c914106566f59844b4461665066613913463a719e0dd15d"},
+ {file = "Cython-3.0.8-cp38-cp38-win_amd64.whl", hash = "sha256:2cdfc32252f3b6dc7c94032ab744dcedb45286733443c294d8f909a4854e7f83"},
+ {file = "Cython-3.0.8-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fa97893d99385386925d00074654aeae3a98867f298d1e12ceaf38a9054a9bae"},
+ {file = "Cython-3.0.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f05c0bf9d085c031df8f583f0d506aa3be1692023de18c45d0aaf78685bbb944"},
+ {file = "Cython-3.0.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de892422582f5758bd8de187e98ac829330ec1007bc42c661f687792999988a7"},
+ {file = "Cython-3.0.8-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:314f2355a1f1d06e3c431eaad4708cf10037b5e91e4b231d89c913989d0bdafd"},
+ {file = "Cython-3.0.8-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:78825a3774211e7d5089730f00cdf7f473042acc9ceb8b9eeebe13ed3a5541de"},
+ {file = "Cython-3.0.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:df8093deabc55f37028190cf5e575c26aad23fc673f34b85d5f45076bc37ce39"},
+ {file = "Cython-3.0.8-cp39-cp39-win32.whl", hash = "sha256:1aca1b97e0095b3a9a6c33eada3f661a4ed0d499067d121239b193e5ba3bb4f0"},
+ {file = "Cython-3.0.8-cp39-cp39-win_amd64.whl", hash = "sha256:16873d78be63bd38ffb759da7ab82814b36f56c769ee02b1d5859560e4c3ac3c"},
+ {file = "Cython-3.0.8-py2.py3-none-any.whl", hash = "sha256:171b27051253d3f9108e9759e504ba59ff06e7f7ba944457f94deaf9c21bf0b6"},
+ {file = "Cython-3.0.8.tar.gz", hash = "sha256:8333423d8fd5765e7cceea3a9985dd1e0a5dfeb2734629e1a2ed2d6233d39de6"},
+]
+
+[[package]]
+name = "datasets"
+version = "2.18.0"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "datasets-2.18.0-py3-none-any.whl", hash = "sha256:f1bbf0e2896917a914de01cbd37075b14deea3837af87ad0d9f697388ccaeb50"},
+ {file = "datasets-2.18.0.tar.gz", hash = "sha256:cdf8b8c6abf7316377ba4f49f9589a4c74556d6b481afd0abd2284f3d69185cb"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+dill = ">=0.3.0,<0.3.9"
+filelock = "*"
+fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
+huggingface-hub = ">=0.19.4"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=12.0.0"
+pyarrow-hotfix = "*"
+pyyaml = ">=5.1"
+requests = ">=2.19.0"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0)"]
+audio = ["librosa", "soundfile (>=0.12.1)"]
+benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
+metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
+quality = ["ruff (>=0.3.0)"]
+s3 = ["s3fs"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+torch = ["torch"]
+vision = ["Pillow (>=6.2.1)"]
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
+ {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+]
+
+[[package]]
+name = "deepspeed"
+version = "0.12.6"
+description = "DeepSpeed library"
+optional = false
+python-versions = "*"
+files = [
+ {file = "deepspeed-0.12.6.tar.gz", hash = "sha256:69ea07c65ef6414f9cd67746672f1c23b4b629dc14c9177de103ac0c5b2e0ce4"},
+]
+
+[package.dependencies]
+hjson = "*"
+ninja = "*"
+numpy = "*"
+packaging = ">=20.0"
+psutil = "*"
+py-cpuinfo = "*"
+pydantic = "*"
+pynvml = "*"
+torch = "*"
+tqdm = "*"
+
+[package.extras]
+1bit-mpi = ["mpi4py"]
+all = ["accelerate", "autodoc_pydantic", "clang-format (==16.0.2)", "coverage", "deepspeed-kernels", "diffusers", "docutils (<0.18)", "future", "google", "hjson", "importlib-metadata (>=4)", "lm-eval (==0.3.0)", "mpi4py", "mup", "neural-compressor (==2.1.0)", "packaging", "pre-commit (>=2.20.0)", "protobuf", "psutil", "py-cpuinfo", "pydantic (<2.0.0)", "pytest", "pytest-forked", "pytest-randomly", "pytest-xdist", "recommonmark", "sphinx", "sphinx-rtd-theme", "sphinx_rtd_theme", "tabulate", "tensorboard", "torch", "torchvision", "tqdm", "transformers (>=4.32.1)", "transformers[sentencepiece]", "triton (==1.0.0)", "triton (>=2.1.0)", "wandb", "xgboost"]
+autotuning = ["tabulate"]
+autotuning-ml = ["hjson", "tabulate", "xgboost"]
+dev = ["accelerate", "clang-format (==16.0.2)", "coverage", "deepspeed-kernels", "docutils (<0.18)", "future", "importlib-metadata (>=4)", "mup", "pre-commit (>=2.20.0)", "pytest", "pytest-forked", "pytest-randomly", "pytest-xdist", "recommonmark", "sphinx", "sphinx-rtd-theme", "tensorboard", "torchvision", "transformers (>=4.32.1)", "wandb"]
+inf = ["google", "lm-eval (==0.3.0)", "protobuf", "transformers (>=4.32.1)", "transformers[sentencepiece]"]
+readthedocs = ["autodoc_pydantic", "docutils (<0.18)", "hjson", "packaging", "psutil", "py-cpuinfo", "pydantic (<2.0.0)", "recommonmark", "sphinx_rtd_theme", "torch", "tqdm"]
+sd = ["diffusers", "triton (>=2.1.0)"]
+sparse = ["neural-compressor (==2.1.0)"]
+sparse-attn = ["triton (==1.0.0)"]
+triton = ["triton (>=2.1.0)"]
+
+[[package]]
+name = "deptry"
+version = "0.12.0"
+description = "A command line utility to check for unused, missing and transitive dependencies in a Python project."
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+ {file = "deptry-0.12.0-py3-none-any.whl", hash = "sha256:69c801a6ae1b39c7b8e0daf40dbe8b75f1f161277d206dd8f921f32cd22dad91"},
+ {file = "deptry-0.12.0.tar.gz", hash = "sha256:ac3cd32d149c92a9af12f63cd9486ddd1760f0277ed0cf306c6ef0388f57ff0a"},
+]
+
+[package.dependencies]
+chardet = ">=4.0.0"
+click = ">=8.0.0,<9.0.0"
+colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\""}
+pathspec = ">=0.9.0"
+tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
+
+[[package]]
+name = "dill"
+version = "0.3.7"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
+ {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+
+[[package]]
+name = "docker"
+version = "6.1.3"
+description = "A Python library for the Docker Engine API."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "docker-6.1.3-py3-none-any.whl", hash = "sha256:aecd2277b8bf8e506e484f6ab7aec39abe0038e29fa4a6d3ba86c3fe01844ed9"},
+ {file = "docker-6.1.3.tar.gz", hash = "sha256:aa6d17830045ba5ef0168d5eaa34d37beeb113948c413affe1d5991fc11f9a20"},
+]
+
+[package.dependencies]
+packaging = ">=14.0"
+pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
+requests = ">=2.26.0"
+urllib3 = ">=1.26.0"
+websocket-client = ">=0.32.0"
+
+[package.extras]
+ssh = ["paramiko (>=2.4.3)"]
+
+[[package]]
+name = "docker-pycreds"
+version = "0.4.0"
+description = "Python bindings for the docker credentials store API"
+optional = false
+python-versions = "*"
+files = [
+ {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"},
+ {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"},
+]
+
+[package.dependencies]
+six = ">=1.4.0"
+
+[[package]]
+name = "evaluate"
+version = "0.4.1"
+description = "HuggingFace community-driven open-source library of evaluation"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+ {file = "evaluate-0.4.1-py3-none-any.whl", hash = "sha256:3ff079ab09572c0a2c1e6d749887c19f6783ab993320412cd39f6fe501d28510"},
+ {file = "evaluate-0.4.1.tar.gz", hash = "sha256:d721d9f2059ced79770d8a0509e954fbd1bbac96a8f9160e29888d8073cda3d9"},
+]
+
+[package.dependencies]
+datasets = ">=2.0.0"
+dill = "*"
+fsspec = {version = ">=2021.05.0", extras = ["http"]}
+huggingface-hub = ">=0.7.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+requests = ">=2.19.0"
+responses = "<0.19"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+dev = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+docs = ["s3fs"]
+evaluator = ["scipy (>=1.7.1)", "transformers"]
+quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
+template = ["cookiecutter", "gradio (>=3.0.0)"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Werkzeug (>=1.0.1)", "absl-py", "accelerate", "bert-score (>=0.3.6)", "cer (>=1.2.0)", "charcut (>=1.1.1)", "jiwer", "mauve-text", "nltk", "pytest", "pytest-datadir", "pytest-xdist", "requests-file (>=1.5.1)", "rouge-score (>=0.1.2)", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1,<=2.10)", "texttable (>=1.6.3)", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "transformers", "trectools", "unidecode (>=1.3.4)"]
+torch = ["torch"]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.2.0"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
+ {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
+[[package]]
+name = "fairseq"
+version = "0.12.2"
+description = "Facebook AI Research Sequence-to-Sequence Toolkit"
+optional = false
+python-versions = "*"
+files = [
+ {file = "fairseq-0.12.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:fe65b07c5121b7cda0c7a17166994a6b0059259ce37881b6daa117b8c209b662"},
+ {file = "fairseq-0.12.2-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:0543905012e39f00bd8c3f3781d9f49e76ab309801eb2eb7de250f5984df0de3"},
+ {file = "fairseq-0.12.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c4877d65346797fc580a3a7e6e2364d2331a0026ef099c22eb8311441e49c2c6"},
+ {file = "fairseq-0.12.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:26454f334ca705c67f898846dff34e14c148fcdaf53b4f52d64209773b509347"},
+ {file = "fairseq-0.12.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:3b8c8b6dc368d2fd23a06ff613a2af05959eee275fe90846d7cffef4a43c522a"},
+ {file = "fairseq-0.12.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:08fa308c760f995cdc13d9c385e2b9d923a78b48275d8b4d78f3a854c71a8f29"},
+ {file = "fairseq-0.12.2.tar.gz", hash = "sha256:34f1b18426bf3844714534162f065ab733e049597476daa35fffb4d06a92b524"},
+]
+
+[package.dependencies]
+bitarray = "*"
+cffi = "*"
+cython = "*"
+hydra-core = ">=1.0.7,<1.1"
+numpy = {version = "*", markers = "python_version >= \"3.7\""}
+omegaconf = "<2.1"
+regex = "*"
+sacrebleu = ">=1.4.12"
+torch = "*"
+torchaudio = ">=0.8.0"
+tqdm = "*"
+
+[[package]]
+name = "fastapi"
+version = "0.109.0"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "fastapi-0.109.0-py3-none-any.whl", hash = "sha256:8c77515984cd8e8cfeb58364f8cc7a28f0692088475e2614f7bf03275eba9093"},
+ {file = "fastapi-0.109.0.tar.gz", hash = "sha256:b978095b9ee01a5cf49b19f4bc1ac9b8ca83aa076e770ef8fd9af09a2b88d191"},
+]
+
+[package.dependencies]
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
+starlette = ">=0.35.0,<0.36.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+
+[[package]]
+name = "ffmpy"
+version = "0.3.1"
+description = "A simple Python wrapper for ffmpeg"
+optional = false
+python-versions = "*"
+files = [
+ {file = "ffmpy-0.3.1.tar.gz", hash = "sha256:a173b8f42c7c669ff722df7fb31e1e870067713697f745224fa6e621b82f0004"},
+]
+
+[[package]]
+name = "filelock"
+version = "3.13.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
+ {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
+]
+
+[package.extras]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+typing = ["typing-extensions (>=4.8)"]
+
+[[package]]
+name = "fonttools"
+version = "4.47.2"
+description = "Tools to manipulate font files"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "fonttools-4.47.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3b629108351d25512d4ea1a8393a2dba325b7b7d7308116b605ea3f8e1be88df"},
+ {file = "fonttools-4.47.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c19044256c44fe299d9a73456aabee4b4d06c6b930287be93b533b4737d70aa1"},
+ {file = "fonttools-4.47.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b8be28c036b9f186e8c7eaf8a11b42373e7e4949f9e9f370202b9da4c4c3f56c"},
+ {file = "fonttools-4.47.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f83a4daef6d2a202acb9bf572958f91cfde5b10c8ee7fb1d09a4c81e5d851fd8"},
+ {file = "fonttools-4.47.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:4a5a5318ba5365d992666ac4fe35365f93004109d18858a3e18ae46f67907670"},
+ {file = "fonttools-4.47.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8f57ecd742545362a0f7186774b2d1c53423ed9ece67689c93a1055b236f638c"},
+ {file = "fonttools-4.47.2-cp310-cp310-win32.whl", hash = "sha256:a1c154bb85dc9a4cf145250c88d112d88eb414bad81d4cb524d06258dea1bdc0"},
+ {file = "fonttools-4.47.2-cp310-cp310-win_amd64.whl", hash = "sha256:3e2b95dce2ead58fb12524d0ca7d63a63459dd489e7e5838c3cd53557f8933e1"},
+ {file = "fonttools-4.47.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:29495d6d109cdbabe73cfb6f419ce67080c3ef9ea1e08d5750240fd4b0c4763b"},
+ {file = "fonttools-4.47.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0a1d313a415eaaba2b35d6cd33536560deeebd2ed758b9bfb89ab5d97dc5deac"},
+ {file = "fonttools-4.47.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:90f898cdd67f52f18049250a6474185ef6544c91f27a7bee70d87d77a8daf89c"},
+ {file = "fonttools-4.47.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3480eeb52770ff75140fe7d9a2ec33fb67b07efea0ab5129c7e0c6a639c40c70"},
+ {file = "fonttools-4.47.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0255dbc128fee75fb9be364806b940ed450dd6838672a150d501ee86523ac61e"},
+ {file = "fonttools-4.47.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:f791446ff297fd5f1e2247c188de53c1bfb9dd7f0549eba55b73a3c2087a2703"},
+ {file = "fonttools-4.47.2-cp311-cp311-win32.whl", hash = "sha256:740947906590a878a4bde7dd748e85fefa4d470a268b964748403b3ab2aeed6c"},
+ {file = "fonttools-4.47.2-cp311-cp311-win_amd64.whl", hash = "sha256:63fbed184979f09a65aa9c88b395ca539c94287ba3a364517698462e13e457c9"},
+ {file = "fonttools-4.47.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:4ec558c543609e71b2275c4894e93493f65d2f41c15fe1d089080c1d0bb4d635"},
+ {file = "fonttools-4.47.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e040f905d542362e07e72e03612a6270c33d38281fd573160e1003e43718d68d"},
+ {file = "fonttools-4.47.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6dd58cc03016b281bd2c74c84cdaa6bd3ce54c5a7f47478b7657b930ac3ed8eb"},
+ {file = "fonttools-4.47.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32ab2e9702dff0dd4510c7bb958f265a8d3dd5c0e2547e7b5f7a3df4979abb07"},
+ {file = "fonttools-4.47.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3a808f3c1d1df1f5bf39be869b6e0c263570cdafb5bdb2df66087733f566ea71"},
+ {file = "fonttools-4.47.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ac71e2e201df041a2891067dc36256755b1229ae167edbdc419b16da78732c2f"},
+ {file = "fonttools-4.47.2-cp312-cp312-win32.whl", hash = "sha256:69731e8bea0578b3c28fdb43dbf95b9386e2d49a399e9a4ad736b8e479b08085"},
+ {file = "fonttools-4.47.2-cp312-cp312-win_amd64.whl", hash = "sha256:b3e1304e5f19ca861d86a72218ecce68f391646d85c851742d265787f55457a4"},
+ {file = "fonttools-4.47.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:254d9a6f7be00212bf0c3159e0a420eb19c63793b2c05e049eb337f3023c5ecc"},
+ {file = "fonttools-4.47.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:eabae77a07c41ae0b35184894202305c3ad211a93b2eb53837c2a1143c8bc952"},
+ {file = "fonttools-4.47.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a86a5ab2873ed2575d0fcdf1828143cfc6b977ac448e3dc616bb1e3d20efbafa"},
+ {file = "fonttools-4.47.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13819db8445a0cec8c3ff5f243af6418ab19175072a9a92f6cc8ca7d1452754b"},
+ {file = "fonttools-4.47.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:4e743935139aa485fe3253fc33fe467eab6ea42583fa681223ea3f1a93dd01e6"},
+ {file = "fonttools-4.47.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d49ce3ea7b7173faebc5664872243b40cf88814ca3eb135c4a3cdff66af71946"},
+ {file = "fonttools-4.47.2-cp38-cp38-win32.whl", hash = "sha256:94208ea750e3f96e267f394d5588579bb64cc628e321dbb1d4243ffbc291b18b"},
+ {file = "fonttools-4.47.2-cp38-cp38-win_amd64.whl", hash = "sha256:0f750037e02beb8b3569fbff701a572e62a685d2a0e840d75816592280e5feae"},
+ {file = "fonttools-4.47.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3d71606c9321f6701642bd4746f99b6089e53d7e9817fc6b964e90d9c5f0ecc6"},
+ {file = "fonttools-4.47.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:86e0427864c6c91cf77f16d1fb9bf1bbf7453e824589e8fb8461b6ee1144f506"},
+ {file = "fonttools-4.47.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a00bd0e68e88987dcc047ea31c26d40a3c61185153b03457956a87e39d43c37"},
+ {file = "fonttools-4.47.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5d77479fb885ef38a16a253a2f4096bc3d14e63a56d6246bfdb56365a12b20c"},
+ {file = "fonttools-4.47.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:5465df494f20a7d01712b072ae3ee9ad2887004701b95cb2cc6dcb9c2c97a899"},
+ {file = "fonttools-4.47.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4c811d3c73b6abac275babb8aa439206288f56fdb2c6f8835e3d7b70de8937a7"},
+ {file = "fonttools-4.47.2-cp39-cp39-win32.whl", hash = "sha256:5b60e3afa9635e3dfd3ace2757039593e3bd3cf128be0ddb7a1ff4ac45fa5a50"},
+ {file = "fonttools-4.47.2-cp39-cp39-win_amd64.whl", hash = "sha256:7ee48bd9d6b7e8f66866c9090807e3a4a56cf43ffad48962725a190e0dd774c8"},
+ {file = "fonttools-4.47.2-py3-none-any.whl", hash = "sha256:7eb7ad665258fba68fd22228a09f347469d95a97fb88198e133595947a20a184"},
+ {file = "fonttools-4.47.2.tar.gz", hash = "sha256:7df26dd3650e98ca45f1e29883c96a0b9f5bb6af8d632a6a108bc744fa0bd9b3"},
+]
+
+[package.extras]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["munkres", "pycairo", "scipy"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=15.1.0)"]
+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+
+[[package]]
+name = "frozenlist"
+version = "1.4.1"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f9aa1878d1083b276b0196f2dfbe00c9b7e752475ed3b682025ff20c1c1f51ac"},
+ {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:29acab3f66f0f24674b7dc4736477bcd4bc3ad4b896f5f45379a67bce8b96868"},
+ {file = "frozenlist-1.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:74fb4bee6880b529a0c6560885fce4dc95936920f9f20f53d99a213f7bf66776"},
+ {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:590344787a90ae57d62511dd7c736ed56b428f04cd8c161fcc5e7232c130c69a"},
+ {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:068b63f23b17df8569b7fdca5517edef76171cf3897eb68beb01341131fbd2ad"},
+ {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5c849d495bf5154cd8da18a9eb15db127d4dba2968d88831aff6f0331ea9bd4c"},
+ {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9750cc7fe1ae3b1611bb8cfc3f9ec11d532244235d75901fb6b8e42ce9229dfe"},
+ {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9b2de4cf0cdd5bd2dee4c4f63a653c61d2408055ab77b151c1957f221cabf2a"},
+ {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0633c8d5337cb5c77acbccc6357ac49a1770b8c487e5b3505c57b949b4b82e98"},
+ {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:27657df69e8801be6c3638054e202a135c7f299267f1a55ed3a598934f6c0d75"},
+ {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:f9a3ea26252bd92f570600098783d1371354d89d5f6b7dfd87359d669f2109b5"},
+ {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:4f57dab5fe3407b6c0c1cc907ac98e8a189f9e418f3b6e54d65a718aaafe3950"},
+ {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e02a0e11cf6597299b9f3bbd3f93d79217cb90cfd1411aec33848b13f5c656cc"},
+ {file = "frozenlist-1.4.1-cp310-cp310-win32.whl", hash = "sha256:a828c57f00f729620a442881cc60e57cfcec6842ba38e1b19fd3e47ac0ff8dc1"},
+ {file = "frozenlist-1.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:f56e2333dda1fe0f909e7cc59f021eba0d2307bc6f012a1ccf2beca6ba362439"},
+ {file = "frozenlist-1.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a0cb6f11204443f27a1628b0e460f37fb30f624be6051d490fa7d7e26d4af3d0"},
+ {file = "frozenlist-1.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b46c8ae3a8f1f41a0d2ef350c0b6e65822d80772fe46b653ab6b6274f61d4a49"},
+ {file = "frozenlist-1.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fde5bd59ab5357e3853313127f4d3565fc7dad314a74d7b5d43c22c6a5ed2ced"},
+ {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:722e1124aec435320ae01ee3ac7bec11a5d47f25d0ed6328f2273d287bc3abb0"},
+ {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2471c201b70d58a0f0c1f91261542a03d9a5e088ed3dc6c160d614c01649c106"},
+ {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c757a9dd70d72b076d6f68efdbb9bc943665ae954dad2801b874c8c69e185068"},
+ {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f146e0911cb2f1da549fc58fc7bcd2b836a44b79ef871980d605ec392ff6b0d2"},
+ {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f9c515e7914626b2a2e1e311794b4c35720a0be87af52b79ff8e1429fc25f19"},
+ {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c302220494f5c1ebeb0912ea782bcd5e2f8308037b3c7553fad0e48ebad6ad82"},
+ {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:442acde1e068288a4ba7acfe05f5f343e19fac87bfc96d89eb886b0363e977ec"},
+ {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:1b280e6507ea8a4fa0c0a7150b4e526a8d113989e28eaaef946cc77ffd7efc0a"},
+ {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:fe1a06da377e3a1062ae5fe0926e12b84eceb8a50b350ddca72dc85015873f74"},
+ {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:db9e724bebd621d9beca794f2a4ff1d26eed5965b004a97f1f1685a173b869c2"},
+ {file = "frozenlist-1.4.1-cp311-cp311-win32.whl", hash = "sha256:e774d53b1a477a67838a904131c4b0eef6b3d8a651f8b138b04f748fccfefe17"},
+ {file = "frozenlist-1.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:fb3c2db03683b5767dedb5769b8a40ebb47d6f7f45b1b3e3b4b51ec8ad9d9825"},
+ {file = "frozenlist-1.4.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:1979bc0aeb89b33b588c51c54ab0161791149f2461ea7c7c946d95d5f93b56ae"},
+ {file = "frozenlist-1.4.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:cc7b01b3754ea68a62bd77ce6020afaffb44a590c2289089289363472d13aedb"},
+ {file = "frozenlist-1.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c9c92be9fd329ac801cc420e08452b70e7aeab94ea4233a4804f0915c14eba9b"},
+ {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c3894db91f5a489fc8fa6a9991820f368f0b3cbdb9cd8849547ccfab3392d86"},
+ {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ba60bb19387e13597fb059f32cd4d59445d7b18b69a745b8f8e5db0346f33480"},
+ {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8aefbba5f69d42246543407ed2461db31006b0f76c4e32dfd6f42215a2c41d09"},
+ {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:780d3a35680ced9ce682fbcf4cb9c2bad3136eeff760ab33707b71db84664e3a"},
+ {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9acbb16f06fe7f52f441bb6f413ebae6c37baa6ef9edd49cdd567216da8600cd"},
+ {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:23b701e65c7b36e4bf15546a89279bd4d8675faabc287d06bbcfac7d3c33e1e6"},
+ {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3e0153a805a98f5ada7e09826255ba99fb4f7524bb81bf6b47fb702666484ae1"},
+ {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:dd9b1baec094d91bf36ec729445f7769d0d0cf6b64d04d86e45baf89e2b9059b"},
+ {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:1a4471094e146b6790f61b98616ab8e44f72661879cc63fa1049d13ef711e71e"},
+ {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:5667ed53d68d91920defdf4035d1cdaa3c3121dc0b113255124bcfada1cfa1b8"},
+ {file = "frozenlist-1.4.1-cp312-cp312-win32.whl", hash = "sha256:beee944ae828747fd7cb216a70f120767fc9f4f00bacae8543c14a6831673f89"},
+ {file = "frozenlist-1.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:64536573d0a2cb6e625cf309984e2d873979709f2cf22839bf2d61790b448ad5"},
+ {file = "frozenlist-1.4.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:20b51fa3f588ff2fe658663db52a41a4f7aa6c04f6201449c6c7c476bd255c0d"},
+ {file = "frozenlist-1.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:410478a0c562d1a5bcc2f7ea448359fcb050ed48b3c6f6f4f18c313a9bdb1826"},
+ {file = "frozenlist-1.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c6321c9efe29975232da3bd0af0ad216800a47e93d763ce64f291917a381b8eb"},
+ {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48f6a4533887e189dae092f1cf981f2e3885175f7a0f33c91fb5b7b682b6bab6"},
+ {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6eb73fa5426ea69ee0e012fb59cdc76a15b1283d6e32e4f8dc4482ec67d1194d"},
+ {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbeb989b5cc29e8daf7f976b421c220f1b8c731cbf22b9130d8815418ea45887"},
+ {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:32453c1de775c889eb4e22f1197fe3bdfe457d16476ea407472b9442e6295f7a"},
+ {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:693945278a31f2086d9bf3df0fe8254bbeaef1fe71e1351c3bd730aa7d31c41b"},
+ {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:1d0ce09d36d53bbbe566fe296965b23b961764c0bcf3ce2fa45f463745c04701"},
+ {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3a670dc61eb0d0eb7080890c13de3066790f9049b47b0de04007090807c776b0"},
+ {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:dca69045298ce5c11fd539682cff879cc1e664c245d1c64da929813e54241d11"},
+ {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a06339f38e9ed3a64e4c4e43aec7f59084033647f908e4259d279a52d3757d09"},
+ {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b7f2f9f912dca3934c1baec2e4585a674ef16fe00218d833856408c48d5beee7"},
+ {file = "frozenlist-1.4.1-cp38-cp38-win32.whl", hash = "sha256:e7004be74cbb7d9f34553a5ce5fb08be14fb33bc86f332fb71cbe5216362a497"},
+ {file = "frozenlist-1.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:5a7d70357e7cee13f470c7883a063aae5fe209a493c57d86eb7f5a6f910fae09"},
+ {file = "frozenlist-1.4.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:bfa4a17e17ce9abf47a74ae02f32d014c5e9404b6d9ac7f729e01562bbee601e"},
+ {file = "frozenlist-1.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b7e3ed87d4138356775346e6845cccbe66cd9e207f3cd11d2f0b9fd13681359d"},
+ {file = "frozenlist-1.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c99169d4ff810155ca50b4da3b075cbde79752443117d89429595c2e8e37fed8"},
+ {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edb678da49d9f72c9f6c609fbe41a5dfb9a9282f9e6a2253d5a91e0fc382d7c0"},
+ {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6db4667b187a6742b33afbbaf05a7bc551ffcf1ced0000a571aedbb4aa42fc7b"},
+ {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:55fdc093b5a3cb41d420884cdaf37a1e74c3c37a31f46e66286d9145d2063bd0"},
+ {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:82e8211d69a4f4bc360ea22cd6555f8e61a1bd211d1d5d39d3d228b48c83a897"},
+ {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89aa2c2eeb20957be2d950b85974b30a01a762f3308cd02bb15e1ad632e22dc7"},
+ {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d3e0c25a2350080e9319724dede4f31f43a6c9779be48021a7f4ebde8b2d742"},
+ {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7268252af60904bf52c26173cbadc3a071cece75f873705419c8681f24d3edea"},
+ {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:0c250a29735d4f15321007fb02865f0e6b6a41a6b88f1f523ca1596ab5f50bd5"},
+ {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:96ec70beabbd3b10e8bfe52616a13561e58fe84c0101dd031dc78f250d5128b9"},
+ {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:23b2d7679b73fe0e5a4560b672a39f98dfc6f60df63823b0a9970525325b95f6"},
+ {file = "frozenlist-1.4.1-cp39-cp39-win32.whl", hash = "sha256:a7496bfe1da7fb1a4e1cc23bb67c58fab69311cc7d32b5a99c2007b4b2a0e932"},
+ {file = "frozenlist-1.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:e6a20a581f9ce92d389a8c7d7c3dd47c81fd5d6e655c8dddf341e14aa48659d0"},
+ {file = "frozenlist-1.4.1-py3-none-any.whl", hash = "sha256:04ced3e6a46b4cfffe20f9ae482818e34eba9b5fb0ce4056e4cc9b6e212d09b7"},
+ {file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"},
+]
+
+[[package]]
+name = "fsspec"
+version = "2023.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
+ {file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
+]
+
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+requests = {version = "*", optional = true, markers = "extra == \"http\""}
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
+[[package]]
+name = "furl"
+version = "2.1.3"
+description = "URL manipulation made simple."
+optional = false
+python-versions = "*"
+files = [
+ {file = "furl-2.1.3-py2.py3-none-any.whl", hash = "sha256:9ab425062c4217f9802508e45feb4a83e54324273ac4b202f1850363309666c0"},
+ {file = "furl-2.1.3.tar.gz", hash = "sha256:5a6188fe2666c484a12159c18be97a1977a71d632ef5bb867ef15f54af39cc4e"},
+]
+
+[package.dependencies]
+orderedmultidict = ">=1.0.1"
+six = ">=1.8.0"
+
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"},
+ {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.40"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "GitPython-3.1.40-py3-none-any.whl", hash = "sha256:cf14627d5a8049ffbf49915732e5eddbe8134c3bdb9d476e6182b676fc573f8a"},
+ {file = "GitPython-3.1.40.tar.gz", hash = "sha256:22b126e9ffb671fdd0c129796343a02bf67bf2994b35449ffc9321aa755e18a4"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"]
+
+[[package]]
+name = "gradio"
+version = "4.16.0"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "gradio-4.16.0-py3-none-any.whl", hash = "sha256:5b9348889f750b61bba702583d7399d0b69baa1f2bb373d8c0b1bbda6c9ea140"},
+ {file = "gradio-4.16.0.tar.gz", hash = "sha256:a89d10bdde41b4c73ec09c760049371e242e3dff8f66d08ccd7710e2ce5c867c"},
+]
+
+[package.dependencies]
+aiofiles = ">=22.0,<24.0"
+altair = ">=4.2.0,<6.0"
+fastapi = "*"
+ffmpy = "*"
+gradio-client = "0.8.1"
+httpx = "*"
+huggingface-hub = ">=0.19.3"
+importlib-resources = ">=1.3,<7.0"
+jinja2 = "<4.0"
+markupsafe = ">=2.0,<3.0"
+matplotlib = ">=3.0,<4.0"
+numpy = ">=1.0,<2.0"
+orjson = ">=3.0,<4.0"
+packaging = "*"
+pandas = ">=1.0,<3.0"
+pillow = ">=8.0,<11.0"
+pydantic = ">=2.0"
+pydub = "*"
+python-multipart = "*"
+pyyaml = ">=5.0,<7.0"
+ruff = ">=0.1.7"
+semantic-version = ">=2.0,<3.0"
+tomlkit = "0.12.0"
+typer = {version = ">=0.9,<1.0", extras = ["all"]}
+typing-extensions = ">=4.0,<5.0"
+uvicorn = ">=0.14.0"
+
+[package.extras]
+oauth = ["authlib", "itsdangerous"]
+
+[[package]]
+name = "gradio-client"
+version = "0.8.1"
+description = "Python library for easily interacting with trained machine learning models"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "gradio_client-0.8.1-py3-none-any.whl", hash = "sha256:5a987492905314baf64b50bc12319d8208bbe23bd0c6fb4c2dcd4d9a8825fb56"},
+ {file = "gradio_client-0.8.1.tar.gz", hash = "sha256:0fa5ba334a174dce918ba4f164d573507b7db740cf3b48704284d568f2e9cfd2"},
+]
+
+[package.dependencies]
+fsspec = "*"
+httpx = "*"
+huggingface-hub = ">=0.19.3"
+packaging = "*"
+typing-extensions = ">=4.0,<5.0"
+websockets = ">=10.0,<12.0"
+
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+ {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "hjson"
+version = "3.1.0"
+description = "Hjson, a user interface for JSON."
+optional = false
+python-versions = "*"
+files = [
+ {file = "hjson-3.1.0-py3-none-any.whl", hash = "sha256:65713cdcf13214fb554eb8b4ef803419733f4f5e551047c9b711098ab7186b89"},
+ {file = "hjson-3.1.0.tar.gz", hash = "sha256:55af475a27cf83a7969c808399d7bccdec8fb836a07ddbd574587593b9cdcf75"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.2"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
+ {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.23.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.26.0"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"},
+ {file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
+[[package]]
+name = "huggingface-hub"
+version = "0.20.2"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "huggingface_hub-0.20.2-py3-none-any.whl", hash = "sha256:53752eda2239d30a470c307a61cf9adcf136bc77b0a734338c7d04941af560d8"},
+ {file = "huggingface_hub-0.20.2.tar.gz", hash = "sha256:215c5fceff631030c7a3d19ba7b588921c908b3f21eef31d160ebc245b200ff6"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
+[[package]]
+name = "humanfriendly"
+version = "10.0"
+description = "Human friendly output for text interfaces using Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+ {file = "humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477"},
+ {file = "humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc"},
+]
+
+[package.dependencies]
+pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
+
+[[package]]
+name = "hydra-core"
+version = "1.0.7"
+description = "A framework for elegantly configuring complex applications"
+optional = false
+python-versions = "*"
+files = [
+ {file = "hydra-core-1.0.7.tar.gz", hash = "sha256:58cc3f7531995b6d8de162ca21f936e17bdaebd4d1e8614d63c32e17c2e41e45"},
+ {file = "hydra_core-1.0.7-py3-none-any.whl", hash = "sha256:e800c6deb8309395508094851fa93bc13408f2285261eb97e626d37193b58a9f"},
+]
+
+[package.dependencies]
+antlr4-python3-runtime = "4.8"
+omegaconf = ">=2.0.5,<2.1"
+
+[[package]]
+name = "idna"
+version = "3.6"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+ {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+]
+
+[[package]]
+name = "importlib-metadata"
+version = "7.0.1"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
+ {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
+]
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+perf = ["ipython"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+
+[[package]]
+name = "importlib-resources"
+version = "6.1.1"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
+ {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
+]
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
+
+[[package]]
+name = "iniconfig"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
+ {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
+]
+
+[[package]]
+name = "isodate"
+version = "0.6.1"
+description = "An ISO 8601 date/time/duration parser and formatter"
+optional = false
+python-versions = "*"
+files = [
+ {file = "isodate-0.6.1-py2.py3-none-any.whl", hash = "sha256:0751eece944162659049d35f4f549ed815792b38793f07cf73381c1c87cbed96"},
+ {file = "isodate-0.6.1.tar.gz", hash = "sha256:48c5881de7e8b0a0d648cb024c8062dc84e7b840ed81e864c7614fd3c127bde9"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "isort"
+version = "5.13.2"
+description = "A Python utility / library to sort Python imports."
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
+ {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
+]
+
+[package.extras]
+colors = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "jeepney"
+version = "0.8.0"
+description = "Low-level, pure Python DBus protocol wrapper."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "jeepney-0.8.0-py3-none-any.whl", hash = "sha256:c0a454ad016ca575060802ee4d590dd912e35c122fa04e70306de3d076cce755"},
+ {file = "jeepney-0.8.0.tar.gz", hash = "sha256:5efe48d255973902f6badc3ce55e2aa6c5c3b3bc642059ef3a91247bcfcc5806"},
+]
+
+[package.extras]
+test = ["async-timeout", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"]
+trio = ["async_generator", "trio"]
+
+[[package]]
+name = "jinja2"
+version = "3.1.2"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
+ {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
+[[package]]
+name = "jiwer"
+version = "3.0.3"
+description = "Evaluate your speech-to-text system with similarity measures such as word error rate (WER)"
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+ {file = "jiwer-3.0.3-py3-none-any.whl", hash = "sha256:190d8238cb0262346781267d94f74c4fc8fc5094cf215a3d5d8317fb0954b842"},
+ {file = "jiwer-3.0.3.tar.gz", hash = "sha256:a777f361a5a5f76012078d758e444e1da2a171fb685dad9cf8fb69181ea26ee6"},
+]
+
+[package.dependencies]
+click = ">=8.1.3,<9.0.0"
+rapidfuzz = ">=3,<4"
+
+[[package]]
+name = "jmespath"
+version = "1.0.1"
+description = "JSON Matching Expressions"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"},
+ {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"},
+]
+
+[[package]]
+name = "joblib"
+version = "1.3.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
+ {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+]
+
+[[package]]
+name = "jsonpickle"
+version = "3.0.2"
+description = "Python library for serializing any arbitrary object graph into JSON"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "jsonpickle-3.0.2-py3-none-any.whl", hash = "sha256:4a8442d97ca3f77978afa58068768dba7bff2dbabe79a9647bc3cdafd4ef019f"},
+ {file = "jsonpickle-3.0.2.tar.gz", hash = "sha256:e37abba4bfb3ca4a4647d28bb9f4706436f7b46c8a8333b4a718abafa8e46b37"},
+]
+
+[package.extras]
+docs = ["jaraco.packaging (>=3.2)", "rst.linker (>=1.9)", "sphinx"]
+testing = ["ecdsa", "feedparser", "gmpy2", "numpy", "pandas", "pymongo", "pytest (>=3.5,!=3.7.3)", "pytest-black-multipy", "pytest-checkdocs (>=1.2.3)", "pytest-cov", "pytest-flake8 (>=1.1.1)", "scikit-learn", "sqlalchemy"]
+testing-libs = ["simplejson", "ujson"]
+
+[[package]]
+name = "jsonschema"
+version = "4.20.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"},
+ {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+ {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.5"
+description = "A fast implementation of the Cassowary constraint solver"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ef7afcd2d281494c0a9101d5c571970708ad911d028137cd558f02b851c08b4"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9eaa8b117dc8337728e834b9c6e2611f10c79e38f65157c4c38e9400286f5cb1"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ec20916e7b4cbfb1f12380e46486ec4bcbaa91a9c448b97023fde0d5bbf9e4ff"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39b42c68602539407884cf70d6a480a469b93b81b7701378ba5e2328660c847a"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa12042de0171fad672b6c59df69106d20d5596e4f87b5e8f76df757a7c399aa"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a40773c71d7ccdd3798f6489aaac9eee213d566850a9533f8d26332d626b82c"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:83d78376d0d4fd884e2c114d0621624b73d2aba4e2788182d286309ebdeed770"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e391b1f0a8a5a10ab3b9bb6afcfd74f2175f24f8975fb87ecae700d1503cdee0"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:852542f9481f4a62dbb5dd99e8ab7aedfeb8fb6342349a181d4036877410f525"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:59edc41b24031bc25108e210c0def6f6c2191210492a972d585a06ff246bb79b"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-win32.whl", hash = "sha256:a6aa6315319a052b4ee378aa171959c898a6183f15c1e541821c5c59beaa0238"},
+ {file = "kiwisolver-1.4.5-cp310-cp310-win_amd64.whl", hash = "sha256:d0ef46024e6a3d79c01ff13801cb19d0cad7fd859b15037aec74315540acc276"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:11863aa14a51fd6ec28688d76f1735f8f69ab1fabf388851a595d0721af042f5"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8ab3919a9997ab7ef2fbbed0cc99bb28d3c13e6d4b1ad36e97e482558a91be90"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fcc700eadbbccbf6bc1bcb9dbe0786b4b1cb91ca0dcda336eef5c2beed37b797"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dfdd7c0b105af050eb3d64997809dc21da247cf44e63dc73ff0fd20b96be55a9"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76c6a5964640638cdeaa0c359382e5703e9293030fe730018ca06bc2010c4437"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bbea0db94288e29afcc4c28afbf3a7ccaf2d7e027489c449cf7e8f83c6346eb9"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ceec1a6bc6cab1d6ff5d06592a91a692f90ec7505d6463a88a52cc0eb58545da"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f91de7223d4c7b793867797bacd1ee53bfe7359bd70d27b7b58a04efbb9436c8"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:faae4860798c31530dd184046a900e652c95513796ef51a12bc086710c2eec4d"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b0157420efcb803e71d1b28e2c287518b8808b7cf1ab8af36718fd0a2c453eb0"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:06f54715b7737c2fecdbf140d1afb11a33d59508a47bf11bb38ecf21dc9ab79f"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fdb7adb641a0d13bdcd4ef48e062363d8a9ad4a182ac7647ec88f695e719ae9f"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-win32.whl", hash = "sha256:bb86433b1cfe686da83ce32a9d3a8dd308e85c76b60896d58f082136f10bffac"},
+ {file = "kiwisolver-1.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:6c08e1312a9cf1074d17b17728d3dfce2a5125b2d791527f33ffbe805200a355"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:32d5cf40c4f7c7b3ca500f8985eb3fb3a7dfc023215e876f207956b5ea26632a"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f846c260f483d1fd217fe5ed7c173fb109efa6b1fc8381c8b7552c5781756192"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5ff5cf3571589b6d13bfbfd6bcd7a3f659e42f96b5fd1c4830c4cf21d4f5ef45"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7269d9e5f1084a653d575c7ec012ff57f0c042258bf5db0954bf551c158466e7"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da802a19d6e15dffe4b0c24b38b3af68e6c1a68e6e1d8f30148c83864f3881db"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3aba7311af82e335dd1e36ffff68aaca609ca6290c2cb6d821a39aa075d8e3ff"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:763773d53f07244148ccac5b084da5adb90bfaee39c197554f01b286cf869228"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2270953c0d8cdab5d422bee7d2007f043473f9d2999631c86a223c9db56cbd16"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d099e745a512f7e3bbe7249ca835f4d357c586d78d79ae8f1dcd4d8adeb9bda9"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:74db36e14a7d1ce0986fa104f7d5637aea5c82ca6326ed0ec5694280942d1162"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:7e5bab140c309cb3a6ce373a9e71eb7e4873c70c2dda01df6820474f9889d6d4"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0f114aa76dc1b8f636d077979c0ac22e7cd8f3493abbab152f20eb8d3cda71f3"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:88a2df29d4724b9237fc0c6eaf2a1adae0cdc0b3e9f4d8e7dc54b16812d2d81a"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-win32.whl", hash = "sha256:72d40b33e834371fd330fb1472ca19d9b8327acb79a5821d4008391db8e29f20"},
+ {file = "kiwisolver-1.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:2c5674c4e74d939b9d91dda0fae10597ac7521768fec9e399c70a1f27e2ea2d9"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:3a2b053a0ab7a3960c98725cfb0bf5b48ba82f64ec95fe06f1d06c99b552e130"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3cd32d6c13807e5c66a7cbb79f90b553642f296ae4518a60d8d76243b0ad2898"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:59ec7b7c7e1a61061850d53aaf8e93db63dce0c936db1fda2658b70e4a1be709"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da4cfb373035def307905d05041c1d06d8936452fe89d464743ae7fb8371078b"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2400873bccc260b6ae184b2b8a4fec0e4082d30648eadb7c3d9a13405d861e89"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:1b04139c4236a0f3aff534479b58f6f849a8b351e1314826c2d230849ed48985"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:4e66e81a5779b65ac21764c295087de82235597a2293d18d943f8e9e32746265"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:7931d8f1f67c4be9ba1dd9c451fb0eeca1a25b89e4d3f89e828fe12a519b782a"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b3f7e75f3015df442238cca659f8baa5f42ce2a8582727981cbfa15fee0ee205"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:bbf1d63eef84b2e8c89011b7f2235b1e0bf7dacc11cac9431fc6468e99ac77fb"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:4c380469bd3f970ef677bf2bcba2b6b0b4d5c75e7a020fb863ef75084efad66f"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-win32.whl", hash = "sha256:9408acf3270c4b6baad483865191e3e582b638b1654a007c62e3efe96f09a9a3"},
+ {file = "kiwisolver-1.4.5-cp37-cp37m-win_amd64.whl", hash = "sha256:5b94529f9b2591b7af5f3e0e730a4e0a41ea174af35a4fd067775f9bdfeee01a"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:11c7de8f692fc99816e8ac50d1d1aef4f75126eefc33ac79aac02c099fd3db71"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:53abb58632235cd154176ced1ae8f0d29a6657aa1aa9decf50b899b755bc2b93"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:88b9f257ca61b838b6f8094a62418421f87ac2a1069f7e896c36a7d86b5d4c29"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3195782b26fc03aa9c6913d5bad5aeb864bdc372924c093b0f1cebad603dd712"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc579bf0f502e54926519451b920e875f433aceb4624a3646b3252b5caa9e0b6"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a580c91d686376f0f7c295357595c5a026e6cbc3d77b7c36e290201e7c11ecb"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cfe6ab8da05c01ba6fbea630377b5da2cd9bcbc6338510116b01c1bc939a2c18"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d2e5a98f0ec99beb3c10e13b387f8db39106d53993f498b295f0c914328b1333"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:a51a263952b1429e429ff236d2f5a21c5125437861baeed77f5e1cc2d2c7c6da"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3edd2fa14e68c9be82c5b16689e8d63d89fe927e56debd6e1dbce7a26a17f81b"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:74d1b44c6cfc897df648cc9fdaa09bc3e7679926e6f96df05775d4fb3946571c"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:76d9289ed3f7501012e05abb8358bbb129149dbd173f1f57a1bf1c22d19ab7cc"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:92dea1ffe3714fa8eb6a314d2b3c773208d865a0e0d35e713ec54eea08a66250"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-win32.whl", hash = "sha256:5c90ae8c8d32e472be041e76f9d2f2dbff4d0b0be8bd4041770eddb18cf49a4e"},
+ {file = "kiwisolver-1.4.5-cp38-cp38-win_amd64.whl", hash = "sha256:c7940c1dc63eb37a67721b10d703247552416f719c4188c54e04334321351ced"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:9407b6a5f0d675e8a827ad8742e1d6b49d9c1a1da5d952a67d50ef5f4170b18d"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15568384086b6df3c65353820a4473575dbad192e35010f622c6ce3eebd57af9"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0dc9db8e79f0036e8173c466d21ef18e1befc02de8bf8aa8dc0813a6dc8a7046"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:cdc8a402aaee9a798b50d8b827d7ecf75edc5fb35ea0f91f213ff927c15f4ff0"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:955e8513d07a283056b1396e9a57ceddbd272d9252c14f154d450d227606eb54"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:346f5343b9e3f00b8db8ba359350eb124b98c99efd0b408728ac6ebf38173958"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9098e0049e88c6a24ff64545cdfc50807818ba6c1b739cae221bbbcbc58aad3"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7b8b454bac16428b22560d0a1cf0a09875339cab69df61d7805bf48919415901"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:f1d072c2eb0ad60d4c183f3fb44ac6f73fb7a8f16a2694a91f988275cbf352f9"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:31a82d498054cac9f6d0b53d02bb85811185bcb477d4b60144f915f3b3126342"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-win32.whl", hash = "sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f"},
+ {file = "kiwisolver-1.4.5-cp39-cp39-win_amd64.whl", hash = "sha256:59415f46a37f7f2efeec758353dd2eae1b07640d8ca0f0c42548ec4125492635"},
+ {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:5c7b3b3a728dc6faf3fc372ef24f21d1e3cee2ac3e9596691d746e5a536de920"},
+ {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:620ced262a86244e2be10a676b646f29c34537d0d9cc8eb26c08f53d98013390"},
+ {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:378a214a1e3bbf5ac4a8708304318b4f890da88c9e6a07699c4ae7174c09a68d"},
+ {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaf7be1207676ac608a50cd08f102f6742dbfc70e8d60c4db1c6897f62f71523"},
+ {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4"},
+ {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892"},
+ {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544"},
+ {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dced8146011d2bc2e883f9bd68618b8247387f4bbec46d7392b3c3b032640126"},
+ {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd"},
+ {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929"},
+ {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e368f200bbc2e4f905b8e71eb38b3c04333bddaa6a2464a6355487b02bb7fb09"},
+ {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7"},
+ {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad"},
+ {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea"},
+ {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee"},
+ {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
+]
+
+[[package]]
+name = "knack"
+version = "0.11.0"
+description = "A Command-Line Interface framework"
+optional = false
+python-versions = "*"
+files = [
+ {file = "knack-0.11.0-py3-none-any.whl", hash = "sha256:6704c867840978a119a193914a90e2e98c7be7dff764c8fcd8a2286c5a978d00"},
+ {file = "knack-0.11.0.tar.gz", hash = "sha256:eb6568001e9110b1b320941431c51033d104cc98cda2254a5c2b09ba569fd494"},
+]
+
+[package.dependencies]
+argcomplete = "*"
+jmespath = "*"
+packaging = "*"
+pygments = "*"
+pyyaml = "*"
+tabulate = "*"
+
+[[package]]
+name = "lazy-loader"
+version = "0.3"
+description = "lazy_loader"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
+ {file = "lazy_loader-0.3.tar.gz", hash = "sha256:3b68898e34f5b2a29daaaac172c6555512d0f32074f147e2254e4a6d9d838f37"},
+]
+
+[package.extras]
+lint = ["pre-commit (>=3.3)"]
+test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+
+[[package]]
+name = "librosa"
+version = "0.10.1"
+description = "Python module for audio and music processing"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "librosa-0.10.1-py3-none-any.whl", hash = "sha256:7ab91d9f5fcb75ea14848a05d3b1f825cf8d0c42ca160d19ae6874f2de2d8223"},
+ {file = "librosa-0.10.1.tar.gz", hash = "sha256:832f7d150d6dd08ed2aa08c0567a4be58330635c32ddd2208de9bc91300802c7"},
+]
+
+[package.dependencies]
+audioread = ">=2.1.9"
+decorator = ">=4.3.0"
+joblib = ">=0.14"
+lazy-loader = ">=0.1"
+msgpack = ">=1.0"
+numba = ">=0.51.0"
+numpy = ">=1.20.3,<1.22.0 || >1.22.0,<1.22.1 || >1.22.1,<1.22.2 || >1.22.2"
+pooch = ">=1.0"
+scikit-learn = ">=0.20.0"
+scipy = ">=1.2.0"
+soundfile = ">=0.12.1"
+soxr = ">=0.3.2"
+typing-extensions = ">=4.1.1"
+
+[package.extras]
+display = ["matplotlib (>=3.3.0)"]
+docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (>=0.51)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=1.2.0)", "sphinxcontrib-svg2pdfconverter"]
+tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
+
+[[package]]
+name = "llvmlite"
+version = "0.41.1"
+description = "lightweight wrapper around basic LLVM functionality"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "llvmlite-0.41.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c1e1029d47ee66d3a0c4d6088641882f75b93db82bd0e6178f7bd744ebce42b9"},
+ {file = "llvmlite-0.41.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:150d0bc275a8ac664a705135e639178883293cf08c1a38de3bbaa2f693a0a867"},
+ {file = "llvmlite-0.41.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1eee5cf17ec2b4198b509272cf300ee6577229d237c98cc6e63861b08463ddc6"},
+ {file = "llvmlite-0.41.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0dd0338da625346538f1173a17cabf21d1e315cf387ca21b294ff209d176e244"},
+ {file = "llvmlite-0.41.1-cp310-cp310-win32.whl", hash = "sha256:fa1469901a2e100c17eb8fe2678e34bd4255a3576d1a543421356e9c14d6e2ae"},
+ {file = "llvmlite-0.41.1-cp310-cp310-win_amd64.whl", hash = "sha256:2b76acee82ea0e9304be6be9d4b3840208d050ea0dcad75b1635fa06e949a0ae"},
+ {file = "llvmlite-0.41.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:210e458723436b2469d61b54b453474e09e12a94453c97ea3fbb0742ba5a83d8"},
+ {file = "llvmlite-0.41.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:855f280e781d49e0640aef4c4af586831ade8f1a6c4df483fb901cbe1a48d127"},
+ {file = "llvmlite-0.41.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b67340c62c93a11fae482910dc29163a50dff3dfa88bc874872d28ee604a83be"},
+ {file = "llvmlite-0.41.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2181bb63ef3c607e6403813421b46982c3ac6bfc1f11fa16a13eaafb46f578e6"},
+ {file = "llvmlite-0.41.1-cp311-cp311-win_amd64.whl", hash = "sha256:9564c19b31a0434f01d2025b06b44c7ed422f51e719ab5d24ff03b7560066c9a"},
+ {file = "llvmlite-0.41.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5940bc901fb0325970415dbede82c0b7f3e35c2d5fd1d5e0047134c2c46b3281"},
+ {file = "llvmlite-0.41.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8b0a9a47c28f67a269bb62f6256e63cef28d3c5f13cbae4fab587c3ad506778b"},
+ {file = "llvmlite-0.41.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8afdfa6da33f0b4226af8e64cfc2b28986e005528fbf944d0a24a72acfc9432"},
+ {file = "llvmlite-0.41.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8454c1133ef701e8c050a59edd85d238ee18bb9a0eb95faf2fca8b909ee3c89a"},
+ {file = "llvmlite-0.41.1-cp38-cp38-win32.whl", hash = "sha256:2d92c51e6e9394d503033ffe3292f5bef1566ab73029ec853861f60ad5c925d0"},
+ {file = "llvmlite-0.41.1-cp38-cp38-win_amd64.whl", hash = "sha256:df75594e5a4702b032684d5481db3af990b69c249ccb1d32687b8501f0689432"},
+ {file = "llvmlite-0.41.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:04725975e5b2af416d685ea0769f4ecc33f97be541e301054c9f741003085802"},
+ {file = "llvmlite-0.41.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:bf14aa0eb22b58c231243dccf7e7f42f7beec48970f2549b3a6acc737d1a4ba4"},
+ {file = "llvmlite-0.41.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:92c32356f669e036eb01016e883b22add883c60739bc1ebee3a1cc0249a50828"},
+ {file = "llvmlite-0.41.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:24091a6b31242bcdd56ae2dbea40007f462260bc9bdf947953acc39dffd54f8f"},
+ {file = "llvmlite-0.41.1-cp39-cp39-win32.whl", hash = "sha256:880cb57ca49e862e1cd077104375b9d1dfdc0622596dfa22105f470d7bacb309"},
+ {file = "llvmlite-0.41.1-cp39-cp39-win_amd64.whl", hash = "sha256:92f093986ab92e71c9ffe334c002f96defc7986efda18397d0f08534f3ebdc4d"},
+ {file = "llvmlite-0.41.1.tar.gz", hash = "sha256:f19f767a018e6ec89608e1f6b13348fa2fcde657151137cb64e56d48598a92db"},
+]
+
+[[package]]
+name = "lxml"
+version = "5.1.0"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "lxml-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:704f5572ff473a5f897745abebc6df40f22d4133c1e0a1f124e4f2bd3330ff7e"},
+ {file = "lxml-5.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9d3c0f8567ffe7502d969c2c1b809892dc793b5d0665f602aad19895f8d508da"},
+ {file = "lxml-5.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5fcfbebdb0c5d8d18b84118842f31965d59ee3e66996ac842e21f957eb76138c"},
+ {file = "lxml-5.1.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2f37c6d7106a9d6f0708d4e164b707037b7380fcd0b04c5bd9cae1fb46a856fb"},
+ {file = "lxml-5.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2befa20a13f1a75c751f47e00929fb3433d67eb9923c2c0b364de449121f447c"},
+ {file = "lxml-5.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22b7ee4c35f374e2c20337a95502057964d7e35b996b1c667b5c65c567d2252a"},
+ {file = "lxml-5.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:bf8443781533b8d37b295016a4b53c1494fa9a03573c09ca5104550c138d5c05"},
+ {file = "lxml-5.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:82bddf0e72cb2af3cbba7cec1d2fd11fda0de6be8f4492223d4a268713ef2147"},
+ {file = "lxml-5.1.0-cp310-cp310-win32.whl", hash = "sha256:b66aa6357b265670bb574f050ffceefb98549c721cf28351b748be1ef9577d93"},
+ {file = "lxml-5.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:4946e7f59b7b6a9e27bef34422f645e9a368cb2be11bf1ef3cafc39a1f6ba68d"},
+ {file = "lxml-5.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:14deca1460b4b0f6b01f1ddc9557704e8b365f55c63070463f6c18619ebf964f"},
+ {file = "lxml-5.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ed8c3d2cd329bf779b7ed38db176738f3f8be637bb395ce9629fc76f78afe3d4"},
+ {file = "lxml-5.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:436a943c2900bb98123b06437cdd30580a61340fbdb7b28aaf345a459c19046a"},
+ {file = "lxml-5.1.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:acb6b2f96f60f70e7f34efe0c3ea34ca63f19ca63ce90019c6cbca6b676e81fa"},
+ {file = "lxml-5.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:af8920ce4a55ff41167ddbc20077f5698c2e710ad3353d32a07d3264f3a2021e"},
+ {file = "lxml-5.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7cfced4a069003d8913408e10ca8ed092c49a7f6cefee9bb74b6b3e860683b45"},
+ {file = "lxml-5.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:9e5ac3437746189a9b4121db2a7b86056ac8786b12e88838696899328fc44bb2"},
+ {file = "lxml-5.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:f4c9bda132ad108b387c33fabfea47866af87f4ea6ffb79418004f0521e63204"},
+ {file = "lxml-5.1.0-cp311-cp311-win32.whl", hash = "sha256:bc64d1b1dab08f679fb89c368f4c05693f58a9faf744c4d390d7ed1d8223869b"},
+ {file = "lxml-5.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:a5ab722ae5a873d8dcee1f5f45ddd93c34210aed44ff2dc643b5025981908cda"},
+ {file = "lxml-5.1.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:9aa543980ab1fbf1720969af1d99095a548ea42e00361e727c58a40832439114"},
+ {file = "lxml-5.1.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:6f11b77ec0979f7e4dc5ae081325a2946f1fe424148d3945f943ceaede98adb8"},
+ {file = "lxml-5.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a36c506e5f8aeb40680491d39ed94670487ce6614b9d27cabe45d94cd5d63e1e"},
+ {file = "lxml-5.1.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f643ffd2669ffd4b5a3e9b41c909b72b2a1d5e4915da90a77e119b8d48ce867a"},
+ {file = "lxml-5.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16dd953fb719f0ffc5bc067428fc9e88f599e15723a85618c45847c96f11f431"},
+ {file = "lxml-5.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:16018f7099245157564d7148165132c70adb272fb5a17c048ba70d9cc542a1a1"},
+ {file = "lxml-5.1.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:82cd34f1081ae4ea2ede3d52f71b7be313756e99b4b5f829f89b12da552d3aa3"},
+ {file = "lxml-5.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:19a1bc898ae9f06bccb7c3e1dfd73897ecbbd2c96afe9095a6026016e5ca97b8"},
+ {file = "lxml-5.1.0-cp312-cp312-win32.whl", hash = "sha256:13521a321a25c641b9ea127ef478b580b5ec82aa2e9fc076c86169d161798b01"},
+ {file = "lxml-5.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:1ad17c20e3666c035db502c78b86e58ff6b5991906e55bdbef94977700c72623"},
+ {file = "lxml-5.1.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:24ef5a4631c0b6cceaf2dbca21687e29725b7c4e171f33a8f8ce23c12558ded1"},
+ {file = "lxml-5.1.0-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8d2900b7f5318bc7ad8631d3d40190b95ef2aa8cc59473b73b294e4a55e9f30f"},
+ {file = "lxml-5.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:601f4a75797d7a770daed8b42b97cd1bb1ba18bd51a9382077a6a247a12aa38d"},
+ {file = "lxml-5.1.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b4b68c961b5cc402cbd99cca5eb2547e46ce77260eb705f4d117fd9c3f932b95"},
+ {file = "lxml-5.1.0-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:afd825e30f8d1f521713a5669b63657bcfe5980a916c95855060048b88e1adb7"},
+ {file = "lxml-5.1.0-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:262bc5f512a66b527d026518507e78c2f9c2bd9eb5c8aeeb9f0eb43fcb69dc67"},
+ {file = "lxml-5.1.0-cp36-cp36m-win32.whl", hash = "sha256:e856c1c7255c739434489ec9c8aa9cdf5179785d10ff20add308b5d673bed5cd"},
+ {file = "lxml-5.1.0-cp36-cp36m-win_amd64.whl", hash = "sha256:c7257171bb8d4432fe9d6fdde4d55fdbe663a63636a17f7f9aaba9bcb3153ad7"},
+ {file = "lxml-5.1.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b9e240ae0ba96477682aa87899d94ddec1cc7926f9df29b1dd57b39e797d5ab5"},
+ {file = "lxml-5.1.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a96f02ba1bcd330807fc060ed91d1f7a20853da6dd449e5da4b09bfcc08fdcf5"},
+ {file = "lxml-5.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e3898ae2b58eeafedfe99e542a17859017d72d7f6a63de0f04f99c2cb125936"},
+ {file = "lxml-5.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:61c5a7edbd7c695e54fca029ceb351fc45cd8860119a0f83e48be44e1c464862"},
+ {file = "lxml-5.1.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:3aeca824b38ca78d9ee2ab82bd9883083d0492d9d17df065ba3b94e88e4d7ee6"},
+ {file = "lxml-5.1.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8f52fe6859b9db71ee609b0c0a70fea5f1e71c3462ecf144ca800d3f434f0764"},
+ {file = "lxml-5.1.0-cp37-cp37m-win32.whl", hash = "sha256:d42e3a3fc18acc88b838efded0e6ec3edf3e328a58c68fbd36a7263a874906c8"},
+ {file = "lxml-5.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:eac68f96539b32fce2c9b47eb7c25bb2582bdaf1bbb360d25f564ee9e04c542b"},
+ {file = "lxml-5.1.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ae15347a88cf8af0949a9872b57a320d2605ae069bcdf047677318bc0bba45b1"},
+ {file = "lxml-5.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:c26aab6ea9c54d3bed716b8851c8bfc40cb249b8e9880e250d1eddde9f709bf5"},
+ {file = "lxml-5.1.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:342e95bddec3a698ac24378d61996b3ee5ba9acfeb253986002ac53c9a5f6f84"},
+ {file = "lxml-5.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:725e171e0b99a66ec8605ac77fa12239dbe061482ac854d25720e2294652eeaa"},
+ {file = "lxml-5.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d184e0d5c918cff04cdde9dbdf9600e960161d773666958c9d7b565ccc60c45"},
+ {file = "lxml-5.1.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:98f3f020a2b736566c707c8e034945c02aa94e124c24f77ca097c446f81b01f1"},
+ {file = "lxml-5.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6d48fc57e7c1e3df57be5ae8614bab6d4e7b60f65c5457915c26892c41afc59e"},
+ {file = "lxml-5.1.0-cp38-cp38-win32.whl", hash = "sha256:7ec465e6549ed97e9f1e5ed51c657c9ede767bc1c11552f7f4d022c4df4a977a"},
+ {file = "lxml-5.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:b21b4031b53d25b0858d4e124f2f9131ffc1530431c6d1321805c90da78388d1"},
+ {file = "lxml-5.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:52427a7eadc98f9e62cb1368a5079ae826f94f05755d2d567d93ee1bc3ceb354"},
+ {file = "lxml-5.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6a2a2c724d97c1eb8cf966b16ca2915566a4904b9aad2ed9a09c748ffe14f969"},
+ {file = "lxml-5.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:843b9c835580d52828d8f69ea4302537337a21e6b4f1ec711a52241ba4a824f3"},
+ {file = "lxml-5.1.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9b99f564659cfa704a2dd82d0684207b1aadf7d02d33e54845f9fc78e06b7581"},
+ {file = "lxml-5.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f8b0c78e7aac24979ef09b7f50da871c2de2def043d468c4b41f512d831e912"},
+ {file = "lxml-5.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9bcf86dfc8ff3e992fed847c077bd875d9e0ba2fa25d859c3a0f0f76f07f0c8d"},
+ {file = "lxml-5.1.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:49a9b4af45e8b925e1cd6f3b15bbba2c81e7dba6dce170c677c9cda547411e14"},
+ {file = "lxml-5.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:280f3edf15c2a967d923bcfb1f8f15337ad36f93525828b40a0f9d6c2ad24890"},
+ {file = "lxml-5.1.0-cp39-cp39-win32.whl", hash = "sha256:ed7326563024b6e91fef6b6c7a1a2ff0a71b97793ac33dbbcf38f6005e51ff6e"},
+ {file = "lxml-5.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:8d7b4beebb178e9183138f552238f7e6613162a42164233e2bda00cb3afac58f"},
+ {file = "lxml-5.1.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:9bd0ae7cc2b85320abd5e0abad5ccee5564ed5f0cc90245d2f9a8ef330a8deae"},
+ {file = "lxml-5.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8c1d679df4361408b628f42b26a5d62bd3e9ba7f0c0e7969f925021554755aa"},
+ {file = "lxml-5.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:2ad3a8ce9e8a767131061a22cd28fdffa3cd2dc193f399ff7b81777f3520e372"},
+ {file = "lxml-5.1.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:304128394c9c22b6569eba2a6d98392b56fbdfbad58f83ea702530be80d0f9df"},
+ {file = "lxml-5.1.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d74fcaf87132ffc0447b3c685a9f862ffb5b43e70ea6beec2fb8057d5d2a1fea"},
+ {file = "lxml-5.1.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:8cf5877f7ed384dabfdcc37922c3191bf27e55b498fecece9fd5c2c7aaa34c33"},
+ {file = "lxml-5.1.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:877efb968c3d7eb2dad540b6cabf2f1d3c0fbf4b2d309a3c141f79c7e0061324"},
+ {file = "lxml-5.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3f14a4fb1c1c402a22e6a341a24c1341b4a3def81b41cd354386dcb795f83897"},
+ {file = "lxml-5.1.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:25663d6e99659544ee8fe1b89b1a8c0aaa5e34b103fab124b17fa958c4a324a6"},
+ {file = "lxml-5.1.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8b9f19df998761babaa7f09e6bc169294eefafd6149aaa272081cbddc7ba4ca3"},
+ {file = "lxml-5.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e53d7e6a98b64fe54775d23a7c669763451340c3d44ad5e3a3b48a1efbdc96f"},
+ {file = "lxml-5.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:c3cd1fc1dc7c376c54440aeaaa0dcc803d2126732ff5c6b68ccd619f2e64be4f"},
+ {file = "lxml-5.1.0.tar.gz", hash = "sha256:3eea6ed6e6c918e468e693c41ef07f3c3acc310b70ddd9cc72d9ef84bc9564ca"},
+]
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["BeautifulSoup4"]
+source = ["Cython (>=3.0.7)"]
+
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
+ {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
+[[package]]
+name = "markupsafe"
+version = "2.1.3"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "MarkupSafe-2.1.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68e78619a61ecf91e76aa3e6e8e33fc4894a2bebe93410754bd28fce0a8a4f9f"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65c1a9bcdadc6c28eecee2c119465aebff8f7a584dd719facdd9e825ec61ab52"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:525808b8019e36eb524b8c68acdd63a37e75714eac50e988180b169d64480a00"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:962f82a3086483f5e5f64dbad880d31038b698494799b097bc59c2edf392fce6"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:aa7bd130efab1c280bed0f45501b7c8795f9fdbeb02e965371bbef3523627779"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c9c804664ebe8f83a211cace637506669e7890fec1b4195b505c214e50dd4eb7"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-win32.whl", hash = "sha256:10bbfe99883db80bdbaff2dcf681dfc6533a614f700da1287707e8a5d78a8431"},
+ {file = "MarkupSafe-2.1.3-cp310-cp310-win_amd64.whl", hash = "sha256:1577735524cdad32f9f694208aa75e422adba74f1baee7551620e43a3141f559"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:ad9e82fb8f09ade1c3e1b996a6337afac2b8b9e365f926f5a61aacc71adc5b3c"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3c0fae6c3be832a0a0473ac912810b2877c8cb9d76ca48de1ed31e1c68386575"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b076b6226fb84157e3f7c971a47ff3a679d837cf338547532ab866c57930dbee"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:338ae27d6b8745585f87218a3f23f1512dbf52c26c28e322dbe54bcede54ccb9"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
+ {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
+ {file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca379055a47383d02a5400cb0d110cef0a776fc644cda797db0c5696cfd7e18e"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:b7ff0f54cb4ff66dd38bebd335a38e2c22c41a8ee45aa608efc890ac3e3931bc"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:c011a4149cfbcf9f03994ec2edffcb8b1dc2d2aede7ca243746df97a5d41ce48"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:56d9f2ecac662ca1611d183feb03a3fa4406469dafe241673d521dd5ae92a155"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-win32.whl", hash = "sha256:8758846a7e80910096950b67071243da3e5a20ed2546e6392603c096778d48e0"},
+ {file = "MarkupSafe-2.1.3-cp37-cp37m-win_amd64.whl", hash = "sha256:787003c0ddb00500e49a10f2844fac87aa6ce977b90b0feaaf9de23c22508b24"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:2ef12179d3a291be237280175b542c07a36e7f60718296278d8593d21ca937d4"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2c1b19b3aaacc6e57b7e25710ff571c24d6c3613a45e905b1fde04d691b98ee0"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8afafd99945ead6e075b973fefa56379c5b5c53fd8937dad92c662da5d8fd5ee"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c41976a29d078bb235fea9b2ecd3da465df42a562910f9022f1a03107bd02be"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:69c0f17e9f5a7afdf2cc9fb2d1ce6aabdb3bafb7f38017c0b77862bcec2bbad8"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:504b320cd4b7eff6f968eddf81127112db685e81f7e36e75f9f84f0df46041c3"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:42de32b22b6b804f42c5d98be4f7e5e977ecdd9ee9b660fda1a3edf03b11792d"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-win32.whl", hash = "sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5"},
+ {file = "MarkupSafe-2.1.3-cp38-cp38-win_amd64.whl", hash = "sha256:1b40069d487e7edb2676d3fbdb2b0829ffa2cd63a2ec26c4938b2d34391b4ecc"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:8023faf4e01efadfa183e863fefde0046de576c6f14659e8782065bcece22198"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6b2b56950d93e41f33b4223ead100ea0fe11f8e6ee5f641eb753ce4b77a7042b"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dcdfd0eaf283af041973bff14a2e143b8bd64e069f4c383416ecd79a81aab58"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:282c2cb35b5b673bbcadb33a585408104df04f14b2d9b01d4c345a3b92861c2c"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ab4a0df41e7c16a1392727727e7998a467472d0ad65f3ad5e6e765015df08636"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-win32.whl", hash = "sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2"},
+ {file = "MarkupSafe-2.1.3-cp39-cp39-win_amd64.whl", hash = "sha256:3fd4abcb888d15a94f32b75d8fd18ee162ca0c064f35b11134be77050296d6ba"},
+ {file = "MarkupSafe-2.1.3.tar.gz", hash = "sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad"},
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.8.2"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.9"
+files = [
+ {file = "matplotlib-3.8.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:09796f89fb71a0c0e1e2f4bdaf63fb2cefc84446bb963ecdeb40dfee7dfa98c7"},
+ {file = "matplotlib-3.8.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f9c6976748a25e8b9be51ea028df49b8e561eed7809146da7a47dbecebab367"},
+ {file = "matplotlib-3.8.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b78e4f2cedf303869b782071b55fdde5987fda3038e9d09e58c91cc261b5ad18"},
+ {file = "matplotlib-3.8.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e208f46cf6576a7624195aa047cb344a7f802e113bb1a06cfd4bee431de5e31"},
+ {file = "matplotlib-3.8.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:46a569130ff53798ea5f50afce7406e91fdc471ca1e0e26ba976a8c734c9427a"},
+ {file = "matplotlib-3.8.2-cp310-cp310-win_amd64.whl", hash = "sha256:830f00640c965c5b7f6bc32f0d4ce0c36dfe0379f7dd65b07a00c801713ec40a"},
+ {file = "matplotlib-3.8.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:d86593ccf546223eb75a39b44c32788e6f6440d13cfc4750c1c15d0fcb850b63"},
+ {file = "matplotlib-3.8.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9a5430836811b7652991939012f43d2808a2db9b64ee240387e8c43e2e5578c8"},
+ {file = "matplotlib-3.8.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9576723858a78751d5aacd2497b8aef29ffea6d1c95981505877f7ac28215c6"},
+ {file = "matplotlib-3.8.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ba9cbd8ac6cf422f3102622b20f8552d601bf8837e49a3afed188d560152788"},
+ {file = "matplotlib-3.8.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:03f9d160a29e0b65c0790bb07f4f45d6a181b1ac33eb1bb0dd225986450148f0"},
+ {file = "matplotlib-3.8.2-cp311-cp311-win_amd64.whl", hash = "sha256:3773002da767f0a9323ba1a9b9b5d00d6257dbd2a93107233167cfb581f64717"},
+ {file = "matplotlib-3.8.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:4c318c1e95e2f5926fba326f68177dee364aa791d6df022ceb91b8221bd0a627"},
+ {file = "matplotlib-3.8.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:091275d18d942cf1ee9609c830a1bc36610607d8223b1b981c37d5c9fc3e46a4"},
+ {file = "matplotlib-3.8.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b0f3b8ea0e99e233a4bcc44590f01604840d833c280ebb8fe5554fd3e6cfe8d"},
+ {file = "matplotlib-3.8.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7b1704a530395aaf73912be741c04d181f82ca78084fbd80bc737be04848331"},
+ {file = "matplotlib-3.8.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:533b0e3b0c6768eef8cbe4b583731ce25a91ab54a22f830db2b031e83cca9213"},
+ {file = "matplotlib-3.8.2-cp312-cp312-win_amd64.whl", hash = "sha256:0f4fc5d72b75e2c18e55eb32292659cf731d9d5b312a6eb036506304f4675630"},
+ {file = "matplotlib-3.8.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:deaed9ad4da0b1aea77fe0aa0cebb9ef611c70b3177be936a95e5d01fa05094f"},
+ {file = "matplotlib-3.8.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:172f4d0fbac3383d39164c6caafd3255ce6fa58f08fc392513a0b1d3b89c4f89"},
+ {file = "matplotlib-3.8.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c7d36c2209d9136cd8e02fab1c0ddc185ce79bc914c45054a9f514e44c787917"},
+ {file = "matplotlib-3.8.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5864bdd7da445e4e5e011b199bb67168cdad10b501750367c496420f2ad00843"},
+ {file = "matplotlib-3.8.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ef8345b48e95cee45ff25192ed1f4857273117917a4dcd48e3905619bcd9c9b8"},
+ {file = "matplotlib-3.8.2-cp39-cp39-win_amd64.whl", hash = "sha256:7c48d9e221b637c017232e3760ed30b4e8d5dfd081daf327e829bf2a72c731b4"},
+ {file = "matplotlib-3.8.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:aa11b3c6928a1e496c1a79917d51d4cd5d04f8a2e75f21df4949eeefdf697f4b"},
+ {file = "matplotlib-3.8.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d1095fecf99eeb7384dabad4bf44b965f929a5f6079654b681193edf7169ec20"},
+ {file = "matplotlib-3.8.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:bddfb1db89bfaa855912261c805bd0e10218923cc262b9159a49c29a7a1c1afa"},
+ {file = "matplotlib-3.8.2.tar.gz", hash = "sha256:01a978b871b881ee76017152f1f1a0cbf6bd5f7b8ff8c96df0df1bd57d8755a1"},
+]
+
+[package.dependencies]
+contourpy = ">=1.0.1"
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
+kiwisolver = ">=1.3.1"
+numpy = ">=1.21,<2"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
+ {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
+]
+
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+ {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
+[[package]]
+name = "msal"
+version = "1.26.0"
+description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
+optional = false
+python-versions = ">=2.7"
+files = [
+ {file = "msal-1.26.0-py2.py3-none-any.whl", hash = "sha256:be77ba6a8f49c9ff598bbcdc5dfcf1c9842f3044300109af738e8c3e371065b5"},
+ {file = "msal-1.26.0.tar.gz", hash = "sha256:224756079fe338be838737682b49f8ebc20a87c1c5eeaf590daae4532b83de15"},
+]
+
+[package.dependencies]
+cryptography = ">=0.6,<44"
+PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
+requests = ">=2.0.0,<3"
+
+[package.extras]
+broker = ["pymsalruntime (>=0.13.2,<0.14)"]
+
+[[package]]
+name = "msal-extensions"
+version = "1.0.0"
+description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
+optional = false
+python-versions = "*"
+files = [
+ {file = "msal-extensions-1.0.0.tar.gz", hash = "sha256:c676aba56b0cce3783de1b5c5ecfe828db998167875126ca4b47dc6436451354"},
+ {file = "msal_extensions-1.0.0-py2.py3-none-any.whl", hash = "sha256:91e3db9620b822d0ed2b4d1850056a0f133cba04455e62f11612e40f5502f2ee"},
+]
+
+[package.dependencies]
+msal = ">=0.4.1,<2.0.0"
+portalocker = [
+ {version = ">=1.0,<3", markers = "python_version >= \"3.5\" and platform_system != \"Windows\""},
+ {version = ">=1.6,<3", markers = "python_version >= \"3.5\" and platform_system == \"Windows\""},
+]
+
+[[package]]
+name = "msgpack"
+version = "1.0.7"
+description = "MessagePack serializer"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "msgpack-1.0.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:04ad6069c86e531682f9e1e71b71c1c3937d6014a7c3e9edd2aa81ad58842862"},
+ {file = "msgpack-1.0.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cca1b62fe70d761a282496b96a5e51c44c213e410a964bdffe0928e611368329"},
+ {file = "msgpack-1.0.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e50ebce52f41370707f1e21a59514e3375e3edd6e1832f5e5235237db933c98b"},
+ {file = "msgpack-1.0.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a7b4f35de6a304b5533c238bee86b670b75b03d31b7797929caa7a624b5dda6"},
+ {file = "msgpack-1.0.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28efb066cde83c479dfe5a48141a53bc7e5f13f785b92ddde336c716663039ee"},
+ {file = "msgpack-1.0.7-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4cb14ce54d9b857be9591ac364cb08dc2d6a5c4318c1182cb1d02274029d590d"},
+ {file = "msgpack-1.0.7-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b573a43ef7c368ba4ea06050a957c2a7550f729c31f11dd616d2ac4aba99888d"},
+ {file = "msgpack-1.0.7-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:ccf9a39706b604d884d2cb1e27fe973bc55f2890c52f38df742bc1d79ab9f5e1"},
+ {file = "msgpack-1.0.7-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:cb70766519500281815dfd7a87d3a178acf7ce95390544b8c90587d76b227681"},
+ {file = "msgpack-1.0.7-cp310-cp310-win32.whl", hash = "sha256:b610ff0f24e9f11c9ae653c67ff8cc03c075131401b3e5ef4b82570d1728f8a9"},
+ {file = "msgpack-1.0.7-cp310-cp310-win_amd64.whl", hash = "sha256:a40821a89dc373d6427e2b44b572efc36a2778d3f543299e2f24eb1a5de65415"},
+ {file = "msgpack-1.0.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:576eb384292b139821c41995523654ad82d1916da6a60cff129c715a6223ea84"},
+ {file = "msgpack-1.0.7-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:730076207cb816138cf1af7f7237b208340a2c5e749707457d70705715c93b93"},
+ {file = "msgpack-1.0.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:85765fdf4b27eb5086f05ac0491090fc76f4f2b28e09d9350c31aac25a5aaff8"},
+ {file = "msgpack-1.0.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3476fae43db72bd11f29a5147ae2f3cb22e2f1a91d575ef130d2bf49afd21c46"},
+ {file = "msgpack-1.0.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d4c80667de2e36970ebf74f42d1088cc9ee7ef5f4e8c35eee1b40eafd33ca5b"},
+ {file = "msgpack-1.0.7-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b0bf0effb196ed76b7ad883848143427a73c355ae8e569fa538365064188b8e"},
+ {file = "msgpack-1.0.7-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f9a7c509542db4eceed3dcf21ee5267ab565a83555c9b88a8109dcecc4709002"},
+ {file = "msgpack-1.0.7-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:84b0daf226913133f899ea9b30618722d45feffa67e4fe867b0b5ae83a34060c"},
+ {file = "msgpack-1.0.7-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:ec79ff6159dffcc30853b2ad612ed572af86c92b5168aa3fc01a67b0fa40665e"},
+ {file = "msgpack-1.0.7-cp311-cp311-win32.whl", hash = "sha256:3e7bf4442b310ff154b7bb9d81eb2c016b7d597e364f97d72b1acc3817a0fdc1"},
+ {file = "msgpack-1.0.7-cp311-cp311-win_amd64.whl", hash = "sha256:3f0c8c6dfa6605ab8ff0611995ee30d4f9fcff89966cf562733b4008a3d60d82"},
+ {file = "msgpack-1.0.7-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f0936e08e0003f66bfd97e74ee530427707297b0d0361247e9b4f59ab78ddc8b"},
+ {file = "msgpack-1.0.7-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:98bbd754a422a0b123c66a4c341de0474cad4a5c10c164ceed6ea090f3563db4"},
+ {file = "msgpack-1.0.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b291f0ee7961a597cbbcc77709374087fa2a9afe7bdb6a40dbbd9b127e79afee"},
+ {file = "msgpack-1.0.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ebbbba226f0a108a7366bf4b59bf0f30a12fd5e75100c630267d94d7f0ad20e5"},
+ {file = "msgpack-1.0.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e2d69948e4132813b8d1131f29f9101bc2c915f26089a6d632001a5c1349672"},
+ {file = "msgpack-1.0.7-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bdf38ba2d393c7911ae989c3bbba510ebbcdf4ecbdbfec36272abe350c454075"},
+ {file = "msgpack-1.0.7-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:993584fc821c58d5993521bfdcd31a4adf025c7d745bbd4d12ccfecf695af5ba"},
+ {file = "msgpack-1.0.7-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:52700dc63a4676669b341ba33520f4d6e43d3ca58d422e22ba66d1736b0a6e4c"},
+ {file = "msgpack-1.0.7-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:e45ae4927759289c30ccba8d9fdce62bb414977ba158286b5ddaf8df2cddb5c5"},
+ {file = "msgpack-1.0.7-cp312-cp312-win32.whl", hash = "sha256:27dcd6f46a21c18fa5e5deed92a43d4554e3df8d8ca5a47bf0615d6a5f39dbc9"},
+ {file = "msgpack-1.0.7-cp312-cp312-win_amd64.whl", hash = "sha256:7687e22a31e976a0e7fc99c2f4d11ca45eff652a81eb8c8085e9609298916dcf"},
+ {file = "msgpack-1.0.7-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5b6ccc0c85916998d788b295765ea0e9cb9aac7e4a8ed71d12e7d8ac31c23c95"},
+ {file = "msgpack-1.0.7-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:235a31ec7db685f5c82233bddf9858748b89b8119bf4538d514536c485c15fe0"},
+ {file = "msgpack-1.0.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:cab3db8bab4b7e635c1c97270d7a4b2a90c070b33cbc00c99ef3f9be03d3e1f7"},
+ {file = "msgpack-1.0.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0bfdd914e55e0d2c9e1526de210f6fe8ffe9705f2b1dfcc4aecc92a4cb4b533d"},
+ {file = "msgpack-1.0.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36e17c4592231a7dbd2ed09027823ab295d2791b3b1efb2aee874b10548b7524"},
+ {file = "msgpack-1.0.7-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:38949d30b11ae5f95c3c91917ee7a6b239f5ec276f271f28638dec9156f82cfc"},
+ {file = "msgpack-1.0.7-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ff1d0899f104f3921d94579a5638847f783c9b04f2d5f229392ca77fba5b82fc"},
+ {file = "msgpack-1.0.7-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:dc43f1ec66eb8440567186ae2f8c447d91e0372d793dfe8c222aec857b81a8cf"},
+ {file = "msgpack-1.0.7-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:dd632777ff3beaaf629f1ab4396caf7ba0bdd075d948a69460d13d44357aca4c"},
+ {file = "msgpack-1.0.7-cp38-cp38-win32.whl", hash = "sha256:4e71bc4416de195d6e9b4ee93ad3f2f6b2ce11d042b4d7a7ee00bbe0358bd0c2"},
+ {file = "msgpack-1.0.7-cp38-cp38-win_amd64.whl", hash = "sha256:8f5b234f567cf76ee489502ceb7165c2a5cecec081db2b37e35332b537f8157c"},
+ {file = "msgpack-1.0.7-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:bfef2bb6ef068827bbd021017a107194956918ab43ce4d6dc945ffa13efbc25f"},
+ {file = "msgpack-1.0.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:484ae3240666ad34cfa31eea7b8c6cd2f1fdaae21d73ce2974211df099a95d81"},
+ {file = "msgpack-1.0.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3967e4ad1aa9da62fd53e346ed17d7b2e922cba5ab93bdd46febcac39be636fc"},
+ {file = "msgpack-1.0.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8dd178c4c80706546702c59529ffc005681bd6dc2ea234c450661b205445a34d"},
+ {file = "msgpack-1.0.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f6ffbc252eb0d229aeb2f9ad051200668fc3a9aaa8994e49f0cb2ffe2b7867e7"},
+ {file = "msgpack-1.0.7-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:822ea70dc4018c7e6223f13affd1c5c30c0f5c12ac1f96cd8e9949acddb48a61"},
+ {file = "msgpack-1.0.7-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:384d779f0d6f1b110eae74cb0659d9aa6ff35aaf547b3955abf2ab4c901c4819"},
+ {file = "msgpack-1.0.7-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:f64e376cd20d3f030190e8c32e1c64582eba56ac6dc7d5b0b49a9d44021b52fd"},
+ {file = "msgpack-1.0.7-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5ed82f5a7af3697b1c4786053736f24a0efd0a1b8a130d4c7bfee4b9ded0f08f"},
+ {file = "msgpack-1.0.7-cp39-cp39-win32.whl", hash = "sha256:f26a07a6e877c76a88e3cecac8531908d980d3d5067ff69213653649ec0f60ad"},
+ {file = "msgpack-1.0.7-cp39-cp39-win_amd64.whl", hash = "sha256:1dc93e8e4653bdb5910aed79f11e165c85732067614f180f70534f056da97db3"},
+ {file = "msgpack-1.0.7.tar.gz", hash = "sha256:572efc93db7a4d27e404501975ca6d2d9775705c2d922390d878fcf768d92c87"},
+]
+
+[[package]]
+name = "msrest"
+version = "0.7.1"
+description = "AutoRest swagger generator Python client runtime."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "msrest-0.7.1-py3-none-any.whl", hash = "sha256:21120a810e1233e5e6cc7fe40b474eeb4ec6f757a15d7cf86702c369f9567c32"},
+ {file = "msrest-0.7.1.zip", hash = "sha256:6e7661f46f3afd88b75667b7187a92829924446c7ea1d169be8c4bb7eeb788b9"},
+]
+
+[package.dependencies]
+azure-core = ">=1.24.0"
+certifi = ">=2017.4.17"
+isodate = ">=0.6.0"
+requests = ">=2.16,<3.0"
+requests-oauthlib = ">=0.5.0"
+
+[package.extras]
+async = ["aiodns", "aiohttp (>=3.0)"]
+
+[[package]]
+name = "msrestazure"
+version = "0.6.4"
+description = "AutoRest swagger generator Python client runtime. Azure-specific module."
+optional = false
+python-versions = "*"
+files = [
+ {file = "msrestazure-0.6.4-py2.py3-none-any.whl", hash = "sha256:3de50f56147ef529b31e099a982496690468ecef33f0544cb0fa0cfe1e1de5b9"},
+ {file = "msrestazure-0.6.4.tar.gz", hash = "sha256:a06f0dabc9a6f5efe3b6add4bd8fb623aeadacf816b7a35b0f89107e0544d189"},
+]
+
+[package.dependencies]
+adal = ">=0.6.0,<2.0.0"
+msrest = ">=0.6.0,<2.0.0"
+six = "*"
+
+[[package]]
+name = "multidict"
+version = "6.0.4"
+description = "multidict implementation"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "multidict-6.0.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0b1a97283e0c85772d613878028fec909f003993e1007eafa715b24b377cb9b8"},
+ {file = "multidict-6.0.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:eeb6dcc05e911516ae3d1f207d4b0520d07f54484c49dfc294d6e7d63b734171"},
+ {file = "multidict-6.0.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d6d635d5209b82a3492508cf5b365f3446afb65ae7ebd755e70e18f287b0adf7"},
+ {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c048099e4c9e9d615545e2001d3d8a4380bd403e1a0578734e0d31703d1b0c0b"},
+ {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ea20853c6dbbb53ed34cb4d080382169b6f4554d394015f1bef35e881bf83547"},
+ {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:16d232d4e5396c2efbbf4f6d4df89bfa905eb0d4dc5b3549d872ab898451f569"},
+ {file = "multidict-6.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36c63aaa167f6c6b04ef2c85704e93af16c11d20de1d133e39de6a0e84582a93"},
+ {file = "multidict-6.0.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:64bdf1086b6043bf519869678f5f2757f473dee970d7abf6da91ec00acb9cb98"},
+ {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:43644e38f42e3af682690876cff722d301ac585c5b9e1eacc013b7a3f7b696a0"},
+ {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:7582a1d1030e15422262de9f58711774e02fa80df0d1578995c76214f6954988"},
+ {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ddff9c4e225a63a5afab9dd15590432c22e8057e1a9a13d28ed128ecf047bbdc"},
+ {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ee2a1ece51b9b9e7752e742cfb661d2a29e7bcdba2d27e66e28a99f1890e4fa0"},
+ {file = "multidict-6.0.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a2e4369eb3d47d2034032a26c7a80fcb21a2cb22e1173d761a162f11e562caa5"},
+ {file = "multidict-6.0.4-cp310-cp310-win32.whl", hash = "sha256:574b7eae1ab267e5f8285f0fe881f17efe4b98c39a40858247720935b893bba8"},
+ {file = "multidict-6.0.4-cp310-cp310-win_amd64.whl", hash = "sha256:4dcbb0906e38440fa3e325df2359ac6cb043df8e58c965bb45f4e406ecb162cc"},
+ {file = "multidict-6.0.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:0dfad7a5a1e39c53ed00d2dd0c2e36aed4650936dc18fd9a1826a5ae1cad6f03"},
+ {file = "multidict-6.0.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:64da238a09d6039e3bd39bb3aee9c21a5e34f28bfa5aa22518581f910ff94af3"},
+ {file = "multidict-6.0.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ff959bee35038c4624250473988b24f846cbeb2c6639de3602c073f10410ceba"},
+ {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:01a3a55bd90018c9c080fbb0b9f4891db37d148a0a18722b42f94694f8b6d4c9"},
+ {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c5cb09abb18c1ea940fb99360ea0396f34d46566f157122c92dfa069d3e0e982"},
+ {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:666daae833559deb2d609afa4490b85830ab0dfca811a98b70a205621a6109fe"},
+ {file = "multidict-6.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11bdf3f5e1518b24530b8241529d2050014c884cf18b6fc69c0c2b30ca248710"},
+ {file = "multidict-6.0.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7d18748f2d30f94f498e852c67d61261c643b349b9d2a581131725595c45ec6c"},
+ {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:458f37be2d9e4c95e2d8866a851663cbc76e865b78395090786f6cd9b3bbf4f4"},
+ {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:b1a2eeedcead3a41694130495593a559a668f382eee0727352b9a41e1c45759a"},
+ {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7d6ae9d593ef8641544d6263c7fa6408cc90370c8cb2bbb65f8d43e5b0351d9c"},
+ {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:5979b5632c3e3534e42ca6ff856bb24b2e3071b37861c2c727ce220d80eee9ed"},
+ {file = "multidict-6.0.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:dcfe792765fab89c365123c81046ad4103fcabbc4f56d1c1997e6715e8015461"},
+ {file = "multidict-6.0.4-cp311-cp311-win32.whl", hash = "sha256:3601a3cece3819534b11d4efc1eb76047488fddd0c85a3948099d5da4d504636"},
+ {file = "multidict-6.0.4-cp311-cp311-win_amd64.whl", hash = "sha256:81a4f0b34bd92df3da93315c6a59034df95866014ac08535fc819f043bfd51f0"},
+ {file = "multidict-6.0.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:67040058f37a2a51ed8ea8f6b0e6ee5bd78ca67f169ce6122f3e2ec80dfe9b78"},
+ {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:853888594621e6604c978ce2a0444a1e6e70c8d253ab65ba11657659dcc9100f"},
+ {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:39ff62e7d0f26c248b15e364517a72932a611a9b75f35b45be078d81bdb86603"},
+ {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af048912e045a2dc732847d33821a9d84ba553f5c5f028adbd364dd4765092ac"},
+ {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1e8b901e607795ec06c9e42530788c45ac21ef3aaa11dbd0c69de543bfb79a9"},
+ {file = "multidict-6.0.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:62501642008a8b9871ddfccbf83e4222cf8ac0d5aeedf73da36153ef2ec222d2"},
+ {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:99b76c052e9f1bc0721f7541e5e8c05db3941eb9ebe7b8553c625ef88d6eefde"},
+ {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:509eac6cf09c794aa27bcacfd4d62c885cce62bef7b2c3e8b2e49d365b5003fe"},
+ {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:21a12c4eb6ddc9952c415f24eef97e3e55ba3af61f67c7bc388dcdec1404a067"},
+ {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:5cad9430ab3e2e4fa4a2ef4450f548768400a2ac635841bc2a56a2052cdbeb87"},
+ {file = "multidict-6.0.4-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ab55edc2e84460694295f401215f4a58597f8f7c9466faec545093045476327d"},
+ {file = "multidict-6.0.4-cp37-cp37m-win32.whl", hash = "sha256:5a4dcf02b908c3b8b17a45fb0f15b695bf117a67b76b7ad18b73cf8e92608775"},
+ {file = "multidict-6.0.4-cp37-cp37m-win_amd64.whl", hash = "sha256:6ed5f161328b7df384d71b07317f4d8656434e34591f20552c7bcef27b0ab88e"},
+ {file = "multidict-6.0.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5fc1b16f586f049820c5c5b17bb4ee7583092fa0d1c4e28b5239181ff9532e0c"},
+ {file = "multidict-6.0.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1502e24330eb681bdaa3eb70d6358e818e8e8f908a22a1851dfd4e15bc2f8161"},
+ {file = "multidict-6.0.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b692f419760c0e65d060959df05f2a531945af31fda0c8a3b3195d4efd06de11"},
+ {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45e1ecb0379bfaab5eef059f50115b54571acfbe422a14f668fc8c27ba410e7e"},
+ {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ddd3915998d93fbcd2566ddf9cf62cdb35c9e093075f862935573d265cf8f65d"},
+ {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:59d43b61c59d82f2effb39a93c48b845efe23a3852d201ed2d24ba830d0b4cf2"},
+ {file = "multidict-6.0.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc8e1d0c705233c5dd0c5e6460fbad7827d5d36f310a0fadfd45cc3029762258"},
+ {file = "multidict-6.0.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6aa0418fcc838522256761b3415822626f866758ee0bc6632c9486b179d0b52"},
+ {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6748717bb10339c4760c1e63da040f5f29f5ed6e59d76daee30305894069a660"},
+ {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:4d1a3d7ef5e96b1c9e92f973e43aa5e5b96c659c9bc3124acbbd81b0b9c8a951"},
+ {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4372381634485bec7e46718edc71528024fcdc6f835baefe517b34a33c731d60"},
+ {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:fc35cb4676846ef752816d5be2193a1e8367b4c1397b74a565a9d0389c433a1d"},
+ {file = "multidict-6.0.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:4b9d9e4e2b37daddb5c23ea33a3417901fa7c7b3dee2d855f63ee67a0b21e5b1"},
+ {file = "multidict-6.0.4-cp38-cp38-win32.whl", hash = "sha256:e41b7e2b59679edfa309e8db64fdf22399eec4b0b24694e1b2104fb789207779"},
+ {file = "multidict-6.0.4-cp38-cp38-win_amd64.whl", hash = "sha256:d6c254ba6e45d8e72739281ebc46ea5eb5f101234f3ce171f0e9f5cc86991480"},
+ {file = "multidict-6.0.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:16ab77bbeb596e14212e7bab8429f24c1579234a3a462105cda4a66904998664"},
+ {file = "multidict-6.0.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc779e9e6f7fda81b3f9aa58e3a6091d49ad528b11ed19f6621408806204ad35"},
+ {file = "multidict-6.0.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4ceef517eca3e03c1cceb22030a3e39cb399ac86bff4e426d4fc6ae49052cc60"},
+ {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:281af09f488903fde97923c7744bb001a9b23b039a909460d0f14edc7bf59706"},
+ {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:52f2dffc8acaba9a2f27174c41c9e57f60b907bb9f096b36b1a1f3be71c6284d"},
+ {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b41156839806aecb3641f3208c0dafd3ac7775b9c4c422d82ee2a45c34ba81ca"},
+ {file = "multidict-6.0.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d5e3fc56f88cc98ef8139255cf8cd63eb2c586531e43310ff859d6bb3a6b51f1"},
+ {file = "multidict-6.0.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8316a77808c501004802f9beebde51c9f857054a0c871bd6da8280e718444449"},
+ {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f70b98cd94886b49d91170ef23ec5c0e8ebb6f242d734ed7ed677b24d50c82cf"},
+ {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bf6774e60d67a9efe02b3616fee22441d86fab4c6d335f9d2051d19d90a40063"},
+ {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:e69924bfcdda39b722ef4d9aa762b2dd38e4632b3641b1d9a57ca9cd18f2f83a"},
+ {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:6b181d8c23da913d4ff585afd1155a0e1194c0b50c54fcfe286f70cdaf2b7176"},
+ {file = "multidict-6.0.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:52509b5be062d9eafc8170e53026fbc54cf3b32759a23d07fd935fb04fc22d95"},
+ {file = "multidict-6.0.4-cp39-cp39-win32.whl", hash = "sha256:27c523fbfbdfd19c6867af7346332b62b586eed663887392cff78d614f9ec313"},
+ {file = "multidict-6.0.4-cp39-cp39-win_amd64.whl", hash = "sha256:33029f5734336aa0d4c0384525da0387ef89148dc7191aae00ca5fb23d7aafc2"},
+ {file = "multidict-6.0.4.tar.gz", hash = "sha256:3666906492efb76453c0e7b97f2cf459b0682e7402c0489a95484965dbc1da49"},
+]
+
+[[package]]
+name = "multiprocess"
+version = "0.70.15"
+description = "better multiprocessing and multithreading in Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "multiprocess-0.70.15-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:aa36c7ed16f508091438687fe9baa393a7a8e206731d321e443745e743a0d4e5"},
+ {file = "multiprocess-0.70.15-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:20e024018c46d0d1602024c613007ac948f9754659e3853b0aa705e83f6931d8"},
+ {file = "multiprocess-0.70.15-pp37-pypy37_pp73-manylinux_2_24_i686.whl", hash = "sha256:e576062981c91f0fe8a463c3d52506e598dfc51320a8dd8d78b987dfca91c5db"},
+ {file = "multiprocess-0.70.15-pp37-pypy37_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:e73f497e6696a0f5433ada2b3d599ae733b87a6e8b008e387c62ac9127add177"},
+ {file = "multiprocess-0.70.15-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:73db2e7b32dcc7f9b0f075c2ffa45c90b6729d3f1805f27e88534c8d321a1be5"},
+ {file = "multiprocess-0.70.15-pp38-pypy38_pp73-manylinux_2_24_i686.whl", hash = "sha256:4271647bd8a49c28ecd6eb56a7fdbd3c212c45529ad5303b40b3c65fc6928e5f"},
+ {file = "multiprocess-0.70.15-pp38-pypy38_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:cf981fb998d6ec3208cb14f0cf2e9e80216e834f5d51fd09ebc937c32b960902"},
+ {file = "multiprocess-0.70.15-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:18f9f2c7063346d1617bd1684fdcae8d33380ae96b99427260f562e1a1228b67"},
+ {file = "multiprocess-0.70.15-pp39-pypy39_pp73-manylinux_2_24_i686.whl", hash = "sha256:0eac53214d664c49a34695e5824872db4006b1a465edd7459a251809c3773370"},
+ {file = "multiprocess-0.70.15-pp39-pypy39_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:1a51dd34096db47fb21fa2b839e615b051d51b97af9a67afbcdaa67186b44883"},
+ {file = "multiprocess-0.70.15-py310-none-any.whl", hash = "sha256:7dd58e33235e83cf09d625e55cffd7b0f0eede7ee9223cdd666a87624f60c21a"},
+ {file = "multiprocess-0.70.15-py311-none-any.whl", hash = "sha256:134f89053d82c9ed3b73edd3a2531eb791e602d4f4156fc92a79259590bd9670"},
+ {file = "multiprocess-0.70.15-py37-none-any.whl", hash = "sha256:f7d4a1629bccb433114c3b4885f69eccc200994323c80f6feee73b0edc9199c5"},
+ {file = "multiprocess-0.70.15-py38-none-any.whl", hash = "sha256:bee9afba476c91f9ebee7beeee0601face9eff67d822e893f9a893725fbd6316"},
+ {file = "multiprocess-0.70.15-py39-none-any.whl", hash = "sha256:3e0953f5d52b4c76f1c973eaf8214554d146f2be5decb48e928e55c7a2d19338"},
+ {file = "multiprocess-0.70.15.tar.gz", hash = "sha256:f20eed3036c0ef477b07a4177cf7c1ba520d9a2677870a4f47fe026f0cd6787e"},
+]
+
+[package.dependencies]
+dill = ">=0.3.7"
+
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
+ {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
+]
+
+[[package]]
+name = "ndg-httpsclient"
+version = "0.5.1"
+description = "Provides enhanced HTTPS support for httplib and urllib2 using PyOpenSSL"
+optional = false
+python-versions = ">=2.7,<3.0.dev0 || >=3.4.dev0"
+files = [
+ {file = "ndg_httpsclient-0.5.1-py2-none-any.whl", hash = "sha256:d2c7225f6a1c6cf698af4ebc962da70178a99bcde24ee6d1961c4f3338130d57"},
+ {file = "ndg_httpsclient-0.5.1-py3-none-any.whl", hash = "sha256:dd174c11d971b6244a891f7be2b32ca9853d3797a72edb34fa5d7b07d8fff7d4"},
+ {file = "ndg_httpsclient-0.5.1.tar.gz", hash = "sha256:d72faed0376ab039736c2ba12e30695e2788c4aa569c9c3e3d72131de2592210"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.1.1"
+PyOpenSSL = "*"
+
+[[package]]
+name = "networkx"
+version = "3.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
+ {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
+developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
+doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
+test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "ninja"
+version = "1.11.1.1"
+description = "Ninja is a small build system with a focus on speed"
+optional = false
+python-versions = "*"
+files = [
+ {file = "ninja-1.11.1.1-py2.py3-none-macosx_10_9_universal2.macosx_10_9_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:376889c76d87b95b5719fdd61dd7db193aa7fd4432e5d52d2e44e4c497bdbbee"},
+ {file = "ninja-1.11.1.1-py2.py3-none-manylinux1_i686.manylinux_2_5_i686.whl", hash = "sha256:ecf80cf5afd09f14dcceff28cb3f11dc90fb97c999c89307aea435889cb66877"},
+ {file = "ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b"},
+ {file = "ninja-1.11.1.1-py2.py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:73b93c14046447c7c5cc892433d4fae65d6364bec6685411cb97a8bcf815f93a"},
+ {file = "ninja-1.11.1.1-py2.py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:18302d96a5467ea98b68e1cae1ae4b4fb2b2a56a82b955193c637557c7273dbd"},
+ {file = "ninja-1.11.1.1-py2.py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:aad34a70ef15b12519946c5633344bc775a7656d789d9ed5fdb0d456383716ef"},
+ {file = "ninja-1.11.1.1-py2.py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:d491fc8d89cdcb416107c349ad1e3a735d4c4af5e1cb8f5f727baca6350fdaea"},
+ {file = "ninja-1.11.1.1-py2.py3-none-musllinux_1_1_i686.whl", hash = "sha256:7563ce1d9fe6ed5af0b8dd9ab4a214bf4ff1f2f6fd6dc29f480981f0f8b8b249"},
+ {file = "ninja-1.11.1.1-py2.py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:9df724344202b83018abb45cb1efc22efd337a1496514e7e6b3b59655be85205"},
+ {file = "ninja-1.11.1.1-py2.py3-none-musllinux_1_1_s390x.whl", hash = "sha256:3e0f9be5bb20d74d58c66cc1c414c3e6aeb45c35b0d0e41e8d739c2c0d57784f"},
+ {file = "ninja-1.11.1.1-py2.py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:76482ba746a2618eecf89d5253c0d1e4f1da1270d41e9f54dfbd91831b0f6885"},
+ {file = "ninja-1.11.1.1-py2.py3-none-win32.whl", hash = "sha256:fa2ba9d74acfdfbfbcf06fad1b8282de8a7a8c481d9dee45c859a8c93fcc1082"},
+ {file = "ninja-1.11.1.1-py2.py3-none-win_amd64.whl", hash = "sha256:95da904130bfa02ea74ff9c0116b4ad266174fafb1c707aa50212bc7859aebf1"},
+ {file = "ninja-1.11.1.1-py2.py3-none-win_arm64.whl", hash = "sha256:185e0641bde601e53841525c4196278e9aaf4463758da6dd1e752c0a0f54136a"},
+ {file = "ninja-1.11.1.1.tar.gz", hash = "sha256:9d793b08dd857e38d0b6ffe9e6b7145d7c485a42dcfea04905ca0cdb6017cc3c"},
+]
+
+[package.extras]
+test = ["codecov (>=2.0.5)", "coverage (>=4.2)", "flake8 (>=3.0.4)", "pytest (>=4.5.0)", "pytest-cov (>=2.7.1)", "pytest-runner (>=5.1)", "pytest-virtualenv (>=1.7.0)", "virtualenv (>=15.0.3)"]
+
+[[package]]
+name = "nltk"
+version = "3.8.1"
+description = "Natural Language Toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "nltk-3.8.1-py3-none-any.whl", hash = "sha256:fd5c9109f976fa86bcadba8f91e47f5e9293bd034474752e92a520f81c93dda5"},
+ {file = "nltk-3.8.1.zip", hash = "sha256:1834da3d0682cba4f2cede2f9aad6b0fafb6461ba451db0efb6f9c39798d64d3"},
+]
+
+[package.dependencies]
+click = "*"
+joblib = "*"
+regex = ">=2021.8.3"
+tqdm = "*"
+
+[package.extras]
+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
+[[package]]
+name = "numba"
+version = "0.58.1"
+description = "compiling Python code using LLVM"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "numba-0.58.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:07f2fa7e7144aa6f275f27260e73ce0d808d3c62b30cff8906ad1dec12d87bbe"},
+ {file = "numba-0.58.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7bf1ddd4f7b9c2306de0384bf3854cac3edd7b4d8dffae2ec1b925e4c436233f"},
+ {file = "numba-0.58.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bc2d904d0319d7a5857bd65062340bed627f5bfe9ae4a495aef342f072880d50"},
+ {file = "numba-0.58.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4e79b6cc0d2bf064a955934a2e02bf676bc7995ab2db929dbbc62e4c16551be6"},
+ {file = "numba-0.58.1-cp310-cp310-win_amd64.whl", hash = "sha256:81fe5b51532478149b5081311b0fd4206959174e660c372b94ed5364cfb37c82"},
+ {file = "numba-0.58.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bcecd3fb9df36554b342140a4d77d938a549be635d64caf8bd9ef6c47a47f8aa"},
+ {file = "numba-0.58.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a1eaa744f518bbd60e1f7ccddfb8002b3d06bd865b94a5d7eac25028efe0e0ff"},
+ {file = "numba-0.58.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bf68df9c307fb0aa81cacd33faccd6e419496fdc621e83f1efce35cdc5e79cac"},
+ {file = "numba-0.58.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:55a01e1881120e86d54efdff1be08381886fe9f04fc3006af309c602a72bc44d"},
+ {file = "numba-0.58.1-cp311-cp311-win_amd64.whl", hash = "sha256:811305d5dc40ae43c3ace5b192c670c358a89a4d2ae4f86d1665003798ea7a1a"},
+ {file = "numba-0.58.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ea5bfcf7d641d351c6a80e8e1826eb4a145d619870016eeaf20bbd71ef5caa22"},
+ {file = "numba-0.58.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e63d6aacaae1ba4ef3695f1c2122b30fa3d8ba039c8f517784668075856d79e2"},
+ {file = "numba-0.58.1-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6fe7a9d8e3bd996fbe5eac0683227ccef26cba98dae6e5cee2c1894d4b9f16c1"},
+ {file = "numba-0.58.1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:898af055b03f09d33a587e9425500e5be84fc90cd2f80b3fb71c6a4a17a7e354"},
+ {file = "numba-0.58.1-cp38-cp38-win_amd64.whl", hash = "sha256:d3e2fe81fe9a59fcd99cc572002101119059d64d31eb6324995ee8b0f144a306"},
+ {file = "numba-0.58.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5c765aef472a9406a97ea9782116335ad4f9ef5c9f93fc05fd44aab0db486954"},
+ {file = "numba-0.58.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9e9356e943617f5e35a74bf56ff6e7cc83e6b1865d5e13cee535d79bf2cae954"},
+ {file = "numba-0.58.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:240e7a1ae80eb6b14061dc91263b99dc8d6af9ea45d310751b780888097c1aaa"},
+ {file = "numba-0.58.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:45698b995914003f890ad839cfc909eeb9c74921849c712a05405d1a79c50f68"},
+ {file = "numba-0.58.1-cp39-cp39-win_amd64.whl", hash = "sha256:bd3dda77955be03ff366eebbfdb39919ce7c2620d86c906203bed92124989032"},
+ {file = "numba-0.58.1.tar.gz", hash = "sha256:487ded0633efccd9ca3a46364b40006dbdaca0f95e99b8b83e778d1195ebcbaa"},
+]
+
+[package.dependencies]
+llvmlite = "==0.41.*"
+numpy = ">=1.22,<1.27"
+
+[[package]]
+name = "numpy"
+version = "1.24.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
+ {file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
+ {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4"},
+ {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6"},
+ {file = "numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc"},
+ {file = "numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e"},
+ {file = "numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810"},
+ {file = "numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254"},
+ {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7"},
+ {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5"},
+ {file = "numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d"},
+ {file = "numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694"},
+ {file = "numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61"},
+ {file = "numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f"},
+ {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e"},
+ {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc"},
+ {file = "numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2"},
+ {file = "numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706"},
+ {file = "numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400"},
+ {file = "numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f"},
+ {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9"},
+ {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d"},
+ {file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
+ {file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
+ {file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
+ {file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
+ {file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
+ {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
+]
+
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.1.3.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
+ {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.1.105"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
+ {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.1.105"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
+ {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.1.105"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
+ {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "8.9.2.26"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.0.2.54"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
+ {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.2.106"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
+ {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.4.5.107"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
+ {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.1.0.106"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
+ {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.18.1"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:1a6c4acefcbebfa6de320f412bf7866de856e786e0462326ba1bac40de0b5e71"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.3.101"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"},
+ {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.1.105"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+files = [
+ {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
+ {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+]
+
+[[package]]
+name = "oauthlib"
+version = "3.2.2"
+description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "oauthlib-3.2.2-py3-none-any.whl", hash = "sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca"},
+ {file = "oauthlib-3.2.2.tar.gz", hash = "sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918"},
+]
+
+[package.extras]
+rsa = ["cryptography (>=3.0.0)"]
+signals = ["blinker (>=1.4.0)"]
+signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
+
+[[package]]
+name = "omegaconf"
+version = "2.0.6"
+description = "A flexible configuration library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "omegaconf-2.0.6-py3-none-any.whl", hash = "sha256:9e349fd76819b95b47aa628edea1ff83fed5b25108608abdd6c7fdca188e302a"},
+ {file = "omegaconf-2.0.6.tar.gz", hash = "sha256:92ca535a788d21651bf4c2eaf5c1ca4c7a8003b2dab4a87cbb09109784268806"},
+]
+
+[package.dependencies]
+PyYAML = ">=5.1"
+typing-extensions = "*"
+
+[[package]]
+name = "orderedmultidict"
+version = "1.0.1"
+description = "Ordered Multivalue Dictionary"
+optional = false
+python-versions = "*"
+files = [
+ {file = "orderedmultidict-1.0.1-py2.py3-none-any.whl", hash = "sha256:43c839a17ee3cdd62234c47deca1a8508a3f2ca1d0678a3bf791c87cf84adbf3"},
+ {file = "orderedmultidict-1.0.1.tar.gz", hash = "sha256:04070bbb5e87291cc9bfa51df413677faf2141c73c61d2a5f7b26bea3cd882ad"},
+]
+
+[package.dependencies]
+six = ">=1.8.0"
+
+[[package]]
+name = "orjson"
+version = "3.9.12"
+description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "orjson-3.9.12-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:6b4e2bed7d00753c438e83b613923afdd067564ff7ed696bfe3a7b073a236e07"},
+ {file = "orjson-3.9.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bd1b8ec63f0bf54a50b498eedeccdca23bd7b658f81c524d18e410c203189365"},
+ {file = "orjson-3.9.12-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ab8add018a53665042a5ae68200f1ad14c7953fa12110d12d41166f111724656"},
+ {file = "orjson-3.9.12-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12756a108875526b76e505afe6d6ba34960ac6b8c5ec2f35faf73ef161e97e07"},
+ {file = "orjson-3.9.12-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:890e7519c0c70296253660455f77e3a194554a3c45e42aa193cdebc76a02d82b"},
+ {file = "orjson-3.9.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d664880d7f016efbae97c725b243b33c2cbb4851ddc77f683fd1eec4a7894146"},
+ {file = "orjson-3.9.12-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:cfdaede0fa5b500314ec7b1249c7e30e871504a57004acd116be6acdda3b8ab3"},
+ {file = "orjson-3.9.12-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6492ff5953011e1ba9ed1bf086835fd574bd0a3cbe252db8e15ed72a30479081"},
+ {file = "orjson-3.9.12-cp310-none-win32.whl", hash = "sha256:29bf08e2eadb2c480fdc2e2daae58f2f013dff5d3b506edd1e02963b9ce9f8a9"},
+ {file = "orjson-3.9.12-cp310-none-win_amd64.whl", hash = "sha256:0fc156fba60d6b50743337ba09f052d8afc8b64595112996d22f5fce01ab57da"},
+ {file = "orjson-3.9.12-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:2849f88a0a12b8d94579b67486cbd8f3a49e36a4cb3d3f0ab352c596078c730c"},
+ {file = "orjson-3.9.12-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3186b18754befa660b31c649a108a915493ea69b4fc33f624ed854ad3563ac65"},
+ {file = "orjson-3.9.12-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cbbf313c9fb9d4f6cf9c22ced4b6682230457741daeb3d7060c5d06c2e73884a"},
+ {file = "orjson-3.9.12-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:99e8cd005b3926c3db9b63d264bd05e1bf4451787cc79a048f27f5190a9a0311"},
+ {file = "orjson-3.9.12-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:59feb148392d9155f3bfed0a2a3209268e000c2c3c834fb8fe1a6af9392efcbf"},
+ {file = "orjson-3.9.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a4ae815a172a1f073b05b9e04273e3b23e608a0858c4e76f606d2d75fcabde0c"},
+ {file = "orjson-3.9.12-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:ed398f9a9d5a1bf55b6e362ffc80ac846af2122d14a8243a1e6510a4eabcb71e"},
+ {file = "orjson-3.9.12-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d3cfb76600c5a1e6be91326b8f3b83035a370e727854a96d801c1ea08b708073"},
+ {file = "orjson-3.9.12-cp311-none-win32.whl", hash = "sha256:a2b6f5252c92bcab3b742ddb3ac195c0fa74bed4319acd74f5d54d79ef4715dc"},
+ {file = "orjson-3.9.12-cp311-none-win_amd64.whl", hash = "sha256:c95488e4aa1d078ff5776b58f66bd29d628fa59adcb2047f4efd3ecb2bd41a71"},
+ {file = "orjson-3.9.12-cp312-cp312-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:d6ce2062c4af43b92b0221ed4f445632c6bf4213f8a7da5396a122931377acd9"},
+ {file = "orjson-3.9.12-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:950951799967558c214cd6cceb7ceceed6f81d2c3c4135ee4a2c9c69f58aa225"},
+ {file = "orjson-3.9.12-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2dfaf71499d6fd4153f5c86eebb68e3ec1bf95851b030a4b55c7637a37bbdee4"},
+ {file = "orjson-3.9.12-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:659a8d7279e46c97661839035a1a218b61957316bf0202674e944ac5cfe7ed83"},
+ {file = "orjson-3.9.12-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af17fa87bccad0b7f6fd8ac8f9cbc9ee656b4552783b10b97a071337616db3e4"},
+ {file = "orjson-3.9.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd52dec9eddf4c8c74392f3fd52fa137b5f2e2bed1d9ae958d879de5f7d7cded"},
+ {file = "orjson-3.9.12-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:640e2b5d8e36b970202cfd0799d11a9a4ab46cf9212332cd642101ec952df7c8"},
+ {file = "orjson-3.9.12-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:daa438bd8024e03bcea2c5a92cd719a663a58e223fba967296b6ab9992259dbf"},
+ {file = "orjson-3.9.12-cp312-none-win_amd64.whl", hash = "sha256:1bb8f657c39ecdb924d02e809f992c9aafeb1ad70127d53fb573a6a6ab59d549"},
+ {file = "orjson-3.9.12-cp38-cp38-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:f4098c7674901402c86ba6045a551a2ee345f9f7ed54eeffc7d86d155c8427e5"},
+ {file = "orjson-3.9.12-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5586a533998267458fad3a457d6f3cdbddbcce696c916599fa8e2a10a89b24d3"},
+ {file = "orjson-3.9.12-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:54071b7398cd3f90e4bb61df46705ee96cb5e33e53fc0b2f47dbd9b000e238e1"},
+ {file = "orjson-3.9.12-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:67426651faa671b40443ea6f03065f9c8e22272b62fa23238b3efdacd301df31"},
+ {file = "orjson-3.9.12-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4a0cd56e8ee56b203abae7d482ac0d233dbfb436bb2e2d5cbcb539fe1200a312"},
+ {file = "orjson-3.9.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a84a0c3d4841a42e2571b1c1ead20a83e2792644c5827a606c50fc8af7ca4bee"},
+ {file = "orjson-3.9.12-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:09d60450cda3fa6c8ed17770c3a88473a16460cd0ff2ba74ef0df663b6fd3bb8"},
+ {file = "orjson-3.9.12-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bc82a4db9934a78ade211cf2e07161e4f068a461c1796465d10069cb50b32a80"},
+ {file = "orjson-3.9.12-cp38-none-win32.whl", hash = "sha256:61563d5d3b0019804d782137a4f32c72dc44c84e7d078b89d2d2a1adbaa47b52"},
+ {file = "orjson-3.9.12-cp38-none-win_amd64.whl", hash = "sha256:410f24309fbbaa2fab776e3212a81b96a1ec6037259359a32ea79fbccfcf76aa"},
+ {file = "orjson-3.9.12-cp39-cp39-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:e773f251258dd82795fd5daeac081d00b97bacf1548e44e71245543374874bcf"},
+ {file = "orjson-3.9.12-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b159baecfda51c840a619948c25817d37733a4d9877fea96590ef8606468b362"},
+ {file = "orjson-3.9.12-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:975e72e81a249174840d5a8df977d067b0183ef1560a32998be340f7e195c730"},
+ {file = "orjson-3.9.12-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:06e42e899dde61eb1851a9fad7f1a21b8e4be063438399b63c07839b57668f6c"},
+ {file = "orjson-3.9.12-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5c157e999e5694475a5515942aebeed6e43f7a1ed52267c1c93dcfde7d78d421"},
+ {file = "orjson-3.9.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dde1bc7c035f2d03aa49dc8642d9c6c9b1a81f2470e02055e76ed8853cfae0c3"},
+ {file = "orjson-3.9.12-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b0e9d73cdbdad76a53a48f563447e0e1ce34bcecef4614eb4b146383e6e7d8c9"},
+ {file = "orjson-3.9.12-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:96e44b21fe407b8ed48afbb3721f3c8c8ce17e345fbe232bd4651ace7317782d"},
+ {file = "orjson-3.9.12-cp39-none-win32.whl", hash = "sha256:cbd0f3555205bf2a60f8812133f2452d498dbefa14423ba90fe89f32276f7abf"},
+ {file = "orjson-3.9.12-cp39-none-win_amd64.whl", hash = "sha256:03ea7ee7e992532c2f4a06edd7ee1553f0644790553a118e003e3c405add41fa"},
+ {file = "orjson-3.9.12.tar.gz", hash = "sha256:da908d23a3b3243632b523344403b128722a5f45e278a8343c2bb67538dff0e4"},
+]
+
+[[package]]
+name = "packaging"
+version = "23.2"
+description = "Core utilities for Python packages"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
+ {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
+]
+
+[[package]]
+name = "pandas"
+version = "2.0.3"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pandas-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e4c7c9f27a4185304c7caf96dc7d91bc60bc162221152de697c98eb0b2648dd8"},
+ {file = "pandas-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f167beed68918d62bffb6ec64f2e1d8a7d297a038f86d4aed056b9493fca407f"},
+ {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce0c6f76a0f1ba361551f3e6dceaff06bde7514a374aa43e33b588ec10420183"},
+ {file = "pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba619e410a21d8c387a1ea6e8a0e49bb42216474436245718d7f2e88a2f8d7c0"},
+ {file = "pandas-2.0.3-cp310-cp310-win32.whl", hash = "sha256:3ef285093b4fe5058eefd756100a367f27029913760773c8bf1d2d8bebe5d210"},
+ {file = "pandas-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:9ee1a69328d5c36c98d8e74db06f4ad518a1840e8ccb94a4ba86920986bb617e"},
+ {file = "pandas-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b084b91d8d66ab19f5bb3256cbd5ea661848338301940e17f4492b2ce0801fe8"},
+ {file = "pandas-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37673e3bdf1551b95bf5d4ce372b37770f9529743d2498032439371fc7b7eb26"},
+ {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9cb1e14fdb546396b7e1b923ffaeeac24e4cedd14266c3497216dd4448e4f2d"},
+ {file = "pandas-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d9cd88488cceb7635aebb84809d087468eb33551097d600c6dad13602029c2df"},
+ {file = "pandas-2.0.3-cp311-cp311-win32.whl", hash = "sha256:694888a81198786f0e164ee3a581df7d505024fbb1f15202fc7db88a71d84ebd"},
+ {file = "pandas-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6a21ab5c89dcbd57f78d0ae16630b090eec626360085a4148693def5452d8a6b"},
+ {file = "pandas-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9e4da0d45e7f34c069fe4d522359df7d23badf83abc1d1cef398895822d11061"},
+ {file = "pandas-2.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:32fca2ee1b0d93dd71d979726b12b61faa06aeb93cf77468776287f41ff8fdc5"},
+ {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:258d3624b3ae734490e4d63c430256e716f488c4fcb7c8e9bde2d3aa46c29089"},
+ {file = "pandas-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9eae3dc34fa1aa7772dd3fc60270d13ced7346fcbcfee017d3132ec625e23bb0"},
+ {file = "pandas-2.0.3-cp38-cp38-win32.whl", hash = "sha256:f3421a7afb1a43f7e38e82e844e2bca9a6d793d66c1a7f9f0ff39a795bbc5e02"},
+ {file = "pandas-2.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:69d7f3884c95da3a31ef82b7618af5710dba95bb885ffab339aad925c3e8ce78"},
+ {file = "pandas-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5247fb1ba347c1261cbbf0fcfba4a3121fbb4029d95d9ef4dc45406620b25c8b"},
+ {file = "pandas-2.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:81af086f4543c9d8bb128328b5d32e9986e0c84d3ee673a2ac6fb57fd14f755e"},
+ {file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1994c789bf12a7c5098277fb43836ce090f1073858c10f9220998ac74f37c69b"},
+ {file = "pandas-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ec591c48e29226bcbb316e0c1e9423622bc7a4eaf1ef7c3c9fa1a3981f89641"},
+ {file = "pandas-2.0.3-cp39-cp39-win32.whl", hash = "sha256:04dbdbaf2e4d46ca8da896e1805bc04eb85caa9a82e259e8eed00254d5e0c682"},
+ {file = "pandas-2.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:1168574b036cd8b93abc746171c9b4f1b83467438a5e45909fed645cf8692dbc"},
+ {file = "pandas-2.0.3.tar.gz", hash = "sha256:c02f372a88e0d17f36d3093a644c73cfc1788e876a7c4bcb4020a77512e2043c"},
+]
+
+[package.dependencies]
+numpy = [
+ {version = ">=1.20.3", markers = "python_version < \"3.10\""},
+ {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+ {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
+]
+python-dateutil = ">=2.8.2"
+pytz = ">=2020.1"
+tzdata = ">=2022.1"
+
+[package.extras]
+all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"]
+aws = ["s3fs (>=2021.08.0)"]
+clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"]
+compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"]
+computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"]
+feather = ["pyarrow (>=7.0.0)"]
+fss = ["fsspec (>=2021.07.0)"]
+gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"]
+hdf5 = ["tables (>=3.6.1)"]
+html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"]
+mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"]
+parquet = ["pyarrow (>=7.0.0)"]
+performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"]
+plot = ["matplotlib (>=3.6.1)"]
+postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"]
+spss = ["pyreadstat (>=1.1.2)"]
+sql-other = ["SQLAlchemy (>=1.4.16)"]
+test = ["hypothesis (>=6.34.2)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
+xml = ["lxml (>=4.6.3)"]
+
+[[package]]
+name = "paramiko"
+version = "3.4.0"
+description = "SSH2 protocol library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "paramiko-3.4.0-py3-none-any.whl", hash = "sha256:43f0b51115a896f9c00f59618023484cb3a14b98bbceab43394a39c6739b7ee7"},
+ {file = "paramiko-3.4.0.tar.gz", hash = "sha256:aac08f26a31dc4dffd92821527d1682d99d52f9ef6851968114a8728f3c274d3"},
+]
+
+[package.dependencies]
+bcrypt = ">=3.2"
+cryptography = ">=3.3"
+pynacl = ">=1.5"
+
+[package.extras]
+all = ["gssapi (>=1.4.1)", "invoke (>=2.0)", "pyasn1 (>=0.1.7)", "pywin32 (>=2.1.8)"]
+gssapi = ["gssapi (>=1.4.1)", "pyasn1 (>=0.1.7)", "pywin32 (>=2.1.8)"]
+invoke = ["invoke (>=2.0)"]
+
+[[package]]
+name = "pathlib2"
+version = "2.3.7.post1"
+description = "Object-oriented filesystem paths"
+optional = false
+python-versions = "*"
+files = [
+ {file = "pathlib2-2.3.7.post1-py2.py3-none-any.whl", hash = "sha256:5266a0fd000452f1b3467d782f079a4343c63aaa119221fbdc4e39577489ca5b"},
+ {file = "pathlib2-2.3.7.post1.tar.gz", hash = "sha256:9fe0edad898b83c0c3e199c842b27ed216645d2e177757b2dd67384d4113c641"},
+]
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
+ {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
+]
+
+[[package]]
+name = "peft"
+version = "0.7.1"
+description = "Parameter-Efficient Fine-Tuning (PEFT)"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "peft-0.7.1-py3-none-any.whl", hash = "sha256:a1b7dc222254ce2161b32b88cc6f2bd7387712d20c478c98c586c59112112e46"},
+ {file = "peft-0.7.1.tar.gz", hash = "sha256:cd8fd190e1aacfdcb6c70df4e13c3e3b0006b1e9cc16ecf0a9804632edfcecd3"},
+]
+
+[package.dependencies]
+accelerate = ">=0.21.0"
+huggingface-hub = ">=0.17.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = "*"
+torch = ">=1.13.0"
+tqdm = "*"
+transformers = "*"
+
+[package.extras]
+dev = ["black (>=22.0,<23.0)", "hf-doc-builder", "ruff (>=0.0.241)", "urllib3 (<=2.0.0)"]
+docs-specific = ["hf-doc-builder"]
+quality = ["black (>=22.0,<23.0)", "ruff (>=0.0.241)", "urllib3 (<=2.0.0)"]
+test = ["black (>=22.0,<23.0)", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.0.241)", "scipy", "urllib3 (<=2.0.0)"]
+
+[[package]]
+name = "pillow"
+version = "10.2.0"
+description = "Python Imaging Library (Fork)"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
+ {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
+ {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fad5ff2f13d69b7e74ce5b4ecd12cc0ec530fcee76356cac6742785ff71c452"},
+ {file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4"},
+ {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:47c0995fc4e7f79b5cfcab1fc437ff2890b770440f7696a3ba065ee0fd496563"},
+ {file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2"},
+ {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:51f1a1bffc50e2e9492e87d8e09a17c5eea8409cda8d3f277eb6edc82813c17c"},
+ {file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69ffdd6120a4737710a9eee73e1d2e37db89b620f702754b8f6e62594471dee0"},
+ {file = "pillow-10.2.0-cp310-cp310-win32.whl", hash = "sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023"},
+ {file = "pillow-10.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:aebb6044806f2e16ecc07b2a2637ee1ef67a11840a66752751714a0d924adf72"},
+ {file = "pillow-10.2.0-cp310-cp310-win_arm64.whl", hash = "sha256:7049e301399273a0136ff39b84c3678e314f2158f50f517bc50285fb5ec847ad"},
+ {file = "pillow-10.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:35bb52c37f256f662abdfa49d2dfa6ce5d93281d323a9af377a120e89a9eafb5"},
+ {file = "pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c23f307202661071d94b5e384e1e1dc7dfb972a28a2310e4ee16103e66ddb67"},
+ {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:773efe0603db30c281521a7c0214cad7836c03b8ccff897beae9b47c0b657d61"},
+ {file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e"},
+ {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:716d30ed977be8b37d3ef185fecb9e5a1d62d110dfbdcd1e2a122ab46fddb03f"},
+ {file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a086c2af425c5f62a65e12fbf385f7c9fcb8f107d0849dba5839461a129cf311"},
+ {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1"},
+ {file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:609448742444d9290fd687940ac0b57fb35e6fd92bdb65386e08e99af60bf757"},
+ {file = "pillow-10.2.0-cp311-cp311-win32.whl", hash = "sha256:823ef7a27cf86df6597fa0671066c1b596f69eba53efa3d1e1cb8b30f3533068"},
+ {file = "pillow-10.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:1da3b2703afd040cf65ec97efea81cfba59cdbed9c11d8efc5ab09df9509fc56"},
+ {file = "pillow-10.2.0-cp311-cp311-win_arm64.whl", hash = "sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1"},
+ {file = "pillow-10.2.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:1b5e1b74d1bd1b78bc3477528919414874748dd363e6272efd5abf7654e68bef"},
+ {file = "pillow-10.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac"},
+ {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7c2286c23cd350b80d2fc9d424fc797575fb16f854b831d16fd47ceec078f2c"},
+ {file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e23412b5c41e58cec602f1135c57dfcf15482013ce6e5f093a86db69646a5aa"},
+ {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:52a50aa3fb3acb9cf7213573ef55d31d6eca37f5709c69e6858fe3bc04a5c2a2"},
+ {file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:127cee571038f252a552760076407f9cff79761c3d436a12af6000cd182a9d04"},
+ {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:8d12251f02d69d8310b046e82572ed486685c38f02176bd08baf216746eb947f"},
+ {file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:54f1852cd531aa981bc0965b7d609f5f6cc8ce8c41b1139f6ed6b3c54ab82bfb"},
+ {file = "pillow-10.2.0-cp312-cp312-win32.whl", hash = "sha256:257d8788df5ca62c980314053197f4d46eefedf4e6175bc9412f14412ec4ea2f"},
+ {file = "pillow-10.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9"},
+ {file = "pillow-10.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48"},
+ {file = "pillow-10.2.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8373c6c251f7ef8bda6675dd6d2b3a0fcc31edf1201266b5cf608b62a37407f9"},
+ {file = "pillow-10.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:870ea1ada0899fd0b79643990809323b389d4d1d46c192f97342eeb6ee0b8483"},
+ {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4b6b1e20608493548b1f32bce8cca185bf0480983890403d3b8753e44077129"},
+ {file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3031709084b6e7852d00479fd1d310b07d0ba82765f973b543c8af5061cf990e"},
+ {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:3ff074fc97dd4e80543a3e91f69d58889baf2002b6be64347ea8cf5533188213"},
+ {file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d"},
+ {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:b1b3020d90c2d8e1dae29cf3ce54f8094f7938460fb5ce8bc5c01450b01fbaf6"},
+ {file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:170aeb00224ab3dc54230c797f8404507240dd868cf52066f66a41b33169bdbe"},
+ {file = "pillow-10.2.0-cp38-cp38-win32.whl", hash = "sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e"},
+ {file = "pillow-10.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39"},
+ {file = "pillow-10.2.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:b792a349405fbc0163190fde0dc7b3fef3c9268292586cf5645598b48e63dc67"},
+ {file = "pillow-10.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364"},
+ {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb"},
+ {file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e"},
+ {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:70c61d4c475835a19b3a5aa42492409878bbca7438554a1f89d20d58a7c75c01"},
+ {file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13"},
+ {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d189550615b4948f45252d7f005e53c2040cea1af5b60d6f79491a6e147eef7"},
+ {file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:49d9ba1ed0ef3e061088cd1e7538a0759aab559e2e0a80a36f9fd9d8c0c21591"},
+ {file = "pillow-10.2.0-cp39-cp39-win32.whl", hash = "sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516"},
+ {file = "pillow-10.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8"},
+ {file = "pillow-10.2.0-cp39-cp39-win_arm64.whl", hash = "sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:322209c642aabdd6207517e9739c704dc9f9db943015535783239022002f054a"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3eedd52442c0a5ff4f887fab0c1c0bb164d8635b32c894bc1faf4c618dd89df2"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:33870dc4653c5017bf4c8873e5488d8f8d5f8935e2f1fb9a2208c47cdd66efd2"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:3c31822339516fb3c82d03f30e22b1d038da87ef27b6a78c9549888f8ceda39a"},
+ {file = "pillow-10.2.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a2b56ba36e05f973d450582fb015594aaa78834fefe8dfb8fcd79b93e64ba4c6"},
+ {file = "pillow-10.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:2247178effb34a77c11c0e8ac355c7a741ceca0a732b27bf11e747bbc950722f"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753cd8f2086b2b80180d9b3010dd4ed147efc167c90d3bf593fe2af21265e5a5"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a"},
+ {file = "pillow-10.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868"},
+ {file = "pillow-10.2.0.tar.gz", hash = "sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e"},
+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
+
+[[package]]
+name = "pkginfo"
+version = "1.9.6"
+description = "Query metadata from sdists / bdists / installed packages."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "pkginfo-1.9.6-py3-none-any.whl", hash = "sha256:4b7a555a6d5a22169fcc9cf7bfd78d296b0361adad412a346c1226849af5e546"},
+ {file = "pkginfo-1.9.6.tar.gz", hash = "sha256:8fd5896e8718a4372f0ea9cc9d96f6417c9b986e23a4d116dda26b62cc29d046"},
+]
+
+[package.extras]
+testing = ["pytest", "pytest-cov"]
+
+[[package]]
+name = "platformdirs"
+version = "4.1.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "platformdirs-4.1.0-py3-none-any.whl", hash = "sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380"},
+ {file = "platformdirs-4.1.0.tar.gz", hash = "sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"},
+]
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+
+[[package]]
+name = "pluggy"
+version = "1.3.0"
+description = "plugin and hook calling mechanisms for python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pluggy-1.3.0-py3-none-any.whl", hash = "sha256:d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7"},
+ {file = "pluggy-1.3.0.tar.gz", hash = "sha256:cf61ae8f126ac6f7c451172cf30e3e43d3ca77615509771b3a984a0730651e12"},
+]
+
+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
+
+[[package]]
+name = "pooch"
+version = "1.8.0"
+description = "\"Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.\""
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pooch-1.8.0-py3-none-any.whl", hash = "sha256:1bfba436d9e2ad5199ccad3583cca8c241b8736b5bb23fe67c213d52650dbb66"},
+ {file = "pooch-1.8.0.tar.gz", hash = "sha256:f59981fd5b9b5d032dcde8f4a11eaa492c2ac6343fae3596a2fdae35fc54b0a0"},
+]
+
+[package.dependencies]
+packaging = ">=20.0"
+platformdirs = ">=2.5.0"
+requests = ">=2.19.0"
+
+[package.extras]
+progress = ["tqdm (>=4.41.0,<5.0.0)"]
+sftp = ["paramiko (>=2.7.0)"]
+xxhash = ["xxhash (>=1.4.3)"]
+
+[[package]]
+name = "portalocker"
+version = "2.8.2"
+description = "Wraps the portalocker recipe for easy usage"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "portalocker-2.8.2-py3-none-any.whl", hash = "sha256:cfb86acc09b9aa7c3b43594e19be1345b9d16af3feb08bf92f23d4dce513a28e"},
+ {file = "portalocker-2.8.2.tar.gz", hash = "sha256:2b035aa7828e46c58e9b31390ee1f169b98e1066ab10b9a6a861fe7e25ee4f33"},
+]
+
+[package.dependencies]
+pywin32 = {version = ">=226", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+docs = ["sphinx (>=1.7.1)"]
+redis = ["redis"]
+tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "pytest-timeout (>=2.1.0)", "redis", "sphinx (>=6.0.0)", "types-redis"]
+
+[[package]]
+name = "protobuf"
+version = "4.25.1"
+description = ""
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "protobuf-4.25.1-cp310-abi3-win32.whl", hash = "sha256:193f50a6ab78a970c9b4f148e7c750cfde64f59815e86f686c22e26b4fe01ce7"},
+ {file = "protobuf-4.25.1-cp310-abi3-win_amd64.whl", hash = "sha256:3497c1af9f2526962f09329fd61a36566305e6c72da2590ae0d7d1322818843b"},
+ {file = "protobuf-4.25.1-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:0bf384e75b92c42830c0a679b0cd4d6e2b36ae0cf3dbb1e1dfdda48a244f4bcd"},
+ {file = "protobuf-4.25.1-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:0f881b589ff449bf0b931a711926e9ddaad3b35089cc039ce1af50b21a4ae8cb"},
+ {file = "protobuf-4.25.1-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:ca37bf6a6d0046272c152eea90d2e4ef34593aaa32e8873fc14c16440f22d4b7"},
+ {file = "protobuf-4.25.1-cp38-cp38-win32.whl", hash = "sha256:abc0525ae2689a8000837729eef7883b9391cd6aa7950249dcf5a4ede230d5dd"},
+ {file = "protobuf-4.25.1-cp38-cp38-win_amd64.whl", hash = "sha256:1484f9e692091450e7edf418c939e15bfc8fc68856e36ce399aed6889dae8bb0"},
+ {file = "protobuf-4.25.1-cp39-cp39-win32.whl", hash = "sha256:8bdbeaddaac52d15c6dce38c71b03038ef7772b977847eb6d374fc86636fa510"},
+ {file = "protobuf-4.25.1-cp39-cp39-win_amd64.whl", hash = "sha256:becc576b7e6b553d22cbdf418686ee4daa443d7217999125c045ad56322dda10"},
+ {file = "protobuf-4.25.1-py3-none-any.whl", hash = "sha256:a19731d5e83ae4737bb2a089605e636077ac001d18781b3cf489b9546c7c80d6"},
+ {file = "protobuf-4.25.1.tar.gz", hash = "sha256:57d65074b4f5baa4ab5da1605c02be90ac20c8b40fb137d6a8df9f416b0d0ce2"},
+]
+
+[[package]]
+name = "psutil"
+version = "5.9.7"
+description = "Cross-platform lib for process and system monitoring in Python."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+files = [
+ {file = "psutil-5.9.7-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:0bd41bf2d1463dfa535942b2a8f0e958acf6607ac0be52265ab31f7923bcd5e6"},
+ {file = "psutil-5.9.7-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:5794944462509e49d4d458f4dbfb92c47539e7d8d15c796f141f474010084056"},
+ {file = "psutil-5.9.7-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:fe361f743cb3389b8efda21980d93eb55c1f1e3898269bc9a2a1d0bb7b1f6508"},
+ {file = "psutil-5.9.7-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:e469990e28f1ad738f65a42dcfc17adaed9d0f325d55047593cb9033a0ab63df"},
+ {file = "psutil-5.9.7-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:3c4747a3e2ead1589e647e64aad601981f01b68f9398ddf94d01e3dc0d1e57c7"},
+ {file = "psutil-5.9.7-cp27-none-win32.whl", hash = "sha256:1d4bc4a0148fdd7fd8f38e0498639ae128e64538faa507df25a20f8f7fb2341c"},
+ {file = "psutil-5.9.7-cp27-none-win_amd64.whl", hash = "sha256:4c03362e280d06bbbfcd52f29acd79c733e0af33d707c54255d21029b8b32ba6"},
+ {file = "psutil-5.9.7-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ea36cc62e69a13ec52b2f625c27527f6e4479bca2b340b7a452af55b34fcbe2e"},
+ {file = "psutil-5.9.7-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1132704b876e58d277168cd729d64750633d5ff0183acf5b3c986b8466cd0284"},
+ {file = "psutil-5.9.7-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fe8b7f07948f1304497ce4f4684881250cd859b16d06a1dc4d7941eeb6233bfe"},
+ {file = "psutil-5.9.7-cp36-cp36m-win32.whl", hash = "sha256:b27f8fdb190c8c03914f908a4555159327d7481dac2f01008d483137ef3311a9"},
+ {file = "psutil-5.9.7-cp36-cp36m-win_amd64.whl", hash = "sha256:44969859757f4d8f2a9bd5b76eba8c3099a2c8cf3992ff62144061e39ba8568e"},
+ {file = "psutil-5.9.7-cp37-abi3-win32.whl", hash = "sha256:c727ca5a9b2dd5193b8644b9f0c883d54f1248310023b5ad3e92036c5e2ada68"},
+ {file = "psutil-5.9.7-cp37-abi3-win_amd64.whl", hash = "sha256:f37f87e4d73b79e6c5e749440c3113b81d1ee7d26f21c19c47371ddea834f414"},
+ {file = "psutil-5.9.7-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:032f4f2c909818c86cea4fe2cc407f1c0f0cde8e6c6d702b28b8ce0c0d143340"},
+ {file = "psutil-5.9.7.tar.gz", hash = "sha256:3f02134e82cfb5d089fddf20bb2e03fd5cd52395321d1c8458a9e58500ff417c"},
+]
+
+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
+[[package]]
+name = "py-cpuinfo"
+version = "9.0.0"
+description = "Get CPU info with pure Python"
+optional = false
+python-versions = "*"
+files = [
+ {file = "py-cpuinfo-9.0.0.tar.gz", hash = "sha256:3cdbbf3fac90dc6f118bfd64384f309edeadd902d7c8fb17f02ffa1fc3f49690"},
+ {file = "py_cpuinfo-9.0.0-py3-none-any.whl", hash = "sha256:859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5"},
+]
+
+[[package]]
+name = "pyarrow"
+version = "14.0.2"
+description = "Python library for Apache Arrow"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pyarrow-14.0.2-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:ba9fe808596c5dbd08b3aeffe901e5f81095baaa28e7d5118e01354c64f22807"},
+ {file = "pyarrow-14.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:22a768987a16bb46220cef490c56c671993fbee8fd0475febac0b3e16b00a10e"},
+ {file = "pyarrow-14.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2dbba05e98f247f17e64303eb876f4a80fcd32f73c7e9ad975a83834d81f3fda"},
+ {file = "pyarrow-14.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a898d134d00b1eca04998e9d286e19653f9d0fcb99587310cd10270907452a6b"},
+ {file = "pyarrow-14.0.2-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:87e879323f256cb04267bb365add7208f302df942eb943c93a9dfeb8f44840b1"},
+ {file = "pyarrow-14.0.2-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:76fc257559404ea5f1306ea9a3ff0541bf996ff3f7b9209fc517b5e83811fa8e"},
+ {file = "pyarrow-14.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:b0c4a18e00f3a32398a7f31da47fefcd7a927545b396e1f15d0c85c2f2c778cd"},
+ {file = "pyarrow-14.0.2-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:87482af32e5a0c0cce2d12eb3c039dd1d853bd905b04f3f953f147c7a196915b"},
+ {file = "pyarrow-14.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:059bd8f12a70519e46cd64e1ba40e97eae55e0cbe1695edd95384653d7626b23"},
+ {file = "pyarrow-14.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f16111f9ab27e60b391c5f6d197510e3ad6654e73857b4e394861fc79c37200"},
+ {file = "pyarrow-14.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06ff1264fe4448e8d02073f5ce45a9f934c0f3db0a04460d0b01ff28befc3696"},
+ {file = "pyarrow-14.0.2-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:6dd4f4b472ccf4042f1eab77e6c8bce574543f54d2135c7e396f413046397d5a"},
+ {file = "pyarrow-14.0.2-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:32356bfb58b36059773f49e4e214996888eeea3a08893e7dbde44753799b2a02"},
+ {file = "pyarrow-14.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:52809ee69d4dbf2241c0e4366d949ba035cbcf48409bf404f071f624ed313a2b"},
+ {file = "pyarrow-14.0.2-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:c87824a5ac52be210d32906c715f4ed7053d0180c1060ae3ff9b7e560f53f944"},
+ {file = "pyarrow-14.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a25eb2421a58e861f6ca91f43339d215476f4fe159eca603c55950c14f378cc5"},
+ {file = "pyarrow-14.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c1da70d668af5620b8ba0a23f229030a4cd6c5f24a616a146f30d2386fec422"},
+ {file = "pyarrow-14.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2cc61593c8e66194c7cdfae594503e91b926a228fba40b5cf25cc593563bcd07"},
+ {file = "pyarrow-14.0.2-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:78ea56f62fb7c0ae8ecb9afdd7893e3a7dbeb0b04106f5c08dbb23f9c0157591"},
+ {file = "pyarrow-14.0.2-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:37c233ddbce0c67a76c0985612fef27c0c92aef9413cf5aa56952f359fcb7379"},
+ {file = "pyarrow-14.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:e4b123ad0f6add92de898214d404e488167b87b5dd86e9a434126bc2b7a5578d"},
+ {file = "pyarrow-14.0.2-cp38-cp38-macosx_10_14_x86_64.whl", hash = "sha256:e354fba8490de258be7687f341bc04aba181fc8aa1f71e4584f9890d9cb2dec2"},
+ {file = "pyarrow-14.0.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:20e003a23a13da963f43e2b432483fdd8c38dc8882cd145f09f21792e1cf22a1"},
+ {file = "pyarrow-14.0.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc0de7575e841f1595ac07e5bc631084fd06ca8b03c0f2ecece733d23cd5102a"},
+ {file = "pyarrow-14.0.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66e986dc859712acb0bd45601229021f3ffcdfc49044b64c6d071aaf4fa49e98"},
+ {file = "pyarrow-14.0.2-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:f7d029f20ef56673a9730766023459ece397a05001f4e4d13805111d7c2108c0"},
+ {file = "pyarrow-14.0.2-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:209bac546942b0d8edc8debda248364f7f668e4aad4741bae58e67d40e5fcf75"},
+ {file = "pyarrow-14.0.2-cp38-cp38-win_amd64.whl", hash = "sha256:1e6987c5274fb87d66bb36816afb6f65707546b3c45c44c28e3c4133c010a881"},
+ {file = "pyarrow-14.0.2-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:a01d0052d2a294a5f56cc1862933014e696aa08cc7b620e8c0cce5a5d362e976"},
+ {file = "pyarrow-14.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a51fee3a7db4d37f8cda3ea96f32530620d43b0489d169b285d774da48ca9785"},
+ {file = "pyarrow-14.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:64df2bf1ef2ef14cee531e2dfe03dd924017650ffaa6f9513d7a1bb291e59c15"},
+ {file = "pyarrow-14.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c0fa3bfdb0305ffe09810f9d3e2e50a2787e3a07063001dcd7adae0cee3601a"},
+ {file = "pyarrow-14.0.2-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:c65bf4fd06584f058420238bc47a316e80dda01ec0dfb3044594128a6c2db794"},
+ {file = "pyarrow-14.0.2-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:63ac901baec9369d6aae1cbe6cca11178fb018a8d45068aaf5bb54f94804a866"},
+ {file = "pyarrow-14.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:75ee0efe7a87a687ae303d63037d08a48ef9ea0127064df18267252cfe2e9541"},
+ {file = "pyarrow-14.0.2.tar.gz", hash = "sha256:36cef6ba12b499d864d1def3e990f97949e0b79400d08b7cf74504ffbd3eb025"},
+]
+
+[package.dependencies]
+numpy = ">=1.16.6"
+
+[[package]]
+name = "pyarrow-hotfix"
+version = "0.6"
+description = ""
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "pyarrow_hotfix-0.6-py3-none-any.whl", hash = "sha256:dcc9ae2d220dff0083be6a9aa8e0cdee5182ad358d4931fce825c545e5c89178"},
+ {file = "pyarrow_hotfix-0.6.tar.gz", hash = "sha256:79d3e030f7ff890d408a100ac16d6f00b14d44a502d7897cd9fc3e3a534e9945"},
+]
+
+[[package]]
+name = "pyasn1"
+version = "0.5.1"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+files = [
+ {file = "pyasn1-0.5.1-py2.py3-none-any.whl", hash = "sha256:4439847c58d40b1d0a573d07e3856e95333f1976294494c325775aeca506eb58"},
+ {file = "pyasn1-0.5.1.tar.gz", hash = "sha256:6d391a96e59b23130a5cfa74d6fd7f388dbbe26cc8f1edf39fdddf08d9d6676c"},
+]
+
+[[package]]
+name = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+ {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
+ {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
+]
+
+[[package]]
+name = "pydantic"
+version = "2.5.3"
+description = "Data validation using Python type hints"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pydantic-2.5.3-py3-none-any.whl", hash = "sha256:d0caf5954bee831b6bfe7e338c32b9e30c85dfe080c843680783ac2b631673b4"},
+ {file = "pydantic-2.5.3.tar.gz", hash = "sha256:b3ef57c62535b0941697cce638c08900d87fcb67e29cfa99e8a68f747f393f7a"},
+]
+
+[package.dependencies]
+annotated-types = ">=0.4.0"
+pydantic-core = "2.14.6"
+typing-extensions = ">=4.6.1"
+
+[package.extras]
+email = ["email-validator (>=2.0.0)"]
+
+[[package]]
+name = "pydantic-core"
+version = "2.14.6"
+description = ""
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pydantic_core-2.14.6-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:72f9a942d739f09cd42fffe5dc759928217649f070056f03c70df14f5770acf9"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6a31d98c0d69776c2576dda4b77b8e0c69ad08e8b539c25c7d0ca0dc19a50d6c"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5aa90562bc079c6c290f0512b21768967f9968e4cfea84ea4ff5af5d917016e4"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:370ffecb5316ed23b667d99ce4debe53ea664b99cc37bfa2af47bc769056d534"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f85f3843bdb1fe80e8c206fe6eed7a1caeae897e496542cee499c374a85c6e08"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9862bf828112e19685b76ca499b379338fd4c5c269d897e218b2ae8fcb80139d"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:036137b5ad0cb0004c75b579445a1efccd072387a36c7f217bb8efd1afbe5245"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:92879bce89f91f4b2416eba4429c7b5ca22c45ef4a499c39f0c5c69257522c7c"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0c08de15d50fa190d577e8591f0329a643eeaed696d7771760295998aca6bc66"},
+ {file = "pydantic_core-2.14.6-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:36099c69f6b14fc2c49d7996cbf4f87ec4f0e66d1c74aa05228583225a07b590"},
+ {file = "pydantic_core-2.14.6-cp310-none-win32.whl", hash = "sha256:7be719e4d2ae6c314f72844ba9d69e38dff342bc360379f7c8537c48e23034b7"},
+ {file = "pydantic_core-2.14.6-cp310-none-win_amd64.whl", hash = "sha256:36fa402dcdc8ea7f1b0ddcf0df4254cc6b2e08f8cd80e7010d4c4ae6e86b2a87"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:dea7fcd62915fb150cdc373212141a30037e11b761fbced340e9db3379b892d4"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ffff855100bc066ff2cd3aa4a60bc9534661816b110f0243e59503ec2df38421"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b027c86c66b8627eb90e57aee1f526df77dc6d8b354ec498be9a757d513b92b"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:00b1087dabcee0b0ffd104f9f53d7d3eaddfaa314cdd6726143af6bc713aa27e"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:75ec284328b60a4e91010c1acade0c30584f28a1f345bc8f72fe8b9e46ec6a96"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7e1f4744eea1501404b20b0ac059ff7e3f96a97d3e3f48ce27a139e053bb370b"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2602177668f89b38b9f84b7b3435d0a72511ddef45dc14446811759b82235a1"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6c8edaea3089bf908dd27da8f5d9e395c5b4dc092dbcce9b65e7156099b4b937"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:478e9e7b360dfec451daafe286998d4a1eeaecf6d69c427b834ae771cad4b622"},
+ {file = "pydantic_core-2.14.6-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b6ca36c12a5120bad343eef193cc0122928c5c7466121da7c20f41160ba00ba2"},
+ {file = "pydantic_core-2.14.6-cp311-none-win32.whl", hash = "sha256:2b8719037e570639e6b665a4050add43134d80b687288ba3ade18b22bbb29dd2"},
+ {file = "pydantic_core-2.14.6-cp311-none-win_amd64.whl", hash = "sha256:78ee52ecc088c61cce32b2d30a826f929e1708f7b9247dc3b921aec367dc1b23"},
+ {file = "pydantic_core-2.14.6-cp311-none-win_arm64.whl", hash = "sha256:a19b794f8fe6569472ff77602437ec4430f9b2b9ec7a1105cfd2232f9ba355e6"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:667aa2eac9cd0700af1ddb38b7b1ef246d8cf94c85637cbb03d7757ca4c3fdec"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cdee837710ef6b56ebd20245b83799fce40b265b3b406e51e8ccc5b85b9099b7"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c5bcf3414367e29f83fd66f7de64509a8fd2368b1edf4351e862910727d3e51"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:26a92ae76f75d1915806b77cf459811e772d8f71fd1e4339c99750f0e7f6324f"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a983cca5ed1dd9a35e9e42ebf9f278d344603bfcb174ff99a5815f953925140a"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cb92f9061657287eded380d7dc455bbf115430b3aa4741bdc662d02977e7d0af"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4ace1e220b078c8e48e82c081e35002038657e4b37d403ce940fa679e57113b"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ef633add81832f4b56d3b4c9408b43d530dfca29e68fb1b797dcb861a2c734cd"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7e90d6cc4aad2cc1f5e16ed56e46cebf4877c62403a311af20459c15da76fd91"},
+ {file = "pydantic_core-2.14.6-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:e8a5ac97ea521d7bde7621d86c30e86b798cdecd985723c4ed737a2aa9e77d0c"},
+ {file = "pydantic_core-2.14.6-cp312-none-win32.whl", hash = "sha256:f27207e8ca3e5e021e2402ba942e5b4c629718e665c81b8b306f3c8b1ddbb786"},
+ {file = "pydantic_core-2.14.6-cp312-none-win_amd64.whl", hash = "sha256:b3e5fe4538001bb82e2295b8d2a39356a84694c97cb73a566dc36328b9f83b40"},
+ {file = "pydantic_core-2.14.6-cp312-none-win_arm64.whl", hash = "sha256:64634ccf9d671c6be242a664a33c4acf12882670b09b3f163cd00a24cffbd74e"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:24368e31be2c88bd69340fbfe741b405302993242ccb476c5c3ff48aeee1afe0"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:e33b0834f1cf779aa839975f9d8755a7c2420510c0fa1e9fa0497de77cd35d2c"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6af4b3f52cc65f8a0bc8b1cd9676f8c21ef3e9132f21fed250f6958bd7223bed"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d15687d7d7f40333bd8266f3814c591c2e2cd263fa2116e314f60d82086e353a"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:095b707bb287bfd534044166ab767bec70a9bba3175dcdc3371782175c14e43c"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:94fc0e6621e07d1e91c44e016cc0b189b48db053061cc22d6298a611de8071bb"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ce830e480f6774608dedfd4a90c42aac4a7af0a711f1b52f807130c2e434c06"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a306cdd2ad3a7d795d8e617a58c3a2ed0f76c8496fb7621b6cd514eb1532cae8"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:2f5fa187bde8524b1e37ba894db13aadd64faa884657473b03a019f625cee9a8"},
+ {file = "pydantic_core-2.14.6-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:438027a975cc213a47c5d70672e0d29776082155cfae540c4e225716586be75e"},
+ {file = "pydantic_core-2.14.6-cp37-none-win32.whl", hash = "sha256:f96ae96a060a8072ceff4cfde89d261837b4294a4f28b84a28765470d502ccc6"},
+ {file = "pydantic_core-2.14.6-cp37-none-win_amd64.whl", hash = "sha256:e646c0e282e960345314f42f2cea5e0b5f56938c093541ea6dbf11aec2862391"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:db453f2da3f59a348f514cfbfeb042393b68720787bbef2b4c6068ea362c8149"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3860c62057acd95cc84044e758e47b18dcd8871a328ebc8ccdefd18b0d26a21b"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36026d8f99c58d7044413e1b819a67ca0e0b8ebe0f25e775e6c3d1fabb3c38fb"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8ed1af8692bd8d2a29d702f1a2e6065416d76897d726e45a1775b1444f5928a7"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:314ccc4264ce7d854941231cf71b592e30d8d368a71e50197c905874feacc8a8"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:982487f8931067a32e72d40ab6b47b1628a9c5d344be7f1a4e668fb462d2da42"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2dbe357bc4ddda078f79d2a36fc1dd0494a7f2fad83a0a684465b6f24b46fe80"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2f6ffc6701a0eb28648c845f4945a194dc7ab3c651f535b81793251e1185ac3d"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:7f5025db12fc6de7bc1104d826d5aee1d172f9ba6ca936bf6474c2148ac336c1"},
+ {file = "pydantic_core-2.14.6-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:dab03ed811ed1c71d700ed08bde8431cf429bbe59e423394f0f4055f1ca0ea60"},
+ {file = "pydantic_core-2.14.6-cp38-none-win32.whl", hash = "sha256:dfcbebdb3c4b6f739a91769aea5ed615023f3c88cb70df812849aef634c25fbe"},
+ {file = "pydantic_core-2.14.6-cp38-none-win_amd64.whl", hash = "sha256:99b14dbea2fdb563d8b5a57c9badfcd72083f6006caf8e126b491519c7d64ca8"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:4ce8299b481bcb68e5c82002b96e411796b844d72b3e92a3fbedfe8e19813eab"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b9a9d92f10772d2a181b5ca339dee066ab7d1c9a34ae2421b2a52556e719756f"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd9e98b408384989ea4ab60206b8e100d8687da18b5c813c11e92fd8212a98e0"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4f86f1f318e56f5cbb282fe61eb84767aee743ebe32c7c0834690ebea50c0a6b"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86ce5fcfc3accf3a07a729779d0b86c5d0309a4764c897d86c11089be61da160"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3dcf1978be02153c6a31692d4fbcc2a3f1db9da36039ead23173bc256ee3b91b"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eedf97be7bc3dbc8addcef4142f4b4164066df0c6f36397ae4aaed3eb187d8ab"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d5f916acf8afbcab6bacbb376ba7dc61f845367901ecd5e328fc4d4aef2fcab0"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:8a14c192c1d724c3acbfb3f10a958c55a2638391319ce8078cb36c02283959b9"},
+ {file = "pydantic_core-2.14.6-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0348b1dc6b76041516e8a854ff95b21c55f5a411c3297d2ca52f5528e49d8411"},
+ {file = "pydantic_core-2.14.6-cp39-none-win32.whl", hash = "sha256:de2a0645a923ba57c5527497daf8ec5df69c6eadf869e9cd46e86349146e5975"},
+ {file = "pydantic_core-2.14.6-cp39-none-win_amd64.whl", hash = "sha256:aca48506a9c20f68ee61c87f2008f81f8ee99f8d7f0104bff3c47e2d148f89d9"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:d5c28525c19f5bb1e09511669bb57353d22b94cf8b65f3a8d141c389a55dec95"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:78d0768ee59baa3de0f4adac9e3748b4b1fffc52143caebddfd5ea2961595277"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b93785eadaef932e4fe9c6e12ba67beb1b3f1e5495631419c784ab87e975670"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a874f21f87c485310944b2b2734cd6d318765bcbb7515eead33af9641816506e"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b89f4477d915ea43b4ceea6756f63f0288941b6443a2b28c69004fe07fde0d0d"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:172de779e2a153d36ee690dbc49c6db568d7b33b18dc56b69a7514aecbcf380d"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:dfcebb950aa7e667ec226a442722134539e77c575f6cfaa423f24371bb8d2e94"},
+ {file = "pydantic_core-2.14.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:55a23dcd98c858c0db44fc5c04fc7ed81c4b4d33c653a7c45ddaebf6563a2f66"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:4241204e4b36ab5ae466ecec5c4c16527a054c69f99bba20f6f75232a6a534e2"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e574de99d735b3fc8364cba9912c2bec2da78775eba95cbb225ef7dda6acea24"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1302a54f87b5cd8528e4d6d1bf2133b6aa7c6122ff8e9dc5220fbc1e07bffebd"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f8e81e4b55930e5ffab4a68db1af431629cf2e4066dbdbfef65348b8ab804ea8"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:c99462ffc538717b3e60151dfaf91125f637e801f5ab008f81c402f1dff0cd0f"},
+ {file = "pydantic_core-2.14.6-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:e4cf2d5829f6963a5483ec01578ee76d329eb5caf330ecd05b3edd697e7d768a"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:cf10b7d58ae4a1f07fccbf4a0a956d705356fea05fb4c70608bb6fa81d103cda"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:399ac0891c284fa8eb998bcfa323f2234858f5d2efca3950ae58c8f88830f145"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c6a5c79b28003543db3ba67d1df336f253a87d3112dac3a51b94f7d48e4c0e1"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:599c87d79cab2a6a2a9df4aefe0455e61e7d2aeede2f8577c1b7c0aec643ee8e"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:43e166ad47ba900f2542a80d83f9fc65fe99eb63ceec4debec160ae729824052"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:3a0b5db001b98e1c649dd55afa928e75aa4087e587b9524a4992316fa23c9fba"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:747265448cb57a9f37572a488a57d873fd96bf51e5bb7edb52cfb37124516da4"},
+ {file = "pydantic_core-2.14.6-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:7ebe3416785f65c28f4f9441e916bfc8a54179c8dea73c23023f7086fa601c5d"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:86c963186ca5e50d5c8287b1d1c9d3f8f024cbe343d048c5bd282aec2d8641f2"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:e0641b506486f0b4cd1500a2a65740243e8670a2549bb02bc4556a83af84ae03"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71d72ca5eaaa8d38c8df16b7deb1a2da4f650c41b58bb142f3fb75d5ad4a611f"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27e524624eace5c59af499cd97dc18bb201dc6a7a2da24bfc66ef151c69a5f2a"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a3dde6cac75e0b0902778978d3b1646ca9f438654395a362cb21d9ad34b24acf"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:00646784f6cd993b1e1c0e7b0fdcbccc375d539db95555477771c27555e3c556"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:23598acb8ccaa3d1d875ef3b35cb6376535095e9405d91a3d57a8c7db5d29341"},
+ {file = "pydantic_core-2.14.6-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7f41533d7e3cf9520065f610b41ac1c76bc2161415955fbcead4981b22c7611e"},
+ {file = "pydantic_core-2.14.6.tar.gz", hash = "sha256:1fd0c1d395372843fba13a51c28e3bb9d59bd7aebfeb17358ffaaa1e4dbbe948"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
+[[package]]
+name = "pydeck"
+version = "0.8.0"
+description = "Widget for deck.gl maps"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pydeck-0.8.0-py2.py3-none-any.whl", hash = "sha256:a8fa7757c6f24bba033af39db3147cb020eef44012ba7e60d954de187f9ed4d5"},
+ {file = "pydeck-0.8.0.tar.gz", hash = "sha256:07edde833f7cfcef6749124351195aa7dcd24663d4909fd7898dbd0b6fbc01ec"},
+]
+
+[package.dependencies]
+jinja2 = ">=2.10.1"
+numpy = ">=1.16.4"
+
+[package.extras]
+carto = ["pydeck-carto"]
+jupyter = ["ipykernel (>=5.1.2)", "ipython (>=5.8.0)", "ipywidgets (>=7,<8)", "traitlets (>=4.3.2)"]
+
+[[package]]
+name = "pydub"
+version = "0.25.1"
+description = "Manipulate audio with an simple and easy high level interface"
+optional = false
+python-versions = "*"
+files = [
+ {file = "pydub-0.25.1-py2.py3-none-any.whl", hash = "sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6"},
+ {file = "pydub-0.25.1.tar.gz", hash = "sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f"},
+]
+
+[[package]]
+name = "pyflakes"
+version = "3.2.0"
+description = "passive checker of Python programs"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "pyflakes-3.2.0-py2.py3-none-any.whl", hash = "sha256:84b5be138a2dfbb40689ca07e2152deb896a65c3a3e24c251c5c62489568074a"},
+ {file = "pyflakes-3.2.0.tar.gz", hash = "sha256:1c61603ff154621fb2a9172037d84dca3500def8c8b630657d1701f026f8af3f"},
+]
+
+[[package]]
+name = "pygments"
+version = "2.17.2"
+description = "Pygments is a syntax highlighting package written in Python."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pygments-2.17.2-py3-none-any.whl", hash = "sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c"},
+ {file = "pygments-2.17.2.tar.gz", hash = "sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367"},
+]
+
+[package.extras]
+plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "pyjwt"
+version = "2.8.0"
+description = "JSON Web Token implementation in Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "PyJWT-2.8.0-py3-none-any.whl", hash = "sha256:59127c392cc44c2da5bb3192169a91f429924e17aff6534d70fdc02ab3e04320"},
+ {file = "PyJWT-2.8.0.tar.gz", hash = "sha256:57e28d156e3d5c10088e0c68abb90bfac3df82b40a71bd0daa20c65ccd5c23de"},
+]
+
+[package.dependencies]
+cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
+
+[package.extras]
+crypto = ["cryptography (>=3.4.0)"]
+dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
+tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
+
+[[package]]
+name = "pynacl"
+version = "1.5.0"
+description = "Python binding to the Networking and Cryptography (NaCl) library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "PyNaCl-1.5.0-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:401002a4aaa07c9414132aaed7f6836ff98f59277a234704ff66878c2ee4a0d1"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_24_aarch64.whl", hash = "sha256:52cb72a79269189d4e0dc537556f4740f7f0a9ec41c1322598799b0bdad4ef92"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a36d4a9dda1f19ce6e03c9a784a2921a4b726b02e1c736600ca9c22029474394"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:0c84947a22519e013607c9be43706dd42513f9e6ae5d39d3613ca1e142fba44d"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06b8f6fa7f5de8d5d2f7573fe8c863c051225a27b61e6860fd047b1775807858"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:a422368fc821589c228f4c49438a368831cb5bbc0eab5ebe1d7fac9dded6567b"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-win32.whl", hash = "sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543"},
+ {file = "PyNaCl-1.5.0-cp36-abi3-win_amd64.whl", hash = "sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93"},
+ {file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"},
+]
+
+[package.dependencies]
+cffi = ">=1.4.1"
+
+[package.extras]
+docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"]
+tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"]
+
+[[package]]
+name = "pynvml"
+version = "11.5.0"
+description = "Python Bindings for the NVIDIA Management Library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "pynvml-11.5.0-py3-none-any.whl", hash = "sha256:5cce014ac01b098d08f06178f86c37be409b80b2e903a5a03ce15eed60f55e25"},
+ {file = "pynvml-11.5.0.tar.gz", hash = "sha256:d027b21b95b1088b9fc278117f9f61b7c67f8e33a787e9f83f735f0f71ac32d0"},
+]
+
+[[package]]
+name = "pyopenssl"
+version = "23.3.0"
+description = "Python wrapper module around the OpenSSL library"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pyOpenSSL-23.3.0-py3-none-any.whl", hash = "sha256:6756834481d9ed5470f4a9393455154bc92fe7a64b7bc6ee2c804e78c52099b2"},
+ {file = "pyOpenSSL-23.3.0.tar.gz", hash = "sha256:6b2cba5cc46e822750ec3e5a81ee12819850b11303630d575e98108a079c2b12"},
+]
+
+[package.dependencies]
+cryptography = ">=41.0.5,<42"
+
+[package.extras]
+docs = ["sphinx (!=5.2.0,!=5.2.0.post0,!=7.2.5)", "sphinx-rtd-theme"]
+test = ["flaky", "pretend", "pytest (>=3.0.1)"]
+
+[[package]]
+name = "pyparsing"
+version = "3.1.1"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+ {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
+ {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "pyrallis"
+version = "0.3.1"
+description = "A framework for simple dataclass-based configurations."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "pyrallis-0.3.1-py3-none-any.whl", hash = "sha256:632370b563486495f5f9e7caf86cddffab8351214a3bdc60ae0d23b261ebdb59"},
+ {file = "pyrallis-0.3.1.tar.gz", hash = "sha256:ab7298f31c633d4858ec3b045a30e6cc9b9f7ab50f21ea93aa3fb28532ac4b6e"},
+]
+
+[package.dependencies]
+pyyaml = "*"
+typing-inspect = "*"
+
+[[package]]
+name = "pyreadline3"
+version = "3.4.1"
+description = "A python implementation of GNU readline."
+optional = false
+python-versions = "*"
+files = [
+ {file = "pyreadline3-3.4.1-py3-none-any.whl", hash = "sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb"},
+ {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
+]
+
+[[package]]
+name = "pysocks"
+version = "1.7.1"
+description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+ {file = "PySocks-1.7.1-py27-none-any.whl", hash = "sha256:08e69f092cc6dbe92a0fdd16eeb9b9ffbc13cadfe5ca4c7bd92ffb078b293299"},
+ {file = "PySocks-1.7.1-py3-none-any.whl", hash = "sha256:2725bd0a9925919b9b51739eea5f9e2bae91e83288108a9ad338b2e3a4435ee5"},
+ {file = "PySocks-1.7.1.tar.gz", hash = "sha256:3f8804571ebe159c380ac6de37643bb4685970655d3bba243530d6558b799aa0"},
+]
+
+[[package]]
+name = "pytest"
+version = "7.4.4"
+description = "pytest: simple powerful testing with Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
+ {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
+iniconfig = "*"
+packaging = "*"
+pluggy = ">=0.12,<2.0"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.8.2"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+ {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
+ {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "python-multipart"
+version = "0.0.6"
+description = "A streaming multipart parser for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "python_multipart-0.0.6-py3-none-any.whl", hash = "sha256:ee698bab5ef148b0a760751c261902cd096e57e10558e11aca17646b74ee1c18"},
+ {file = "python_multipart-0.0.6.tar.gz", hash = "sha256:e9925a80bb668529f1b67c7fdb0a5dacdd7cbfc6fb0bff3ea443fe22bdd62132"},
+]
+
+[package.extras]
+dev = ["atomicwrites (==1.2.1)", "attrs (==19.2.0)", "coverage (==6.5.0)", "hatch", "invoke (==1.7.3)", "more-itertools (==4.3.0)", "pbr (==4.3.0)", "pluggy (==1.0.0)", "py (==1.11.0)", "pytest (==7.2.0)", "pytest-cov (==4.0.0)", "pytest-timeout (==2.1.0)", "pyyaml (==5.1)"]
+
+[[package]]
+name = "pytz"
+version = "2023.3.post1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+ {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
+ {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
+]
+
+[[package]]
+name = "pywin32"
+version = "306"
+description = "Python for Window Extensions"
+optional = false
+python-versions = "*"
+files = [
+ {file = "pywin32-306-cp310-cp310-win32.whl", hash = "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d"},
+ {file = "pywin32-306-cp310-cp310-win_amd64.whl", hash = "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8"},
+ {file = "pywin32-306-cp311-cp311-win32.whl", hash = "sha256:e65028133d15b64d2ed8f06dd9fbc268352478d4f9289e69c190ecd6818b6407"},
+ {file = "pywin32-306-cp311-cp311-win_amd64.whl", hash = "sha256:a7639f51c184c0272e93f244eb24dafca9b1855707d94c192d4a0b4c01e1100e"},
+ {file = "pywin32-306-cp311-cp311-win_arm64.whl", hash = "sha256:70dba0c913d19f942a2db25217d9a1b726c278f483a919f1abfed79c9cf64d3a"},
+ {file = "pywin32-306-cp312-cp312-win32.whl", hash = "sha256:383229d515657f4e3ed1343da8be101000562bf514591ff383ae940cad65458b"},
+ {file = "pywin32-306-cp312-cp312-win_amd64.whl", hash = "sha256:37257794c1ad39ee9be652da0462dc2e394c8159dfd913a8a4e8eb6fd346da0e"},
+ {file = "pywin32-306-cp312-cp312-win_arm64.whl", hash = "sha256:5821ec52f6d321aa59e2db7e0a35b997de60c201943557d108af9d4ae1ec7040"},
+ {file = "pywin32-306-cp37-cp37m-win32.whl", hash = "sha256:1c73ea9a0d2283d889001998059f5eaaba3b6238f767c9cf2833b13e6a685f65"},
+ {file = "pywin32-306-cp37-cp37m-win_amd64.whl", hash = "sha256:72c5f621542d7bdd4fdb716227be0dd3f8565c11b280be6315b06ace35487d36"},
+ {file = "pywin32-306-cp38-cp38-win32.whl", hash = "sha256:e4c092e2589b5cf0d365849e73e02c391c1349958c5ac3e9d5ccb9a28e017b3a"},
+ {file = "pywin32-306-cp38-cp38-win_amd64.whl", hash = "sha256:e8ac1ae3601bee6ca9f7cb4b5363bf1c0badb935ef243c4733ff9a393b1690c0"},
+ {file = "pywin32-306-cp39-cp39-win32.whl", hash = "sha256:e25fd5b485b55ac9c057f67d94bc203f3f6595078d1fb3b458c9c28b7153a802"},
+ {file = "pywin32-306-cp39-cp39-win_amd64.whl", hash = "sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4"},
+]
+
+[[package]]
+name = "pyyaml"
+version = "6.0.1"
+description = "YAML parser and emitter for Python"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"},
+ {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"},
+ {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
+ {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
+ {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
+ {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
+ {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
+ {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
+ {file = "PyYAML-6.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f003ed9ad21d6a4713f0a9b5a7a0a79e08dd0f221aff4525a2be4c346ee60aab"},
+ {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
+ {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
+ {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
+ {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
+ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afd7e57eddb1a54f0f1a974bc4391af8bcce0b444685d936840f125cf046d5bd"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-win32.whl", hash = "sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585"},
+ {file = "PyYAML-6.0.1-cp36-cp36m-win_amd64.whl", hash = "sha256:f22ac1c3cac4dbc50079e965eba2c1058622631e526bd9afd45fedd49ba781fa"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b1275ad35a5d18c62a7220633c913e1b42d44b46ee12554e5fd39c70a243d6a3"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18aeb1bf9a78867dc38b259769503436b7c72f7a1f1f4c93ff9a17de54319b27"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:596106435fa6ad000c2991a98fa58eeb8656ef2325d7e158344fb33864ed87e3"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa90d3f661d43131ca170712d903e6295d1f7a0f595074f151c0aed377c9b9c"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-win32.whl", hash = "sha256:9046c58c4395dff28dd494285c82ba00b546adfc7ef001486fbf0324bc174fba"},
+ {file = "PyYAML-6.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:4fb147e7a67ef577a588a0e2c17b6db51dda102c71de36f8549b6816a96e1867"},
+ {file = "PyYAML-6.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1d4c7e777c441b20e32f52bd377e0c409713e8bb1386e1099c2415f26e479595"},
+ {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
+ {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
+ {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
+ {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
+ {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
+ {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
+ {file = "PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c8098ddcc2a85b61647b2590f825f3db38891662cfc2fc776415143f599bb859"},
+ {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
+ {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
+ {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
+ {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
+ {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
+ {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
+]
+
+[[package]]
+name = "rapidfuzz"
+version = "3.6.1"
+description = "rapid fuzzy string matching"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "rapidfuzz-3.6.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ac434fc71edda30d45db4a92ba5e7a42c7405e1a54cb4ec01d03cc668c6dcd40"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2a791168e119cfddf4b5a40470620c872812042f0621e6a293983a2d52372db0"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5a2f3e9df346145c2be94e4d9eeffb82fab0cbfee85bd4a06810e834fe7c03fa"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:23de71e7f05518b0bbeef55d67b5dbce3bcd3e2c81e7e533051a2e9401354eb0"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d056e342989248d2bdd67f1955bb7c3b0ecfa239d8f67a8dfe6477b30872c607"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:01835d02acd5d95c1071e1da1bb27fe213c84a013b899aba96380ca9962364bc"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ed0f712e0bb5fea327e92aec8a937afd07ba8de4c529735d82e4c4124c10d5a0"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96cd19934f76a1264e8ecfed9d9f5291fde04ecb667faef5f33bdbfd95fe2d1f"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e06c4242a1354cf9d48ee01f6f4e6e19c511d50bb1e8d7d20bcadbb83a2aea90"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:d73dcfe789d37c6c8b108bf1e203e027714a239e50ad55572ced3c004424ed3b"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:06e98ff000e2619e7cfe552d086815671ed09b6899408c2c1b5103658261f6f3"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:08b6fb47dd889c69fbc0b915d782aaed43e025df6979b6b7f92084ba55edd526"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a1788ebb5f5b655a15777e654ea433d198f593230277e74d51a2a1e29a986283"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-win32.whl", hash = "sha256:c65f92881753aa1098c77818e2b04a95048f30edbe9c3094dc3707d67df4598b"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-win_amd64.whl", hash = "sha256:4243a9c35667a349788461aae6471efde8d8800175b7db5148a6ab929628047f"},
+ {file = "rapidfuzz-3.6.1-cp310-cp310-win_arm64.whl", hash = "sha256:f59d19078cc332dbdf3b7b210852ba1f5db8c0a2cd8cc4c0ed84cc00c76e6802"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:fbc07e2e4ac696497c5f66ec35c21ddab3fc7a406640bffed64c26ab2f7ce6d6"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:40cced1a8852652813f30fb5d4b8f9b237112a0bbaeebb0f4cc3611502556764"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:82300e5f8945d601c2daaaac139d5524d7c1fdf719aa799a9439927739917460"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edf97c321fd641fea2793abce0e48fa4f91f3c202092672f8b5b4e781960b891"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7420e801b00dee4a344ae2ee10e837d603461eb180e41d063699fb7efe08faf0"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:060bd7277dc794279fa95522af355034a29c90b42adcb7aa1da358fc839cdb11"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b7e3375e4f2bfec77f907680328e4cd16cc64e137c84b1886d547ab340ba6928"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a490cd645ef9d8524090551016f05f052e416c8adb2d8b85d35c9baa9d0428ab"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:2e03038bfa66d2d7cffa05d81c2f18fd6acbb25e7e3c068d52bb7469e07ff382"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:2b19795b26b979c845dba407fe79d66975d520947b74a8ab6cee1d22686f7967"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:064c1d66c40b3a0f488db1f319a6e75616b2e5fe5430a59f93a9a5e40a656d15"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:3c772d04fb0ebeece3109d91f6122b1503023086a9591a0b63d6ee7326bd73d9"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:841eafba6913c4dfd53045835545ba01a41e9644e60920c65b89c8f7e60c00a9"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-win32.whl", hash = "sha256:266dd630f12696ea7119f31d8b8e4959ef45ee2cbedae54417d71ae6f47b9848"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-win_amd64.whl", hash = "sha256:d79aec8aeee02ab55d0ddb33cea3ecd7b69813a48e423c966a26d7aab025cdfe"},
+ {file = "rapidfuzz-3.6.1-cp311-cp311-win_arm64.whl", hash = "sha256:484759b5dbc5559e76fefaa9170147d1254468f555fd9649aea3bad46162a88b"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:b2ef4c0fd3256e357b70591ffb9e8ed1d439fb1f481ba03016e751a55261d7c1"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:588c4b20fa2fae79d60a4e438cf7133d6773915df3cc0a7f1351da19eb90f720"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7142ee354e9c06e29a2636b9bbcb592bb00600a88f02aa5e70e4f230347b373e"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1dfc557c0454ad22382373ec1b7df530b4bbd974335efe97a04caec936f2956a"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:03f73b381bdeccb331a12c3c60f1e41943931461cdb52987f2ecf46bfc22f50d"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6b0ccc2ec1781c7e5370d96aef0573dd1f97335343e4982bdb3a44c133e27786"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da3e8c9f7e64bb17faefda085ff6862ecb3ad8b79b0f618a6cf4452028aa2222"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fde9b14302a31af7bdafbf5cfbb100201ba21519be2b9dedcf4f1048e4fbe65d"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:c1a23eee225dfb21c07f25c9fcf23eb055d0056b48e740fe241cbb4b22284379"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:e49b9575d16c56c696bc7b06a06bf0c3d4ef01e89137b3ddd4e2ce709af9fe06"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:0a9fc714b8c290261669f22808913aad49553b686115ad0ee999d1cb3df0cd66"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:a3ee4f8f076aa92184e80308fc1a079ac356b99c39408fa422bbd00145be9854"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:f056ba42fd2f32e06b2c2ba2443594873cfccc0c90c8b6327904fc2ddf6d5799"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-win32.whl", hash = "sha256:5d82b9651e3d34b23e4e8e201ecd3477c2baa17b638979deeabbb585bcb8ba74"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:dad55a514868dae4543ca48c4e1fc0fac704ead038dafedf8f1fc0cc263746c1"},
+ {file = "rapidfuzz-3.6.1-cp312-cp312-win_arm64.whl", hash = "sha256:3c84294f4470fcabd7830795d754d808133329e0a81d62fcc2e65886164be83b"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e19d519386e9db4a5335a4b29f25b8183a1c3f78cecb4c9c3112e7f86470e37f"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:01eb03cd880a294d1bf1a583fdd00b87169b9cc9c9f52587411506658c864d73"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:be368573255f8fbb0125a78330a1a40c65e9ba3c5ad129a426ff4289099bfb41"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b3e5af946f419c30f5cb98b69d40997fe8580efe78fc83c2f0f25b60d0e56efb"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f382f7ffe384ce34345e1c0b2065451267d3453cadde78946fbd99a59f0cc23c"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:be156f51f3a4f369e758505ed4ae64ea88900dcb2f89d5aabb5752676d3f3d7e"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1936d134b6c513fbe934aeb668b0fee1ffd4729a3c9d8d373f3e404fbb0ce8a0"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:12ff8eaf4a9399eb2bebd838f16e2d1ded0955230283b07376d68947bbc2d33d"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ae598a172e3a95df3383634589660d6b170cc1336fe7578115c584a99e0ba64d"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:cd4ba4c18b149da11e7f1b3584813159f189dc20833709de5f3df8b1342a9759"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:0402f1629e91a4b2e4aee68043a30191e5e1b7cd2aa8dacf50b1a1bcf6b7d3ab"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:1e12319c6b304cd4c32d5db00b7a1e36bdc66179c44c5707f6faa5a889a317c0"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:0bbfae35ce4de4c574b386c43c78a0be176eeddfdae148cb2136f4605bebab89"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-win32.whl", hash = "sha256:7fec74c234d3097612ea80f2a80c60720eec34947066d33d34dc07a3092e8105"},
+ {file = "rapidfuzz-3.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:a553cc1a80d97459d587529cc43a4c7c5ecf835f572b671107692fe9eddf3e24"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:757dfd7392ec6346bd004f8826afb3bf01d18a723c97cbe9958c733ab1a51791"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2963f4a3f763870a16ee076796be31a4a0958fbae133dbc43fc55c3968564cf5"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d2f0274595cc5b2b929c80d4e71b35041104b577e118cf789b3fe0a77b37a4c5"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f211e366e026de110a4246801d43a907cd1a10948082f47e8a4e6da76fef52"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a59472b43879012b90989603aa5a6937a869a72723b1bf2ff1a0d1edee2cc8e6"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a03863714fa6936f90caa7b4b50ea59ea32bb498cc91f74dc25485b3f8fccfe9"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5dd95b6b7bfb1584f806db89e1e0c8dbb9d25a30a4683880c195cc7f197eaf0c"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7183157edf0c982c0b8592686535c8b3e107f13904b36d85219c77be5cefd0d8"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ad9d74ef7c619b5b0577e909582a1928d93e07d271af18ba43e428dc3512c2a1"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b53137d81e770c82189e07a8f32722d9e4260f13a0aec9914029206ead38cac3"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:49b9ed2472394d306d5dc967a7de48b0aab599016aa4477127b20c2ed982dbf9"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:dec307b57ec2d5054d77d03ee4f654afcd2c18aee00c48014cb70bfed79597d6"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4381023fa1ff32fd5076f5d8321249a9aa62128eb3f21d7ee6a55373e672b261"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-win32.whl", hash = "sha256:8d7a072f10ee57c8413c8ab9593086d42aaff6ee65df4aa6663eecdb7c398dca"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:ebcfb5bfd0a733514352cfc94224faad8791e576a80ffe2fd40b2177bf0e7198"},
+ {file = "rapidfuzz-3.6.1-cp39-cp39-win_arm64.whl", hash = "sha256:1c47d592e447738744905c18dda47ed155620204714e6df20eb1941bb1ba315e"},
+ {file = "rapidfuzz-3.6.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:eef8b346ab331bec12bbc83ac75641249e6167fab3d84d8f5ca37fd8e6c7a08c"},
+ {file = "rapidfuzz-3.6.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:53251e256017e2b87f7000aee0353ba42392c442ae0bafd0f6b948593d3f68c6"},
+ {file = "rapidfuzz-3.6.1-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6dede83a6b903e3ebcd7e8137e7ff46907ce9316e9d7e7f917d7e7cdc570ee05"},
+ {file = "rapidfuzz-3.6.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e4da90e4c2b444d0a171d7444ea10152e07e95972bb40b834a13bdd6de1110c"},
+ {file = "rapidfuzz-3.6.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:ca3dfcf74f2b6962f411c33dd95b0adf3901266e770da6281bc96bb5a8b20de9"},
+ {file = "rapidfuzz-3.6.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bcc957c0a8bde8007f1a8a413a632a1a409890f31f73fe764ef4eac55f59ca87"},
+ {file = "rapidfuzz-3.6.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:692c9a50bea7a8537442834f9bc6b7d29d8729a5b6379df17c31b6ab4df948c2"},
+ {file = "rapidfuzz-3.6.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:76c23ceaea27e790ddd35ef88b84cf9d721806ca366199a76fd47cfc0457a81b"},
+ {file = "rapidfuzz-3.6.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b155e67fff215c09f130555002e42f7517d0ea72cbd58050abb83cb7c880cec"},
+ {file = "rapidfuzz-3.6.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3028ee8ecc48250607fa8a0adce37b56275ec3b1acaccd84aee1f68487c8557b"},
+ {file = "rapidfuzz-3.6.1.tar.gz", hash = "sha256:35660bee3ce1204872574fa041c7ad7ec5175b3053a4cb6e181463fc07013de7"},
+]
+
+[package.extras]
+full = ["numpy"]
+
+[[package]]
+name = "referencing"
+version = "0.32.1"
+description = "JSON Referencing + Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "referencing-0.32.1-py3-none-any.whl", hash = "sha256:7e4dc12271d8e15612bfe35792f5ea1c40970dadf8624602e33db2758f7ee554"},
+ {file = "referencing-0.32.1.tar.gz", hash = "sha256:3c57da0513e9563eb7e203ebe9bb3a1b509b042016433bd1e45a2853466c3dd3"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+rpds-py = ">=0.7.0"
+
+[[package]]
+name = "regex"
+version = "2023.12.25"
+description = "Alternative regular expression module, to replace re."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "regex-2023.12.25-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0694219a1d54336fd0445ea382d49d36882415c0134ee1e8332afd1529f0baa5"},
+ {file = "regex-2023.12.25-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b014333bd0217ad3d54c143de9d4b9a3ca1c5a29a6d0d554952ea071cff0f1f8"},
+ {file = "regex-2023.12.25-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d865984b3f71f6d0af64d0d88f5733521698f6c16f445bb09ce746c92c97c586"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e0eabac536b4cc7f57a5f3d095bfa557860ab912f25965e08fe1545e2ed8b4c"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c25a8ad70e716f96e13a637802813f65d8a6760ef48672aa3502f4c24ea8b400"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9b6d73353f777630626f403b0652055ebfe8ff142a44ec2cf18ae470395766e"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9cc99d6946d750eb75827cb53c4371b8b0fe89c733a94b1573c9dd16ea6c9e4"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:88d1f7bef20c721359d8675f7d9f8e414ec5003d8f642fdfd8087777ff7f94b5"},
+ {file = "regex-2023.12.25-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:cb3fe77aec8f1995611f966d0c656fdce398317f850d0e6e7aebdfe61f40e1cd"},
+ {file = "regex-2023.12.25-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:7aa47c2e9ea33a4a2a05f40fcd3ea36d73853a2aae7b4feab6fc85f8bf2c9704"},
+ {file = "regex-2023.12.25-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:df26481f0c7a3f8739fecb3e81bc9da3fcfae34d6c094563b9d4670b047312e1"},
+ {file = "regex-2023.12.25-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:c40281f7d70baf6e0db0c2f7472b31609f5bc2748fe7275ea65a0b4601d9b392"},
+ {file = "regex-2023.12.25-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:d94a1db462d5690ebf6ae86d11c5e420042b9898af5dcf278bd97d6bda065423"},
+ {file = "regex-2023.12.25-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:ba1b30765a55acf15dce3f364e4928b80858fa8f979ad41f862358939bdd1f2f"},
+ {file = "regex-2023.12.25-cp310-cp310-win32.whl", hash = "sha256:150c39f5b964e4d7dba46a7962a088fbc91f06e606f023ce57bb347a3b2d4630"},
+ {file = "regex-2023.12.25-cp310-cp310-win_amd64.whl", hash = "sha256:09da66917262d9481c719599116c7dc0c321ffcec4b1f510c4f8a066f8768105"},
+ {file = "regex-2023.12.25-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:1b9d811f72210fa9306aeb88385b8f8bcef0dfbf3873410413c00aa94c56c2b6"},
+ {file = "regex-2023.12.25-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d902a43085a308cef32c0d3aea962524b725403fd9373dea18110904003bac97"},
+ {file = "regex-2023.12.25-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d166eafc19f4718df38887b2bbe1467a4f74a9830e8605089ea7a30dd4da8887"},
+ {file = "regex-2023.12.25-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c7ad32824b7f02bb3c9f80306d405a1d9b7bb89362d68b3c5a9be53836caebdb"},
+ {file = "regex-2023.12.25-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:636ba0a77de609d6510235b7f0e77ec494d2657108f777e8765efc060094c98c"},
+ {file = "regex-2023.12.25-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0fda75704357805eb953a3ee15a2b240694a9a514548cd49b3c5124b4e2ad01b"},
+ {file = "regex-2023.12.25-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f72cbae7f6b01591f90814250e636065850c5926751af02bb48da94dfced7baa"},
+ {file = "regex-2023.12.25-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:db2a0b1857f18b11e3b0e54ddfefc96af46b0896fb678c85f63fb8c37518b3e7"},
+ {file = "regex-2023.12.25-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:7502534e55c7c36c0978c91ba6f61703faf7ce733715ca48f499d3dbbd7657e0"},
+ {file = "regex-2023.12.25-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:e8c7e08bb566de4faaf11984af13f6bcf6a08f327b13631d41d62592681d24fe"},
+ {file = "regex-2023.12.25-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:283fc8eed679758de38fe493b7d7d84a198b558942b03f017b1f94dda8efae80"},
+ {file = "regex-2023.12.25-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:f44dd4d68697559d007462b0a3a1d9acd61d97072b71f6d1968daef26bc744bd"},
+ {file = "regex-2023.12.25-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:67d3ccfc590e5e7197750fcb3a2915b416a53e2de847a728cfa60141054123d4"},
+ {file = "regex-2023.12.25-cp311-cp311-win32.whl", hash = "sha256:68191f80a9bad283432385961d9efe09d783bcd36ed35a60fb1ff3f1ec2efe87"},
+ {file = "regex-2023.12.25-cp311-cp311-win_amd64.whl", hash = "sha256:7d2af3f6b8419661a0c421584cfe8aaec1c0e435ce7e47ee2a97e344b98f794f"},
+ {file = "regex-2023.12.25-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:8a0ccf52bb37d1a700375a6b395bff5dd15c50acb745f7db30415bae3c2b0715"},
+ {file = "regex-2023.12.25-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c3c4a78615b7762740531c27cf46e2f388d8d727d0c0c739e72048beb26c8a9d"},
+ {file = "regex-2023.12.25-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ad83e7545b4ab69216cef4cc47e344d19622e28aabec61574b20257c65466d6a"},
+ {file = "regex-2023.12.25-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7a635871143661feccce3979e1727c4e094f2bdfd3ec4b90dfd4f16f571a87a"},
+ {file = "regex-2023.12.25-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d498eea3f581fbe1b34b59c697512a8baef88212f92e4c7830fcc1499f5b45a5"},
+ {file = "regex-2023.12.25-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:43f7cd5754d02a56ae4ebb91b33461dc67be8e3e0153f593c509e21d219c5060"},
+ {file = "regex-2023.12.25-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51f4b32f793812714fd5307222a7f77e739b9bc566dc94a18126aba3b92b98a3"},
+ {file = "regex-2023.12.25-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ba99d8077424501b9616b43a2d208095746fb1284fc5ba490139651f971d39d9"},
+ {file = "regex-2023.12.25-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:4bfc2b16e3ba8850e0e262467275dd4d62f0d045e0e9eda2bc65078c0110a11f"},
+ {file = "regex-2023.12.25-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8c2c19dae8a3eb0ea45a8448356ed561be843b13cbc34b840922ddf565498c1c"},
+ {file = "regex-2023.12.25-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:60080bb3d8617d96f0fb7e19796384cc2467447ef1c491694850ebd3670bc457"},
+ {file = "regex-2023.12.25-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b77e27b79448e34c2c51c09836033056a0547aa360c45eeeb67803da7b0eedaf"},
+ {file = "regex-2023.12.25-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:518440c991f514331f4850a63560321f833979d145d7d81186dbe2f19e27ae3d"},
+ {file = "regex-2023.12.25-cp312-cp312-win32.whl", hash = "sha256:e2610e9406d3b0073636a3a2e80db05a02f0c3169b5632022b4e81c0364bcda5"},
+ {file = "regex-2023.12.25-cp312-cp312-win_amd64.whl", hash = "sha256:cc37b9aeebab425f11f27e5e9e6cf580be7206c6582a64467a14dda211abc232"},
+ {file = "regex-2023.12.25-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:da695d75ac97cb1cd725adac136d25ca687da4536154cdc2815f576e4da11c69"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d126361607b33c4eb7b36debc173bf25d7805847346dd4d99b5499e1fef52bc7"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4719bb05094d7d8563a450cf8738d2e1061420f79cfcc1fa7f0a44744c4d8f73"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5dd58946bce44b53b06d94aa95560d0b243eb2fe64227cba50017a8d8b3cd3e2"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22a86d9fff2009302c440b9d799ef2fe322416d2d58fc124b926aa89365ec482"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2aae8101919e8aa05ecfe6322b278f41ce2994c4a430303c4cd163fef746e04f"},
+ {file = "regex-2023.12.25-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:e692296c4cc2873967771345a876bcfc1c547e8dd695c6b89342488b0ea55cd8"},
+ {file = "regex-2023.12.25-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:263ef5cc10979837f243950637fffb06e8daed7f1ac1e39d5910fd29929e489a"},
+ {file = "regex-2023.12.25-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:d6f7e255e5fa94642a0724e35406e6cb7001c09d476ab5fce002f652b36d0c39"},
+ {file = "regex-2023.12.25-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:88ad44e220e22b63b0f8f81f007e8abbb92874d8ced66f32571ef8beb0643b2b"},
+ {file = "regex-2023.12.25-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:3a17d3ede18f9cedcbe23d2daa8a2cd6f59fe2bf082c567e43083bba3fb00347"},
+ {file = "regex-2023.12.25-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:d15b274f9e15b1a0b7a45d2ac86d1f634d983ca40d6b886721626c47a400bf39"},
+ {file = "regex-2023.12.25-cp37-cp37m-win32.whl", hash = "sha256:ed19b3a05ae0c97dd8f75a5d8f21f7723a8c33bbc555da6bbe1f96c470139d3c"},
+ {file = "regex-2023.12.25-cp37-cp37m-win_amd64.whl", hash = "sha256:a6d1047952c0b8104a1d371f88f4ab62e6275567d4458c1e26e9627ad489b445"},
+ {file = "regex-2023.12.25-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:b43523d7bc2abd757119dbfb38af91b5735eea45537ec6ec3a5ec3f9562a1c53"},
+ {file = "regex-2023.12.25-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:efb2d82f33b2212898f1659fb1c2e9ac30493ac41e4d53123da374c3b5541e64"},
+ {file = "regex-2023.12.25-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b7fca9205b59c1a3d5031f7e64ed627a1074730a51c2a80e97653e3e9fa0d415"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:086dd15e9435b393ae06f96ab69ab2d333f5d65cbe65ca5a3ef0ec9564dfe770"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e81469f7d01efed9b53740aedd26085f20d49da65f9c1f41e822a33992cb1590"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:34e4af5b27232f68042aa40a91c3b9bb4da0eeb31b7632e0091afc4310afe6cb"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9852b76ab558e45b20bf1893b59af64a28bd3820b0c2efc80e0a70a4a3ea51c1"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ff100b203092af77d1a5a7abe085b3506b7eaaf9abf65b73b7d6905b6cb76988"},
+ {file = "regex-2023.12.25-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:cc038b2d8b1470364b1888a98fd22d616fba2b6309c5b5f181ad4483e0017861"},
+ {file = "regex-2023.12.25-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:094ba386bb5c01e54e14434d4caabf6583334090865b23ef58e0424a6286d3dc"},
+ {file = "regex-2023.12.25-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:5cd05d0f57846d8ba4b71d9c00f6f37d6b97d5e5ef8b3c3840426a475c8f70f4"},
+ {file = "regex-2023.12.25-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:9aa1a67bbf0f957bbe096375887b2505f5d8ae16bf04488e8b0f334c36e31360"},
+ {file = "regex-2023.12.25-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:98a2636994f943b871786c9e82bfe7883ecdaba2ef5df54e1450fa9869d1f756"},
+ {file = "regex-2023.12.25-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:37f8e93a81fc5e5bd8db7e10e62dc64261bcd88f8d7e6640aaebe9bc180d9ce2"},
+ {file = "regex-2023.12.25-cp38-cp38-win32.whl", hash = "sha256:d78bd484930c1da2b9679290a41cdb25cc127d783768a0369d6b449e72f88beb"},
+ {file = "regex-2023.12.25-cp38-cp38-win_amd64.whl", hash = "sha256:b521dcecebc5b978b447f0f69b5b7f3840eac454862270406a39837ffae4e697"},
+ {file = "regex-2023.12.25-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:f7bc09bc9c29ebead055bcba136a67378f03d66bf359e87d0f7c759d6d4ffa31"},
+ {file = "regex-2023.12.25-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e14b73607d6231f3cc4622809c196b540a6a44e903bcfad940779c80dffa7be7"},
+ {file = "regex-2023.12.25-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9eda5f7a50141291beda3edd00abc2d4a5b16c29c92daf8d5bd76934150f3edc"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cc6bb9aa69aacf0f6032c307da718f61a40cf970849e471254e0e91c56ffca95"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:298dc6354d414bc921581be85695d18912bea163a8b23cac9a2562bbcd5088b1"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2f4e475a80ecbd15896a976aa0b386c5525d0ed34d5c600b6d3ebac0a67c7ddf"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:531ac6cf22b53e0696f8e1d56ce2396311254eb806111ddd3922c9d937151dae"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:22f3470f7524b6da61e2020672df2f3063676aff444db1daa283c2ea4ed259d6"},
+ {file = "regex-2023.12.25-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:89723d2112697feaa320c9d351e5f5e7b841e83f8b143dba8e2d2b5f04e10923"},
+ {file = "regex-2023.12.25-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0ecf44ddf9171cd7566ef1768047f6e66975788258b1c6c6ca78098b95cf9a3d"},
+ {file = "regex-2023.12.25-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:905466ad1702ed4acfd67a902af50b8db1feeb9781436372261808df7a2a7bca"},
+ {file = "regex-2023.12.25-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:4558410b7a5607a645e9804a3e9dd509af12fb72b9825b13791a37cd417d73a5"},
+ {file = "regex-2023.12.25-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:7e316026cc1095f2a3e8cc012822c99f413b702eaa2ca5408a513609488cb62f"},
+ {file = "regex-2023.12.25-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:3b1de218d5375cd6ac4b5493e0b9f3df2be331e86520f23382f216c137913d20"},
+ {file = "regex-2023.12.25-cp39-cp39-win32.whl", hash = "sha256:11a963f8e25ab5c61348d090bf1b07f1953929c13bd2309a0662e9ff680763c9"},
+ {file = "regex-2023.12.25-cp39-cp39-win_amd64.whl", hash = "sha256:e693e233ac92ba83a87024e1d32b5f9ab15ca55ddd916d878146f4e3406b5c91"},
+ {file = "regex-2023.12.25.tar.gz", hash = "sha256:29171aa128da69afdf4bde412d5bedc335f2ca8fcfe4489038577d05f16181e5"},
+]
+
+[[package]]
+name = "requests"
+version = "2.31.0"
+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
+ {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
+]
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset-normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+PySocks = {version = ">=1.5.6,<1.5.7 || >1.5.7", optional = true, markers = "extra == \"socks\""}
+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "requests-oauthlib"
+version = "1.3.1"
+description = "OAuthlib authentication support for Requests."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+ {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
+ {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
+]
+
+[package.dependencies]
+oauthlib = ">=3.0.0"
+requests = ">=2.0.0"
+
+[package.extras]
+rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
+
+[[package]]
+name = "responses"
+version = "0.18.0"
+description = "A utility library for mocking out the `requests` Python library."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "responses-0.18.0-py3-none-any.whl", hash = "sha256:15c63ad16de13ee8e7182d99c9334f64fd81f1ee79f90748d527c28f7ca9dd51"},
+ {file = "responses-0.18.0.tar.gz", hash = "sha256:380cad4c1c1dc942e5e8a8eaae0b4d4edf708f4f010db8b7bcfafad1fcd254ff"},
+]
+
+[package.dependencies]
+requests = ">=2.0,<3.0"
+urllib3 = ">=1.25.10"
+
+[package.extras]
+tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"]
+
+[[package]]
+name = "rich"
+version = "13.7.0"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+ {file = "rich-13.7.0-py3-none-any.whl", hash = "sha256:6da14c108c4866ee9520bbffa71f6fe3962e193b7da68720583850cd4548e235"},
+ {file = "rich-13.7.0.tar.gz", hash = "sha256:5cb5123b5cf9ee70584244246816e9114227e0b98ad9176eede6ad54bf5403fa"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
+[[package]]
+name = "rpds-py"
+version = "0.16.2"
+description = "Python bindings to Rust's persistent data structures (rpds)"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "rpds_py-0.16.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:509b617ac787cd1149600e731db9274ebbef094503ca25158e6f23edaba1ca8f"},
+ {file = "rpds_py-0.16.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:413b9c17388bbd0d87a329d8e30c1a4c6e44e2bb25457f43725a8e6fe4161e9e"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2946b120718eba9af2b4dd103affc1164a87b9e9ebff8c3e4c05d7b7a7e274e2"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:35ae5ece284cf36464eb160880018cf6088a9ac5ddc72292a6092b6ef3f4da53"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3dc6a7620ba7639a3db6213da61312cb4aa9ac0ca6e00dc1cbbdc21c2aa6eb57"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8cb6fe8ecdfffa0e711a75c931fb39f4ba382b4b3ccedeca43f18693864fe850"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dace7b26a13353e24613417ce2239491b40a6ad44e5776a18eaff7733488b44"},
+ {file = "rpds_py-0.16.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1bdbc5fcb04a7309074de6b67fa9bc4b418ab3fc435fec1f2779a0eced688d04"},
+ {file = "rpds_py-0.16.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:f42e25c016927e2a6b1ce748112c3ab134261fc2ddc867e92d02006103e1b1b7"},
+ {file = "rpds_py-0.16.2-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:eab36eae3f3e8e24b05748ec9acc66286662f5d25c52ad70cadab544e034536b"},
+ {file = "rpds_py-0.16.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:0474df4ade9a3b4af96c3d36eb81856cb9462e4c6657d4caecfd840d2a13f3c9"},
+ {file = "rpds_py-0.16.2-cp310-none-win32.whl", hash = "sha256:84c5a4d1f9dd7e2d2c44097fb09fffe728629bad31eb56caf97719e55575aa82"},
+ {file = "rpds_py-0.16.2-cp310-none-win_amd64.whl", hash = "sha256:2bd82db36cd70b3628c0c57d81d2438e8dd4b7b32a6a9f25f24ab0e657cb6c4e"},
+ {file = "rpds_py-0.16.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:adc0c3d6fc6ae35fee3e4917628983f6ce630d513cbaad575b4517d47e81b4bb"},
+ {file = "rpds_py-0.16.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ec23fcad480e77ede06cf4127a25fc440f7489922e17fc058f426b5256ee0edb"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:07aab64e2808c3ebac2a44f67e9dc0543812b715126dfd6fe4264df527556cb6"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a4ebb8b20bd09c5ce7884c8f0388801100f5e75e7f733b1b6613c713371feefc"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a3d7e2ea25d3517c6d7e5a1cc3702cffa6bd18d9ef8d08d9af6717fc1c700eed"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f28ac0e8e7242d140f99402a903a2c596ab71550272ae9247ad78f9a932b5698"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19f00f57fdd38db4bb5ad09f9ead1b535332dbf624200e9029a45f1f35527ebb"},
+ {file = "rpds_py-0.16.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3da5a4c56953bdbf6d04447c3410309616c54433146ccdb4a277b9cb499bc10e"},
+ {file = "rpds_py-0.16.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:ec2e1cf025b2c0f48ec17ff3e642661da7ee332d326f2e6619366ce8e221f018"},
+ {file = "rpds_py-0.16.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:e0441fb4fdd39a230477b2ca9be90868af64425bfe7b122b57e61e45737a653b"},
+ {file = "rpds_py-0.16.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9f0350ef2fba5f34eb0c9000ea328e51b9572b403d2f7f3b19f24085f6f598e8"},
+ {file = "rpds_py-0.16.2-cp311-none-win32.whl", hash = "sha256:5a80e2f83391ad0808b4646732af2a7b67550b98f0cae056cb3b40622a83dbb3"},
+ {file = "rpds_py-0.16.2-cp311-none-win_amd64.whl", hash = "sha256:e04e56b4ca7a770593633556e8e9e46579d66ec2ada846b401252a2bdcf70a6d"},
+ {file = "rpds_py-0.16.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:5e6caa3809e50690bd92fa490f5c38caa86082c8c3315aa438bce43786d5e90d"},
+ {file = "rpds_py-0.16.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2e53b9b25cac9065328901713a7e9e3b12e4f57ef4280b370fbbf6fef2052eef"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:af27423662f32d7501a00c5e7342f7dbd1e4a718aea7a239781357d15d437133"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:43d4dd5fb16eb3825742bad8339d454054261ab59fed2fbac84e1d84d5aae7ba"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e061de3b745fe611e23cd7318aec2c8b0e4153939c25c9202a5811ca911fd733"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3b811d182ad17ea294f2ec63c0621e7be92a1141e1012383461872cead87468f"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5552f328eaef1a75ff129d4d0c437bf44e43f9436d3996e8eab623ea0f5fcf73"},
+ {file = "rpds_py-0.16.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dcbe1f8dd179e4d69b70b1f1d9bb6fd1e7e1bdc9c9aad345cdeb332e29d40748"},
+ {file = "rpds_py-0.16.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8aad80645a011abae487d356e0ceb359f4938dfb6f7bcc410027ed7ae4f7bb8b"},
+ {file = "rpds_py-0.16.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:b6f5549d6ed1da9bfe3631ca9483ae906f21410be2445b73443fa9f017601c6f"},
+ {file = "rpds_py-0.16.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:d452817e0d9c749c431a1121d56a777bd7099b720b3d1c820f1725cb40928f58"},
+ {file = "rpds_py-0.16.2-cp312-none-win32.whl", hash = "sha256:888a97002e986eca10d8546e3c8b97da1d47ad8b69726dcfeb3e56348ebb28a3"},
+ {file = "rpds_py-0.16.2-cp312-none-win_amd64.whl", hash = "sha256:d8dda2a806dfa4a9b795950c4f5cc56d6d6159f7d68080aedaff3bdc9b5032f5"},
+ {file = "rpds_py-0.16.2-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:071980663c273bf3d388fe5c794c547e6f35ba3335477072c713a3176bf14a60"},
+ {file = "rpds_py-0.16.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:726ac36e8a3bb8daef2fd482534cabc5e17334052447008405daca7ca04a3108"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e9e557db6a177470316c82f023e5d571811c9a4422b5ea084c85da9aa3c035fc"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:90123853fc8b1747f80b0d354be3d122b4365a93e50fc3aacc9fb4c2488845d6"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a61f659665a39a4d17d699ab3593d7116d66e1e2e3f03ef3fb8f484e91908808"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cc97f0640e91d7776530f06e6836c546c1c752a52de158720c4224c9e8053cad"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44a54e99a2b9693a37ebf245937fd6e9228b4cbd64b9cc961e1f3391ec6c7391"},
+ {file = "rpds_py-0.16.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:bd4b677d929cf1f6bac07ad76e0f2d5de367e6373351c01a9c0a39f6b21b4a8b"},
+ {file = "rpds_py-0.16.2-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:5ef00873303d678aaf8b0627e111fd434925ca01c657dbb2641410f1cdaef261"},
+ {file = "rpds_py-0.16.2-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:349cb40897fd529ca15317c22c0eab67f5ac5178b5bd2c6adc86172045210acc"},
+ {file = "rpds_py-0.16.2-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:2ddef620e70eaffebed5932ce754d539c0930f676aae6212f8e16cd9743dd365"},
+ {file = "rpds_py-0.16.2-cp38-none-win32.whl", hash = "sha256:882ce6e25e585949c3d9f9abd29202367175e0aab3aba0c58c9abbb37d4982ff"},
+ {file = "rpds_py-0.16.2-cp38-none-win_amd64.whl", hash = "sha256:f4bd4578e44f26997e9e56c96dedc5f1af43cc9d16c4daa29c771a00b2a26851"},
+ {file = "rpds_py-0.16.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:69ac7ea9897ec201ce68b48582f3eb34a3f9924488a5432a93f177bf76a82a7e"},
+ {file = "rpds_py-0.16.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a9880b4656efe36ccad41edc66789e191e5ee19a1ea8811e0aed6f69851a82f4"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ee94cb58c0ba2c62ee108c2b7c9131b2c66a29e82746e8fa3aa1a1effbd3dcf1"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:24f7a2eb3866a9e91f4599851e0c8d39878a470044875c49bd528d2b9b88361c"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ca57468da2d9a660bcf8961637c85f2fbb2aa64d9bc3f9484e30c3f9f67b1dd7"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ccd4e400309e1f34a5095bf9249d371f0fd60f8a3a5c4a791cad7b99ce1fd38d"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:80443fe2f7b3ea3934c5d75fb0e04a5dbb4a8e943e5ff2de0dec059202b70a8b"},
+ {file = "rpds_py-0.16.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4d6a9f052e72d493efd92a77f861e45bab2f6be63e37fa8ecf0c6fd1a58fedb0"},
+ {file = "rpds_py-0.16.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:35953f4f2b3216421af86fd236b7c0c65935936a94ea83ddbd4904ba60757773"},
+ {file = "rpds_py-0.16.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:981d135c7cdaf6cd8eadae1c950de43b976de8f09d8e800feed307140d3d6d00"},
+ {file = "rpds_py-0.16.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d0dd7ed2f16df2e129496e7fbe59a34bc2d7fc8db443a606644d069eb69cbd45"},
+ {file = "rpds_py-0.16.2-cp39-none-win32.whl", hash = "sha256:703d95c75a72e902544fda08e965885525e297578317989fd15a6ce58414b41d"},
+ {file = "rpds_py-0.16.2-cp39-none-win_amd64.whl", hash = "sha256:e93ec1b300acf89730cf27975ef574396bc04edecc358e9bd116fb387a123239"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:44627b6ca7308680a70766454db5249105fa6344853af6762eaad4158a2feebe"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:3f91df8e6dbb7360e176d1affd5fb0246d2b88d16aa5ebc7db94fd66b68b61da"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6d904c5693e08bad240f16d79305edba78276be87061c872a4a15e2c301fa2c0"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:290a81cfbe4673285cdf140ec5cd1658ffbf63ab359f2b352ebe172e7cfa5bf0"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b634c5ec0103c5cbebc24ebac4872b045cccb9456fc59efdcf6fe39775365bd2"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a297a4d08cc67c7466c873c78039d87840fb50d05473db0ec1b7b03d179bf322"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2e75e17bd0bb66ee34a707da677e47c14ee51ccef78ed6a263a4cc965a072a1"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f1b9d9260e06ea017feb7172976ab261e011c1dc2f8883c7c274f6b2aabfe01a"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:162d7cd9cd311c1b0ff1c55a024b8f38bd8aad1876b648821da08adc40e95734"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-musllinux_1_2_i686.whl", hash = "sha256:9b32f742ce5b57201305f19c2ef7a184b52f6f9ba6871cc042c2a61f0d6b49b8"},
+ {file = "rpds_py-0.16.2-pp310-pypy310_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:ac08472f41ea77cd6a5dae36ae7d4ed3951d6602833af87532b556c1b4601d63"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:495a14b72bbe217f2695dcd9b5ab14d4f8066a00f5d209ed94f0aca307f85f6e"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:8d6b6937ae9eac6d6c0ca3c42774d89fa311f55adff3970fb364b34abde6ed3d"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a61226465bda9283686db8f17d02569a98e4b13c637be5a26d44aa1f1e361c2"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5cf6af100ffb5c195beec11ffaa8cf8523057f123afa2944e6571d54da84cdc9"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6df15846ee3fb2e6397fe25d7ca6624af9f89587f3f259d177b556fed6bebe2c"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1be2f033df1b8be8c3167ba3c29d5dca425592ee31e35eac52050623afba5772"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96f957d6ab25a78b9e7fc9749d754b98eac825a112b4e666525ce89afcbd9ed5"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:088396c7c70e59872f67462fcac3ecbded5233385797021976a09ebd55961dfe"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:4c46ad6356e1561f2a54f08367d1d2e70a0a1bb2db2282d2c1972c1d38eafc3b"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-musllinux_1_2_i686.whl", hash = "sha256:47713dc4fce213f5c74ca8a1f6a59b622fc1b90868deb8e8e4d993e421b4b39d"},
+ {file = "rpds_py-0.16.2-pp38-pypy38_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:f811771019f063bbd0aa7bb72c8a934bc13ebacb4672d712fc1639cfd314cccc"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f19afcfc0dd0dca35694df441e9b0f95bc231b512f51bded3c3d8ca32153ec19"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a4b682c5775d6a3d21e314c10124599976809455ee67020e8e72df1769b87bc3"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c647ca87fc0ebe808a41de912e9a1bfef9acb85257e5d63691364ac16b81c1f0"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:302bd4983bbd47063e452c38be66153760112f6d3635c7eeefc094299fa400a9"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bf721ede3eb7b829e4a9b8142bd55db0bdc82902720548a703f7e601ee13bdc3"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:358dafc89ce3894c7f486c615ba914609f38277ef67f566abc4c854d23b997fa"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cad0f59ee3dc35526039f4bc23642d52d5f6616b5f687d846bfc6d0d6d486db0"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cffa76b385dfe1e38527662a302b19ffb0e7f5cf7dd5e89186d2c94a22dd9d0c"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:83640a5d7cd3bff694747d50436b8b541b5b9b9782b0c8c1688931d6ee1a1f2d"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-musllinux_1_2_i686.whl", hash = "sha256:ed99b4f7179d2111702020fd7d156e88acd533f5a7d3971353e568b6051d5c97"},
+ {file = "rpds_py-0.16.2-pp39-pypy39_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:4022b9dc620e14f30201a8a73898a873c8e910cb642bcd2f3411123bc527f6ac"},
+ {file = "rpds_py-0.16.2.tar.gz", hash = "sha256:781ef8bfc091b19960fc0142a23aedadafa826bc32b433fdfe6fd7f964d7ef44"},
+]
+
+[[package]]
+name = "ruff"
+version = "0.1.14"
+description = "An extremely fast Python linter and code formatter, written in Rust."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "ruff-0.1.14-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:96f76536df9b26622755c12ed8680f159817be2f725c17ed9305b472a757cdbb"},
+ {file = "ruff-0.1.14-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ab3f71f64498c7241123bb5a768544cf42821d2a537f894b22457a543d3ca7a9"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7060156ecc572b8f984fd20fd8b0fcb692dd5d837b7606e968334ab7ff0090ab"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a53d8e35313d7b67eb3db15a66c08434809107659226a90dcd7acb2afa55faea"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bea9be712b8f5b4ebed40e1949379cfb2a7d907f42921cf9ab3aae07e6fba9eb"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:2270504d629a0b064247983cbc495bed277f372fb9eaba41e5cf51f7ba705a6a"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80258bb3b8909b1700610dfabef7876423eed1bc930fe177c71c414921898efa"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:653230dd00aaf449eb5ff25d10a6e03bc3006813e2cb99799e568f55482e5cae"},
+ {file = "ruff-0.1.14-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87b3acc6c4e6928459ba9eb7459dd4f0c4bf266a053c863d72a44c33246bfdbf"},
+ {file = "ruff-0.1.14-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:6b3dadc9522d0eccc060699a9816e8127b27addbb4697fc0c08611e4e6aeb8b5"},
+ {file = "ruff-0.1.14-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:1c8eca1a47b4150dc0fbec7fe68fc91c695aed798532a18dbb1424e61e9b721f"},
+ {file = "ruff-0.1.14-py3-none-musllinux_1_2_i686.whl", hash = "sha256:62ce2ae46303ee896fc6811f63d6dabf8d9c389da0f3e3f2bce8bc7f15ef5488"},
+ {file = "ruff-0.1.14-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b2027dde79d217b211d725fc833e8965dc90a16d0d3213f1298f97465956661b"},
+ {file = "ruff-0.1.14-py3-none-win32.whl", hash = "sha256:722bafc299145575a63bbd6b5069cb643eaa62546a5b6398f82b3e4403329cab"},
+ {file = "ruff-0.1.14-py3-none-win_amd64.whl", hash = "sha256:e3d241aa61f92b0805a7082bd89a9990826448e4d0398f0e2bc8f05c75c63d99"},
+ {file = "ruff-0.1.14-py3-none-win_arm64.whl", hash = "sha256:269302b31ade4cde6cf6f9dd58ea593773a37ed3f7b97e793c8594b262466b67"},
+ {file = "ruff-0.1.14.tar.gz", hash = "sha256:ad3f8088b2dfd884820289a06ab718cde7d38b94972212cc4ba90d5fbc9955f3"},
+]
+
+[[package]]
+name = "sacrebleu"
+version = "2.4.0"
+description = "Hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "sacrebleu-2.4.0-py3-none-any.whl", hash = "sha256:fc7c34464a56d691bf5e37c4b5292142d2273b02516ac61e264cd19035fff981"},
+ {file = "sacrebleu-2.4.0.tar.gz", hash = "sha256:d9e918147dc0777b2e159bff3246b8eb22d76f3b4ee3e6c6cbda05dc25dbb9c0"},
+]
+
+[package.dependencies]
+colorama = "*"
+lxml = "*"
+numpy = ">=1.17"
+portalocker = "*"
+regex = "*"
+tabulate = ">=0.8.9"
+
+[package.extras]
+ja = ["ipadic (>=1.0,<2.0)", "mecab-python3 (>=1.0.5,<=1.0.6)"]
+ko = ["mecab-ko (>=1.0.0,<=1.0.1)", "mecab-ko-dic (>=1.0,<2.0)"]
+
+[[package]]
+name = "safetensors"
+version = "0.4.1"
+description = ""
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "safetensors-0.4.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:cba01c6b76e01ec453933b3b3c0157c59b52881c83eaa0f7666244e71aa75fd1"},
+ {file = "safetensors-0.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7a8f6f679d97ea0135c7935c202feefbd042c149aa70ee759855e890c01c7814"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbc2ce1f5ae5143a7fb72b71fa71db6a42b4f6cf912aa3acdc6b914084778e68"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2d87d993eaefe6611a9c241a8bd364a5f1ffed5771c74840363a6c4ed8d868f6"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:097e9af2efa8778cd2f0cba451784253e62fa7cc9fc73c0744d27212f7294e25"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d10a9f7bae608ccfdc009351f01dc3d8535ff57f9488a58a4c38e45bf954fe93"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:270b99885ec14abfd56c1d7f28ada81740a9220b4bae960c3de1c6fe84af9e4d"},
+ {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:285b52a481e7ba93e29ad4ec5841ef2c4479ef0a6c633c4e2629e0508453577b"},
+ {file = "safetensors-0.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:c3c9f0ca510e0de95abd6424789dcbc879942a3a4e29b0dfa99d9427bf1da75c"},
+ {file = "safetensors-0.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:88b4653059c903015284a9722f9a46838c654257173b279c8f6f46dbe80b612d"},
+ {file = "safetensors-0.4.1-cp310-none-win32.whl", hash = "sha256:2fe6926110e3d425c4b684a4379b7796fdc26ad7d16922ea1696c8e6ea7e920f"},
+ {file = "safetensors-0.4.1-cp310-none-win_amd64.whl", hash = "sha256:a79e16222106b2f5edbca1b8185661477d8971b659a3c814cc6f15181a9b34c8"},
+ {file = "safetensors-0.4.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:d93321eea0dd7e81b283e47a1d20dee6069165cc158286316d0d06d340de8fe8"},
+ {file = "safetensors-0.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8ff8e41c8037db17de0ea2a23bc684f43eaf623be7d34906fe1ac10985b8365e"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39d36f1d88468a87c437a1bc27c502e71b6ca44c385a9117a9f9ba03a75cc9c6"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7ef010e9afcb4057fb6be3d0a0cfa07aac04fe97ef73fe4a23138d8522ba7c17"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b287304f2b2220d51ccb51fd857761e78bcffbeabe7b0238f8dc36f2edfd9542"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e09000b2599e1836314430f81a3884c66a5cbabdff5d9f175b5d560d4de38d78"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e9c80ce0001efa16066358d2dd77993adc25f5a6c61850e4ad096a2232930bce"},
+ {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:413e1f6ac248f7d1b755199a06635e70c3515493d3b41ba46063dec33aa2ebb7"},
+ {file = "safetensors-0.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d3ac139377cfe71ba04573f1cda66e663b7c3e95be850e9e6c2dd4b5984bd513"},
+ {file = "safetensors-0.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:04157d008385bea66d12fe90844a80d4a76dc25ec5230b5bd9a630496d1b7c03"},
+ {file = "safetensors-0.4.1-cp311-none-win32.whl", hash = "sha256:5f25297148ec665f0deb8bd67e9564634d8d6841041ab5393ccfe203379ea88b"},
+ {file = "safetensors-0.4.1-cp311-none-win_amd64.whl", hash = "sha256:b2f8877990a72ff595507b80f4b69036a9a1986a641f8681adf3425d97d3d2a5"},
+ {file = "safetensors-0.4.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:eb2c1da1cc39509d1a55620a5f4d14f8911c47a89c926a96e6f4876e864375a3"},
+ {file = "safetensors-0.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:303d2c0415cf15a28f8d7f17379ea3c34c2b466119118a34edd9965983a1a8a6"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb4cb3e37a9b961ddd68e873b29fe9ab4a081e3703412e34aedd2b7a8e9cafd9"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ae5497adc68669db2fed7cb2dad81e6a6106e79c9a132da3efdb6af1db1014fa"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3b30abd0cddfe959d1daedf92edcd1b445521ebf7ddefc20860ed01486b33c90"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d784a98c492c751f228a4a894c3b8a092ff08b24e73b5568938c28b8c0e8f8df"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e57a5ab08b0ec7a7caf30d2ac79bb30c89168431aca4f8854464bb9461686925"},
+ {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:edcf3121890b5f0616aa5a54683b1a5d2332037b970e507d6bb7841a3a596556"},
+ {file = "safetensors-0.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:fdb58dee173ef33634c3016c459d671ca12d11e6acf9db008261cbe58107e579"},
+ {file = "safetensors-0.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:780dc21eb3fd32ddd0e8c904bdb0290f2454f4ac21ae71e94f9ce72db1900a5a"},
+ {file = "safetensors-0.4.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:48901bd540f8a3c1791314bc5c8a170927bf7f6acddb75bf0a263d081a3637d4"},
+ {file = "safetensors-0.4.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:3b0b7b2d5976fbed8a05e2bbdce5816a59e6902e9e7c7e07dc723637ed539787"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f69903ff49cb30b9227fb5d029bea276ea20d04b06803877a420c5b1b74c689"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0ddd050e01f3e843aa8c1c27bf68675b8a08e385d0045487af4d70418c3cb356"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9a82bc2bd7a9a0e08239bdd6d7774d64121f136add93dfa344a2f1a6d7ef35fa"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6ace9e66a40f98a216ad661245782483cf79cf56eb2b112650bb904b0baa9db5"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:82cbb8f4d022f2e94498cbefca900698b8ded3d4f85212f47da614001ff06652"},
+ {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:791edc10a3c359a2f5f52d5cddab0df8a45107d91027d86c3d44e57162e5d934"},
+ {file = "safetensors-0.4.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:83c2cfbe8c6304f0891e7bb378d56f66d2148972eeb5f747cd8a2246886f0d8c"},
+ {file = "safetensors-0.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:04dd14f53f5500eb4c4149674216ba1000670efbcf4b1b5c2643eb244e7882ea"},
+ {file = "safetensors-0.4.1-cp37-none-win32.whl", hash = "sha256:d5b3defa74f3723a388bfde2f5d488742bc4879682bd93267c09a3bcdf8f869b"},
+ {file = "safetensors-0.4.1-cp37-none-win_amd64.whl", hash = "sha256:25a043cbb59d4f75e9dd87fdf5c009dd8830105a2c57ace49b72167dd9808111"},
+ {file = "safetensors-0.4.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:3f6a520af7f2717c5ecba112041f2c8af1ca6480b97bf957aba81ed9642e654c"},
+ {file = "safetensors-0.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c3807ac3b16288dffebb3474b555b56fe466baa677dfc16290dcd02dca1ab228"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b58ba13a9e82b4bc3fc221914f6ef237fe6c2adb13cede3ace64d1aacf49610"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:dac4bb42f8679aadc59bd91a4c5a1784a758ad49d0912995945cd674089f628e"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:911b48dc09e321a194def3a7431662ff4f03646832f3a8915bbf0f449b8a5fcb"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:82571d20288c975c1b30b08deb9b1c3550f36b31191e1e81fae87669a92217d0"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da52ee0dc8ba03348ffceab767bd8230842fdf78f8a996e2a16445747143a778"},
+ {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2536b11ce665834201072e9397404170f93f3be10cca9995b909f023a04501ee"},
+ {file = "safetensors-0.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:998fbac99ca956c3a09fe07cc0b35fac26a521fa8865a690686d889f0ff4e4a6"},
+ {file = "safetensors-0.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:845be0aafabf2a60c2d482d4e93023fecffe5e5443d801d7a7741bae9de41233"},
+ {file = "safetensors-0.4.1-cp38-none-win32.whl", hash = "sha256:ce7a28bc8af685a69d7e869d09d3e180a275e3281e29cf5f1c7319e231932cc7"},
+ {file = "safetensors-0.4.1-cp38-none-win_amd64.whl", hash = "sha256:e056fb9e22d118cc546107f97dc28b449d88274207dd28872bd668c86216e4f6"},
+ {file = "safetensors-0.4.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:bdc0d039e44a727824639824090bd8869535f729878fa248addd3dc01db30eae"},
+ {file = "safetensors-0.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3c1b1d510c7aba71504ece87bf393ea82638df56303e371e5e2cf09d18977dd7"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0bd0afd95c1e497f520e680ea01e0397c0868a3a3030e128438cf6e9e3fcd671"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f603bdd8deac6726d39f41688ed353c532dd53935234405d79e9eb53f152fbfb"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d8a85e3e47e0d4eebfaf9a58b40aa94f977a56050cb5598ad5396a9ee7c087c6"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0ccb5aa0f3be2727117e5631200fbb3a5b3a2b3757545a92647d6dd8be6658f"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d784938534e255473155e4d9f276ee69eb85455b6af1292172c731409bf9adee"},
+ {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a257de175c254d39ccd6a21341cd62eb7373b05c1e618a78096a56a857e0c316"},
+ {file = "safetensors-0.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:6fd80f7794554091836d4d613d33a7d006e2b8d6ba014d06f97cebdfda744f64"},
+ {file = "safetensors-0.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:35803201d980efcf964b75a0a2aee97fe5e9ecc5f3ad676b38fafdfe98e0620d"},
+ {file = "safetensors-0.4.1-cp39-none-win32.whl", hash = "sha256:7ff8a36e0396776d3ed9a106fc9a9d7c55d4439ca9a056a24bf66d343041d3e6"},
+ {file = "safetensors-0.4.1-cp39-none-win_amd64.whl", hash = "sha256:bfa2e20342b81921b98edba52f8deb68843fa9c95250739a56b52ceda5ea5c61"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:ae2d5a31cfb8a973a318f7c4d2cffe0bd1fe753cdf7bb41a1939d45a0a06f964"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1a45dbf03e8334d3a5dc93687d98b6dc422f5d04c7d519dac09b84a3c87dd7c6"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2297b359d91126c0f9d4fd17bae3cfa2fe3a048a6971b8db07db746ad92f850c"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bda3d98e2bcece388232cfc551ebf063b55bdb98f65ab54df397da30efc7dcc5"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f8934bdfd202ebd0697040a3dff40dd77bc4c5bbf3527ede0532f5e7fb4d970f"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:42c3710cec7e5c764c7999697516370bee39067de0aa089b7e2cfb97ac8c6b20"},
+ {file = "safetensors-0.4.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:53134226053e56bd56e73f7db42596e7908ed79f3c9a1016e4c1dade593ac8e5"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:257d59e40a1b367cb544122e7451243d65b33c3f34d822a347f4eea6fdf97fdf"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d54c2f1826e790d1eb2d2512bfd0ee443f0206b423d6f27095057c7f18a0687"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:645b3f1138fce6e818e79d4128afa28f0657430764cc045419c1d069ff93f732"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e9a7ffb1e551c6df51d267f5a751f042b183df22690f6feceac8d27364fd51d7"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:44e230fbbe120de564b64f63ef3a8e6ff02840fa02849d9c443d56252a1646d4"},
+ {file = "safetensors-0.4.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:9d16b3b2fcc6fca012c74bd01b5619c655194d3e3c13e4d4d0e446eefa39a463"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:5d95ea4d8b32233910734a904123bdd3979c137c461b905a5ed32511defc075f"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:dab431699b5d45e0ca043bc580651ce9583dda594e62e245b7497adb32e99809"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16d8bbb7344e39cb9d4762e85c21df94ebeb03edac923dd94bb9ed8c10eac070"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1faf5111c66a6ba91f85dff2e36edaaf36e6966172703159daeef330de4ddc7b"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:660ca1d8bff6c7bc7c6b30b9b32df74ef3ab668f5df42cefd7588f0d40feadcb"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ae2f67f04ed0bb2e56fd380a8bd3eef03f609df53f88b6f5c7e89c08e52aae00"},
+ {file = "safetensors-0.4.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:c8ed5d2c04cdc1afc6b3c28d59580448ac07732c50d94c15e14670f9c473a2ce"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:2b6a2814278b6660261aa9a9aae524616de9f1ec364e3716d219b6ed8f91801f"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:3cfd1ca35eacc635f0eaa894e5c5ed83ffebd0f95cac298fd430014fa7323631"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4177b456c6b0c722d82429127b5beebdaf07149d265748e97e0a34ff0b3694c8"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:313e8472197bde54e3ec54a62df184c414582979da8f3916981b6a7954910a1b"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fdb4adb76e21bad318210310590de61c9f4adcef77ee49b4a234f9dc48867869"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:1d568628e9c43ca15eb96c217da73737c9ccb07520fafd8a1eba3f2750614105"},
+ {file = "safetensors-0.4.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:573b6023a55a2f28085fc0a84e196c779b6cbef4d9e73acea14c8094fee7686f"},
+ {file = "safetensors-0.4.1.tar.gz", hash = "sha256:2304658e6ada81a5223225b4efe84748e760c46079bffedf7e321763cafb36c9"},
+]
+
+[package.extras]
+all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
+dev = ["safetensors[all]"]
+jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
+numpy = ["numpy (>=1.21.6)"]
+paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
+pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
+quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
+tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
+testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
+torch = ["safetensors[numpy]", "torch (>=1.10)"]
+
+[[package]]
+name = "scikit-learn"
+version = "1.3.2"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "scikit-learn-1.3.2.tar.gz", hash = "sha256:a2f54c76accc15a34bfb9066e6c7a56c1e7235dda5762b990792330b52ccfb05"},
+ {file = "scikit_learn-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e326c0eb5cf4d6ba40f93776a20e9a7a69524c4db0757e7ce24ba222471ee8a1"},
+ {file = "scikit_learn-1.3.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:535805c2a01ccb40ca4ab7d081d771aea67e535153e35a1fd99418fcedd1648a"},
+ {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1215e5e58e9880b554b01187b8c9390bf4dc4692eedeaf542d3273f4785e342c"},
+ {file = "scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ee107923a623b9f517754ea2f69ea3b62fc898a3641766cb7deb2f2ce450161"},
+ {file = "scikit_learn-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:35a22e8015048c628ad099da9df5ab3004cdbf81edc75b396fd0cff8699ac58c"},
+ {file = "scikit_learn-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6fb6bc98f234fda43163ddbe36df8bcde1d13ee176c6dc9b92bb7d3fc842eb66"},
+ {file = "scikit_learn-1.3.2-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:18424efee518a1cde7b0b53a422cde2f6625197de6af36da0b57ec502f126157"},
+ {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3271552a5eb16f208a6f7f617b8cc6d1f137b52c8a1ef8edf547db0259b2c9fb"},
+ {file = "scikit_learn-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc4144a5004a676d5022b798d9e573b05139e77f271253a4703eed295bde0433"},
+ {file = "scikit_learn-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:67f37d708f042a9b8d59551cf94d30431e01374e00dc2645fa186059c6c5d78b"},
+ {file = "scikit_learn-1.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:8db94cd8a2e038b37a80a04df8783e09caac77cbe052146432e67800e430c028"},
+ {file = "scikit_learn-1.3.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:61a6efd384258789aa89415a410dcdb39a50e19d3d8410bd29be365bcdd512d5"},
+ {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb06f8dce3f5ddc5dee1715a9b9f19f20d295bed8e3cd4fa51e1d050347de525"},
+ {file = "scikit_learn-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b2de18d86f630d68fe1f87af690d451388bb186480afc719e5f770590c2ef6c"},
+ {file = "scikit_learn-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:0402638c9a7c219ee52c94cbebc8fcb5eb9fe9c773717965c1f4185588ad3107"},
+ {file = "scikit_learn-1.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:a19f90f95ba93c1a7f7924906d0576a84da7f3b2282ac3bfb7a08a32801add93"},
+ {file = "scikit_learn-1.3.2-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:b8692e395a03a60cd927125eef3a8e3424d86dde9b2370d544f0ea35f78a8073"},
+ {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15e1e94cc23d04d39da797ee34236ce2375ddea158b10bee3c343647d615581d"},
+ {file = "scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:785a2213086b7b1abf037aeadbbd6d67159feb3e30263434139c98425e3dcfcf"},
+ {file = "scikit_learn-1.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:64381066f8aa63c2710e6b56edc9f0894cc7bf59bd71b8ce5613a4559b6145e0"},
+ {file = "scikit_learn-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6c43290337f7a4b969d207e620658372ba3c1ffb611f8bc2b6f031dc5c6d1d03"},
+ {file = "scikit_learn-1.3.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:dc9002fc200bed597d5d34e90c752b74df516d592db162f756cc52836b38fe0e"},
+ {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d08ada33e955c54355d909b9c06a4789a729977f165b8bae6f225ff0a60ec4a"},
+ {file = "scikit_learn-1.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:763f0ae4b79b0ff9cca0bf3716bcc9915bdacff3cebea15ec79652d1cc4fa5c9"},
+ {file = "scikit_learn-1.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:ed932ea780517b00dae7431e031faae6b49b20eb6950918eb83bd043237950e0"},
+]
+
+[package.dependencies]
+joblib = ">=1.1.1"
+numpy = ">=1.17.3,<2.0"
+scipy = ">=1.5.0"
+threadpoolctl = ">=2.0.0"
+
+[package.extras]
+benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.10.1)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
+tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"]
+
+[[package]]
+name = "scipy"
+version = "1.9.3"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0"},
+ {file = "scipy-1.9.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd"},
+ {file = "scipy-1.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b"},
+ {file = "scipy-1.9.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9"},
+ {file = "scipy-1.9.3-cp310-cp310-win_amd64.whl", hash = "sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523"},
+ {file = "scipy-1.9.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096"},
+ {file = "scipy-1.9.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c"},
+ {file = "scipy-1.9.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab"},
+ {file = "scipy-1.9.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb"},
+ {file = "scipy-1.9.3-cp311-cp311-win_amd64.whl", hash = "sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31"},
+ {file = "scipy-1.9.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840"},
+ {file = "scipy-1.9.3-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5"},
+ {file = "scipy-1.9.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108"},
+ {file = "scipy-1.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc"},
+ {file = "scipy-1.9.3-cp38-cp38-win_amd64.whl", hash = "sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e"},
+ {file = "scipy-1.9.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c"},
+ {file = "scipy-1.9.3-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95"},
+ {file = "scipy-1.9.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e"},
+ {file = "scipy-1.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0"},
+ {file = "scipy-1.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58"},
+ {file = "scipy-1.9.3.tar.gz", hash = "sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027"},
+]
+
+[package.dependencies]
+numpy = ">=1.18.5,<1.26.0"
+
+[package.extras]
+dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
+doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
+test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "secretstorage"
+version = "3.3.3"
+description = "Python bindings to FreeDesktop.org Secret Service API"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "SecretStorage-3.3.3-py3-none-any.whl", hash = "sha256:f356e6628222568e3af06f2eba8df495efa13b3b63081dafd4f7d9a7b7bc9f99"},
+ {file = "SecretStorage-3.3.3.tar.gz", hash = "sha256:2403533ef369eca6d2ba81718576c5e0f564d5cca1b58f73a8b23e7d4eeebd77"},
+]
+
+[package.dependencies]
+cryptography = ">=2.0"
+jeepney = ">=0.6"
+
+[[package]]
+name = "semantic-version"
+version = "2.10.0"
+description = "A library implementing the 'SemVer' scheme."
+optional = false
+python-versions = ">=2.7"
+files = [
+ {file = "semantic_version-2.10.0-py2.py3-none-any.whl", hash = "sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177"},
+ {file = "semantic_version-2.10.0.tar.gz", hash = "sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c"},
+]
+
+[package.extras]
+dev = ["Django (>=1.11)", "check-manifest", "colorama (<=0.4.1)", "coverage", "flake8", "nose2", "readme-renderer (<25.0)", "tox", "wheel", "zest.releaser[recommended]"]
+doc = ["Sphinx", "sphinx-rtd-theme"]
+
+[[package]]
+name = "sentry-sdk"
+version = "1.39.1"
+description = "Python client for Sentry (https://sentry.io)"
+optional = false
+python-versions = "*"
+files = [
+ {file = "sentry-sdk-1.39.1.tar.gz", hash = "sha256:320a55cdf9da9097a0bead239c35b7e61f53660ef9878861824fd6d9b2eaf3b5"},
+ {file = "sentry_sdk-1.39.1-py2.py3-none-any.whl", hash = "sha256:81b5b9ffdd1a374e9eb0c053b5d2012155db9cbe76393a8585677b753bd5fdc1"},
+]
+
+[package.dependencies]
+certifi = "*"
+urllib3 = {version = ">=1.26.11", markers = "python_version >= \"3.6\""}
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.5)"]
+arq = ["arq (>=0.23)"]
+asyncpg = ["asyncpg (>=0.23)"]
+beam = ["apache-beam (>=2.12)"]
+bottle = ["bottle (>=0.12.13)"]
+celery = ["celery (>=3)"]
+chalice = ["chalice (>=1.16.0)"]
+clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
+django = ["django (>=1.8)"]
+falcon = ["falcon (>=1.4)"]
+fastapi = ["fastapi (>=0.79.0)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+grpcio = ["grpcio (>=1.21.1)"]
+httpx = ["httpx (>=0.16.0)"]
+huey = ["huey (>=2)"]
+loguru = ["loguru (>=0.5)"]
+opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
+opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
+pure-eval = ["asttokens", "executing", "pure-eval"]
+pymongo = ["pymongo (>=3.1)"]
+pyspark = ["pyspark (>=2.4.4)"]
+quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
+rq = ["rq (>=0.6)"]
+sanic = ["sanic (>=0.8)"]
+sqlalchemy = ["sqlalchemy (>=1.2)"]
+starlette = ["starlette (>=0.19.1)"]
+starlite = ["starlite (>=1.48)"]
+tornado = ["tornado (>=5)"]
+
+[[package]]
+name = "setproctitle"
+version = "1.3.3"
+description = "A Python module to customize the process title"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:897a73208da48db41e687225f355ce993167079eda1260ba5e13c4e53be7f754"},
+ {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8c331e91a14ba4076f88c29c777ad6b58639530ed5b24b5564b5ed2fd7a95452"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbbd6c7de0771c84b4aa30e70b409565eb1fc13627a723ca6be774ed6b9d9fa3"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c05ac48ef16ee013b8a326c63e4610e2430dbec037ec5c5b58fcced550382b74"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1342f4fdb37f89d3e3c1c0a59d6ddbedbde838fff5c51178a7982993d238fe4f"},
+ {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc74e84fdfa96821580fb5e9c0b0777c1c4779434ce16d3d62a9c4d8c710df39"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9617b676b95adb412bb69645d5b077d664b6882bb0d37bfdafbbb1b999568d85"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6a249415f5bb88b5e9e8c4db47f609e0bf0e20a75e8d744ea787f3092ba1f2d0"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:38da436a0aaace9add67b999eb6abe4b84397edf4a78ec28f264e5b4c9d53cd5"},
+ {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:da0d57edd4c95bf221b2ebbaa061e65b1788f1544977288bdf95831b6e44e44d"},
+ {file = "setproctitle-1.3.3-cp310-cp310-win32.whl", hash = "sha256:a1fcac43918b836ace25f69b1dca8c9395253ad8152b625064415b1d2f9be4fb"},
+ {file = "setproctitle-1.3.3-cp310-cp310-win_amd64.whl", hash = "sha256:200620c3b15388d7f3f97e0ae26599c0c378fdf07ae9ac5a13616e933cbd2086"},
+ {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:334f7ed39895d692f753a443102dd5fed180c571eb6a48b2a5b7f5b3564908c8"},
+ {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:950f6476d56ff7817a8fed4ab207727fc5260af83481b2a4b125f32844df513a"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:195c961f54a09eb2acabbfc90c413955cf16c6e2f8caa2adbf2237d1019c7dd8"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f05e66746bf9fe6a3397ec246fe481096664a9c97eb3fea6004735a4daf867fd"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b5901a31012a40ec913265b64e48c2a4059278d9f4e6be628441482dd13fb8b5"},
+ {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64286f8a995f2cd934082b398fc63fca7d5ffe31f0e27e75b3ca6b4efda4e353"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:184239903bbc6b813b1a8fc86394dc6ca7d20e2ebe6f69f716bec301e4b0199d"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:664698ae0013f986118064b6676d7dcd28fefd0d7d5a5ae9497cbc10cba48fa5"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:e5119a211c2e98ff18b9908ba62a3bd0e3fabb02a29277a7232a6fb4b2560aa0"},
+ {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:417de6b2e214e837827067048f61841f5d7fc27926f2e43954567094051aff18"},
+ {file = "setproctitle-1.3.3-cp311-cp311-win32.whl", hash = "sha256:6a143b31d758296dc2f440175f6c8e0b5301ced3b0f477b84ca43cdcf7f2f476"},
+ {file = "setproctitle-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:a680d62c399fa4b44899094027ec9a1bdaf6f31c650e44183b50d4c4d0ccc085"},
+ {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:d4460795a8a7a391e3567b902ec5bdf6c60a47d791c3b1d27080fc203d11c9dc"},
+ {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bdfd7254745bb737ca1384dee57e6523651892f0ea2a7344490e9caefcc35e64"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:477d3da48e216d7fc04bddab67b0dcde633e19f484a146fd2a34bb0e9dbb4a1e"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ab2900d111e93aff5df9fddc64cf51ca4ef2c9f98702ce26524f1acc5a786ae7"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:088b9efc62d5aa5d6edf6cba1cf0c81f4488b5ce1c0342a8b67ae39d64001120"},
+ {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6d50252377db62d6a0bb82cc898089916457f2db2041e1d03ce7fadd4a07381"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:87e668f9561fd3a457ba189edfc9e37709261287b52293c115ae3487a24b92f6"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:287490eb90e7a0ddd22e74c89a92cc922389daa95babc833c08cf80c84c4df0a"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:4fe1c49486109f72d502f8be569972e27f385fe632bd8895f4730df3c87d5ac8"},
+ {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4a6ba2494a6449b1f477bd3e67935c2b7b0274f2f6dcd0f7c6aceae10c6c6ba3"},
+ {file = "setproctitle-1.3.3-cp312-cp312-win32.whl", hash = "sha256:2df2b67e4b1d7498632e18c56722851ba4db5d6a0c91aaf0fd395111e51cdcf4"},
+ {file = "setproctitle-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:f38d48abc121263f3b62943f84cbaede05749047e428409c2c199664feb6abc7"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:816330675e3504ae4d9a2185c46b573105d2310c20b19ea2b4596a9460a4f674"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68f960bc22d8d8e4ac886d1e2e21ccbd283adcf3c43136161c1ba0fa509088e0"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:00e6e7adff74796ef12753ff399491b8827f84f6c77659d71bd0b35870a17d8f"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:53bc0d2358507596c22b02db079618451f3bd720755d88e3cccd840bafb4c41c"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad6d20f9541f5f6ac63df553b6d7a04f313947f550eab6a61aa758b45f0d5657"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c1c84beab776b0becaa368254801e57692ed749d935469ac10e2b9b825dbdd8e"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:507e8dc2891021350eaea40a44ddd887c9f006e6b599af8d64a505c0f718f170"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b1067647ac7aba0b44b591936118a22847bda3c507b0a42d74272256a7a798e9"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:2e71f6365744bf53714e8bd2522b3c9c1d83f52ffa6324bd7cbb4da707312cd8"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-win32.whl", hash = "sha256:7f1d36a1e15a46e8ede4e953abb104fdbc0845a266ec0e99cc0492a4364f8c44"},
+ {file = "setproctitle-1.3.3-cp37-cp37m-win_amd64.whl", hash = "sha256:c9a402881ec269d0cc9c354b149fc29f9ec1a1939a777f1c858cdb09c7a261df"},
+ {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ff814dea1e5c492a4980e3e7d094286077054e7ea116cbeda138819db194b2cd"},
+ {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:accb66d7b3ccb00d5cd11d8c6e07055a4568a24c95cf86109894dcc0c134cc89"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:554eae5a5b28f02705b83a230e9d163d645c9a08914c0ad921df363a07cf39b1"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a911b26264dbe9e8066c7531c0591cfab27b464459c74385b276fe487ca91c12"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2982efe7640c4835f7355fdb4da313ad37fb3b40f5c69069912f8048f77b28c8"},
+ {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df3f4274b80709d8bcab2f9a862973d453b308b97a0b423a501bcd93582852e3"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:af2c67ae4c795d1674a8d3ac1988676fa306bcfa1e23fddb5e0bd5f5635309ca"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:af4061f67fd7ec01624c5e3c21f6b7af2ef0e6bab7fbb43f209e6506c9ce0092"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:37a62cbe16d4c6294e84670b59cf7adcc73faafe6af07f8cb9adaf1f0e775b19"},
+ {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:a83ca086fbb017f0d87f240a8f9bbcf0809f3b754ee01cec928fff926542c450"},
+ {file = "setproctitle-1.3.3-cp38-cp38-win32.whl", hash = "sha256:059f4ce86f8cc92e5860abfc43a1dceb21137b26a02373618d88f6b4b86ba9b2"},
+ {file = "setproctitle-1.3.3-cp38-cp38-win_amd64.whl", hash = "sha256:ab92e51cd4a218208efee4c6d37db7368fdf182f6e7ff148fb295ecddf264287"},
+ {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c7951820b77abe03d88b114b998867c0f99da03859e5ab2623d94690848d3e45"},
+ {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5bc94cf128676e8fac6503b37763adb378e2b6be1249d207630f83fc325d9b11"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f5d9027eeda64d353cf21a3ceb74bb1760bd534526c9214e19f052424b37e42"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e4a8104db15d3462e29d9946f26bed817a5b1d7a47eabca2d9dc2b995991503"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c32c41ace41f344d317399efff4cffb133e709cec2ef09c99e7a13e9f3b9483c"},
+ {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbf16381c7bf7f963b58fb4daaa65684e10966ee14d26f5cc90f07049bfd8c1e"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e18b7bd0898398cc97ce2dfc83bb192a13a087ef6b2d5a8a36460311cb09e775"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:69d565d20efe527bd8a9b92e7f299ae5e73b6c0470f3719bd66f3cd821e0d5bd"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:ddedd300cd690a3b06e7eac90ed4452348b1348635777ce23d460d913b5b63c3"},
+ {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:415bfcfd01d1fbf5cbd75004599ef167a533395955305f42220a585f64036081"},
+ {file = "setproctitle-1.3.3-cp39-cp39-win32.whl", hash = "sha256:21112fcd2195d48f25760f0eafa7a76510871bbb3b750219310cf88b04456ae3"},
+ {file = "setproctitle-1.3.3-cp39-cp39-win_amd64.whl", hash = "sha256:5a740f05d0968a5a17da3d676ce6afefebeeeb5ce137510901bf6306ba8ee002"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6b9e62ddb3db4b5205c0321dd69a406d8af9ee1693529d144e86bd43bcb4b6c0"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9e3b99b338598de0bd6b2643bf8c343cf5ff70db3627af3ca427a5e1a1a90dd9"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38ae9a02766dad331deb06855fb7a6ca15daea333b3967e214de12cfae8f0ef5"},
+ {file = "setproctitle-1.3.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:200ede6fd11233085ba9b764eb055a2a191fb4ffb950c68675ac53c874c22e20"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0d3a953c50776751e80fe755a380a64cb14d61e8762bd43041ab3f8cc436092f"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5e08e232b78ba3ac6bc0d23ce9e2bee8fad2be391b7e2da834fc9a45129eb87"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1da82c3e11284da4fcbf54957dafbf0655d2389cd3d54e4eaba636faf6d117a"},
+ {file = "setproctitle-1.3.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:aeaa71fb9568ebe9b911ddb490c644fbd2006e8c940f21cb9a1e9425bd709574"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:59335d000c6250c35989394661eb6287187854e94ac79ea22315469ee4f4c244"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c3ba57029c9c50ecaf0c92bb127224cc2ea9fda057b5d99d3f348c9ec2855ad3"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d876d355c53d975c2ef9c4f2487c8f83dad6aeaaee1b6571453cb0ee992f55f6"},
+ {file = "setproctitle-1.3.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:224602f0939e6fb9d5dd881be1229d485f3257b540f8a900d4271a2c2aa4e5f4"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d7f27e0268af2d7503386e0e6be87fb9b6657afd96f5726b733837121146750d"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f5e7266498cd31a4572378c61920af9f6b4676a73c299fce8ba93afd694f8ae7"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33c5609ad51cd99d388e55651b19148ea99727516132fb44680e1f28dd0d1de9"},
+ {file = "setproctitle-1.3.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:eae8988e78192fd1a3245a6f4f382390b61bce6cfcc93f3809726e4c885fa68d"},
+ {file = "setproctitle-1.3.3.tar.gz", hash = "sha256:c913e151e7ea01567837ff037a23ca8740192880198b7fbb90b16d181607caae"},
+]
+
+[package.extras]
+test = ["pytest"]
+
+[[package]]
+name = "setuptools"
+version = "68.2.2"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "setuptools-68.2.2-py3-none-any.whl", hash = "sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"},
+ {file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
+ {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
+]
+
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+ {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+ {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
+ {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.0"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
+ {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
+]
+
+[[package]]
+name = "sounddevice"
+version = "0.4.6"
+description = "Play and Record Sound with Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "sounddevice-0.4.6-py3-none-any.whl", hash = "sha256:5de768ba6fe56ad2b5aaa2eea794b76b73e427961c95acad2ee2ed7f866a4b20"},
+ {file = "sounddevice-0.4.6-py3-none-macosx_10_6_x86_64.macosx_10_6_universal2.whl", hash = "sha256:8b0b806c205dd3e3cd5a97262b2482624fd21db7d47083b887090148a08051c8"},
+ {file = "sounddevice-0.4.6-py3-none-win32.whl", hash = "sha256:e3ba6e674ffa8f79a591d744a1d4ab922fe5bdfd4faf8b25069a08e051010b7b"},
+ {file = "sounddevice-0.4.6-py3-none-win_amd64.whl", hash = "sha256:7830d4f8f8570f2e5552942f81d96999c5fcd9a0b682d6fc5d5c5529df23be2c"},
+ {file = "sounddevice-0.4.6.tar.gz", hash = "sha256:3236b78f15f0415bdf006a620cef073d0c0522851d66f4a961ed6d8eb1482fe9"},
+]
+
+[package.dependencies]
+CFFI = ">=1.0"
+
+[package.extras]
+numpy = ["NumPy"]
+
+[[package]]
+name = "soundfile"
+version = "0.12.1"
+description = "An audio library based on libsndfile, CFFI and NumPy"
+optional = false
+python-versions = "*"
+files = [
+ {file = "soundfile-0.12.1-py2.py3-none-any.whl", hash = "sha256:828a79c2e75abab5359f780c81dccd4953c45a2c4cd4f05ba3e233ddf984b882"},
+ {file = "soundfile-0.12.1-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:d922be1563ce17a69582a352a86f28ed8c9f6a8bc951df63476ffc310c064bfa"},
+ {file = "soundfile-0.12.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:bceaab5c4febb11ea0554566784bcf4bc2e3977b53946dda2b12804b4fe524a8"},
+ {file = "soundfile-0.12.1-py2.py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:2dc3685bed7187c072a46ab4ffddd38cef7de9ae5eb05c03df2ad569cf4dacbc"},
+ {file = "soundfile-0.12.1-py2.py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:074247b771a181859d2bc1f98b5ebf6d5153d2c397b86ee9e29ba602a8dfe2a6"},
+ {file = "soundfile-0.12.1-py2.py3-none-win32.whl", hash = "sha256:59dfd88c79b48f441bbf6994142a19ab1de3b9bb7c12863402c2bc621e49091a"},
+ {file = "soundfile-0.12.1-py2.py3-none-win_amd64.whl", hash = "sha256:0d86924c00b62552b650ddd28af426e3ff2d4dc2e9047dae5b3d8452e0a49a77"},
+ {file = "soundfile-0.12.1.tar.gz", hash = "sha256:e8e1017b2cf1dda767aef19d2fd9ee5ebe07e050d430f77a0a7c66ba08b8cdae"},
+]
+
+[package.dependencies]
+cffi = ">=1.0"
+
+[package.extras]
+numpy = ["numpy"]
+
+[[package]]
+name = "soxr"
+version = "0.3.7"
+description = "High quality, one-dimensional sample-rate conversion library"
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "soxr-0.3.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac81c4af6a993d5b7c0b466bbac4835bad2b14ec32f342b2c1f83e4cf825e301"},
+ {file = "soxr-0.3.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8d8a2b3e7f8d0255e2484fb82cb66c86da6fb25b342ef793cceca9ce9a61aa16"},
+ {file = "soxr-0.3.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd6eb6f6bbda2e8de36672cf2f0529ced6e638773150744ef075be0cc4f52c"},
+ {file = "soxr-0.3.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e47d86af35b942c92606fc2d5dfccf3f01309329475571ae2312bbf9edc3a790"},
+ {file = "soxr-0.3.7-cp310-cp310-win_amd64.whl", hash = "sha256:0e291adfaf9f2a7c4dd180a1b8c280f9beb1c84cb381853e4f4b3434d002ed7f"},
+ {file = "soxr-0.3.7-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9e811450f0e91972932bd37ac58e32e44002c2c99db2aa926a9e7ba164545034"},
+ {file = "soxr-0.3.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9cea63014ce91035074e1228c9340e2b8609faf964e268705fcac5135d05060c"},
+ {file = "soxr-0.3.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bfab27830f6217a15b83445988225c3aeea3bbccfa9399ced291e53e1b05925d"},
+ {file = "soxr-0.3.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:286858e3078d76c11b6d490b66fed3c9bb2a4229759f6be03ceef5c02189bf2c"},
+ {file = "soxr-0.3.7-cp311-cp311-win_amd64.whl", hash = "sha256:54985ff33292192d2937be80df3e5f3a44d6d53e6835f727d6b99b7cdd3f1611"},
+ {file = "soxr-0.3.7-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:83c74ef6d61d7dcd81be26f91bee0a420f792f5c1982266f2a80e655f0650a98"},
+ {file = "soxr-0.3.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cb1e14663a43fe88b8fbc287822a159028366a820abe1a0a9670fb53618cb47b"},
+ {file = "soxr-0.3.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48acdfbcf870ab54f645b1cfd641bce92c1e3a67346c3bf0f6c0ad2873c1dd35"},
+ {file = "soxr-0.3.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea663b76f2b0ec1576b8a43aef317aec080abc0a67a4015fcd9f3407039f260a"},
+ {file = "soxr-0.3.7-cp312-cp312-win_amd64.whl", hash = "sha256:42da0d9eb79c70e5a41917f1b48a032e241a48eb4a1bcea7c80577302ff26974"},
+ {file = "soxr-0.3.7-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:511c6b2279c8ddd83459d129d69f628f7aae4616ae0a1912963985bd89e35df7"},
+ {file = "soxr-0.3.7-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a37c518c0b5d70162956d808d6c2e249bae0672e414e0dcfc101e200d8c31f3c"},
+ {file = "soxr-0.3.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27f2890528d2b2e358938ab660a6b8346802863f5b6b646204d7ff8ab0ca2c66"},
+ {file = "soxr-0.3.7-cp37-cp37m-win_amd64.whl", hash = "sha256:52467c8c012495544a6dcfcce6b5bcbbc653d24fe9bb33c0b6191acecdb5e297"},
+ {file = "soxr-0.3.7-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ce12b93747958f2769d6b297e6e27c73d9ad635fe8104ef052bece9c8a322824"},
+ {file = "soxr-0.3.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1cd65dc7b96ea3cb6c8c48e6020e859680556cc42dd3d4de44779530cce21037"},
+ {file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d994f1a7690b1b13ab639ea33e0c1d78415b64d88d6df4af705a9443f97b9687"},
+ {file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e87b58bc9e8c2caa16f07726f666bd043f0a49ca937baa803ce7708003b27833"},
+ {file = "soxr-0.3.7-cp38-cp38-win_amd64.whl", hash = "sha256:07f4c0c6125ea1482fa187ad5f007216712ee0a93586a9b2f80e79c0bf944cf7"},
+ {file = "soxr-0.3.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e5267c3ba34d4b873d9bbe3a9e58418b01ae4fd04349a4f944d9943b9ddac0f7"},
+ {file = "soxr-0.3.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6e39668c250e221db888cf3b290a16fbe10a702d9a4eb604a127f720040de583"},
+ {file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8ceeb74e5a55d903cc286d3bd12c2d8f8c85d02894071e9ec92ab405430907c"},
+ {file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0eed6bf58192dd1bb93becd2444de4d712689713d727b32fd55623ae9aae7df7"},
+ {file = "soxr-0.3.7-cp39-cp39-win_amd64.whl", hash = "sha256:7221302b4547d02a3f38dd3cd15317ab2b78873c75921db5f4a070848f0c71be"},
+ {file = "soxr-0.3.7.tar.gz", hash = "sha256:436ddff00c6eb2c75b79c19cfdca7527b1e31b5fad738652f044045ba6258593"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
+test = ["pytest"]
+
+[[package]]
+name = "starlette"
+version = "0.35.1"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "starlette-0.35.1-py3-none-any.whl", hash = "sha256:50bbbda9baa098e361f398fda0928062abbaf1f54f4fadcbe17c092a01eb9a25"},
+ {file = "starlette-0.35.1.tar.gz", hash = "sha256:3e2639dac3520e4f58734ed22553f950d3f3cb1001cd2eaac4d57e8cdc5f66bc"},
+]
+
+[package.dependencies]
+anyio = ">=3.4.0,<5"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
+
+[[package]]
+name = "streamlit"
+version = "1.30.0"
+description = "A faster way to build and share data apps"
+optional = false
+python-versions = ">=3.8, !=3.9.7"
+files = [
+ {file = "streamlit-1.30.0-py2.py3-none-any.whl", hash = "sha256:536494a4edfe9b66ed70c437176cfd6c7e36b1d99d0587b0be64245fa89c241b"},
+ {file = "streamlit-1.30.0.tar.gz", hash = "sha256:90333915d9df8ce3b06de31b8a5bbab51e8cf0982dc6c32da9d6b1f2b4a9fa78"},
+]
+
+[package.dependencies]
+altair = ">=4.0,<6"
+blinker = ">=1.0.0,<2"
+cachetools = ">=4.0,<6"
+click = ">=7.0,<9"
+gitpython = ">=3.0.7,<3.1.19 || >3.1.19,<4"
+importlib-metadata = ">=1.4,<8"
+numpy = ">=1.19.3,<2"
+packaging = ">=16.8,<24"
+pandas = ">=1.3.0,<3"
+pillow = ">=7.1.0,<11"
+protobuf = ">=3.20,<5"
+pyarrow = ">=6.0"
+pydeck = ">=0.8.0b4,<1"
+python-dateutil = ">=2.7.3,<3"
+requests = ">=2.27,<3"
+rich = ">=10.14.0,<14"
+tenacity = ">=8.1.0,<9"
+toml = ">=0.10.1,<2"
+tornado = ">=6.0.3,<7"
+typing-extensions = ">=4.3.0,<5"
+tzlocal = ">=1.1,<6"
+validators = ">=0.2,<1"
+watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""}
+
+[package.extras]
+snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python (>=0.9.0)"]
+
+[[package]]
+name = "sympy"
+version = "1.12"
+description = "Computer algebra system (CAS) in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
+ {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+]
+
+[package.dependencies]
+mpmath = ">=0.19"
+
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"},
+ {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
+[[package]]
+name = "tenacity"
+version = "8.2.3"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
+ {file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx", "tornado (>=4.5)"]
+
+[[package]]
+name = "tensorboardx"
+version = "2.6.2.2"
+description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
+optional = false
+python-versions = "*"
+files = [
+ {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
+ {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
+]
+
+[package.dependencies]
+numpy = "*"
+packaging = "*"
+protobuf = ">=3.20"
+
+[[package]]
+name = "threadpoolctl"
+version = "3.2.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
+ {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
+]
+
+[[package]]
+name = "tokenizers"
+version = "0.15.0"
+description = ""
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tokenizers-0.15.0-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:cd3cd0299aaa312cd2988957598f80becd04d5a07338741eca076057a2b37d6e"},
+ {file = "tokenizers-0.15.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8a922c492c721744ee175f15b91704be2d305569d25f0547c77cd6c9f210f9dc"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:331dd786d02fc38698f835fff61c99480f98b73ce75a4c65bd110c9af5e4609a"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88dd0961c437d413ab027f8b115350c121d49902cfbadf08bb8f634b15fa1814"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6fdcc55339df7761cd52e1fbe8185d3b3963bc9e3f3545faa6c84f9e8818259a"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f1480b0051d8ab5408e8e4db2dc832f7082ea24aa0722c427bde2418c6f3bd07"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9855e6c258918f9cf62792d4f6ddfa6c56dccd8c8118640f867f6393ecaf8bd7"},
+ {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de9529fe75efcd54ba8d516aa725e1851df9199f0669b665c55e90df08f5af86"},
+ {file = "tokenizers-0.15.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:8edcc90a36eab0705fe9121d6c77c6e42eeef25c7399864fd57dfb27173060bf"},
+ {file = "tokenizers-0.15.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:ae17884aafb3e94f34fb7cfedc29054f5f54e142475ebf8a265a4e388fee3f8b"},
+ {file = "tokenizers-0.15.0-cp310-none-win32.whl", hash = "sha256:9a3241acdc9b44cff6e95c4a55b9be943ef3658f8edb3686034d353734adba05"},
+ {file = "tokenizers-0.15.0-cp310-none-win_amd64.whl", hash = "sha256:4b31807cb393d6ea31926b307911c89a1209d5e27629aa79553d1599c8ffdefe"},
+ {file = "tokenizers-0.15.0-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:af7e9be8c05d30bb137b9fd20f9d99354816599e5fd3d58a4b1e28ba3b36171f"},
+ {file = "tokenizers-0.15.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c3d7343fa562ea29661783344a2d83662db0d3d17a6fa6a403cac8e512d2d9fd"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:32371008788aeeb0309a9244809a23e4c0259625e6b74a103700f6421373f395"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca9db64c7c9954fbae698884c5bb089764edc549731e5f9b7fa1dd4e4d78d77f"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:dbed5944c31195514669cf6381a0d8d47f164943000d10f93d6d02f0d45c25e0"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aab16c4a26d351d63e965b0c792f5da7227a37b69a6dc6d922ff70aa595b1b0c"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3c2b60b12fdd310bf85ce5d7d3f823456b9b65eed30f5438dd7761879c495983"},
+ {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0344d6602740e44054a9e5bbe9775a5e149c4dddaff15959bb07dcce95a5a859"},
+ {file = "tokenizers-0.15.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:4525f6997d81d9b6d9140088f4f5131f6627e4c960c2c87d0695ae7304233fc3"},
+ {file = "tokenizers-0.15.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:65975094fef8cc68919644936764efd2ce98cf1bacbe8db2687155d2b0625bee"},
+ {file = "tokenizers-0.15.0-cp311-none-win32.whl", hash = "sha256:ff5d2159c5d93015f5a4542aac6c315506df31853123aa39042672031768c301"},
+ {file = "tokenizers-0.15.0-cp311-none-win_amd64.whl", hash = "sha256:2dd681b53cf615e60a31a115a3fda3980e543d25ca183797f797a6c3600788a3"},
+ {file = "tokenizers-0.15.0-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:c9cce6ee149a3d703f86877bc2a6d997e34874b2d5a2d7839e36b2273f31d3d9"},
+ {file = "tokenizers-0.15.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a0a94bc3370e6f1cc8a07a8ae867ce13b7c1b4291432a773931a61f256d44ea"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:309cfcccfc7e502cb1f1de2c9c1c94680082a65bfd3a912d5a5b2c90c677eb60"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8413e994dd7d875ab13009127fc85633916c71213917daf64962bafd488f15dc"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d0ebf9430f901dbdc3dcb06b493ff24a3644c9f88c08e6a1d6d0ae2228b9b818"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:10361e9c7864b22dd791ec5126327f6c9292fb1d23481d4895780688d5e298ac"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:babe42635b8a604c594bdc56d205755f73414fce17ba8479d142a963a6c25cbc"},
+ {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3768829861e964c7a4556f5f23307fce6a23872c2ebf030eb9822dbbbf7e9b2a"},
+ {file = "tokenizers-0.15.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9c91588a630adc88065e1c03ac6831e3e2112558869b9ebcb2b8afd8a14c944d"},
+ {file = "tokenizers-0.15.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:77606994e793ca54ecf3a3619adc8a906a28ca223d9354b38df41cb8766a0ed6"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:6fe143939f3b596681922b2df12a591a5b010e7dcfbee2202482cd0c1c2f2459"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:b7bee0f1795e3e3561e9a557061b1539e5255b8221e3f928f58100282407e090"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5d37e7f4439b4c46192ab4f2ff38ab815e4420f153caa13dec9272ef14403d34"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:caadf255cf7f951b38d10097836d1f3bcff4aeaaffadfdf748bab780bf5bff95"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:05accb9162bf711a941b1460b743d62fec61c160daf25e53c5eea52c74d77814"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:26a2ef890740127cb115ee5260878f4a677e36a12831795fd7e85887c53b430b"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e54c5f26df14913620046b33e822cb3bcd091a332a55230c0e63cc77135e2169"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:669b8ed653a578bcff919566631156f5da3aab84c66f3c0b11a6281e8b4731c7"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:0ea480d943297df26f06f508dab6e012b07f42bf3dffdd36e70799368a5f5229"},
+ {file = "tokenizers-0.15.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:bc80a0a565ebfc7cd89de7dd581da8c2b3238addfca6280572d27d763f135f2f"},
+ {file = "tokenizers-0.15.0-cp37-none-win32.whl", hash = "sha256:cdd945e678bbdf4517d5d8de66578a5030aeefecdb46f5320b034de9cad8d4dd"},
+ {file = "tokenizers-0.15.0-cp37-none-win_amd64.whl", hash = "sha256:1ab96ab7dc706e002c32b2ea211a94c1c04b4f4de48354728c3a6e22401af322"},
+ {file = "tokenizers-0.15.0-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:f21c9eb71c9a671e2a42f18b456a3d118e50c7f0fc4dd9fa8f4eb727fea529bf"},
+ {file = "tokenizers-0.15.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2a5f4543a35889679fc3052086e69e81880b2a5a28ff2a52c5a604be94b77a3f"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f8aa81afec893e952bd39692b2d9ef60575ed8c86fce1fd876a06d2e73e82dca"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1574a5a4af22c3def93fe8fe4adcc90a39bf5797ed01686a4c46d1c3bc677d2f"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7c7982fd0ec9e9122d03b209dac48cebfea3de0479335100ef379a9a959b9a5a"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f8d16b647032df2ce2c1f9097236e046ea9fedd969b25637b9d5d734d78aa53b"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b3cdf29e6f9653da330515dc8fa414be5a93aae79e57f8acc50d4028dd843edf"},
+ {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7286f3df10de840867372e3e64b99ef58c677210e3ceb653cd0e740a5c53fe78"},
+ {file = "tokenizers-0.15.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:aabc83028baa5a36ce7a94e7659250f0309c47fa4a639e5c2c38e6d5ea0de564"},
+ {file = "tokenizers-0.15.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:72f78b0e0e276b1fc14a672fa73f3acca034ba8db4e782124a2996734a9ba9cf"},
+ {file = "tokenizers-0.15.0-cp38-none-win32.whl", hash = "sha256:9680b0ecc26e7e42f16680c1aa62e924d58d1c2dd992707081cc10a374896ea2"},
+ {file = "tokenizers-0.15.0-cp38-none-win_amd64.whl", hash = "sha256:f17cbd88dab695911cbdd385a5a7e3709cc61dff982351f5d1b5939f074a2466"},
+ {file = "tokenizers-0.15.0-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:3661862df7382c5eb23ac4fbf7c75e69b02dc4f5784e4c5a734db406b5b24596"},
+ {file = "tokenizers-0.15.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c3045d191dad49647f5a5039738ecf1c77087945c7a295f7bcf051c37067e883"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:a9fcaad9ab0801f14457d7c820d9f246b5ab590c407fc6b073819b1573097aa7"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a79f17027f24fe9485701c8dbb269b9c713954ec3bdc1e7075a66086c0c0cd3c"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:01a3aa332abc4bee7640563949fcfedca4de8f52691b3b70f2fc6ca71bfc0f4e"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:05b83896a893cdfedad8785250daa3ba9f0504848323471524d4783d7291661e"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cbbf2489fcf25d809731ba2744ff278dd07d9eb3f8b7482726bd6cae607073a4"},
+ {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab806ad521a5e9de38078b7add97589c313915f6f5fec6b2f9f289d14d607bd6"},
+ {file = "tokenizers-0.15.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:4a522612d5c88a41563e3463226af64e2fa00629f65cdcc501d1995dd25d23f5"},
+ {file = "tokenizers-0.15.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:e58a38c4e6075810bdfb861d9c005236a72a152ebc7005941cc90d1bbf16aca9"},
+ {file = "tokenizers-0.15.0-cp39-none-win32.whl", hash = "sha256:b8034f1041fd2bd2b84ff9f4dc4ae2e1c3b71606820a9cd5c562ebd291a396d1"},
+ {file = "tokenizers-0.15.0-cp39-none-win_amd64.whl", hash = "sha256:edde9aa964145d528d0e0dbf14f244b8a85ebf276fb76869bc02e2530fa37a96"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:309445d10d442b7521b98083dc9f0b5df14eca69dbbfebeb98d781ee2cef5d30"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:d3125a6499226d4d48efc54f7498886b94c418e93a205b673bc59364eecf0804"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed56ddf0d54877bb9c6d885177db79b41576e61b5ef6defeb579dcb803c04ad5"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b22cd714706cc5b18992a232b023f736e539495f5cc61d2d28d176e55046f6c"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fac2719b1e9bc8e8e7f6599b99d0a8e24f33d023eb8ef644c0366a596f0aa926"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:85ddae17570ec7e5bfaf51ffa78d044f444a8693e1316e1087ee6150596897ee"},
+ {file = "tokenizers-0.15.0-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:76f1bed992e396bf6f83e3df97b64ff47885e45e8365f8983afed8556a0bc51f"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:3bb0f4df6dce41a1c7482087b60d18c372ef4463cb99aa8195100fcd41e0fd64"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:22c27672c27a059a5f39ff4e49feed8c7f2e1525577c8a7e3978bd428eb5869d"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78104f5d035c9991f92831fc0efe9e64a05d4032194f2a69f67aaa05a4d75bbb"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a40b73dc19d82c3e3ffb40abdaacca8fbc95eeb26c66b7f9f860aebc07a73998"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:d801d1368188c74552cd779b1286e67cb9fd96f4c57a9f9a2a09b6def9e1ab37"},
+ {file = "tokenizers-0.15.0-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:82641ffb13a4da1293fcc9f437d457647e60ed0385a9216cd135953778b3f0a1"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:160f9d1810f2c18fffa94aa98bf17632f6bd2dabc67fcb01a698ca80c37d52ee"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:8d7d6eea831ed435fdeeb9bcd26476226401d7309d115a710c65da4088841948"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f6456bec6c557d63d8ec0023758c32f589e1889ed03c055702e84ce275488bed"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1eef39a502fad3bf104b9e1906b4fb0cee20e44e755e51df9a98f8922c3bf6d4"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1e4664c5b797e093c19b794bbecc19d2367e782b4a577d8b7c1821db5dc150d"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ca003fb5f3995ff5cf676db6681b8ea5d54d3b30bea36af1120e78ee1a4a4cdf"},
+ {file = "tokenizers-0.15.0-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7f17363141eb0c53752c89e10650b85ef059a52765d0802ba9613dbd2d21d425"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:8a765db05581c7d7e1280170f2888cda351760d196cc059c37ea96f121125799"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:2a0dd641a72604486cd7302dd8f87a12c8a9b45e1755e47d2682733f097c1af5"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:0a1a3c973e4dc97797fc19e9f11546c95278ffc55c4492acb742f69e035490bc"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4fab75642aae4e604e729d6f78e0addb9d7e7d49e28c8f4d16b24da278e5263"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65f80be77f6327a86d8fd35a4467adcfe6174c159b4ab52a1a8dd4c6f2d7d9e1"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:a8da7533dbe66b88afd430c56a2f2ce1fd82e2681868f857da38eeb3191d7498"},
+ {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:fa8eb4584fc6cbe6a84d7a7864be3ed28e23e9fd2146aa8ef1814d579df91958"},
+ {file = "tokenizers-0.15.0.tar.gz", hash = "sha256:10c7e6e7b4cabd757da59e93f5f8d1126291d16f8b54f28510825ef56a3e5d0e"},
+]
+
+[package.dependencies]
+huggingface_hub = ">=0.16.4,<1.0"
+
+[package.extras]
+dev = ["tokenizers[testing]"]
+docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+
+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+ {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
+ {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
+ {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+]
+
+[[package]]
+name = "tomlkit"
+version = "0.12.0"
+description = "Style preserving TOML library"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tomlkit-0.12.0-py3-none-any.whl", hash = "sha256:926f1f37a1587c7a4f6c7484dae538f1345d96d793d9adab5d3675957b1d0766"},
+ {file = "tomlkit-0.12.0.tar.gz", hash = "sha256:01f0477981119c7d8ee0f67ebe0297a7c95b14cf9f4b102b45486deb77018716"},
+]
+
+[[package]]
+name = "toolz"
+version = "0.12.1"
+description = "List processing tools and functional utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "toolz-0.12.1-py3-none-any.whl", hash = "sha256:d22731364c07d72eea0a0ad45bafb2c2937ab6fd38a3507bf55eae8744aa7d85"},
+ {file = "toolz-0.12.1.tar.gz", hash = "sha256:ecca342664893f177a13dac0e6b41cbd8ac25a358e5f215316d43e2100224f4d"},
+]
+
+[[package]]
+name = "torch"
+version = "2.1.2"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "torch-2.1.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:3a871edd6c02dae77ad810335c0833391c1a4ce49af21ea8cf0f6a5d2096eea8"},
+ {file = "torch-2.1.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:bef6996c27d8f6e92ea4e13a772d89611da0e103b48790de78131e308cf73076"},
+ {file = "torch-2.1.2-cp310-cp310-win_amd64.whl", hash = "sha256:0e13034fd5fb323cbbc29e56d0637a3791e50dd589616f40c79adfa36a5a35a1"},
+ {file = "torch-2.1.2-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:d9b535cad0df3d13997dbe8bd68ac33e0e3ae5377639c9881948e40794a61403"},
+ {file = "torch-2.1.2-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:f9a55d55af02826ebfbadf4e9b682f0f27766bc33df8236b48d28d705587868f"},
+ {file = "torch-2.1.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:a6ebbe517097ef289cc7952783588c72de071d4b15ce0f8b285093f0916b1162"},
+ {file = "torch-2.1.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:8f32ce591616a30304f37a7d5ea80b69ca9e1b94bba7f308184bf616fdaea155"},
+ {file = "torch-2.1.2-cp311-cp311-win_amd64.whl", hash = "sha256:e0ee6cf90c8970e05760f898d58f9ac65821c37ffe8b04269ec787aa70962b69"},
+ {file = "torch-2.1.2-cp311-none-macosx_10_9_x86_64.whl", hash = "sha256:76d37967c31c99548ad2c4d3f2cf191db48476f2e69b35a0937137116da356a1"},
+ {file = "torch-2.1.2-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:e2d83f07b4aac983453ea5bf8f9aa9dacf2278a8d31247f5d9037f37befc60e4"},
+ {file = "torch-2.1.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:f41fe0c7ecbf903a568c73486139a75cfab287a0f6c17ed0698fdea7a1e8641d"},
+ {file = "torch-2.1.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e3225f47d50bb66f756fe9196a768055d1c26b02154eb1f770ce47a2578d3aa7"},
+ {file = "torch-2.1.2-cp38-cp38-win_amd64.whl", hash = "sha256:33d59cd03cb60106857f6c26b36457793637512998666ee3ce17311f217afe2b"},
+ {file = "torch-2.1.2-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:8e221deccd0def6c2badff6be403e0c53491805ed9915e2c029adbcdb87ab6b5"},
+ {file = "torch-2.1.2-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:05b18594f60a911a0c4f023f38a8bda77131fba5fd741bda626e97dcf5a3dd0a"},
+ {file = "torch-2.1.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9ca96253b761e9aaf8e06fb30a66ee301aecbf15bb5a303097de1969077620b6"},
+ {file = "torch-2.1.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d93ba70f67b08c2ae5598ee711cbc546a1bc8102cef938904b8c85c2089a51a0"},
+ {file = "torch-2.1.2-cp39-cp39-win_amd64.whl", hash = "sha256:255b50bc0608db177e6a3cc118961d77de7e5105f07816585fa6f191f33a9ff3"},
+ {file = "torch-2.1.2-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:6984cd5057c0c977b3c9757254e989d3f1124f4ce9d07caa6cb637783c71d42a"},
+ {file = "torch-2.1.2-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:bc195d7927feabc0eb7c110e457c955ed2ab616f3c7c28439dd4188cf589699f"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = "*"
+jinja2 = "*"
+networkx = "*"
+nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+sympy = "*"
+triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = "*"
+
+[package.extras]
+dynamo = ["jinja2"]
+opt-einsum = ["opt-einsum (>=3.3)"]
+
+[[package]]
+name = "torchaudio"
+version = "2.1.2"
+description = "An audio package for PyTorch"
+optional = false
+python-versions = "*"
+files = [
+ {file = "torchaudio-2.1.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:06f8c02814e6cdd78626bbf44ad2bb8afa5b39ab650c6af18328a32311461058"},
+ {file = "torchaudio-2.1.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9d676673c1ce4dd11fca145e3a6cd9b4e5b897cffad0f617d2906f2d3fc8c3a9"},
+ {file = "torchaudio-2.1.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:23c1b34e98664a0ac239efd4e1a0af407b3dd0a86a5869114bae582c3e5437d7"},
+ {file = "torchaudio-2.1.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:f82657fc4ec3b473bf6c752c0ee62d7f511af9ef37e5143f8339ec049504d767"},
+ {file = "torchaudio-2.1.2-cp310-cp310-win_amd64.whl", hash = "sha256:683eaa721e016ca1f27bb28fa89feae37a6f7b98ff1ceee0d5e5aedd19bd982c"},
+ {file = "torchaudio-2.1.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:c1084eedf4ced1af9fdd18910690ff615f89baeb30b32030806543fbc6f3657e"},
+ {file = "torchaudio-2.1.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:860acc32e6507063f2c13d81e26718199e215f34a2bcd6c9609a25e9bf21aa36"},
+ {file = "torchaudio-2.1.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:50c651d60bde7a4e096bf376eddb9ea32da6e37c3827536d6e918798ad203dbf"},
+ {file = "torchaudio-2.1.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:9a6bade8a2495a724f4ee6acb5e86828ff4083dc6c7c57c6386b54a0ea7afe71"},
+ {file = "torchaudio-2.1.2-cp311-cp311-win_amd64.whl", hash = "sha256:47f322708c282e0b1b7548cdbe4e12451c531061761885d7c7fe2e479a4a3861"},
+ {file = "torchaudio-2.1.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:d0efd008c35dec962b80f5dce3468bd1b88301cf65152bbfa7f74c0005a17e89"},
+ {file = "torchaudio-2.1.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:30ad97112412592518953f3cc2cd1b6ae153d6563dd5bd9eab6a972315fe9d9e"},
+ {file = "torchaudio-2.1.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:c33e05c2305bc4d659aaf77a385433e3f8ac07ae235d3b15d6ef4ff995258746"},
+ {file = "torchaudio-2.1.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:ff7156b30eb05e9124286c30c80da84b93e227d009adb96eb19489600b459332"},
+ {file = "torchaudio-2.1.2-cp38-cp38-win_amd64.whl", hash = "sha256:dbaefae9ca0b208ce0157e0358cea8ab796c9e26a2c61c3d181246e4010b04d2"},
+ {file = "torchaudio-2.1.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:ae50dcf34d5c6f73180cf694195ee31194b9d6090328575c30a5960bc716fa52"},
+ {file = "torchaudio-2.1.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:14729cc9df52defa674fcf5ed4de0d6507038ef18012b96a2f56a77ed70676dd"},
+ {file = "torchaudio-2.1.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:03b4cf02ee468b25280f9593cca95a32b517a88512a1e5f41129e24cd0c17e64"},
+ {file = "torchaudio-2.1.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b3bbb5324e705ded77616e546591b249ae7588d35a3e8c2c4c1d986a5ea51ef4"},
+ {file = "torchaudio-2.1.2-cp39-cp39-win_amd64.whl", hash = "sha256:03d2a3c9a806486f2d9646381a564a922a880b6df8f18336b6f0e4a0d8356743"},
+]
+
+[package.dependencies]
+torch = "2.1.2"
+
+[[package]]
+name = "tornado"
+version = "6.4"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+optional = false
+python-versions = ">= 3.8"
+files = [
+ {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
+ {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
+ {file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
+ {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
+ {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
+ {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
+ {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
+ {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
+ {file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
+ {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
+ {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
+]
+
+[[package]]
+name = "tqdm"
+version = "4.66.1"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
+ {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "transformers"
+version = "4.37.2"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+ {file = "transformers-4.37.2-py3-none-any.whl", hash = "sha256:595a8b12a1fcc4ad0ced49ce206c58e17be68c85d7aee3d7546d04a32c910d2e"},
+ {file = "transformers-4.37.2.tar.gz", hash = "sha256:f307082ae5d528b8480611a4879a4a11651012d0e9aaea3f6cf17219ffd95542"},
+]
+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.19.3,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.1"
+tokenizers = ">=0.14,<0.19"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.21.0)"]
+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.11,!=1.12.0)"]
+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.14,<0.19)"]
+torch = ["accelerate (>=0.21.0)", "torch (>=1.11,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.11,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "2.1.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+ {file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
+ {file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
+ {file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
+ {file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
+ {file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
+ {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
+ {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
+ {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
+[[package]]
+name = "truecase"
+version = "0.0.14"
+description = "A library to restore capitalization for text"
+optional = false
+python-versions = "*"
+files = [
+ {file = "truecase-0.0.14-py3-none-any.whl", hash = "sha256:80e93b9d45a430d4bce4d9fe19fe0c185976ecf244779cf92e0901531ce86ced"},
+ {file = "truecase-0.0.14.tar.gz", hash = "sha256:3a47b58c1724fcca7268cbeaf4056bb2c0cd041bd81f3b99a85ea263d7fc2d20"},
+]
+
+[package.dependencies]
+nltk = "*"
+
+[[package]]
+name = "typer"
+version = "0.9.0"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.6"
+files = [
+ {file = "typer-0.9.0-py3-none-any.whl", hash = "sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee"},
+ {file = "typer-0.9.0.tar.gz", hash = "sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2"},
+]
+
+[package.dependencies]
+click = ">=7.1.1,<9.0.0"
+colorama = {version = ">=0.4.3,<0.5.0", optional = true, markers = "extra == \"all\""}
+rich = {version = ">=10.11.0,<14.0.0", optional = true, markers = "extra == \"all\""}
+shellingham = {version = ">=1.3.0,<2.0.0", optional = true, markers = "extra == \"all\""}
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
+doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
+test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.9.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+ {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+]
+
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+description = "Runtime inspection utilities for typing module."
+optional = false
+python-versions = "*"
+files = [
+ {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
+ {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=0.3.0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "tzdata"
+version = "2023.4"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+ {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
+ {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+]
+
+[[package]]
+name = "tzlocal"
+version = "5.2"
+description = "tzinfo object for the local timezone"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "tzlocal-5.2-py3-none-any.whl", hash = "sha256:49816ef2fe65ea8ac19d19aa7a1ae0551c834303d5014c6d5a62e4cbda8047b8"},
+ {file = "tzlocal-5.2.tar.gz", hash = "sha256:8d399205578f1a9342816409cc1e46a93ebd5755e39ea2d85334bea911bf0e6e"},
+]
+
+[package.dependencies]
+tzdata = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
+
+[[package]]
+name = "urllib3"
+version = "2.1.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
+ {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "uvicorn"
+version = "0.27.0.post1"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "uvicorn-0.27.0.post1-py3-none-any.whl", hash = "sha256:4b85ba02b8a20429b9b205d015cbeb788a12da527f731811b643fd739ef90d5f"},
+ {file = "uvicorn-0.27.0.post1.tar.gz", hash = "sha256:54898fcd80c13ff1cd28bf77b04ec9dbd8ff60c5259b499b4b12bb0917f22907"},
+]
+
+[package.dependencies]
+click = ">=7.0"
+h11 = ">=0.8"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+
+[[package]]
+name = "validators"
+version = "0.22.0"
+description = "Python Data Validation for Humans™"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "validators-0.22.0-py3-none-any.whl", hash = "sha256:61cf7d4a62bbae559f2e54aed3b000cea9ff3e2fdbe463f51179b92c58c9585a"},
+ {file = "validators-0.22.0.tar.gz", hash = "sha256:77b2689b172eeeb600d9605ab86194641670cdb73b60afd577142a9397873370"},
+]
+
+[package.extras]
+docs-offline = ["myst-parser (>=2.0.0)", "pypandoc-binary (>=1.11)", "sphinx (>=7.1.1)"]
+docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"]
+hooks = ["pre-commit (>=3.3.3)"]
+package = ["build (>=1.0.0)", "twine (>=4.0.2)"]
+runner = ["tox (>=4.11.1)"]
+sast = ["bandit[toml] (>=1.7.5)"]
+testing = ["pytest (>=7.4.0)"]
+tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"]
+tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"]
+
+[[package]]
+name = "wandb"
+version = "0.16.1"
+description = "A CLI and library for interacting with the Weights & Biases API."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "wandb-0.16.1-py3-none-any.whl", hash = "sha256:1d7423f92520984585bae9693bb637ae08d3e0c1d75ad4b34215bc44431f114c"},
+ {file = "wandb-0.16.1.tar.gz", hash = "sha256:ffe6e8dd8cc8fcd72010c1246fb3d6d226b37c4f111f3f94308a1c0ae28a2fec"},
+]
+
+[package.dependencies]
+appdirs = ">=1.4.3"
+Click = ">=7.1,<8.0.0 || >8.0.0"
+docker-pycreds = ">=0.4.0"
+GitPython = ">=1.0.0,<3.1.29 || >3.1.29"
+protobuf = [
+ {version = ">=3.15.0,<4.21.0 || >4.21.0,<5", markers = "python_version == \"3.9\" and sys_platform == \"linux\""},
+ {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""},
+]
+psutil = ">=5.0.0"
+PyYAML = "*"
+requests = ">=2.0.0,<3"
+sentry-sdk = ">=1.0.0"
+setproctitle = "*"
+setuptools = "*"
+typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+
+[package.extras]
+async = ["httpx (>=0.23.0)"]
+aws = ["boto3"]
+azure = ["azure-identity", "azure-storage-blob"]
+core = ["wandb-core (>=0.17.0b2)"]
+gcp = ["google-cloud-storage"]
+kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
+models = ["cloudpickle"]
+perf = ["orjson"]
+sweeps = ["sweeps (>=0.2.0)"]
+
+[[package]]
+name = "watchdog"
+version = "3.0.0"
+description = "Filesystem events monitoring"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"},
+ {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397"},
+ {file = "watchdog-3.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:adfdeab2da79ea2f76f87eb42a3ab1966a5313e5a69a0213a3cc06ef692b0e96"},
+ {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b57a1e730af3156d13b7fdddfc23dea6487fceca29fc75c5a868beed29177ae"},
+ {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7ade88d0d778b1b222adebcc0927428f883db07017618a5e684fd03b83342bd9"},
+ {file = "watchdog-3.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7e447d172af52ad204d19982739aa2346245cc5ba6f579d16dac4bfec226d2e7"},
+ {file = "watchdog-3.0.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9fac43a7466eb73e64a9940ac9ed6369baa39b3bf221ae23493a9ec4d0022674"},
+ {file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:8ae9cda41fa114e28faf86cb137d751a17ffd0316d1c34ccf2235e8a84365c7f"},
+ {file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:25f70b4aa53bd743729c7475d7ec41093a580528b100e9a8c5b5efe8899592fc"},
+ {file = "watchdog-3.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4f94069eb16657d2c6faada4624c39464f65c05606af50bb7902e036e3219be3"},
+ {file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7c5f84b5194c24dd573fa6472685b2a27cc5a17fe5f7b6fd40345378ca6812e3"},
+ {file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3aa7f6a12e831ddfe78cdd4f8996af9cf334fd6346531b16cec61c3b3c0d8da0"},
+ {file = "watchdog-3.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:233b5817932685d39a7896b1090353fc8efc1ef99c9c054e46c8002561252fb8"},
+ {file = "watchdog-3.0.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:13bbbb462ee42ec3c5723e1205be8ced776f05b100e4737518c67c8325cf6100"},
+ {file = "watchdog-3.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8f3ceecd20d71067c7fd4c9e832d4e22584318983cabc013dbf3f70ea95de346"},
+ {file = "watchdog-3.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:c9d8c8ec7efb887333cf71e328e39cffbf771d8f8f95d308ea4125bf5f90ba64"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0e06ab8858a76e1219e68c7573dfeba9dd1c0219476c5a44d5333b01d7e1743a"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:d00e6be486affb5781468457b21a6cbe848c33ef43f9ea4a73b4882e5f188a44"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:c07253088265c363d1ddf4b3cdb808d59a0468ecd017770ed716991620b8f77a"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:5113334cf8cf0ac8cd45e1f8309a603291b614191c9add34d33075727a967709"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:51f90f73b4697bac9c9a78394c3acbbd331ccd3655c11be1a15ae6fe289a8c83"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:ba07e92756c97e3aca0912b5cbc4e5ad802f4557212788e72a72a47ff376950d"},
+ {file = "watchdog-3.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d429c2430c93b7903914e4db9a966c7f2b068dd2ebdd2fa9b9ce094c7d459f33"},
+ {file = "watchdog-3.0.0-py3-none-win32.whl", hash = "sha256:3ed7c71a9dccfe838c2f0b6314ed0d9b22e77d268c67e015450a29036a81f60f"},
+ {file = "watchdog-3.0.0-py3-none-win_amd64.whl", hash = "sha256:4c9956d27be0bb08fc5f30d9d0179a855436e655f046d288e2bcc11adfae893c"},
+ {file = "watchdog-3.0.0-py3-none-win_ia64.whl", hash = "sha256:5d9f3a10e02d7371cd929b5d8f11e87d4bad890212ed3901f9b4d68767bee759"},
+ {file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
+[[package]]
+name = "websocket-client"
+version = "1.7.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
+ {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "websockets"
+version = "11.0.3"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "websockets-11.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3ccc8a0c387629aec40f2fc9fdcb4b9d5431954f934da3eaf16cdc94f67dbfac"},
+ {file = "websockets-11.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d67ac60a307f760c6e65dad586f556dde58e683fab03323221a4e530ead6f74d"},
+ {file = "websockets-11.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:84d27a4832cc1a0ee07cdcf2b0629a8a72db73f4cf6de6f0904f6661227f256f"},
+ {file = "websockets-11.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffd7dcaf744f25f82190856bc26ed81721508fc5cbf2a330751e135ff1283564"},
+ {file = "websockets-11.0.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7622a89d696fc87af8e8d280d9b421db5133ef5b29d3f7a1ce9f1a7bf7fcfa11"},
+ {file = "websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bceab846bac555aff6427d060f2fcfff71042dba6f5fca7dc4f75cac815e57ca"},
+ {file = "websockets-11.0.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:54c6e5b3d3a8936a4ab6870d46bdd6ec500ad62bde9e44462c32d18f1e9a8e54"},
+ {file = "websockets-11.0.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:41f696ba95cd92dc047e46b41b26dd24518384749ed0d99bea0a941ca87404c4"},
+ {file = "websockets-11.0.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:86d2a77fd490ae3ff6fae1c6ceaecad063d3cc2320b44377efdde79880e11526"},
+ {file = "websockets-11.0.3-cp310-cp310-win32.whl", hash = "sha256:2d903ad4419f5b472de90cd2d40384573b25da71e33519a67797de17ef849b69"},
+ {file = "websockets-11.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:1d2256283fa4b7f4c7d7d3e84dc2ece74d341bce57d5b9bf385df109c2a1a82f"},
+ {file = "websockets-11.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e848f46a58b9fcf3d06061d17be388caf70ea5b8cc3466251963c8345e13f7eb"},
+ {file = "websockets-11.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:aa5003845cdd21ac0dc6c9bf661c5beddd01116f6eb9eb3c8e272353d45b3288"},
+ {file = "websockets-11.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b58cbf0697721120866820b89f93659abc31c1e876bf20d0b3d03cef14faf84d"},
+ {file = "websockets-11.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:660e2d9068d2bedc0912af508f30bbeb505bbbf9774d98def45f68278cea20d3"},
+ {file = "websockets-11.0.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c1f0524f203e3bd35149f12157438f406eff2e4fb30f71221c8a5eceb3617b6b"},
+ {file = "websockets-11.0.3-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:def07915168ac8f7853812cc593c71185a16216e9e4fa886358a17ed0fd9fcf6"},
+ {file = "websockets-11.0.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:b30c6590146e53149f04e85a6e4fcae068df4289e31e4aee1fdf56a0dead8f97"},
+ {file = "websockets-11.0.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:619d9f06372b3a42bc29d0cd0354c9bb9fb39c2cbc1a9c5025b4538738dbffaf"},
+ {file = "websockets-11.0.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:01f5567d9cf6f502d655151645d4e8b72b453413d3819d2b6f1185abc23e82dd"},
+ {file = "websockets-11.0.3-cp311-cp311-win32.whl", hash = "sha256:e1459677e5d12be8bbc7584c35b992eea142911a6236a3278b9b5ce3326f282c"},
+ {file = "websockets-11.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:e7837cb169eca3b3ae94cc5787c4fed99eef74c0ab9506756eea335e0d6f3ed8"},
+ {file = "websockets-11.0.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9f59a3c656fef341a99e3d63189852be7084c0e54b75734cde571182c087b152"},
+ {file = "websockets-11.0.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2529338a6ff0eb0b50c7be33dc3d0e456381157a31eefc561771ee431134a97f"},
+ {file = "websockets-11.0.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34fd59a4ac42dff6d4681d8843217137f6bc85ed29722f2f7222bd619d15e95b"},
+ {file = "websockets-11.0.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:332d126167ddddec94597c2365537baf9ff62dfcc9db4266f263d455f2f031cb"},
+ {file = "websockets-11.0.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:6505c1b31274723ccaf5f515c1824a4ad2f0d191cec942666b3d0f3aa4cb4007"},
+ {file = "websockets-11.0.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:f467ba0050b7de85016b43f5a22b46383ef004c4f672148a8abf32bc999a87f0"},
+ {file = "websockets-11.0.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:9d9acd80072abcc98bd2c86c3c9cd4ac2347b5a5a0cae7ed5c0ee5675f86d9af"},
+ {file = "websockets-11.0.3-cp37-cp37m-win32.whl", hash = "sha256:e590228200fcfc7e9109509e4d9125eace2042fd52b595dd22bbc34bb282307f"},
+ {file = "websockets-11.0.3-cp37-cp37m-win_amd64.whl", hash = "sha256:b16fff62b45eccb9c7abb18e60e7e446998093cdcb50fed33134b9b6878836de"},
+ {file = "websockets-11.0.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:fb06eea71a00a7af0ae6aefbb932fb8a7df3cb390cc217d51a9ad7343de1b8d0"},
+ {file = "websockets-11.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:8a34e13a62a59c871064dfd8ffb150867e54291e46d4a7cf11d02c94a5275bae"},
+ {file = "websockets-11.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4841ed00f1026dfbced6fca7d963c4e7043aa832648671b5138008dc5a8f6d99"},
+ {file = "websockets-11.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a073fc9ab1c8aff37c99f11f1641e16da517770e31a37265d2755282a5d28aa"},
+ {file = "websockets-11.0.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:68b977f21ce443d6d378dbd5ca38621755f2063d6fdb3335bda981d552cfff86"},
+ {file = "websockets-11.0.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1a99a7a71631f0efe727c10edfba09ea6bee4166a6f9c19aafb6c0b5917d09c"},
+ {file = "websockets-11.0.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:bee9fcb41db2a23bed96c6b6ead6489702c12334ea20a297aa095ce6d31370d0"},
+ {file = "websockets-11.0.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:4b253869ea05a5a073ebfdcb5cb3b0266a57c3764cf6fe114e4cd90f4bfa5f5e"},
+ {file = "websockets-11.0.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:1553cb82942b2a74dd9b15a018dce645d4e68674de2ca31ff13ebc2d9f283788"},
+ {file = "websockets-11.0.3-cp38-cp38-win32.whl", hash = "sha256:f61bdb1df43dc9c131791fbc2355535f9024b9a04398d3bd0684fc16ab07df74"},
+ {file = "websockets-11.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:03aae4edc0b1c68498f41a6772d80ac7c1e33c06c6ffa2ac1c27a07653e79d6f"},
+ {file = "websockets-11.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:777354ee16f02f643a4c7f2b3eff8027a33c9861edc691a2003531f5da4f6bc8"},
+ {file = "websockets-11.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8c82f11964f010053e13daafdc7154ce7385ecc538989a354ccc7067fd7028fd"},
+ {file = "websockets-11.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3580dd9c1ad0701169e4d6fc41e878ffe05e6bdcaf3c412f9d559389d0c9e016"},
+ {file = "websockets-11.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6f1a3f10f836fab6ca6efa97bb952300b20ae56b409414ca85bff2ad241d2a61"},
+ {file = "websockets-11.0.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:df41b9bc27c2c25b486bae7cf42fccdc52ff181c8c387bfd026624a491c2671b"},
+ {file = "websockets-11.0.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:279e5de4671e79a9ac877427f4ac4ce93751b8823f276b681d04b2156713b9dd"},
+ {file = "websockets-11.0.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:1fdf26fa8a6a592f8f9235285b8affa72748dc12e964a5518c6c5e8f916716f7"},
+ {file = "websockets-11.0.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:69269f3a0b472e91125b503d3c0b3566bda26da0a3261c49f0027eb6075086d1"},
+ {file = "websockets-11.0.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:97b52894d948d2f6ea480171a27122d77af14ced35f62e5c892ca2fae9344311"},
+ {file = "websockets-11.0.3-cp39-cp39-win32.whl", hash = "sha256:c7f3cb904cce8e1be667c7e6fef4516b98d1a6a0635a58a57528d577ac18a128"},
+ {file = "websockets-11.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:c792ea4eabc0159535608fc5658a74d1a81020eb35195dd63214dcf07556f67e"},
+ {file = "websockets-11.0.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:f2e58f2c36cc52d41f2659e4c0cbf7353e28c8c9e63e30d8c6d3494dc9fdedcf"},
+ {file = "websockets-11.0.3-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:de36fe9c02995c7e6ae6efe2e205816f5f00c22fd1fbf343d4d18c3d5ceac2f5"},
+ {file = "websockets-11.0.3-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0ac56b661e60edd453585f4bd68eb6a29ae25b5184fd5ba51e97652580458998"},
+ {file = "websockets-11.0.3-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e052b8467dd07d4943936009f46ae5ce7b908ddcac3fda581656b1b19c083d9b"},
+ {file = "websockets-11.0.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:42cc5452a54a8e46a032521d7365da775823e21bfba2895fb7b77633cce031bb"},
+ {file = "websockets-11.0.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e6316827e3e79b7b8e7d8e3b08f4e331af91a48e794d5d8b099928b6f0b85f20"},
+ {file = "websockets-11.0.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8531fdcad636d82c517b26a448dcfe62f720e1922b33c81ce695d0edb91eb931"},
+ {file = "websockets-11.0.3-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c114e8da9b475739dde229fd3bc6b05a6537a88a578358bc8eb29b4030fac9c9"},
+ {file = "websockets-11.0.3-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e063b1865974611313a3849d43f2c3f5368093691349cf3c7c8f8f75ad7cb280"},
+ {file = "websockets-11.0.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:92b2065d642bf8c0a82d59e59053dd2fdde64d4ed44efe4870fa816c1232647b"},
+ {file = "websockets-11.0.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0ee68fe502f9031f19d495dae2c268830df2760c0524cbac5d759921ba8c8e82"},
+ {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dcacf2c7a6c3a84e720d1bb2b543c675bf6c40e460300b628bab1b1efc7c034c"},
+ {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b67c6f5e5a401fc56394f191f00f9b3811fe843ee93f4a70df3c389d1adf857d"},
+ {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d5023a4b6a5b183dc838808087033ec5df77580485fc533e7dab2567851b0a4"},
+ {file = "websockets-11.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ed058398f55163a79bb9f06a90ef9ccc063b204bb346c4de78efc5d15abfe602"},
+ {file = "websockets-11.0.3-py3-none-any.whl", hash = "sha256:6681ba9e7f8f3b19440921e99efbb40fc89f26cd71bf539e45d8c8a25c976dc6"},
+ {file = "websockets-11.0.3.tar.gz", hash = "sha256:88fc51d9a26b10fc331be344f1781224a375b78488fc343620184e95a4b27016"},
+]
+
+[[package]]
+name = "xxhash"
+version = "3.4.1"
+description = "Python binding for xxHash"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "xxhash-3.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:91dbfa55346ad3e18e738742236554531a621042e419b70ad8f3c1d9c7a16e7f"},
+ {file = "xxhash-3.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:665a65c2a48a72068fcc4d21721510df5f51f1142541c890491afc80451636d2"},
+ {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb11628470a6004dc71a09fe90c2f459ff03d611376c1debeec2d648f44cb693"},
+ {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5bef2a7dc7b4f4beb45a1edbba9b9194c60a43a89598a87f1a0226d183764189"},
+ {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c0f7b2d547d72c7eda7aa817acf8791f0146b12b9eba1d4432c531fb0352228"},
+ {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00f2fdef6b41c9db3d2fc0e7f94cb3db86693e5c45d6de09625caad9a469635b"},
+ {file = "xxhash-3.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:23cfd9ca09acaf07a43e5a695143d9a21bf00f5b49b15c07d5388cadf1f9ce11"},
+ {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:6a9ff50a3cf88355ca4731682c168049af1ca222d1d2925ef7119c1a78e95b3b"},
+ {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:f1d7c69a1e9ca5faa75546fdd267f214f63f52f12692f9b3a2f6467c9e67d5e7"},
+ {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:672b273040d5d5a6864a36287f3514efcd1d4b1b6a7480f294c4b1d1ee1b8de0"},
+ {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:4178f78d70e88f1c4a89ff1ffe9f43147185930bb962ee3979dba15f2b1cc799"},
+ {file = "xxhash-3.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:9804b9eb254d4b8cc83ab5a2002128f7d631dd427aa873c8727dba7f1f0d1c2b"},
+ {file = "xxhash-3.4.1-cp310-cp310-win32.whl", hash = "sha256:c09c49473212d9c87261d22c74370457cfff5db2ddfc7fd1e35c80c31a8c14ce"},
+ {file = "xxhash-3.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:ebbb1616435b4a194ce3466d7247df23499475c7ed4eb2681a1fa42ff766aff6"},
+ {file = "xxhash-3.4.1-cp310-cp310-win_arm64.whl", hash = "sha256:25dc66be3db54f8a2d136f695b00cfe88018e59ccff0f3b8f545869f376a8a46"},
+ {file = "xxhash-3.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:58c49083801885273e262c0f5bbeac23e520564b8357fbb18fb94ff09d3d3ea5"},
+ {file = "xxhash-3.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b526015a973bfbe81e804a586b703f163861da36d186627e27524f5427b0d520"},
+ {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36ad4457644c91a966f6fe137d7467636bdc51a6ce10a1d04f365c70d6a16d7e"},
+ {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:248d3e83d119770f96003271fe41e049dd4ae52da2feb8f832b7a20e791d2920"},
+ {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2070b6d5bbef5ee031666cf21d4953c16e92c2f8a24a94b5c240f8995ba3b1d0"},
+ {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2746035f518f0410915e247877f7df43ef3372bf36cfa52cc4bc33e85242641"},
+ {file = "xxhash-3.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2a8ba6181514681c2591840d5632fcf7356ab287d4aff1c8dea20f3c78097088"},
+ {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0aac5010869240e95f740de43cd6a05eae180c59edd182ad93bf12ee289484fa"},
+ {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4cb11d8debab1626181633d184b2372aaa09825bde709bf927704ed72765bed1"},
+ {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b29728cff2c12f3d9f1d940528ee83918d803c0567866e062683f300d1d2eff3"},
+ {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:a15cbf3a9c40672523bdb6ea97ff74b443406ba0ab9bca10ceccd9546414bd84"},
+ {file = "xxhash-3.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:6e66df260fed01ed8ea790c2913271641c58481e807790d9fca8bfd5a3c13844"},
+ {file = "xxhash-3.4.1-cp311-cp311-win32.whl", hash = "sha256:e867f68a8f381ea12858e6d67378c05359d3a53a888913b5f7d35fbf68939d5f"},
+ {file = "xxhash-3.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:200a5a3ad9c7c0c02ed1484a1d838b63edcf92ff538770ea07456a3732c577f4"},
+ {file = "xxhash-3.4.1-cp311-cp311-win_arm64.whl", hash = "sha256:1d03f1c0d16d24ea032e99f61c552cb2b77d502e545187338bea461fde253583"},
+ {file = "xxhash-3.4.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c4bbba9b182697a52bc0c9f8ec0ba1acb914b4937cd4a877ad78a3b3eeabefb3"},
+ {file = "xxhash-3.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9fd28a9da300e64e434cfc96567a8387d9a96e824a9be1452a1e7248b7763b78"},
+ {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6066d88c9329ab230e18998daec53d819daeee99d003955c8db6fc4971b45ca3"},
+ {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:93805bc3233ad89abf51772f2ed3355097a5dc74e6080de19706fc447da99cd3"},
+ {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:64da57d5ed586ebb2ecdde1e997fa37c27fe32fe61a656b77fabbc58e6fbff6e"},
+ {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a97322e9a7440bf3c9805cbaac090358b43f650516486746f7fa482672593df"},
+ {file = "xxhash-3.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbe750d512982ee7d831838a5dee9e9848f3fb440e4734cca3f298228cc957a6"},
+ {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:fd79d4087727daf4d5b8afe594b37d611ab95dc8e29fe1a7517320794837eb7d"},
+ {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:743612da4071ff9aa4d055f3f111ae5247342931dedb955268954ef7201a71ff"},
+ {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:b41edaf05734092f24f48c0958b3c6cbaaa5b7e024880692078c6b1f8247e2fc"},
+ {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:a90356ead70d715fe64c30cd0969072de1860e56b78adf7c69d954b43e29d9fa"},
+ {file = "xxhash-3.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ac56eebb364e44c85e1d9e9cc5f6031d78a34f0092fea7fc80478139369a8b4a"},
+ {file = "xxhash-3.4.1-cp312-cp312-win32.whl", hash = "sha256:911035345932a153c427107397c1518f8ce456f93c618dd1c5b54ebb22e73747"},
+ {file = "xxhash-3.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:f31ce76489f8601cc7b8713201ce94b4bd7b7ce90ba3353dccce7e9e1fee71fa"},
+ {file = "xxhash-3.4.1-cp312-cp312-win_arm64.whl", hash = "sha256:b5beb1c6a72fdc7584102f42c4d9df232ee018ddf806e8c90906547dfb43b2da"},
+ {file = "xxhash-3.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6d42b24d1496deb05dee5a24ed510b16de1d6c866c626c2beb11aebf3be278b9"},
+ {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b685fab18876b14a8f94813fa2ca80cfb5ab6a85d31d5539b7cd749ce9e3624"},
+ {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:419ffe34c17ae2df019a4685e8d3934d46b2e0bbe46221ab40b7e04ed9f11137"},
+ {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0e041ce5714f95251a88670c114b748bca3bf80cc72400e9f23e6d0d59cf2681"},
+ {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc860d887c5cb2f524899fb8338e1bb3d5789f75fac179101920d9afddef284b"},
+ {file = "xxhash-3.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:312eba88ffe0a05e332e3a6f9788b73883752be63f8588a6dc1261a3eaaaf2b2"},
+ {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:e01226b6b6a1ffe4e6bd6d08cfcb3ca708b16f02eb06dd44f3c6e53285f03e4f"},
+ {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:9f3025a0d5d8cf406a9313cd0d5789c77433ba2004b1c75439b67678e5136537"},
+ {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:6d3472fd4afef2a567d5f14411d94060099901cd8ce9788b22b8c6f13c606a93"},
+ {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:43984c0a92f06cac434ad181f329a1445017c33807b7ae4f033878d860a4b0f2"},
+ {file = "xxhash-3.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:a55e0506fdb09640a82ec4f44171273eeabf6f371a4ec605633adb2837b5d9d5"},
+ {file = "xxhash-3.4.1-cp37-cp37m-win32.whl", hash = "sha256:faec30437919555b039a8bdbaba49c013043e8f76c999670aef146d33e05b3a0"},
+ {file = "xxhash-3.4.1-cp37-cp37m-win_amd64.whl", hash = "sha256:c9e1b646af61f1fc7083bb7b40536be944f1ac67ef5e360bca2d73430186971a"},
+ {file = "xxhash-3.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:961d948b7b1c1b6c08484bbce3d489cdf153e4122c3dfb07c2039621243d8795"},
+ {file = "xxhash-3.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:719a378930504ab159f7b8e20fa2aa1896cde050011af838af7e7e3518dd82de"},
+ {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74fb5cb9406ccd7c4dd917f16630d2e5e8cbbb02fc2fca4e559b2a47a64f4940"},
+ {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5dab508ac39e0ab988039bc7f962c6ad021acd81fd29145962b068df4148c476"},
+ {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8c59f3e46e7daf4c589e8e853d700ef6607afa037bfad32c390175da28127e8c"},
+ {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8cc07256eff0795e0f642df74ad096f8c5d23fe66bc138b83970b50fc7f7f6c5"},
+ {file = "xxhash-3.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e9f749999ed80f3955a4af0eb18bb43993f04939350b07b8dd2f44edc98ffee9"},
+ {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:7688d7c02149a90a3d46d55b341ab7ad1b4a3f767be2357e211b4e893efbaaf6"},
+ {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a8b4977963926f60b0d4f830941c864bed16aa151206c01ad5c531636da5708e"},
+ {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:8106d88da330f6535a58a8195aa463ef5281a9aa23b04af1848ff715c4398fb4"},
+ {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:4c76a77dbd169450b61c06fd2d5d436189fc8ab7c1571d39265d4822da16df22"},
+ {file = "xxhash-3.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:11f11357c86d83e53719c592021fd524efa9cf024dc7cb1dfb57bbbd0d8713f2"},
+ {file = "xxhash-3.4.1-cp38-cp38-win32.whl", hash = "sha256:0c786a6cd74e8765c6809892a0d45886e7c3dc54de4985b4a5eb8b630f3b8e3b"},
+ {file = "xxhash-3.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:aabf37fb8fa27430d50507deeab2ee7b1bcce89910dd10657c38e71fee835594"},
+ {file = "xxhash-3.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6127813abc1477f3a83529b6bbcfeddc23162cece76fa69aee8f6a8a97720562"},
+ {file = "xxhash-3.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ef2e194262f5db16075caea7b3f7f49392242c688412f386d3c7b07c7733a70a"},
+ {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71be94265b6c6590f0018bbf73759d21a41c6bda20409782d8117e76cd0dfa8b"},
+ {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:10e0a619cdd1c0980e25eb04e30fe96cf8f4324758fa497080af9c21a6de573f"},
+ {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fa122124d2e3bd36581dd78c0efa5f429f5220313479fb1072858188bc2d5ff1"},
+ {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e17032f5a4fea0a074717fe33477cb5ee723a5f428de7563e75af64bfc1b1e10"},
+ {file = "xxhash-3.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca7783b20e3e4f3f52f093538895863f21d18598f9a48211ad757680c3bd006f"},
+ {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d77d09a1113899fad5f354a1eb4f0a9afcf58cefff51082c8ad643ff890e30cf"},
+ {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:21287bcdd299fdc3328cc0fbbdeaa46838a1c05391264e51ddb38a3f5b09611f"},
+ {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:dfd7a6cc483e20b4ad90224aeb589e64ec0f31e5610ab9957ff4314270b2bf31"},
+ {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:543c7fcbc02bbb4840ea9915134e14dc3dc15cbd5a30873a7a5bf66039db97ec"},
+ {file = "xxhash-3.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:fe0a98d990e433013f41827b62be9ab43e3cf18e08b1483fcc343bda0d691182"},
+ {file = "xxhash-3.4.1-cp39-cp39-win32.whl", hash = "sha256:b9097af00ebf429cc7c0e7d2fdf28384e4e2e91008130ccda8d5ae653db71e54"},
+ {file = "xxhash-3.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:d699b921af0dcde50ab18be76c0d832f803034d80470703700cb7df0fbec2832"},
+ {file = "xxhash-3.4.1-cp39-cp39-win_arm64.whl", hash = "sha256:2be491723405e15cc099ade1280133ccfbf6322d2ef568494fb7d07d280e7eee"},
+ {file = "xxhash-3.4.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:431625fad7ab5649368c4849d2b49a83dc711b1f20e1f7f04955aab86cd307bc"},
+ {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fc6dbd5fc3c9886a9e041848508b7fb65fd82f94cc793253990f81617b61fe49"},
+ {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3ff8dbd0ec97aec842476cb8ccc3e17dd288cd6ce3c8ef38bff83d6eb927817"},
+ {file = "xxhash-3.4.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef73a53fe90558a4096e3256752268a8bdc0322f4692ed928b6cd7ce06ad4fe3"},
+ {file = "xxhash-3.4.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:450401f42bbd274b519d3d8dcf3c57166913381a3d2664d6609004685039f9d3"},
+ {file = "xxhash-3.4.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a162840cf4de8a7cd8720ff3b4417fbc10001eefdd2d21541a8226bb5556e3bb"},
+ {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b736a2a2728ba45017cb67785e03125a79d246462dfa892d023b827007412c52"},
+ {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d0ae4c2e7698adef58710d6e7a32ff518b66b98854b1c68e70eee504ad061d8"},
+ {file = "xxhash-3.4.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d6322c4291c3ff174dcd104fae41500e75dad12be6f3085d119c2c8a80956c51"},
+ {file = "xxhash-3.4.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:dd59ed668801c3fae282f8f4edadf6dc7784db6d18139b584b6d9677ddde1b6b"},
+ {file = "xxhash-3.4.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:92693c487e39523a80474b0394645b393f0ae781d8db3474ccdcead0559ccf45"},
+ {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4603a0f642a1e8d7f3ba5c4c25509aca6a9c1cc16f85091004a7028607ead663"},
+ {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fa45e8cbfbadb40a920fe9ca40c34b393e0b067082d94006f7f64e70c7490a6"},
+ {file = "xxhash-3.4.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:595b252943b3552de491ff51e5bb79660f84f033977f88f6ca1605846637b7c6"},
+ {file = "xxhash-3.4.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:562d8b8f783c6af969806aaacf95b6c7b776929ae26c0cd941d54644ea7ef51e"},
+ {file = "xxhash-3.4.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:41ddeae47cf2828335d8d991f2d2b03b0bdc89289dc64349d712ff8ce59d0647"},
+ {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c44d584afdf3c4dbb3277e32321d1a7b01d6071c1992524b6543025fb8f4206f"},
+ {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd7bddb3a5b86213cc3f2c61500c16945a1b80ecd572f3078ddbbe68f9dabdfb"},
+ {file = "xxhash-3.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9ecb6c987b62437c2f99c01e97caf8d25660bf541fe79a481d05732e5236719c"},
+ {file = "xxhash-3.4.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:696b4e18b7023527d5c50ed0626ac0520edac45a50ec7cf3fc265cd08b1f4c03"},
+ {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
+]
+
+[[package]]
+name = "yarl"
+version = "1.9.4"
+description = "Yet another URL library"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e"},
+ {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a3a6ed1d525bfb91b3fc9b690c5a21bb52de28c018530ad85093cc488bee2dd2"},
+ {file = "yarl-1.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c38c9ddb6103ceae4e4498f9c08fac9b590c5c71b0370f98714768e22ac6fa66"},
+ {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d9e09c9d74f4566e905a0b8fa668c58109f7624db96a2171f21747abc7524234"},
+ {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b8477c1ee4bd47c57d49621a062121c3023609f7a13b8a46953eb6c9716ca392"},
+ {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5ff2c858f5f6a42c2a8e751100f237c5e869cbde669a724f2062d4c4ef93551"},
+ {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:357495293086c5b6d34ca9616a43d329317feab7917518bc97a08f9e55648455"},
+ {file = "yarl-1.9.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:54525ae423d7b7a8ee81ba189f131054defdb122cde31ff17477951464c1691c"},
+ {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:801e9264d19643548651b9db361ce3287176671fb0117f96b5ac0ee1c3530d53"},
+ {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e516dc8baf7b380e6c1c26792610230f37147bb754d6426462ab115a02944385"},
+ {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:7d5aaac37d19b2904bb9dfe12cdb08c8443e7ba7d2852894ad448d4b8f442863"},
+ {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:54beabb809ffcacbd9d28ac57b0db46e42a6e341a030293fb3185c409e626b8b"},
+ {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bac8d525a8dbc2a1507ec731d2867025d11ceadcb4dd421423a5d42c56818541"},
+ {file = "yarl-1.9.4-cp310-cp310-win32.whl", hash = "sha256:7855426dfbddac81896b6e533ebefc0af2f132d4a47340cee6d22cac7190022d"},
+ {file = "yarl-1.9.4-cp310-cp310-win_amd64.whl", hash = "sha256:848cd2a1df56ddbffeb375535fb62c9d1645dde33ca4d51341378b3f5954429b"},
+ {file = "yarl-1.9.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:35a2b9396879ce32754bd457d31a51ff0a9d426fd9e0e3c33394bf4b9036b099"},
+ {file = "yarl-1.9.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c7d56b293cc071e82532f70adcbd8b61909eec973ae9d2d1f9b233f3d943f2c"},
+ {file = "yarl-1.9.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d8a1c6c0be645c745a081c192e747c5de06e944a0d21245f4cf7c05e457c36e0"},
+ {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4b3c1ffe10069f655ea2d731808e76e0f452fc6c749bea04781daf18e6039525"},
+ {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:549d19c84c55d11687ddbd47eeb348a89df9cb30e1993f1b128f4685cd0ebbf8"},
+ {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a7409f968456111140c1c95301cadf071bd30a81cbd7ab829169fb9e3d72eae9"},
+ {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e23a6d84d9d1738dbc6e38167776107e63307dfc8ad108e580548d1f2c587f42"},
+ {file = "yarl-1.9.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d8b889777de69897406c9fb0b76cdf2fd0f31267861ae7501d93003d55f54fbe"},
+ {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:03caa9507d3d3c83bca08650678e25364e1843b484f19986a527630ca376ecce"},
+ {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4e9035df8d0880b2f1c7f5031f33f69e071dfe72ee9310cfc76f7b605958ceb9"},
+ {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:c0ec0ed476f77db9fb29bca17f0a8fcc7bc97ad4c6c1d8959c507decb22e8572"},
+ {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:ee04010f26d5102399bd17f8df8bc38dc7ccd7701dc77f4a68c5b8d733406958"},
+ {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:49a180c2e0743d5d6e0b4d1a9e5f633c62eca3f8a86ba5dd3c471060e352ca98"},
+ {file = "yarl-1.9.4-cp311-cp311-win32.whl", hash = "sha256:81eb57278deb6098a5b62e88ad8281b2ba09f2f1147c4767522353eaa6260b31"},
+ {file = "yarl-1.9.4-cp311-cp311-win_amd64.whl", hash = "sha256:d1d2532b340b692880261c15aee4dc94dd22ca5d61b9db9a8a361953d36410b1"},
+ {file = "yarl-1.9.4-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0d2454f0aef65ea81037759be5ca9947539667eecebca092733b2eb43c965a81"},
+ {file = "yarl-1.9.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:44d8ffbb9c06e5a7f529f38f53eda23e50d1ed33c6c869e01481d3fafa6b8142"},
+ {file = "yarl-1.9.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:aaaea1e536f98754a6e5c56091baa1b6ce2f2700cc4a00b0d49eca8dea471074"},
+ {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3777ce5536d17989c91696db1d459574e9a9bd37660ea7ee4d3344579bb6f129"},
+ {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9fc5fc1eeb029757349ad26bbc5880557389a03fa6ada41703db5e068881e5f2"},
+ {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ea65804b5dc88dacd4a40279af0cdadcfe74b3e5b4c897aa0d81cf86927fee78"},
+ {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aa102d6d280a5455ad6a0f9e6d769989638718e938a6a0a2ff3f4a7ff8c62cc4"},
+ {file = "yarl-1.9.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09efe4615ada057ba2d30df871d2f668af661e971dfeedf0c159927d48bbeff0"},
+ {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:008d3e808d03ef28542372d01057fd09168419cdc8f848efe2804f894ae03e51"},
+ {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:6f5cb257bc2ec58f437da2b37a8cd48f666db96d47b8a3115c29f316313654ff"},
+ {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:992f18e0ea248ee03b5a6e8b3b4738850ae7dbb172cc41c966462801cbf62cf7"},
+ {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0e9d124c191d5b881060a9e5060627694c3bdd1fe24c5eecc8d5d7d0eb6faabc"},
+ {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3986b6f41ad22988e53d5778f91855dc0399b043fc8946d4f2e68af22ee9ff10"},
+ {file = "yarl-1.9.4-cp312-cp312-win32.whl", hash = "sha256:4b21516d181cd77ebd06ce160ef8cc2a5e9ad35fb1c5930882baff5ac865eee7"},
+ {file = "yarl-1.9.4-cp312-cp312-win_amd64.whl", hash = "sha256:a9bd00dc3bc395a662900f33f74feb3e757429e545d831eef5bb280252631984"},
+ {file = "yarl-1.9.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:63b20738b5aac74e239622d2fe30df4fca4942a86e31bf47a81a0e94c14df94f"},
+ {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7d7f7de27b8944f1fee2c26a88b4dabc2409d2fea7a9ed3df79b67277644e17"},
+ {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c74018551e31269d56fab81a728f683667e7c28c04e807ba08f8c9e3bba32f14"},
+ {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ca06675212f94e7a610e85ca36948bb8fc023e458dd6c63ef71abfd482481aa5"},
+ {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5aef935237d60a51a62b86249839b51345f47564208c6ee615ed2a40878dccdd"},
+ {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2b134fd795e2322b7684155b7855cc99409d10b2e408056db2b93b51a52accc7"},
+ {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:d25039a474c4c72a5ad4b52495056f843a7ff07b632c1b92ea9043a3d9950f6e"},
+ {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:f7d6b36dd2e029b6bcb8a13cf19664c7b8e19ab3a58e0fefbb5b8461447ed5ec"},
+ {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:957b4774373cf6f709359e5c8c4a0af9f6d7875db657adb0feaf8d6cb3c3964c"},
+ {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:d7eeb6d22331e2fd42fce928a81c697c9ee2d51400bd1a28803965883e13cead"},
+ {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:6a962e04b8f91f8c4e5917e518d17958e3bdee71fd1d8b88cdce74dd0ebbf434"},
+ {file = "yarl-1.9.4-cp37-cp37m-win32.whl", hash = "sha256:f3bc6af6e2b8f92eced34ef6a96ffb248e863af20ef4fde9448cc8c9b858b749"},
+ {file = "yarl-1.9.4-cp37-cp37m-win_amd64.whl", hash = "sha256:ad4d7a90a92e528aadf4965d685c17dacff3df282db1121136c382dc0b6014d2"},
+ {file = "yarl-1.9.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ec61d826d80fc293ed46c9dd26995921e3a82146feacd952ef0757236fc137be"},
+ {file = "yarl-1.9.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:8be9e837ea9113676e5754b43b940b50cce76d9ed7d2461df1af39a8ee674d9f"},
+ {file = "yarl-1.9.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:bef596fdaa8f26e3d66af846bbe77057237cb6e8efff8cd7cc8dff9a62278bbf"},
+ {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d47552b6e52c3319fede1b60b3de120fe83bde9b7bddad11a69fb0af7db32f1"},
+ {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84fc30f71689d7fc9168b92788abc977dc8cefa806909565fc2951d02f6b7d57"},
+ {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4aa9741085f635934f3a2583e16fcf62ba835719a8b2b28fb2917bb0537c1dfa"},
+ {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:206a55215e6d05dbc6c98ce598a59e6fbd0c493e2de4ea6cc2f4934d5a18d130"},
+ {file = "yarl-1.9.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:07574b007ee20e5c375a8fe4a0789fad26db905f9813be0f9fef5a68080de559"},
+ {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5a2e2433eb9344a163aced6a5f6c9222c0786e5a9e9cac2c89f0b28433f56e23"},
+ {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6ad6d10ed9b67a382b45f29ea028f92d25bc0bc1daf6c5b801b90b5aa70fb9ec"},
+ {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:6fe79f998a4052d79e1c30eeb7d6c1c1056ad33300f682465e1b4e9b5a188b78"},
+ {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a825ec844298c791fd28ed14ed1bffc56a98d15b8c58a20e0e08c1f5f2bea1be"},
+ {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8619d6915b3b0b34420cf9b2bb6d81ef59d984cb0fde7544e9ece32b4b3043c3"},
+ {file = "yarl-1.9.4-cp38-cp38-win32.whl", hash = "sha256:686a0c2f85f83463272ddffd4deb5e591c98aac1897d65e92319f729c320eece"},
+ {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"},
+ {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:604f31d97fa493083ea21bd9b92c419012531c4e17ea6da0f65cacdcf5d0bd27"},
+ {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a854227cf581330ffa2c4824d96e52ee621dd571078a252c25e3a3b3d94a1b1"},
+ {file = "yarl-1.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ba6f52cbc7809cd8d74604cce9c14868306ae4aa0282016b641c661f981a6e91"},
+ {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a6327976c7c2f4ee6816eff196e25385ccc02cb81427952414a64811037bbc8b"},
+ {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"},
+ {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"},
+ {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"},
+ {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"},
+ {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"},
+ {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+ {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+ {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+ {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+ {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+ {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+ {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+ {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zipp"
+version = "3.17.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+ {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9,<3.9.7 || >3.9.7,<4.0"
+content-hash = "b6e7ef9dddaf5bb68459f701899f39aa3fd64cfd2766eb0af6bfa4d9841bc237"
diff --git a/legacy/src/ultravox/pyproject.toml b/legacy/src/ultravox/pyproject.toml
new file mode 100644
index 00000000..fecdeaaa
--- /dev/null
+++ b/legacy/src/ultravox/pyproject.toml
@@ -0,0 +1,43 @@
+[tool.poetry]
+name = "ultravox"
+version = "0.1.0"
+description = "Training engine for E2E voice models"
+authors = ["Farzad Abdolhosseini "]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.9.7 || >3.9.7,<4.0"
+torch = "^2.1.2"
+torchaudio = "^2.1.2"
+librosa = "^0.10.1"
+transformers = "4.37.2"
+datasets = "^2.16.1"
+deepspeed = "^0.12.6"
+peft = "^0.7.1"
+wandb = "^0.16.1"
+clearml = "^1.13.2"
+pyrallis = "^0.3.1"
+truecase = "^0.0.14"
+tensorboardx = "^2.6.2.2"
+evaluate = "^0.4.1"
+azure-keyvault-secrets = "^4.7.0"
+azure-identity = "^1.15.0"
+azureml-core = "^1.54.0.post1"
+bitsandbytes = "^0.42.0"
+jiwer = "^3.0.3"
+streamlit = "^1.30.0"
+gradio = "^4.16.0"
+fairseq = "^0.12.2"
+sounddevice = "^0.4.6"
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.4"
+black = "^23.12.1"
+isort = "^5.13.2"
+autoflake = "^2.2.1"
+deptry = "^0.12.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/legacy/src/ultravox/requirements.txt b/legacy/src/ultravox/requirements.txt
new file mode 100644
index 00000000..0623a0c17
--- /dev/null
+++ b/legacy/src/ultravox/requirements.txt
@@ -0,0 +1,21 @@
+torch
+torchaudio
+librosa
+# transformers
+git+https://github.com/huggingface/transformers
+bitsandbytes
+datasets
+deepspeed
+peft
+wandb
+clearml
+pyrallis
+truecase
+tensorboardx
+evaluate
+jiwer
+
+# azure python client
+azure-keyvault-secrets
+azure-identity
+azureml-core
diff --git a/legacy/src/ultravox/runjob.sh b/legacy/src/ultravox/runjob.sh
new file mode 100755
index 00000000..3e87f6cf
--- /dev/null
+++ b/legacy/src/ultravox/runjob.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+set -x
+which python
+# python -m venv .venv
+# source .venv/bin/activate
+
+# pip install poetry
+# poetry install --sync --no-root
+
+pip install -r requirements.txt
+echo "Done installing requirements. Running task."
+# TODO: nproc depends on the number of GPUs, right? How to set automatically?
+torchrun --nproc_per_node=8 --master_port=1234 -m train.train --config_path $@
+# python -m train.train --config_path $@
+# sleep infinity
+
+
+sleep 20 && pkill -f wandb
\ No newline at end of file
diff --git a/legacy/src/ultravox/train/__init__.py b/legacy/src/ultravox/train/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/legacy/src/ultravox/train/config.yaml b/legacy/src/ultravox/train/config.yaml
new file mode 100644
index 00000000..1d922df1
--- /dev/null
+++ b/legacy/src/ultravox/train/config.yaml
@@ -0,0 +1,72 @@
+# exp_name: tinyllama-hubertL-CV-10Hz-3earlyEOU-8random-shiftright-30pCrop-40pSil-sysprompt-Reload
+# exp_name: tinyllama-hubertL-GS-10Hz
+exp_name: tinyllamaR8-hubertL-GS-10Hz-EPD+late-Reload
+
+model:
+ # llm_name: "llama2-7b"
+ llm_name: "tinyllama"
+ # audio_enc_name: "wav2vec2"
+ # audio_enc_name: "wav2vec2-bert"
+ # audio_enc_name: "wav2vec2-bert-enconly"
+ audio_enc_name: "hubert-large"
+ # audio_enc_name: "whisper-large-v3"
+ audio_stride: 5
+ # init_type: "small"
+
+model_load_path: "runs/tinyllama-hubertL-GS-10Hz-bs1/model*.safetensors"
+
+dataset_name: "gigaspeech"
+# dataset_name: "librispeech"
+# dataset_name: "commonvoice"
+
+audio_tokenizer_config:
+ add_audio_tag_ratio: 0
+ # prompt: "Transcribe speech to text: {audio}"
+ prompt: "Transcribe speech to text and indicate whether user is done talking or they might continue with [END] or [...]: {audio}"
+ late_eou_label: True
+ # early_eou_labels: True
+ early_eou_count: 3
+ early_mid_count: 3
+ crop_audio_prob: 0.6
+ crop_audio_band: [0.4, 0.8]
+ # crop_silence_prob: 0.4
+ # system_prompt: "Note: After the end of a user turn, either 'END' or '...' is used to indicate whether the user is done or if his/her audio was cut-off before the end of their turn."
+
+lr: 5e-5
+# lr_warmup_ratio: 0.05
+lr_warmup_steps: 2000
+lr_scheduler_type: constant_with_warmup
+# lr_scheduler_type: linear
+# lr_scheduler_type: cosine_with_restarts
+# lr_scheduler_kwargs:
+# num_cycles: 6
+# optimizer_type: adamw_bnb_8bit
+# weight_decay: 0.02
+optimizer_type: adamw_torch
+
+freezing_config:
+ llm_lora_config:
+ r: 8
+ target_modules: [k_proj, q_proj]
+
+ audio_enc_lora_config:
+ r: 0
+ target_modules: [k_proj, q_proj, linear_k, linear_q]
+
+ freeze_text_embeds: True
+ freeze_audio_embeds: True
+
+num_workers: 4
+per_device_train_batch_size: 1
+per_device_eval_batch_size: 1
+gradient_accumulation_steps: 1
+eval_accumulation_steps: 1
+# device: gpu
+max_steps: 80000
+logging_steps: 100
+save_steps: 10000
+eval_steps: 2000
+bf16: True
+# fp16: True
+report_logs_to: ["tensorboard", "wandb"]
+# workers: 32
diff --git a/legacy/src/ultravox/train/configs_base.py b/legacy/src/ultravox/train/configs_base.py
new file mode 100644
index 00000000..e2c29beb
--- /dev/null
+++ b/legacy/src/ultravox/train/configs_base.py
@@ -0,0 +1,88 @@
+import logging
+import typing as t
+from dataclasses import dataclass
+from dataclasses import field
+from pathlib import Path
+
+import torch
+
+from . import data
+from .models import multimodal as multimodal_models
+
+
+@dataclass
+class TrainConfig:
+ """Training config for Machine Learning"""
+
+ model: multimodal_models.SpeechLMConfig
+ lr: float
+ per_device_train_batch_size: int
+ per_device_eval_batch_size: int
+ model_load_path: t.Optional[Path] = None
+ freezing_config: multimodal_models.FreezingConfig = field(
+ default_factory=multimodal_models.FreezingConfig
+ )
+ num_epochs: int = 0
+ max_audio_duration_in_seconds: float = 20.0
+ lr_warmup_ratio: float = 0.0
+ lr_warmup_steps: int = 0
+ lr_scheduler_type: str = "linear"
+ lr_scheduler_kwargs: t.Dict[str, float] = None
+ weight_decay: float = 0.0
+ optimizer_type: str = "adamw_torch"
+ gradient_accumulation_steps: int = 1
+ eval_accumulation_steps: int = 1
+ dataset_name: data.DatasetType = data.DatasetType.GIGASPEECH
+ dataset_streaming: bool = True
+ audio_tokenizer_config: data.AudioTextTokenizerConfig = field(
+ default_factory=data.AudioTextTokenizerConfig
+ )
+ num_workers: int = 0 # The number of workers for data loader
+ max_steps: t.Optional[int] = None
+ use_cpu: t.Optional[bool] = None
+ output_dir: Path = None
+ resume_from_checkpoint: bool = False
+ allow_tf32: bool = True
+ fp16: bool = False
+ # WARNING: currently the code cannot handle FP16 in some cases
+ # Left as TODO since on A100s we'd use BF16 anyway and it's not a priority
+ bf16: bool = False
+ seed: int = 42
+ deepspeed: t.Optional[Path] = None
+ exp_name: str = "test" # The experiment name
+ eval_steps: t.Union[int, float] = 100
+ logging_steps: int = 5
+ save_steps: t.Union[int, float] = 0.1
+ report_logs_to: t.List[str] = ("tensorboard", "wandb", "clearml")
+
+ def __post_init__(self):
+ # A builtin method of dataclasses, used for post-processing our configuration.
+ if self.use_cpu is None:
+ self.use_cpu = not torch.cuda.is_available()
+
+ # Note: it's possible to do BF16 on CPU too, but I don't think we care
+ self.bf16 = self.bf16 and not self.use_cpu and torch.cuda.is_bf16_supported()
+
+ # FP16 is not supported right now
+ # if self.fp16:
+ # logging.warning(
+ # "Currently the code cannot handle FP16 in some cases, hence disabling it."
+ # )
+ # self.fp16 = False
+ if self.bf16 and self.fp16:
+ logging.warning(
+ "Using BF16 and FP16 at the same time is not supported. Disabling FP16."
+ )
+ self.fp16 = False
+
+ if self.output_dir is None:
+ self.output_dir = Path("runs") / self.exp_name
+
+ if self.per_device_eval_batch_size is None:
+ self.per_device_eval_batch_size = self.per_device_train_batch_size
+
+ if self.optimizer_type == "adamw_bnb_8bit" and not torch.cuda.is_available():
+ logging.warning(
+ "Using CPU with adamw_bnb_8bit is not supported. Switching to adamw_torch"
+ )
+ self.optimizer_type = "adamw_torch"
diff --git a/legacy/src/ultravox/train/data/__init__.py b/legacy/src/ultravox/train/data/__init__.py
new file mode 100644
index 00000000..a0970396
--- /dev/null
+++ b/legacy/src/ultravox/train/data/__init__.py
@@ -0,0 +1,15 @@
+from .base import AudioTextTokenizer
+from .base import AudioTextTokenizerConfig
+from .base import DataCollatorForSeq2SeqWithAudio
+from .base import DatasetType
+from .base import get_dataset
+from .base import get_dataset_split
+
+__all__ = [
+ "get_dataset",
+ "get_dataset_split",
+ "DatasetType",
+ "AudioTextTokenizer",
+ "AudioTextTokenizerConfig",
+ "DataCollatorForSeq2SeqWithAudio",
+]
diff --git a/legacy/src/ultravox/train/data/base.py b/legacy/src/ultravox/train/data/base.py
new file mode 100644
index 00000000..e768a94c
--- /dev/null
+++ b/legacy/src/ultravox/train/data/base.py
@@ -0,0 +1,365 @@
+import enum
+import os
+import random
+import typing as t
+from dataclasses import dataclass
+from dataclasses import field
+
+import datasets
+import pyrallis
+import torch
+import transformers
+from train.data import epd
+
+from .gigaspeech import clean_text_for_training
+from .prompts import get_random_asr_prompt
+
+
+class DatasetType(enum.Enum):
+ LIBRISPEECH = "librispeech"
+ GIGASPEECH = "gigaspeech"
+ COMMON_VOICE = "commonvoice"
+
+
+pyrallis.decode.register(DatasetType, lambda x: DatasetType(x))
+
+
+def get_dataset(
+ dataset_name: datasets.Dataset,
+ dev_env: bool = False,
+ streaming: bool = True,
+ shuffle: bool = True,
+ sampling_rate: int = 16_000,
+ max_duration_in_seconds: float = 20.0,
+ val_max_num_samples: t.Optional[int] = 256,
+) -> t.Tuple[datasets.IterableDataset, datasets.IterableDataset]:
+ train_ds = get_dataset_split(
+ dataset_name=dataset_name,
+ train=True,
+ streaming=streaming,
+ shuffle=shuffle,
+ dev_env=dev_env,
+ sampling_rate=sampling_rate,
+ max_duration_in_seconds=max_duration_in_seconds,
+ )
+ validation_ds = get_dataset_split(
+ dataset_name=dataset_name,
+ train=False,
+ streaming=streaming,
+ shuffle=shuffle,
+ dev_env=dev_env,
+ sampling_rate=sampling_rate,
+ max_duration_in_seconds=max_duration_in_seconds,
+ max_num_samples=val_max_num_samples, # FIXME: remove this limit? I'm worried about OOM right now
+ )
+
+ return train_ds, validation_ds
+
+
+def is_text_unempty(sample):
+ return bool(sample.get("text", None))
+
+
+def get_dataset_split(
+ dataset_name: datasets.Dataset,
+ train: bool = True,
+ dev_env: bool = False,
+ streaming: bool = True,
+ shuffle: bool = True,
+ sampling_rate: int = 16_000,
+ max_duration_in_seconds: float = 20.0,
+ max_num_samples: t.Optional[int] = None,
+) -> datasets.IterableDataset:
+ """
+ Args:
+ dataset_name: The name of the dataset to load.
+ train: Whether to load the training or validation split.
+ dev_env: Whether to use a development environment (e.g. for testing).
+ streaming: Whether to use the streaming version of the dataset.
+ This is almost always True for training.
+ shuffle: Whether to shuffle the dataset.
+ sampling_rate: The sampling rate to use for the audio.
+ max_duration_in_seconds: The maximum duration of audio to use.
+ max_num_samples: The maximum number of samples to load.
+ """
+ kwargs = {
+ "trust_remote_code": True,
+ "token": os.environ.get("HF_ACCESS_TOKEN", None),
+ "streaming": streaming,
+ }
+
+ if dev_env and not train:
+ max_num_samples = 10
+
+ if dataset_name == DatasetType.LIBRISPEECH:
+ kwargs["path"] = "librispeech_asr"
+ kwargs["split"] = "train.clean.360" if train else "validation.clean"
+ # kwargs["streaming"] = True # TODO?
+ elif dataset_name == DatasetType.GIGASPEECH:
+ kwargs["path"] = "speechcolab/gigaspeech"
+ # kwargs["name"] = "xs" if dev_env else "s"
+ kwargs["name"] = "s"
+ kwargs["split"] = "train" if train else "validation"
+ elif dataset_name == DatasetType.COMMON_VOICE:
+ kwargs["path"] = "mozilla-foundation/common_voice_16_1"
+ # kwargs["path"] = "mozilla-foundation/common_voice_11_0"
+ kwargs["name"] = "en" # TODO: combine multiple languages
+ kwargs["split"] = "train" if train else "validation"
+ else:
+ raise ValueError(f"Unknown dataset name: {dataset_name}")
+
+ ds: datasets.IterableDataset = datasets.load_dataset(**kwargs)
+
+ if shuffle:
+ ds = ds.shuffle()
+
+ if isinstance(ds, datasets.Dataset):
+ ds = ds.to_iterable_dataset(num_shards=4)
+
+ if max_num_samples:
+ if isinstance(ds, datasets.IterableDataset):
+ ds = ds.take(max_num_samples)
+ else:
+ ds = ds.select(range(max_num_samples))
+
+ ds = ds.cast_column("audio", datasets.Audio(sampling_rate=sampling_rate))
+ ds = ds.filter(IsAudioLengthInRange(max_duration_in_seconds))
+
+ if dataset_name == DatasetType.COMMON_VOICE:
+ # Common Voice text already seems clean, but it's called "sentence"
+ ds = ds.rename_column("sentence", "text")
+ else:
+ ds = ds.map(clean_text_for_training, input_columns=["text"])
+
+ ds = ds.filter(is_text_unempty)
+
+ return ds
+
+
+@dataclass
+class IsAudioLengthInRange:
+ max_duration_in_seconds: float
+
+ def __call__(self, sample):
+ audio_len = len(sample["audio"]["array"]) / sample["audio"]["sampling_rate"]
+ return audio_len < self.max_duration_in_seconds
+
+
+@dataclass
+class AudioTextTokenizerConfig:
+ system_prompt: t.Optional[str] = None
+ prompt: t.Optional[str] = None
+ add_audio_tag_ratio: float = 0.5
+ asr_label: bool = True
+ inference_mode: bool = False
+ late_eou_label: bool = False
+ early_eou_labels: bool = False
+ early_eou_count: int = 1
+ early_mid_count: int = -1
+ crop_audio_prob: float = 0.0
+ crop_audio_band: t.Tuple[float, float] = field(default_factory=lambda: (0.2, 0.8))
+ crop_silence_prob: float = 0.0
+ crop_silence_band: t.Tuple[float, float] = field(default_factory=lambda: (0, 1))
+
+ def __post_init__(self):
+ if self.crop_audio_prob > 0:
+ if (
+ self.crop_audio_band[0] <= 0
+ or self.crop_audio_band[1] >= 1
+ or self.crop_audio_band[0] >= self.crop_audio_band[1]
+ ):
+ raise ValueError(
+ "crop_audio_band should be a tuple of two floats in (0, 1)."
+ )
+
+
+@dataclass
+class AudioTextTokenizer:
+ audio_processor: transformers.Wav2Vec2Processor
+ tokenizer: transformers.LlamaTokenizer
+ audio_to_tokens_ratio: int
+ """Defines how many frames of audio_features correspond to one token."""
+ cfg: AudioTextTokenizerConfig
+
+ def __post_init__(self):
+ if self.cfg.late_eou_label:
+ self.cropped_transcriber = epd.CroppedTranscriber()
+ eou = self.tokenizer.encode("END")
+ mid = self.tokenizer.encode("...")
+ self.eou_token_id = eou[-1]
+ self.mid_token_id = mid[-1]
+ if self.cfg.early_eou_labels:
+ if len(eou) > 2 or len(mid) > 2:
+ raise ValueError(
+ "When early_eou_labels are enabled, the tokenizer should not split the 'END' or '...' tokens. Cannot recover."
+ )
+
+ def generate_prompt_tokens(
+ self,
+ num_audio_tokens: int,
+ transcription: str = None,
+ prompt: str = None,
+ remove_last_eos: bool = False,
+ ) -> t.List[int]:
+ # Putting unk token at the beginning of the sequence.
+ # This should be replaced with audio features inside the model
+ audio_placeholder = self.tokenizer.unk_token * num_audio_tokens
+ if random.random() < self.cfg.add_audio_tag_ratio:
+ audio_placeholder = f"{audio_placeholder} "
+
+ if prompt is None:
+ prompt = self.cfg.prompt
+ if prompt is None:
+ prompt = get_random_asr_prompt()
+
+ if "{audio}" not in prompt:
+ prompt = "{audio}\n" + prompt
+
+ chat = []
+ if self.cfg.system_prompt:
+ chat.append({"role": "system", "content": self.cfg.system_prompt})
+
+ chat.append({"role": "user", "content": prompt.format(audio=audio_placeholder)})
+
+ if transcription:
+ chat.append(
+ {"role": "assistant", "content": f"Transcript: {transcription}"}
+ )
+
+ tokens: t.List[int] = self.tokenizer.apply_chat_template(chat, tokenize=True)
+
+ if remove_last_eos:
+ for i in range(len(tokens) - 1, -1, -1):
+ if tokens[i] == self.tokenizer.eos_token_id:
+ tokens = tokens[:i]
+ break
+
+ return tokens
+
+ def __call__(self, sample: t.Dict[str, t.Any]):
+ audio_array = sample["audio"]["array"]
+ audio_sr = sample["audio"]["sampling_rate"]
+ text = sample.get("text", None)
+ cropped = random.random() < self.cfg.crop_audio_prob
+
+ if cropped:
+ keep_portion = random.uniform(*self.cfg.crop_audio_band)
+ audio_array = audio_array[..., : int(len(audio_array) * keep_portion)]
+ if random.random() < self.cfg.crop_silence_prob:
+ # Add silence to the end of the audio
+ sil_len = random.uniform(*self.cfg.crop_silence_band) * audio_sr
+ audio_array[..., -int(sil_len) :] *= 100
+ if self.cfg.late_eou_label:
+ text = self.cropped_transcriber(
+ audio_array, audio_sr, full_transcript=text
+ )
+ else:
+ # In this case we can ignore the whole text (no ASR task, just EPD-early)
+ text = None
+
+ audio_input = self.audio_processor(audio_array, sampling_rate=audio_sr)
+
+ if "input_features" in audio_input:
+ audio_feats = audio_input.input_features[0]
+ else:
+ audio_feats = audio_input.input_values[0]
+
+ processed = {}
+ processed["audio_features"] = audio_feats
+
+ if isinstance(self.audio_processor, transformers.WhisperProcessor):
+ # Whisper pads all inputs to the same length of 30 seconds
+ num_audio_tokens = 30 * audio_sr // self.audio_to_tokens_ratio
+ else:
+ num_audio_tokens = audio_array.shape[-1] // self.audio_to_tokens_ratio
+
+ suffix = ""
+ if self.cfg.late_eou_label:
+ suffix = " [...]" if cropped else " [END]"
+
+ tokens = self.generate_prompt_tokens(
+ num_audio_tokens,
+ transcription=text + suffix if text is not None else None,
+ prompt=sample.get("prompt", None),
+ )
+ input_tokens_only = self.generate_prompt_tokens(
+ num_audio_tokens,
+ prompt=sample.get("prompt", None),
+ )
+ tokens_without_suffix = self.generate_prompt_tokens(
+ num_audio_tokens,
+ transcription=text,
+ prompt=sample.get("prompt", None),
+ remove_last_eos=True,
+ )
+
+ if self.cfg.asr_label:
+ # The input tokens are not labelled (hence -100)
+ num_unlabelled = len(input_tokens_only)
+ else:
+ # Transcript tokens are not labelled in this case
+ # Only the suffix (EOU + EOS) is labelled.
+ # TODO: do we need to label EOS? Probably okay, huh?
+ num_unlabelled = len(tokens_without_suffix)
+
+ labels = [-100] * num_unlabelled + tokens[num_unlabelled:]
+
+ if self.cfg.inference_mode:
+ tokens = input_tokens_only
+
+ # Simple way to keep track of which tokens are audio and which are text
+ audio_token_mask = [
+ 1 if t == self.tokenizer.unk_token_id else 0 for t in input_tokens_only
+ ]
+
+ # gotta assert that they are continuous
+ processed["audio_token_start_idx"] = audio_token_mask.index(1)
+ processed["audio_token_len"] = audio_token_mask.count(1)
+
+ if self.cfg.early_eou_labels:
+ # Set audio labels as a bunch of ... tokens followed by a single END
+ start = processed["audio_token_start_idx"]
+ end = start + processed["audio_token_len"]
+ # Shift everything to the right by 1 to match "next"-word prediction task
+ start += 1
+ end += 1
+
+ # The last early_eou_count places are labeled as END if not cropped, otherwise ...
+ for i in range(end - self.cfg.early_eou_count, end):
+ labels[i] = self.mid_token_id if cropped else self.eou_token_id
+ end -= self.cfg.early_eou_count
+
+ # If early_mid_count is -1, we label all the remaining tokens as `...`
+ # otherwise, we randomly select that many tokens to be labeled as `...`
+ mid_labels = range(start, end)
+ if 0 <= self.cfg.early_mid_count < len(mid_labels):
+ mid_labels = random.sample(mid_labels, self.cfg.early_mid_count)
+ for i in mid_labels:
+ labels[i] = self.mid_token_id
+
+ processed["input_ids"] = tokens
+ processed["labels"] = labels
+ processed["attention_mask"] = [1] * len(tokens)
+
+ return processed
+
+
+@dataclass
+class DataCollatorForSeq2SeqWithAudio(transformers.DataCollatorForSeq2Seq):
+ audio_dtype: torch.dtype = torch.float32
+
+ def __call__(self, features, *args, **kwargs):
+ audio_features = [f.pop("audio_features") for f in features]
+ # audio_token_mask = [f.pop("audio_token_mask") for f in features]
+
+ batch = super().__call__(features, *args, **kwargs)
+
+ batch["audio_features"] = torch.nn.utils.rnn.pad_sequence(
+ [torch.tensor(f, dtype=self.audio_dtype) for f in audio_features],
+ batch_first=True,
+ )
+ # batch["audio_token_mask"] = torch.nn.utils.rnn.pad_sequence(
+ # [torch.tensor(f) for f in audio_token_mask], batch_first=True
+ # )
+
+ return batch
diff --git a/legacy/src/ultravox/train/data/epd.py b/legacy/src/ultravox/train/data/epd.py
new file mode 100644
index 00000000..4fd8ae77
--- /dev/null
+++ b/legacy/src/ultravox/train/data/epd.py
@@ -0,0 +1,62 @@
+import re
+import typing as t
+
+import numpy as np
+import regex
+import transformers
+
+
+def find_prefix_match(full: str, partial: str, cer_limit: float = 0) -> t.Optional[str]:
+ allowed_errors = int(round(len(partial) * cer_limit)) + 10
+ # Remove whitespaces and trailing punctuation
+ partial = partial.strip()
+ if partial[-1] in ".,;:!?":
+ partial = partial[:-1]
+ # Fuzzy search for a prefix match
+ ## (?i) - case insensitive ## (?e) - enhance match ## {e<=10} - number of errors allowed ## .* - any characters after the match
+ partial_regex = "^(?i)(?e)(" + partial + "){e<=" + str(allowed_errors) + "}.*$"
+ match: t.Optional[regex.Match[str]] = regex.fullmatch(partial_regex, full)
+ if match is None:
+ return basic_prefix_match(full, partial)
+ return match.groups()[0].strip()
+
+
+def basic_prefix_match(full: str, partial: str):
+ """
+ Simply want to do full[:len(partial)], but make sure we don't cut off in the middle of a word.
+ """
+ words = re.split(r" |,|\.|;|:|\?|!", full)
+ word_end_lens = np.cumsum(np.ones(len(words)) + [len(w) for w in words])
+ word_end_lens = np.insert(word_end_lens, 0, 0)
+ closest_ind = np.abs(word_end_lens - len(partial)).argmin()
+ return full[: int(word_end_lens[closest_ind])].strip()
+
+
+class CroppedTranscriber:
+ def __init__(self, model_name="openai/whisper-tiny", device="cpu"):
+ self.model = transformers.AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
+ self.model.to(device)
+
+ self.processor = transformers.WhisperProcessor.from_pretrained(model_name)
+
+ def __call__(self, cropped_raw_audio: list, sr: int, full_transcript: str):
+ input_features = self.processor(
+ cropped_raw_audio, sampling_rate=sr, return_tensors="pt"
+ ).input_features
+ predicted_ids = self.model.generate(input_features)
+ partial_transcript = self.processor.batch_decode(
+ predicted_ids, skip_special_tokens=True
+ )[0]
+ return basic_prefix_match(full_transcript, partial_transcript)
+ # return find_prefix_match(full_transcript, partial_transcript)
+
+
+def test_cropped_transcriber():
+ ct = CroppedTranscriber()
+ assert ct(np.zeros(1000), 16000, "hello there, how's it going?!") == "hello"
+ assert ct(np.zeros(1000), 16000, "helloooo there, how's it going?!") == ""
+
+
+if __name__ == "__main__":
+ ct = CroppedTranscriber()
+ print(ct(np.zeros(1000), 16000, "hello there, how's it going?!"))
diff --git a/legacy/src/ultravox/train/data/gigaspeech.py b/legacy/src/ultravox/train/data/gigaspeech.py
new file mode 100644
index 00000000..5c80dec0
--- /dev/null
+++ b/legacy/src/ultravox/train/data/gigaspeech.py
@@ -0,0 +1,82 @@
+import nltk # needed for truecase
+import truecase
+
+nltk.download("punkt")
+
+
+def clean_text_for_training(text: str):
+ """
+ Cleans the text for training. Most of these are for Gigaspeech:
+ - Convert punctuations
+ - Remove non-scoring words to reduce noise
+ - Convert to true case
+ - This is not perfect, but it's better than nothing
+
+ Example:
+ "I SEE LOTS OF PEOPLE HAVE AH DRONES HERE AH MAVERICK AH AS WELL "
+ --> "I see lots of people have drones here, maverick as well."
+ """
+ remaining_words = []
+ for word in text.split():
+ if word in gigaspeech_punctuations:
+ word = gigaspeech_punctuations[word]
+ elif word in non_scoring_words:
+ continue
+ remaining_words.append(word)
+
+ text = " ".join(remaining_words)
+ text = truecase.get_true_case(text)
+
+ return {"text": text}
+
+
+# Source: https://github.com/SpeechColab/GigaSpeech/blob/main/utils/gigaspeech_scoring.py
+def asr_text_post_processing(text):
+ # 1. convert to uppercase
+ text = text.upper()
+
+ # 2. remove hyphen
+ # "E-COMMERCE" -> "E COMMERCE", "STATE-OF-THE-ART" -> "STATE OF THE ART"
+ text = text.replace("-", " ")
+
+ # 3. remove non-scoring words from evaluation
+ remaining_words = []
+ for word in text.split():
+ if word in non_scoring_words:
+ continue
+ remaining_words.append(word)
+
+ return " ".join(remaining_words)
+
+
+conversational_filler = [
+ "UH",
+ "UHH",
+ "UM",
+ "EH",
+ "MM",
+ "HM",
+ "AH",
+ "HUH",
+ "HA",
+ "ER",
+ "OOF",
+ "HEE",
+ "ACH",
+ "EEE",
+ "EW",
+]
+special_tags = ["", "", ""]
+gigaspeech_punctuations = {
+ "": ",",
+ "": ".",
+ "": "?",
+ "": "!",
+}
+gigaspeech_garbage_utterance_tags = ["", "", "", ""]
+non_scoring_words = set(
+ conversational_filler
+ + special_tags
+ + list(gigaspeech_punctuations.keys())
+ + gigaspeech_garbage_utterance_tags
+)
diff --git a/legacy/src/ultravox/train/data/prompts.py b/legacy/src/ultravox/train/data/prompts.py
new file mode 100644
index 00000000..17ac1eec
--- /dev/null
+++ b/legacy/src/ultravox/train/data/prompts.py
@@ -0,0 +1,113 @@
+import random
+
+# https://chat.openai.com/share/39361db5-041b-4a3e-8a03-8905653dd084
+ASR_PROMPTS = [
+ "Transcribe speech to text.",
+ # "Transcribe speech to text.",
+ # "Transcribe speech to text.",
+ # "Transcribe speech to text.",
+ # "Extract spoken words precisely.",
+ # "Convert speech into accurate text.",
+ # "Decipher spoken language to written form.",
+ # "Transform spoken words into textual representation.",
+ # "Render spoken content into written format.",
+ # "Capture spoken language with precision.",
+ # "Translate spoken words to written text.",
+ # "Convert audible speech to written words.",
+ # "Transform spoken phrases into written sentences.",
+ # "Transcribe spoken dialogue verbatim.",
+ # "Capture spoken words exactly.",
+ # "Render spoken language into precise text.",
+ # "Transform audible speech into written form.",
+ # "Transcribe spoken content accurately.",
+ # "Convert spoken words into textual form.",
+ # "Extract spoken phrases with precision.",
+ # "Translate audible speech into written words.",
+ # "Decipher spoken words to text.",
+ # "Transform spoken language into written sentences.",
+ # "Convert spoken dialogue to verbatim text.",
+ # "Capture spoken content precisely.",
+ # "Render spoken words into accurate text.",
+ # "Transcribe spoken language exactly.",
+ # "Transform audible speech into written sentences.",
+ # "Convert spoken phrases into textual representation.",
+ # "Extract spoken words accurately.",
+ # "Decipher audible speech to written text.",
+ # "Capture spoken language verbatim.",
+ # "Translate spoken phrases into precise text.",
+ # "Convert spoken dialogue into written form.",
+ # "Transform spoken content to exact text.",
+ # "Render spoken words precisely.",
+ # "Transcribe spoken language verbatim.",
+ # "Convert spoken phrases into written words.",
+ # "Extract spoken words with precision.",
+ # "Transform audible speech into written sentences.",
+ # "Capture spoken dialogue accurately.",
+ # "Decipher spoken language precisely.",
+ # "Render spoken words into verbatim text.",
+ # "Translate spoken phrases to written form.",
+ # "Convert audible speech into written sentences.",
+ # "Transform spoken content accurately.",
+ # "Transcribe spoken words verbatim.",
+ # "Capture spoken language exactly.",
+ # "Decipher spoken dialogue with precision.",
+ # "Convert spoken phrases into textual form.",
+ # "Extract spoken words precisely.",
+ # "Render spoken language into verbatim text.",
+ # "Translate spoken content into written sentences.",
+ # "Transform audible speech into accurate text.",
+ # "Transcribe spoken words accurately.",
+ # "Convert spoken dialogue into textual representation.",
+ # "Capture spoken phrases verbatim.",
+ # "Decipher spoken language to written form.",
+ # "Extract spoken words into written sentences.",
+ # "Render spoken content accurately.",
+ # "Convert spoken phrases into precise text.",
+ # "Transcribe spoken dialogue verbatim.",
+ # "Capture spoken language with precision.",
+ # "Transform audible speech into written words.",
+ # "Decipher spoken words accurately.",
+ # "Convert spoken content to exact text.",
+ # "Translate spoken phrases into written sentences.",
+ # "Extract spoken words verbatim.",
+ # "Render spoken language into textual representation.",
+ # "Transcribe spoken dialogue precisely.",
+ # "Convert spoken phrases into written form.",
+ # "Capture spoken words exactly.",
+ # "Transform audible speech into written sentences.",
+ # "Decipher spoken language verbatim.",
+ # "Translate spoken content with precision.",
+ # "Convert spoken dialogue into accurate text.",
+ # "Extract spoken words into precise text.",
+ # "Transform spoken language into verbatim text.",
+ # "Render spoken phrases into written words.",
+ # "Capture spoken content accurately.",
+ # "Transcribe spoken words verbatim.",
+ # "Convert audible speech into written sentences.",
+ # "Decipher spoken language precisely.",
+ # "Translate spoken phrases to exact text.",
+ # "Transform spoken dialogue into written form.",
+ # "Extract spoken words into textual representation.",
+ # "Capture spoken language verbatim.",
+ # "Convert spoken phrases accurately.",
+ # "Render spoken content into precise text.",
+ # "Transcribe spoken words with precision.",
+ # "Transform audible speech into written words.",
+ # "Decipher spoken dialogue exactly.",
+ # "Convert spoken language to verbatim text.",
+ # "Extract spoken words into written sentences.",
+ # "Capture spoken phrases precisely.",
+ # "Render spoken content into accurate text.",
+ # "Transcribe spoken dialogue verbatim.",
+ # "Convert audible speech into written form.",
+ # "Decipher spoken words accurately.",
+ # "Transform spoken language into written words.",
+ # "Translate spoken phrases precisely.",
+ # "Convert spoken content into verbatim text.",
+ # "Capture spoken words with precision.",
+ # "Transcribe spoken language accurately.",
+]
+
+
+def get_random_asr_prompt() -> str:
+ return random.choice(ASR_PROMPTS)
diff --git a/legacy/src/ultravox/train/env.py b/legacy/src/ultravox/train/env.py
new file mode 100644
index 00000000..68cafefe
--- /dev/null
+++ b/legacy/src/ultravox/train/env.py
@@ -0,0 +1,29 @@
+import logging
+import os
+
+try:
+ from azureml.core import Run
+except ImportError:
+ Run = None
+
+
+def set_env_vars_azure():
+ if "TOKENIZERS_PARALLELISM" not in os.environ:
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+ if os.environ.get("WANDB_PROJECT", default=None) is None:
+ os.environ["WANDB_PROJECT"] = "ultravox"
+
+ try:
+ run = Run.get_context()
+
+ os.environ["HF_ACCESS_TOKEN"] = run.get_secret(name="hf-access-token")
+ os.environ["WANDB_API_KEY"] = run.get_secret("wandb-api-key")
+
+ os.environ["CLEARML_WEB_HOST"] = "https://app.clear.ml"
+ os.environ["CLEARML_API_HOST"] = "https://api.clear.ml"
+ os.environ["CLEARML_FILES_HOST"] = "https://files.clear.ml"
+ os.environ["CLEARML_API_ACCESS_KEY"] = run.get_secret("clearml-api-access-key")
+ os.environ["CLEARML_API_SECRET_KEY"] = run.get_secret("clearml-api-secret-key")
+ except:
+ logging.warning("Failed to set environment variables from Azure Key Vault")
diff --git a/legacy/src/ultravox/train/models/__init__.py b/legacy/src/ultravox/train/models/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/legacy/src/ultravox/train/models/audio/__init__.py b/legacy/src/ultravox/train/models/audio/__init__.py
new file mode 100644
index 00000000..68eb4d8f
--- /dev/null
+++ b/legacy/src/ultravox/train/models/audio/__init__.py
@@ -0,0 +1,3 @@
+from .encoders import AudioEncLoader
+
+__all__ = ["AudioEncLoader"]
diff --git a/legacy/src/ultravox/train/models/audio/encoders.py b/legacy/src/ultravox/train/models/audio/encoders.py
new file mode 100644
index 00000000..28205508
--- /dev/null
+++ b/legacy/src/ultravox/train/models/audio/encoders.py
@@ -0,0 +1,143 @@
+import enum
+import logging
+import os
+import typing as t
+
+import torch
+import transformers
+import transformers.models.wav2vec2_bert.modeling_wav2vec2_bert as w2v2bert
+import transformers.models.whisper.modeling_whisper as whisper
+
+AUDIO_ENC_NAME_MAP = {
+ "wav2vec2": "facebook/wav2vec2-base-960h",
+ # ----------
+ # TODO: use this or `ylacombe/w2v-bert-2.0`?
+ # read comment in encoders.py
+ "wav2vec2-bert": "hf-audio/wav2vec2-bert-CV16-en",
+ "wav2vec2-bert-enconly": "hf-audio/wav2vec2-bert-CV16-en",
+ # ----- HuBERT -----
+ "hubert-base": "facebook/hubert-base-ls960",
+ "hubert-large": "facebook/hubert-large-ls960-ft",
+ "hubert-xlarge": "facebook/hubert-xlarge-ls960-ft",
+ # ----- Whisper -----
+ "whisper-large-v3": "openai/whisper-large-v3",
+ "whisper-medium": "openai/whisper-medium",
+}
+
+
+class AudioEncType(enum.Enum):
+ Wav2Vec2 = "wav2vec2"
+ Wav2Vec2Bert = "wav2vec2-bert"
+ # Wav2Vec2BertEncOnly = "wav2vec2-bert-enconly"
+ HuBERT = "hubert"
+ Whisper = "whisper"
+
+ @staticmethod
+ def from_str(s: str) -> "AudioEncType":
+ if "whisper" in s:
+ return AudioEncType.Whisper
+ if "hubert" in s:
+ return AudioEncType.HuBERT
+ if "wav2vec2-bert" in s:
+ return AudioEncType.Wav2Vec2Bert
+ if "wav2vec2" in s:
+ return AudioEncType.Wav2Vec2
+ raise ValueError(f"Unknown model_name: {s}")
+
+
+class AudioEncLoader:
+ model_class = transformers.AutoModel
+ audio_factor: int = 320
+ """
+ Describes the ratio: len(waveform) / len(audio_feates)
+ Given a fixed sampling rate of 16kHz, the frequency of the audio features is:
+ 16kHz / audio_factor, which is 50Hz in all cases by the full w2v2bert model.
+ """
+
+ def __init__(self, model_name: str, dtype: torch.dtype = torch.float32):
+ self.model_name = model_name
+ self.processor_name = model_name
+ self.dtype = dtype
+
+ model_type = AudioEncType.from_str(model_name)
+
+ if model_type in (AudioEncType.Wav2Vec2, AudioEncType.HuBERT):
+ # All HuBERT models use the same processor as Wav2Vec2
+ # The HuBERT-base processor is broken in HF, so I'm forcefully replacing the name for all
+ self.processor_name = "facebook/wav2vec2-base-960h"
+
+ if model_type != AudioEncType.Wav2Vec2Bert:
+ if self.dtype != torch.float32:
+ logging.warn(
+ f"Wav2Vec2, HuBERT, and Whisper do not work with Half precision dtypes. You chose {dtype}. Forcing Float32."
+ )
+ self.dtype = torch.float32
+
+ if model_type == AudioEncType.Wav2Vec2Bert:
+ raise ValueError("Wav2Vec2Bert is not supported at the moment.")
+
+ # TODO: this doesn't actually work, as a result wav2vec2-bert is broken
+ # if "bert-enconly" in model_name:
+ # # The encoder only model (without the adapter head)
+ # self.model_class = w2v2bert.Wav2Vec2BertEncoder
+ # assert False
+ # elif "wav2vec2-bert" in model_name:
+ # # The adapter reduces number of frames by 2
+ # self.audio_factor *= 2
+ # assert False
+
+ def get_model(
+ self,
+ ) -> t.Union[
+ transformers.Wav2Vec2Model,
+ # transformers.Wav2Vec2BertModel,
+ transformers.HubertModel,
+ whisper.WhisperEncoder,
+ w2v2bert.Wav2Vec2BertEncoder,
+ ]:
+ model = self.model_class.from_pretrained(
+ self.model_name,
+ torch_dtype=self.dtype,
+ token=os.environ.get("HF_ACCESS_TOKEN", None),
+ )
+
+ if isinstance(model, transformers.WhisperForConditionalGeneration):
+ # WhisperForConditionalGeneration -> WhisperModel
+ model = model.model
+ if isinstance(model, (w2v2bert.Wav2Vec2BertModel, whisper.WhisperModel)):
+ # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
+ # WhisperModel -> WhisperEncoder
+ model = model.encoder
+
+ assert isinstance(
+ model,
+ (
+ transformers.Wav2Vec2Model,
+ transformers.HubertModel,
+ w2v2bert.Wav2Vec2BertEncoder,
+ whisper.WhisperEncoder,
+ ),
+ )
+ return model
+
+ def get_processor(
+ self,
+ ) -> t.Union[
+ transformers.Wav2Vec2Processor,
+ transformers.Wav2Vec2BertProcessor,
+ transformers.WhisperProcessor,
+ ]:
+ return transformers.AutoProcessor.from_pretrained(self.processor_name)
+
+
+# TODO: use which model_name?
+# processor = transformers.AutoProcessor.from_pretrained(
+# "hf-audio/wav2vec2-bert-CV16-en"
+# )
+# # I checked and this is the same as `facebook/w2v-bert-2.0`
+# # The only difference is that the processor for this one works
+# model = transformers.Wav2Vec2BertModel.from_pretrained("ylacombe/w2v-bert-2.0")
+
+# W2V-BERT-2.0 token rate is
+# - 50Hz (20ms) after processor is applied and up to last conv layer (extract_features)
+# - 25Hz (40ms) after model (last_hidden_state)
diff --git a/legacy/src/ultravox/train/models/audio/mhubert.py b/legacy/src/ultravox/train/models/audio/mhubert.py
new file mode 100644
index 00000000..b8e813d9
--- /dev/null
+++ b/legacy/src/ultravox/train/models/audio/mhubert.py
@@ -0,0 +1,194 @@
+# Code taken from https://github.com/0nutation/SpeechGPT with some modifications
+# Original code is under Apache License 2.0
+import argparse
+import os
+
+import fairseq
+import joblib
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchaudio
+from torchaudio.functional import resample
+
+# logging.basicConfig(
+# format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+# datefmt="%Y-%m-%d %H:%M:%S",
+# level=os.environ.get("LOGLEVEL", "INFO").upper(),
+# stream=sys.stdout,
+# )
+# logger = logging.getLogger("generate_pseudo_language")
+
+# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class FeatureReader(object):
+ def __init__(
+ self,
+ ckpt_path,
+ layer,
+ max_chunk=1600000,
+ fp16=False,
+ sampling_rate=16000,
+ device="cuda" if torch.cuda.is_available() else "cpu",
+ ):
+ (
+ model,
+ cfg,
+ task,
+ ) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
+ self.model = model[0].eval().to(device)
+ self.task = task
+ self.layer = layer
+ self.max_chunk = max_chunk
+ self.fp16 = fp16
+ if fp16:
+ self.model.half()
+
+ self.layer_shift = 0
+ self.target_sample_hz = sampling_rate
+
+ logger.info(f"TASK CONFIG:\n{self.task.cfg}")
+
+ def read_audio(self, path):
+ wav, sr = torchaudio.load(path)
+ if sr != self.target_sample_hz:
+ wav = resample(wav, sr, self.target_sample_hz)
+ return wav
+
+ @torch.no_grad()
+ def get_feats(self, waveform):
+ x = waveform
+ with torch.no_grad():
+ if self.fp16:
+ x = x.half().cuda()
+ else:
+ x = x.float().cuda()
+ if self.task.cfg.normalize:
+ x = F.layer_norm(x, x.shape)
+ x = x.view(1, -1)
+
+ feat = []
+ for start in range(0, x.size(1), self.max_chunk):
+ x_chunk = x[:, start : start + self.max_chunk]
+ feat_chunk, _ = self.model.extract_features(
+ source=x_chunk,
+ padding_mask=None,
+ mask=False,
+ output_layer=self.layer + self.layer_shift,
+ )
+
+ feat.append(feat_chunk)
+ if len(feat) == 0:
+ return torch.zeros(0, 0)
+ return torch.cat(feat, 1).squeeze(0)
+
+
+class ApplyKmeans(object):
+ def __init__(self, km_path):
+ self.km_model = joblib.load(km_path)
+ self.C_np = self.km_model.cluster_centers_.transpose()
+ self.Cnorm_np = (self.C_np**2).sum(0, keepdims=True)
+
+ self.C = torch.from_numpy(self.C_np)
+ self.Cnorm = torch.from_numpy(self.Cnorm_np)
+ if torch.cuda.is_available():
+ self.C = self.C.cuda()
+ self.Cnorm = self.Cnorm.cuda()
+
+ def __call__(self, x):
+ if isinstance(x, torch.Tensor):
+ self.C = self.C.to(x)
+ self.Cnorm = self.Cnorm.to(x)
+ dist = (
+ x.pow(2).sum(1, keepdim=True) - 2 * torch.matmul(x, self.C) + self.Cnorm
+ )
+ return dist.argmin(dim=1).cpu().numpy()
+ else:
+ dist = (
+ (x**2).sum(1, keepdims=True)
+ - 2 * np.matmul(x, self.C_np)
+ + self.Cnorm_np
+ )
+ return np.argmin(dist, axis=1)
+
+
+class Speech2Unit(torch.nn.Module):
+ def __init__(
+ self,
+ ckpt_dir,
+ layer=11,
+ max_chunk=1600000,
+ fp16=False,
+ sampling_rate=16000,
+ device="cuda" if torch.cuda.is_available() else "cpu",
+ ):
+ """
+ Args:
+ ckpt_dir(str): path to hubert model dir(e.g. hubert_base_ls960.pt)
+ layer(int): feat from which layer of hubert models defauly by 9
+ max_chunk(int): default by 1600000
+ fp16(bool): default by False
+ sampling_rate(int): sampling_rate default by 16000
+ """
+ super().__init__()
+ self.device = device
+
+ ckpt_path = os.path.join(ckpt_dir, "mhubert_base_vp_en_es_fr_it3.pt")
+ km_path = os.path.join(ckpt_dir, "mhubert_base_vp_en_es_fr_it3_L11_km1000.bin")
+
+ self.feature_reader = FeatureReader(
+ ckpt_path, layer, max_chunk, fp16, sampling_rate, device=device
+ )
+ self.apply_kmeans = ApplyKmeans(km_path)
+
+ @staticmethod
+ def merge_duplicates(cluster_ids):
+ dup_cluster_list = []
+ duration_list = []
+ count = 1
+ for i in range(0, len(cluster_ids)):
+ if i + 1 < len(cluster_ids) and cluster_ids[i] == cluster_ids[i + 1]:
+ count += 1
+ else:
+ dup_cluster_list.append(cluster_ids[i])
+ duration_list.append(count)
+ count = 1
+ return dup_cluster_list, duration_list
+
+ def __call__(self, path=None, waveform=None, merged=True):
+ if waveform is None:
+ waveform = self.feature_reader.read_audio(path)
+
+ waveform = waveform.to(self.device)
+
+ feat = self.feature_reader.get_feats(waveform)
+ cluster_ids = self.apply_kmeans(feat).tolist()
+ dup_cluster_list, duration_list = self.merge_duplicates(cluster_ids)
+
+ # merged_units = (
+ # "" + "".join([f"<{str(x)}>" for x in dup_cluster_list]) + ""
+ # )
+ # unmerged_units = (
+ # "" + "".join([f"<{str(x)}>" for x in cluster_ids]) + ""
+ # )
+ merged_units = dup_cluster_list
+ unmerged_units = cluster_ids
+
+ if merged:
+ return merged_units
+ else:
+ return unmerged_units
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--wav", type=str)
+ args = parser.parse_args()
+
+ ckpt_dir = "speechgpt/utils/speech2unit/"
+
+ s2u = Speech2Unit(ckpt_dir=ckpt_dir)
+
+ units = s2u(args.wav)
+ print(units)
diff --git a/legacy/src/ultravox/train/models/multimodal/__init__.py b/legacy/src/ultravox/train/models/multimodal/__init__.py
new file mode 100644
index 00000000..2978aa2b
--- /dev/null
+++ b/legacy/src/ultravox/train/models/multimodal/__init__.py
@@ -0,0 +1,6 @@
+from .audio_llm import SpeechLM
+from .config import FreezingConfig
+from .config import SpeechLMConfig
+from .processors import SpeechLMProcessor
+
+__all__ = ["SpeechLM", "SpeechLMConfig", "FreezingConfig", "SpeechLMProcessor"]
diff --git a/legacy/src/ultravox/train/models/multimodal/audio_llm.py b/legacy/src/ultravox/train/models/multimodal/audio_llm.py
new file mode 100644
index 00000000..fa0ce8d9
--- /dev/null
+++ b/legacy/src/ultravox/train/models/multimodal/audio_llm.py
@@ -0,0 +1,391 @@
+import os
+import typing as t
+
+import peft
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import transformers
+import transformers.models
+from train.models import audio as audio_models
+from train.models import text as text_models
+
+from .config import FreezingConfig
+from .config import SpeechLMConfig
+
+
+class SpeechLM(transformers.LlamaPreTrainedModel, transformers.GenerationMixin):
+ config_class = SpeechLMConfig
+ base_model_prefix = "llm.model"
+ _tied_weights_keys = ["llm.lm_head.weight", "llm.embed_tokens.weight"]
+ # TODO: other generation kwargs?
+
+ def __init__(self, config: SpeechLMConfig):
+ self.keep_params = set()
+ self.config = config # Do not move this line after the init. It's point is just for the type hints
+ super().__init__(config)
+
+ self.llm: transformers.LlamaForCausalLM = (
+ transformers.LlamaForCausalLM.from_pretrained(
+ config.llm_name,
+ torch_dtype=config.torch_dtype,
+ token=os.environ.get("HF_ACCESS_TOKEN", None),
+ low_cpu_mem_usage=False,
+ # device_map=config.device_map,
+ )
+ )
+ self.generation_config = self.llm.generation_config
+
+ self.audio_enc = audio_models.AudioEncLoader(
+ config.audio_enc_name, dtype=config.torch_dtype
+ ).get_model()
+
+ # Taking out embed_tokens out of LLM so we can apply it to text only
+ self.embed_tokens: nn.Embedding = self.llm.get_input_embeddings()
+ self.llm.set_input_embeddings(nn.Identity())
+
+ # self.token_embed_dim: int = self.llm.config.hidden_size
+ self.token_embed_dim: int = self.embed_tokens.embedding_dim
+
+ if config.audio_squeeze_type in ["stride", "mean", "random"]:
+ audio_embed_in_dim = self.audio_enc.config.hidden_size
+ elif config.audio_squeeze_type == "stack":
+ audio_embed_in_dim = self.audio_enc.config.hidden_size * config.audio_stride
+ else:
+ raise ValueError(f"Unknown audio_squeeze_type: {config.audio_squeeze_type}")
+
+ self.audio_to_embed = nn.Sequential(
+ nn.LayerNorm(audio_embed_in_dim),
+ nn.Linear(
+ audio_embed_in_dim, self.token_embed_dim, dtype=config.torch_dtype
+ ),
+ nn.ReLU(), # SiLU?
+ nn.LayerNorm(self.token_embed_dim),
+ nn.Linear(
+ self.token_embed_dim, self.token_embed_dim, dtype=config.torch_dtype
+ ),
+ # nn.LayerNorm(self.token_embed_dim, elementwise_affine=False),
+ # nn.MultiheadAttention(
+ # embed_dim=self.token_embed_dim, num_heads=8, dtype=config.torch_dtype
+ # ),
+ )
+
+ if config.init_type == "small":
+ for layer in self.audio_to_embed:
+ if isinstance(layer, nn.Linear):
+ layer.weight.data.div_(10)
+ layer.bias.data.div_(10)
+
+ # Fuck me ...
+ transformers.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[
+ "audio-llm"
+ ] = self.__class__.__name__
+ if isinstance(self.audio_enc.config, transformers.HubertConfig):
+ self.audio_enc.config.mask_time_length = 6
+
+ def state_dict(self, *args, **kwargs):
+ named_params = dict(self.named_parameters())
+ state_dict: dict = super().state_dict(*args, **kwargs)
+
+ state_dict = {
+ k: v
+ for k, v in state_dict.items()
+ if k in self.keep_params
+ or (k in named_params and named_params[k].requires_grad)
+ }
+ return state_dict
+
+ def load_state_dict(
+ self,
+ state_dict: t.Dict[str, t.Any],
+ *args,
+ **kwargs,
+ ):
+ self.keep_params.update(set(state_dict.keys()))
+ return super().load_state_dict(state_dict, *args, **kwargs)
+
+ def apply_lora_configs(self, config: FreezingConfig):
+ # Freeze parameters or apply LoRA as needed
+ if config.llm_lora_config:
+ self.llm = text_models.apply_lora(
+ self.llm, lora_config=config.llm_lora_config
+ )
+ if config.audio_enc_lora_config:
+ self.audio_enc = text_models.apply_lora(
+ self.audio_enc, lora_config=config.audio_enc_lora_config
+ )
+ if config.freeze_text_embeds:
+ text_models.freeze_parameters(self.embed_tokens)
+ if config.freeze_audio_embeds:
+ text_models.freeze_parameters(self.audio_to_embed)
+
+ def forward_audio(self, audio_features: torch.Tensor) -> torch.Tensor:
+ # B: batch size, A: audio length, T: text length
+ # 768 is the hidden dim of Wave2Vec2 (audio_dim)
+ # BxA -> B x A/320 x 768
+ audio_features = audio_features.to(dtype=self.audio_enc.dtype)
+
+ audio_hidden: torch.Tensor = self.audio_enc.forward(
+ audio_features
+ ).last_hidden_state
+
+ audio_hidden = audio_hidden.to(self.dtype)
+
+ stride: int = self.config.audio_stride
+
+ # B x A/3200 x 768
+ audio_hidden = self._downsample_audio_features(audio_hidden, stride)
+
+ # B x A/3200 x 768 -> B x A/3200 x 2048
+ audio_embed = self.audio_to_embed(audio_hidden.contiguous())
+
+ # Lingo:
+ # * audio_features are the raw audio features
+ # * audio_hidden are the output of the audio encoder
+ # * audio_embed are the embeddings and by-pass the LLM embedding layer
+
+ return audio_embed
+
+ def _downsample_audio_features(self, audio_hidden: torch.Tensor, stride: int):
+ """Downsamples audio frames."""
+ # throw away frames to make sure audio_seq_len is divisible by audio_stride
+ hidden_seq_len = audio_hidden.shape[-2]
+ hidden_seq_len = hidden_seq_len - hidden_seq_len % stride
+ audio_hidden = audio_hidden[..., :hidden_seq_len, :]
+
+ # combine frames by applying: stack, drop, or average operation
+ if self.config.audio_squeeze_type == "stride":
+ # Throw away some frames to get to increase stride of audio
+ # Given a ~20ms initial stride and a 10x here we get ~200ms audio-tokens
+ # B x A/320 x 768 -> B x A/3200 x 768
+ audio_hidden = audio_hidden[..., ::stride, :]
+ elif self.config.audio_squeeze_type == "mean":
+ # reshape to B x A/3200 x 10 x 768 then average over the 10 frames
+ audio_hidden = audio_hidden.view(
+ *audio_hidden.shape[:-2],
+ -1,
+ stride,
+ audio_hidden.shape[-1],
+ ).mean(dim=-2)
+ elif self.config.audio_squeeze_type == "stack":
+ # reshape to B x A/3200 x 10 x 768 then stack the 10 frames
+ audio_hidden = audio_hidden.view(
+ *audio_hidden.shape[:-2],
+ -1,
+ stride * audio_hidden.shape[-1],
+ )
+ else:
+ # TODO: random drop type (Google SLM)
+ raise ValueError(
+ f"Unknown audio_squeeze_type: {self.config.audio_squeeze_type}"
+ )
+
+ return audio_hidden
+
+ # def inference(self, audio, tokenizer: transformers.LlamaTokenizer):
+ # with torch.no_grad():
+ # audio_embed = self.forward_audio(audio.unsqueeze(0))
+ # tokens = tokenizer.apply_chat_template(
+ # ['Repeat the following"'],
+ # tokenize=False,
+ # add_generation_prompt=True,
+ # return_tensors="pt",
+ # )
+
+ # prompt: please repeat the following: "[audio]"
+ # also todo: compute metrics ...
+
+ def _reorder_cache(self, past_key_values, beam_idx):
+ return self.llm._reorder_cache(past_key_values, beam_idx)
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ audio_features=None,
+ audio_token_start_idx=None,
+ audio_token_len=None,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ **kwargs,
+ # input_ids: torch.Tensor,
+ # audio_features: torch.Tensor,
+ # audio_token_mask: t.Optional[torch.Tensor] = None,
+ # *args,
+ # **kwargs
+ ) -> t.Dict[str, t.Any]:
+ model_input = self.llm.prepare_inputs_for_generation(
+ input_ids=input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ **kwargs,
+ )
+ if past_key_values is None:
+ # We only want to use audio features in the 1st generation step
+ model_input["audio_features"] = audio_features
+ model_input["audio_token_start_idx"] = audio_token_start_idx
+ model_input["audio_token_len"] = audio_token_len
+ # TODO: but why does LTU do this if embeds is not present?
+ # What are embeds?
+ return model_input
+
+ def forward(
+ self,
+ input_ids: torch.Tensor, # this is the text
+ audio_features: t.Optional[torch.Tensor] = None,
+ # TODO: this should be optional to allow for inference
+ inputs_embeds: t.Optional[torch.FloatTensor] = None,
+ labels: t.Optional[torch.Tensor] = None,
+ attention_mask: t.Optional[torch.Tensor] = None,
+ audio_token_start_idx: t.Optional[torch.Tensor] = None,
+ audio_token_len: t.Optional[torch.Tensor] = None,
+ past_key_values: t.Optional[t.Tuple] = None,
+ **kwargs,
+ ):
+ # V (vocab size for TinyLlama): 32000
+ # D (embedding dim): 2048
+ # B x T -> B x T x D
+ if inputs_embeds is None:
+ input_embeds = self.embed_tokens.forward(input_ids)
+
+ if audio_features is not None:
+ # TODO: there is a bug here if you use fp16. I couldn't figure it out, but it
+ # has to do with unscaling the gradients in automatic mixed precision. Not a priority to fix.
+
+ # B x A/3200 x D
+ audio_embed = self.forward_audio(audio_features)
+
+ input_embeds = self._combine_embeds(
+ audio_embed, input_embeds, audio_token_start_idx, audio_token_len
+ )
+ else:
+ input_embeds = input_embeds
+ audio_embed = None
+
+ input_embeds = input_embeds.contiguous()
+
+ if labels is not None and labels.shape[1] != input_embeds.shape[1]:
+ # This is a hack to allow full labels to be passed in even
+ # if we want to generate the response.
+ labels = labels[:, : input_embeds.shape[1]]
+ # labels = None
+
+ llm_output = self.llm.forward(
+ inputs_embeds=input_embeds,
+ labels=labels,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ **kwargs,
+ )
+
+ # print(f"{input_ids[0, :7]} ... {input_ids[0, -7:]}")
+ # max_logit = llm_output.logits.argmax(-1)
+ # print(f"{max_logit[0, :7]} ... {max_logit[0, -7:]}")
+ return llm_output
+
+ def _combine_embeds(
+ self,
+ audio_embed: torch.Tensor,
+ text_embeds: torch.Tensor,
+ audio_token_start_idx: t.Optional[torch.Tensor] = None,
+ audio_token_len: t.Optional[torch.Tensor] = None,
+ ):
+ """
+ Combining text and audio embeddings into the same tensor.
+
+ The tokens should roughly be: `concat(preample, audio, text)`
+ The above format doesn't account for some minor templating, but it's close enough.
+
+ The preamble is the same for all samples, so we can just take the first one
+ """
+
+ embeds = text_embeds
+
+ for i, (audio, start, length) in enumerate(
+ zip(audio_embed, audio_token_start_idx, audio_token_len)
+ ):
+ length = min(length, audio.shape[0])
+ embeds[i, start : start + length] = audio[:length]
+
+ return embeds
+
+ def print_trainable_parameters(self):
+ """
+ Prints the number of trainable parameters in the model (reuses Peft model's method)
+ """
+ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
+
+ trainable_params, all_param = count_params(self)
+
+ print(
+ f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
+ f" || trainable%: {100 * trainable_params / all_param:.1f}%"
+ )
+
+ llm_trainable_params, llm_all_params = count_params(self.llm)
+ audio_enc_trainable_params, audio_enc_all_params = count_params(self.audio_enc)
+
+ glue_trainable_params = (
+ trainable_params - llm_trainable_params - audio_enc_trainable_params
+ )
+ glue_all_params = all_param - llm_all_params - audio_enc_all_params
+
+ print(
+ f"LLM trainable%: {100 * llm_trainable_params / llm_all_params:.1f}%"
+ f" || Audio Encoder trainable%: {100 * audio_enc_trainable_params / audio_enc_all_params:.1f}%"
+ f" || Glue trainable%: {100 * glue_trainable_params / glue_all_params:.1f}%"
+ )
+
+ # def to(
+ # self,
+ # device: t.Optional[t.Union[int, torch.device]] = None,
+ # dtype: t.Optional[t.Union[torch.dtype, str]] = None,
+ # **kwargs,
+ # ):
+ # for name, child in self.named_children():
+ # if (
+ # name != "audio_enc"
+ # or dtype != torch.bfloat16
+ # or not self.config.is_auido_enc_w2vbert()
+ # ):
+ # child.to(device=device, dtype=dtype, **kwargs)
+ # else:
+ # logging.warning(
+ # "Skipping conversion of audio_enc to bfloat16 since it's not supported."
+ # )
+ # child.to(device=device, **kwargs)
+
+
+def audio_text_matching_loss(
+ audio_embed: torch.Tensor,
+ text_embeds: torch.Tensor,
+ text_mask: torch.LongTensor,
+):
+ audio_embed = audio_embed.float()
+ text_embeds = text_embeds.float()
+ text_mask = text_mask.float()
+
+ audio_token_count = audio_embed.shape[-2]
+ # text_token_count = text_embeds.shape[-2]
+
+ # We want to expand text embddings to match audio embeddings
+ # Values in channel are interpolated independently, hence the transpose.
+ # B x T x 2048 -> B x A/3200 x 2048
+ expanded_text_embeds = F.interpolate(
+ text_embeds.transpose(-1, -2), size=audio_token_count, mode="linear"
+ ).transpose(-1, -2)
+
+ # B x T -> B x 1 x T -> B x T x 1
+ expanded_mask = F.interpolate(
+ text_mask.unsqueeze(1), size=audio_token_count, mode="linear"
+ ).transpose(-1, -2)
+
+ # B x T x 2048
+ # element-wise loss: no reduction to allow masking padded tokens
+ # loss_ew = F.mse_loss(audio_embed, expanded_text_embeds, reduction="none") + F.l1_loss(audio_embed, expanded_text_embeds, reduction="none")
+ loss_ew = F.l1_loss(audio_embed, expanded_text_embeds, reduction="none")
+ # BxTx2048 * BxTx1 -> BxTx2048 -> 1 (scalar loss)
+ masked_loss = (loss_ew * expanded_mask).mean() / expanded_mask.mean()
+
+ return masked_loss
diff --git a/legacy/src/ultravox/train/models/multimodal/config.py b/legacy/src/ultravox/train/models/multimodal/config.py
new file mode 100644
index 00000000..77669750
--- /dev/null
+++ b/legacy/src/ultravox/train/models/multimodal/config.py
@@ -0,0 +1,90 @@
+import typing as t
+from dataclasses import asdict
+from dataclasses import dataclass
+from dataclasses import field
+from dataclasses import fields
+
+import peft
+import transformers
+from train.models.audio import encoders
+
+LLM_NAME_MAP = {
+ "tinyllama": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+ # ----------
+ "llama2": "meta-llama/Llama-2-7b-chat-hf",
+ "llama2-7b": "meta-llama/Llama-2-7b-chat-hf",
+ "llama2-13b": "meta-llama/Llama-2-13b-chat-hf",
+ "llama2-70b": "meta-llama/Llama-2-70b-chat-hf",
+ # ----------
+ "vicuna": "lmsys/vicuna-7b-v1.5",
+ "vicuna-13b": "lmsys/vicuna-13b-v1.5",
+ # ----------
+ "misral-7b": "mistralai/Mistral-7B-Instruct-v0.2",
+}
+
+
+@dataclass(init=False)
+class SpeechLMConfig(transformers.PretrainedConfig):
+ llm_name: str = "tinyllama"
+ audio_enc_name: str = "wav2vec2-base"
+ audio_stride: int = 10
+ use_cpu: bool = False
+ audio_squeeze_type: str = "stack"
+ init_type: str = "default" # "default" or "small"
+ # device_map: t.Union[str, t.Dict[str, int]] = "auto"
+
+ def __init__(self, **kwargs):
+ names = set([f.name for f in fields(self)])
+ hf_names = set(kwargs.keys()) - names
+ for k, v in kwargs.items():
+ if k in names:
+ setattr(self, k, v)
+ super().__init__(**{k: v for k, v in kwargs.items() if k in hf_names})
+ self.__post_init__()
+
+ def is_audio_enc_w2vbert(self):
+ return "bert" in self.audio_enc_name
+
+ def __post_init__(self):
+ if self.audio_squeeze_type not in ["stride", "mean", "stack", "random"]:
+ raise ValueError(f"Unknown audio_squeeze_type: {self.audio_squeeze_type}")
+
+ # support shortened names
+ if self.llm_name in LLM_NAME_MAP:
+ self.llm_name = LLM_NAME_MAP[self.llm_name]
+
+ if self.audio_enc_name in encoders.AUDIO_ENC_NAME_MAP:
+ self.audio_enc_name = encoders.AUDIO_ENC_NAME_MAP[self.audio_enc_name]
+
+ super().__init__()
+
+
+@dataclass
+class LoraConfigSimplified:
+ """
+ For some reason I get an error when I try to use the peft.LoraConfig class
+ So this is a simplified proxy for that class
+ """
+
+ r: int = 0
+ lora_alpha: float = 8
+ target_modules: t.Optional[t.Union[t.List[str], str]] = field(
+ default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
+ )
+
+
+@dataclass
+class FreezingConfig:
+ llm_lora_config: LoraConfigSimplified = None
+ audio_enc_lora_config: LoraConfigSimplified = None
+ freeze_text_embeds: bool = False
+ freeze_audio_embeds: bool = False
+
+ def __post_init__(self):
+ if self.llm_lora_config is not None:
+ self.llm_lora_config = peft.LoraConfig(**asdict(self.llm_lora_config))
+
+ if self.audio_enc_lora_config is not None:
+ self.audio_enc_lora_config = peft.LoraConfig(
+ **asdict(self.audio_enc_lora_config)
+ )
diff --git a/legacy/src/ultravox/train/models/multimodal/processors.py b/legacy/src/ultravox/train/models/multimodal/processors.py
new file mode 100644
index 00000000..30c23b51
--- /dev/null
+++ b/legacy/src/ultravox/train/models/multimodal/processors.py
@@ -0,0 +1,26 @@
+import typing as t
+from dataclasses import dataclass
+
+import transformers
+from train.models import audio as audio_models
+
+from .config import SpeechLMConfig
+
+
+@dataclass
+class SpeechLMProcessor(transformers.ProcessorMixin):
+ tokenizer: transformers.LlamaTokenizer
+ audio_processor: t.Union[
+ transformers.Wav2Vec2Processor, transformers.Wav2Vec2BertProcessor
+ ]
+ total_audio_stride: int
+
+ @staticmethod
+ def from_config(config: SpeechLMConfig):
+ audio_model_loader = audio_models.AudioEncLoader(config.audio_enc_name)
+ total_audio_stride = audio_model_loader.audio_factor * config.audio_stride
+ return SpeechLMProcessor(
+ tokenizer=transformers.LlamaTokenizerFast.from_pretrained(config.llm_name),
+ audio_processor=audio_model_loader.get_processor(),
+ total_audio_stride=total_audio_stride,
+ )
diff --git a/legacy/src/ultravox/train/models/text/__init__.py b/legacy/src/ultravox/train/models/text/__init__.py
new file mode 100644
index 00000000..709f0c61
--- /dev/null
+++ b/legacy/src/ultravox/train/models/text/__init__.py
@@ -0,0 +1,5 @@
+from .llms import apply_lora
+from .llms import freeze_parameters
+from .llms import get_llm
+
+__all__ = ["get_llm", "apply_lora", "freeze_parameters"]
diff --git a/legacy/src/ultravox/train/models/text/llms.py b/legacy/src/ultravox/train/models/text/llms.py
new file mode 100644
index 00000000..6f5576bc
--- /dev/null
+++ b/legacy/src/ultravox/train/models/text/llms.py
@@ -0,0 +1,38 @@
+import os
+import typing as t
+
+import peft
+import torch
+import transformers
+
+
+def get_llm(model_name: str, cpu=False, dtype=torch.float32):
+ pipe = transformers.pipeline(
+ "text-generation",
+ model=model_name,
+ torch_dtype=dtype,
+ device_map="cpu" if cpu else "cuda",
+ token=os.environ.get("HF_ACCESS_TOKEN", None),
+ )
+
+ if "pad_token" not in pipe.tokenizer.special_tokens_map:
+ pipe.tokenizer.add_special_tokens({"pad_token": ""})
+
+ return pipe.model, pipe.tokenizer
+
+
+def apply_lora(
+ model: torch.nn.Module,
+ lora_config: t.Optional[peft.LoraConfig] = None,
+):
+ if lora_config.r == 0:
+ freeze_parameters(model)
+ else:
+ model = peft.get_peft_model(model, lora_config)
+
+ return model
+
+
+def freeze_parameters(model: torch.nn.Module):
+ for param in model.parameters():
+ param.requires_grad = False
diff --git a/legacy/src/ultravox/train/models/utils.py b/legacy/src/ultravox/train/models/utils.py
new file mode 100644
index 00000000..175f81c3
--- /dev/null
+++ b/legacy/src/ultravox/train/models/utils.py
@@ -0,0 +1,34 @@
+import typing as t
+
+import transformers
+
+from . import audio as audio_models
+from . import multimodal as multimodal_models
+from . import text as text_models
+
+
+def create_audiollm_model(
+ config: multimodal_models.SpeechLMConfig,
+) -> t.Tuple[
+ multimodal_models.SpeechLM,
+ transformers.LlamaTokenizer,
+ transformers.Wav2Vec2Processor,
+ int,
+]:
+ audio_enc, audio_proc, audio_factor = audio_models.get_audio_encoder(
+ config.audio_enc_name, dtype=config.dtype
+ )
+ llm, tokenizer = text_models.get_llm(
+ model_name=config.llm_name, cpu=config.use_cpu, dtype=config.dtype
+ )
+
+ model = multimodal_models.SpeechLM(
+ audio_enc=audio_enc,
+ llm=llm,
+ audio_stride=config.audio_stride,
+ dtype=config.dtype,
+ )
+
+ # TODO: model to device
+
+ return model, tokenizer, audio_proc, audio_factor * config.audio_stride
diff --git a/legacy/src/ultravox/train/train.py b/legacy/src/ultravox/train/train.py
new file mode 100644
index 00000000..0d0fb22f
--- /dev/null
+++ b/legacy/src/ultravox/train/train.py
@@ -0,0 +1,258 @@
+import glob
+import logging
+import os
+import typing as t
+from dataclasses import asdict
+from dataclasses import dataclass
+
+import evaluate
+import numpy as np
+import pyrallis
+import safetensors.torch
+import torch
+import torch.distributed.elastic.multiprocessing.errors
+import transformers
+import transformers.models
+import wandb
+
+from . import configs_base
+from . import data
+from . import env
+from .models import multimodal as multimodal_models
+
+
+def training_function(config: configs_base.TrainConfig):
+ # set seed
+ transformers.set_seed(config.seed)
+ np.random.seed(config.seed)
+
+ if config.allow_tf32:
+ torch.backends.cuda.matmul.allow_tf32 = True
+
+ dtype = (
+ torch.bfloat16
+ if config.bf16
+ else (torch.float16 if config.fp16 else torch.float32)
+ )
+
+ # config.model.torch_dtype = dtype
+
+ model = multimodal_models.SpeechLM(config.model)
+ model.apply_lora_configs(config=config.freezing_config)
+ model.print_trainable_parameters()
+ processor = multimodal_models.SpeechLMProcessor.from_config(config.model)
+
+ if not config.use_cpu:
+ model = model.to("cuda")
+
+ model.llm.config.use_cache = False
+ # set pad token to unk. we want this to be different from the eos token
+ processor.tokenizer.pad_token_id = processor.tokenizer.eos_token_id
+ processor.tokenizer.padding_side = "left" # to allow for fast generation
+
+ # TODO: move inside model
+ sampling_rate = 16_000 # 16kHz
+
+ print(f"Audio token freq: {round(sampling_rate / processor.total_audio_stride)} Hz")
+
+ if config.model_load_path:
+ logging.info(f"Loading model state dict from {config.model_load_path}")
+ for path in glob.glob(str(config.model_load_path)):
+ state_dict = safetensors.torch.load_file(path)
+ mismatch = model.load_state_dict(state_dict, strict=False)
+ if mismatch.unexpected_keys:
+ raise ValueError(
+ f"Unexpected keys in state dict: {mismatch.unexpected_keys}"
+ )
+
+ train_val_dataset = data.get_dataset(
+ dataset_name=config.dataset_name,
+ sampling_rate=sampling_rate,
+ dev_env=config.use_cpu,
+ streaming=config.dataset_streaming,
+ max_duration_in_seconds=config.max_audio_duration_in_seconds,
+ val_max_num_samples=128,
+ # val_max_num_samples=8,
+ )
+ preproc = data.AudioTextTokenizer(
+ processor.audio_processor,
+ processor.tokenizer,
+ processor.total_audio_stride,
+ cfg=config.audio_tokenizer_config,
+ # inference_mode=True,
+ )
+ train_ds, val_ds = [ds.map(preproc) for ds in train_val_dataset]
+
+ data_collator = data.DataCollatorForSeq2SeqWithAudio(
+ processor.tokenizer,
+ pad_to_multiple_of=8, # This won't be needed when we move back to concat model
+ return_tensors="pt",
+ padding=True,
+ audio_dtype=dtype,
+ )
+
+ # eval_dataset = data.get_dataset_librispeech(train=False)
+ # TODO: wrap with pytorch dataloader and sampler?
+
+ # Define training args
+ training_args = transformers.Seq2SeqTrainingArguments(
+ # torch_compile=True,
+ optim=config.optimizer_type,
+ output_dir=config.output_dir,
+ per_device_train_batch_size=config.per_device_train_batch_size,
+ per_device_eval_batch_size=config.per_device_eval_batch_size,
+ gradient_accumulation_steps=config.gradient_accumulation_steps,
+ seed=config.seed,
+ use_cpu=config.use_cpu,
+ fp16=config.fp16, # Be careful when using FP16
+ bf16=config.bf16, # Use BF16 if available
+ learning_rate=config.lr,
+ warmup_ratio=config.lr_warmup_ratio,
+ warmup_steps=config.lr_warmup_steps,
+ weight_decay=config.weight_decay,
+ # TODO: couldn't find "exponential decaying schedule" in HF trainer.
+ lr_scheduler_type=config.lr_scheduler_type,
+ lr_scheduler_kwargs=config.lr_scheduler_kwargs,
+ num_train_epochs=config.num_epochs,
+ max_steps=config.max_steps,
+ deepspeed=config.deepspeed,
+ # gradient_checkpointing=config.gradient_checkpointing,
+ # logging & evaluation strategies
+ logging_dir=config.output_dir / "logs",
+ logging_strategy="steps",
+ logging_steps=config.logging_steps,
+ logging_first_step=True,
+ dataloader_pin_memory=False,
+ evaluation_strategy="steps",
+ eval_steps=config.eval_steps,
+ eval_accumulation_steps=config.eval_accumulation_steps,
+ save_strategy="steps",
+ save_steps=config.save_steps,
+ run_name=config.exp_name,
+ dataloader_num_workers=config.num_workers,
+ report_to=config.report_logs_to,
+ predict_with_generate=True,
+ generation_config=transformers.GenerationConfig(max_new_tokens=1),
+ )
+
+ # TODO: are we shuffling the data?
+
+ # Create Trainer instance
+ trainer = transformers.Seq2SeqTrainer(
+ model=model,
+ tokenizer=processor.tokenizer,
+ args=training_args,
+ train_dataset=train_ds,
+ eval_dataset=val_ds,
+ data_collator=data_collator,
+ compute_metrics=ComputeMetrics(processor.tokenizer),
+ )
+
+ # Start training
+
+ if "wandb" in config.report_logs_to:
+ wandb.init(
+ project=os.getenv("WANDB_PROJECT", "ultravox"),
+ config=asdict(config),
+ name=config.exp_name,
+ # TODO: run name, etc from HF callback
+ )
+
+ print("Initial evaluation:", trainer.evaluate())
+ trainer.train(resume_from_checkpoint=config.resume_from_checkpoint)
+
+ trainer.save_model(config.output_dir)
+
+
+@dataclass
+class ComputeMetrics:
+ tokenizer: transformers.LlamaTokenizer
+ preamble: str = "Transcript:"
+
+ def __post_init__(self):
+ self.cer_metric = evaluate.load("cer")
+ self.wer_metric = evaluate.load("wer")
+
+ def _remove_preamble(self, text: str) -> str:
+ """
+ If a preamble is present, remove it and return the text after it.
+ Will only remove the first occurence of the preamble.
+ Will not modify the text (other than stripping spaces) if the preamble is not found.
+
+ Examples:
+ >>> _remove_preamble("hello world")
+ "hello world"
+ >>> _remove_preamble("Transcript: hello")
+ "hello"
+ >>> _remove_preamble("Transcript: hello Transcript: world")
+ "hello Transcript: world"
+ """
+ return text.split(self.preamble, maxsplit=1)[-1].strip()
+
+ def __call__(self, eval_preds: transformers.EvalPrediction):
+ targets = eval_preds.label_ids
+ logits = eval_preds.predictions
+ if logits.ndim > targets.ndim:
+ logits = logits.argmax(axis=-1)
+
+ if logits.shape[1] == targets.shape[1] + 1:
+ # This is because we set max_new_tokens to 1 so that we can use
+ # predict_with_generate which is faster.
+ # I can make this cover more cases, but I actually want it to fail
+ # since I don't know whether that would be a bug or not.
+ logits = logits[:, : targets.shape[1]]
+
+ ref_strs: t.List[str] = []
+ pred_strs: t.List[str] = []
+ accuracies: t.List[float] = []
+ # for logit, target in zip(logits[..., :-1], targets[..., 1:]):
+ # No need to shift logits left? Is that because of Seq2Seq?
+ for logit, target in zip(logits, targets):
+ idx = target > 0
+ # Remove special tokens : 1, : 0, and : 2
+ ref_ids = [x for x in target[idx] if x > 2]
+ pred_ids = [x for x in logit[idx] if x > 2]
+ # CER complains if the string is empty
+ ref_strs.append(self.tokenizer.decode(ref_ids).strip() or "")
+ pred_strs.append(self.tokenizer.decode(pred_ids).strip() or "")
+
+ # TODO: is text sometimes empty?
+ accuracies.append(
+ np.mean(target[idx] == logit[idx]) if idx.sum() > 0 else np.nan
+ )
+
+ ref_strs = [self._remove_preamble(x).lower() for x in ref_strs]
+ pred_strs = [self._remove_preamble(x).lower() for x in pred_strs]
+
+ # for ref, pred in zip(ref_strs, pred_strs):
+ # print(f"REF: {ref}\nPRED: {pred}\n")
+
+ return {
+ # "perplexity" TODO?
+ "cer_assisted": self.cer_metric.compute(
+ predictions=pred_strs, references=ref_strs
+ ),
+ "wer_assisted": self.wer_metric.compute(
+ predictions=pred_strs, references=ref_strs
+ ),
+ # "cheat_ratio": cheat_ratio,
+ "token_accuracy": np.nanmean(accuracies),
+ }
+
+
+def main():
+ logging.basicConfig(level=logging.DEBUG)
+ pyrallis.decode.register(
+ t.List[str], lambda x: x.split(",") if isinstance(x, str) else x
+ )
+ cfg = pyrallis.parse(config_class=configs_base.TrainConfig)
+ logging.warning(f"\n\nRunning with config:\n{cfg}\n\n")
+
+ training_function(cfg)
+
+ wandb.finish()
+
+
+if __name__ == "__main__":
+ env.set_env_vars_azure()
+ main()
diff --git a/mcloud.yaml b/mcloud.yaml
new file mode 100644
index 00000000..e02fa95c
--- /dev/null
+++ b/mcloud.yaml
@@ -0,0 +1,18 @@
+# Gazelle POC training configuration
+
+name: gazelle-poc
+image: mosaicml/composer:latest
+compute:
+ gpus: 8
+ cluster: r7z22
+integrations:
+ - integration_type: git_repo
+ git_repo: fixie-ai/ultravox
+ git_branch: $UV_BRANCH
+ pip_install: -r requirements.txt
+command: >-
+ cd ultravox && torchrun --nproc_per_node=8 -m ultravox.training.train --config_path ultravox/training/configs/$UV_CONFIG
+env_variables:
+ MLFLOW_TRACKING_URI: databricks
+ UV_BRANCH: main
+ UV_CONFIG: asr_llama.yaml
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 00000000..0183ffbd
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,5 @@
+[mypy]
+ignore_missing_imports = True
+
+[mypy-ultravox/model/gazelle.*]
+ignore_errors = True
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..04d4aee0
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,30 @@
+[tool.poetry]
+name = "ultravox"
+version = "0.1.0"
+description = ""
+authors = ["Fixie.ai Team "]
+packages = [{ include = "ultravox/" }]
+
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = true
+warn_unused_configs = true
+
+[tool.isort]
+profile = "black"
+single_line_exclusions = ["typing", "collections.abc", "typing_extensions"]
+skip = [
+ "ultravox/model/gazelle",
+ "venv",
+ ".venv",
+ "third_party",
+]
+
+[tool.black]
+extend-exclude = '''
+/(
+ | ultravox/model/gazelle",
+ | third_party
+ | venv
+)/
+'''
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 00000000..eea2c180
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1 @@
+[pytest]
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 00000000..18722d41
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,19 @@
+# dev dependencies for formatting and linting
+black
+isort
+mypy
+autoflake
+
+# dev dependencies for testing
+pytest
+
+# dependencies for tools
+fsspec
+sounddevice
+
+mosaicml-cli
+gcsfs
+gradio
+gradio_client
+
+gpustat # for monitoring GPU usage: run "gpustat" on the machine
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..72fbdeae
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,27 @@
+# Core
+transformers[torch]>=4.39.3
+bitsandbytes>=0.42.0
+peft
+simple_parsing
+
+# Data processing
+librosa>=0.10.0
+requests==2.22.0
+datasets
+mosaicml-streaming
+nltk
+truecase
+sentencepiece
+protobuf
+
+# evals
+dataclasses-json
+openai
+jiwer
+
+# Monitoring
+tensorboardx
+wandb
+neptune
+mlflow
+
diff --git a/scripts/vscode_tunnel.sh b/scripts/vscode_tunnel.sh
new file mode 100755
index 00000000..db36f8ce
--- /dev/null
+++ b/scripts/vscode_tunnel.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Copied from https://docs.mosaicml.com/projects/mcli/en/latest/training/interactive.html
+# This script is used to create a tunnel to the MosaicML training environment
+# It allows you to use the VSCode interface to interact with the training environment
+# Very useful for debugging and monitoring training runs
+
+trap '/tmp/code tunnel unregister' EXIT
+cd /tmp && curl -Lk 'https://code.visualstudio.com/sha/download?build=stable&os=cli-alpine-x64' --output vscode_cli.tar.gz
+tar -xf vscode_cli.tar.gz
+/tmp/code tunnel --accept-server-license-terms --no-sleep --name mml-dev-01
\ No newline at end of file
diff --git a/setup.sh b/setup.sh
new file mode 100644
index 00000000..4cd89885
--- /dev/null
+++ b/setup.sh
@@ -0,0 +1,9 @@
+cd $HOME
+mkdir workspace
+cd workspace
+git clone git@github.com:fixie-ai/ultravox.git -b main
+cd ultravox
+mkdir -p ~/.local/bin
+curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to ~/.local/bin
+just install
+bash ./scripts/vscode_tunnel.sh
diff --git a/ultravox/__init__.py b/ultravox/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/data/__init__.py b/ultravox/data/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/data/datasets.py b/ultravox/data/datasets.py
new file mode 100644
index 00000000..8944ea1c
--- /dev/null
+++ b/ultravox/data/datasets.py
@@ -0,0 +1,639 @@
+import abc
+import base64
+import dataclasses
+import enum
+import io
+import logging
+import os
+import tempfile
+from typing import Any, Callable, Dict, List, Optional, Sequence
+
+import datasets
+import librosa
+import numpy as np
+import requests
+import soundfile as sf
+import streaming as mds
+import torch
+import transformers
+from torch.utils import data
+
+from ultravox.data import text_proc
+
+SAMPLE_RATE = 16000
+
+TRANSCRIBE_INPUT_TASK = "transcribe_input"
+TRANSCRIBE_OUTPUT_TASK = "transcribe_output"
+ANSWER_TASK = "answer"
+
+TRANSCRIBE_PROMPTS = [
+ # from Gazelle
+ "Transcribe <|audio|>",
+ "Transcribe exactly what is said here <|audio|>",
+ "Repeat exactly what is written here: <|audio|>",
+ "Write exactly what was said: <|audio|>",
+ "First listen to the clip. Then, transcribe exactly what is said. <|audio|>",
+ # from https://arxiv.org/pdf/2402.08846
+ "Transcribe speech to text: <|audio|>",
+ # from GPT-4
+ "Capture every word from <|audio|> verbatim",
+ "Convert speech to text from <|audio|>",
+ "Listen and transcribe the complete text from <|audio|>",
+ "Record in writing what is spoken in <|audio|>",
+ "Transcribe the spoken words from <|audio|> with exact wording and punctuation",
+]
+ANSWER_PROMPTS = [
+ # from Gazelle
+ "Listen to <|audio|> and respond to it",
+ "Listen and respond: <|audio|>",
+ "Respond to <|audio|>",
+ "Respond to the user <|audio|>",
+ "<|audio|>",
+ "<|audio|>", # repeated to emphasize not needing a prompt for Q&A tasks
+ "Respond to this question: \n<|audio|>",
+ "Continue the conversation after <|audio|>",
+ "First listen to the clip: <|audio|>\n How would you respond?",
+ "<|audio|> - respond",
+ "<|audio|>\n Respond to the question",
+]
+
+# TODO(juberti): set these in the environment so they don't need to be hard-coded here.
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "service_account.json"
+os.environ["GOOGLE_CLOUD_PROJECT"] = "fixie-training"
+
+
+# Silence the spurious warnings coming from the MosaicML streaming library.
+logging.getLogger("streaming.base.dataset").setLevel(logging.ERROR)
+
+
+@dataclasses.dataclass
+class DataCollatorForSeq2SeqWithAudio(transformers.DataCollatorForSeq2Seq):
+ def __call__(self, features, *args, **kwargs):
+ audio_features = [f.pop("audio_values") for f in features]
+ batch = super().__call__(features, *args, **kwargs)
+ batch["audio_values"] = torch.nn.utils.rnn.pad_sequence(
+ audio_features, batch_first=True
+ )
+ return batch
+
+
+def audio_from_file(path: str) -> np.ndarray:
+ """Load audio from a file, converting to float32 PCM @ 16 kHz."""
+ audio, _ = librosa.load(path, sr=SAMPLE_RATE)
+ assert audio.dtype == np.float32
+ return audio
+
+
+def audio_from_buf(buf: bytes) -> np.ndarray:
+ """Load audio from a buffer, converting to float32 PCM @ 16 kHz."""
+ audio, _ = librosa.load(io.BytesIO(buf), sr=SAMPLE_RATE)
+ assert audio.dtype == np.float32
+ return audio
+
+
+def audio_to_wav(audio: np.ndarray, sample_rate: int = SAMPLE_RATE) -> bytes:
+ """Convert audio to WAV format, 16-bit PCM @ 16 kHz."""
+ assert audio.dtype == np.float32
+ with io.BytesIO() as buf:
+ sf.write(buf, audio, sample_rate, format="WAV", subtype="PCM_16")
+ return buf.getvalue()
+
+
+def audio_to_wav_base64(audio: np.ndarray, sample_rate: int = SAMPLE_RATE) -> str:
+ """Convert audio to a base64-encoded WAV file."""
+ return base64.b64encode(audio_to_wav(audio, sample_rate)).decode("utf-8")
+
+
+def audio_to_data_uri(audio: np.ndarray, sample_rate: int = SAMPLE_RATE) -> str:
+ """Convert audio to a data URI."""
+ return f"data:audio/wav;base64,{audio_to_wav_base64(audio, sample_rate)}"
+
+
+def messages_from_prompt(prompt: str) -> List[Dict[str, str]]:
+ return [{"role": "user", "content": prompt}]
+
+
+@dataclasses.dataclass
+class VoiceSample:
+ @staticmethod
+ def from_json(data: Dict[str, Any]) -> "VoiceSample":
+ """Convert from JSON format; audio is expected as base64ed WAV."""
+ bytes = base64.b64decode(data["audio"])
+ return VoiceSample(data["messages"], audio_from_buf(bytes))
+
+ @staticmethod
+ def from_prompt(prompt: str) -> "VoiceSample":
+ """Create a VoiceSample from a prompt only."""
+ return VoiceSample(messages_from_prompt(prompt), None)
+
+ @staticmethod
+ def from_prompt_and_file(prompt: str, path: str) -> "VoiceSample":
+ """Create a VoiceSample from a prompt and an audio file."""
+ return VoiceSample(messages_from_prompt(prompt), audio_from_file(path))
+
+ @staticmethod
+ def from_prompt_and_buf(prompt: str, buf: bytes) -> "VoiceSample":
+ """Create a VoiceSample from a prompt and an encoded audio buffer."""
+ return VoiceSample(messages_from_prompt(prompt), audio_from_buf(buf))
+
+ @staticmethod
+ def from_prompt_and_raw(
+ prompt: str, buf: np.ndarray, sample_rate: int
+ ) -> "VoiceSample":
+ """Create a VoiceSample from a prompt and raw audio data with sample rate."""
+ # Keep in native sample rate; we'll resample later if needed.
+ return VoiceSample(messages_from_prompt(prompt), buf, sample_rate)
+
+ def to_json(self) -> Dict[str, Any]:
+ """Convert to JSON format; audio is written as base64ed WAV."""
+ obj: Dict[str, Any] = {"messages": self.messages}
+ if self.audio is not None:
+ obj["audio"] = audio_to_wav_base64(self.audio, self.sample_rate)
+ return obj
+
+ def __post_init__(self):
+ """Ensure audio is float32 PCM."""
+ if self.audio is not None:
+ if self.audio.dtype == np.float64:
+ self.audio = self.audio.astype(np.float32)
+ assert (
+ self.audio.dtype == np.float32
+ ), f"Unexpected audio dtype: {self.audio.dtype}"
+ assert self.audio.ndim == 1, f"Unexpected audio shape: {self.audio.shape}"
+
+ messages: List[Dict[str, str]]
+ """List of messages, each with a "role" and "content" field."""
+ audio: Optional[np.ndarray] = None
+ """Audio data as float32 PCM @ `sample_rate`."""
+ sample_rate: int = SAMPLE_RATE
+ """Audio sample rate in Hz."""
+ audio_transcript: Optional[str] = None
+ """For evaluations, the known transcript of the audio."""
+
+
+class DatasetSplit(str, enum.Enum):
+ TRAIN = "train"
+ VALIDATION = "validation"
+
+
+@dataclasses.dataclass
+class VoiceDatasetArgs:
+ data_dir: Optional[str] = None
+ num_prompts: int = 1
+ shuffle: bool = False
+ shuffle_seed: int = 42
+ max_audio_duration_secs: Optional[float] = None
+ use_mds: bool = False
+ mds_batch_size: int = 32
+ split: DatasetSplit = DatasetSplit.TRAIN
+
+ def __post_init__(self):
+ if isinstance(self.split, str):
+ self.split = DatasetSplit(self.split.lower())
+
+
+class VoiceDataset(abc.ABC, data.IterableDataset):
+ """
+ Base class for streaming voice datasets.
+ Wraps a Hugging Face dataset or MDS-formatted dataset from GCP.
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__()
+ self._args = args
+ self._session: Optional[requests.Session] = None
+
+ def _init_dataset(self, dataset: data.Dataset) -> None:
+ self._dataset = dataset
+
+ def _load_audio_dataset(
+ self,
+ path: str,
+ name: Optional[str] = None,
+ *,
+ split: Optional[str] = None,
+ shuffle: Optional[bool] = None,
+ streaming: bool = True,
+ ) -> data.Dataset:
+ logging.info(f"Loading dataset {path} {name} {split} {shuffle} {streaming}")
+ if shuffle is None:
+ shuffle = self._args.shuffle
+ if self._args.use_mds:
+ gcs_path = path.replace("/", "_")
+ if name:
+ gcs_path += f"/{name}"
+ if split:
+ gcs_path += f"/{split}"
+ url = f"gs://fixie-datasets/mds/{gcs_path}"
+ temp_dir = os.path.join(
+ tempfile.gettempdir(), f"mds_{gcs_path.replace('/', '_')}"
+ )
+ return mds.StreamingDataset(
+ remote=url,
+ local=temp_dir,
+ batch_size=self._args.mds_batch_size,
+ shuffle=shuffle,
+ shuffle_seed=self._args.shuffle_seed,
+ )
+ else:
+ dataset = datasets.load_dataset(
+ path, name, split=split, trust_remote_code=True, streaming=streaming
+ ).cast_column("audio", datasets.Audio(sampling_rate=SAMPLE_RATE))
+ if shuffle:
+ dataset = dataset.shuffle(seed=self._args.shuffle_seed)
+ return dataset
+
+ def __iter__(self):
+ for i, row in enumerate(self._dataset):
+ sample = self._get_sample(i, row)
+ if (
+ self._args.max_audio_duration_secs is None
+ or sample.audio.shape[-1] / SAMPLE_RATE
+ <= self._args.max_audio_duration_secs
+ ):
+ yield sample
+
+ @abc.abstractmethod
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ pass
+
+ def _get_answer_prompt(self, idx: int) -> str:
+ prompt_idx = idx % min(self._args.num_prompts, len(ANSWER_PROMPTS))
+ return ANSWER_PROMPTS[prompt_idx]
+
+ def _get_transcribe_prompt(self, idx: int) -> str:
+ prompt_idx = idx % min(self._args.num_prompts, len(TRANSCRIBE_PROMPTS))
+ return TRANSCRIBE_PROMPTS[prompt_idx]
+
+ def _get_answer_messages(self, idx: int, text: str) -> List[Dict[str, str]]:
+ return [
+ {"role": "user", "content": self._get_answer_prompt(idx)},
+ {"role": "assistant", "content": text},
+ ]
+
+ def _get_transcribe_messages(self, idx: int, text: str) -> List[Dict[str, str]]:
+ return [
+ {"role": "user", "content": self._get_transcribe_prompt(idx)},
+ {"role": "assistant", "content": text},
+ ]
+
+ def _get_audio(self, row: transformers.BatchFeature) -> np.ndarray:
+ # Hugging Face datasets have an Audio object, with array and sampling_rate fields.
+ # For MDS, this object is flattened into audio_array and audio_sampling_rate fields.
+ if "audio" in row:
+ audio = row["audio"]["array"]
+ sampling_rate = row["audio"]["sampling_rate"]
+ elif "audio_array" in row:
+ audio = row["audio_array"]
+ sampling_rate = row["audio_sampling_rate"]
+ else:
+ raise ValueError("No audio field found in row.")
+ assert sampling_rate == SAMPLE_RATE
+ return audio
+
+ def _get_transcribe_sample(
+ self,
+ idx: int,
+ row: transformers.BatchFeature,
+ tcol: str = "text",
+ tproc: Optional[Callable[[str], str]] = None,
+ ) -> VoiceSample:
+ text = tproc(row[tcol]) if tproc else row[tcol]
+ return VoiceSample(
+ self._get_transcribe_messages(idx, text),
+ self._get_audio(row),
+ audio_transcript=text,
+ )
+
+ def _load_audio(self, base_url: str, folder: str, filename: str) -> np.ndarray:
+ if self._args.data_dir:
+ audio_path = f"{self._args.data_dir}/{folder}/{filename}"
+ audio = audio_from_file(audio_path)
+ else:
+ url = f"{base_url}/{filename}" # hack for GCS bucket naming
+ if self._session is None:
+ self._session = requests.Session()
+ response = self._session.get(url)
+ response.raise_for_status()
+ audio = audio_from_buf(response.content)
+ return audio
+
+
+class LibriSpeechDummyDataset(VoiceDataset):
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+ dataset = self._load_audio_dataset(
+ "hf-internal-testing/librispeech_asr_dummy",
+ "clean",
+ split="validation",
+ streaming=False, # not supported by the dummy dataset
+ )
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tproc=text_proc.format_asr_text)
+
+
+class EmptyDataset(data.IterableDataset):
+ def __iter__(self):
+ return iter([])
+
+
+class AnyInstructDataset(VoiceDataset):
+ """
+ Metadata file format:
+ {"chat": [
+ {"role": "USER", "message": "Write a sentence based on this summary: iraqi embassy in jakarta removes saddam hussein 's photo", "speech": "chunk_00000/0001.mp3"},
+ {"role": "AnyGPT", "message": "The building in Jakarta where people from Iraq work, took down a picture of a man named Saddam Hussein.", "speech": "chunk_00000/0002.mp3"}
+ ]}
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ # TODO(juberti): convert to MDS
+ # The last 7 samples are missing audio files, so we exclude them.
+ NUM_SAMPLES = 108193 - 7
+ super().__init__(args)
+ dataset = (
+ datasets.load_dataset(
+ "json",
+ "anyinstruct",
+ data_files="https://huggingface.co/datasets/fnlp/AnyInstruct/resolve/main/speech_conv/metadata.jsonl",
+ split="train",
+ ).select(range(NUM_SAMPLES))
+ # TODO: make num_shards configurable if need be
+ .to_iterable_dataset(num_shards=16)
+ )
+ if args.shuffle:
+ dataset = dataset.shuffle(seed=args.shuffle_seed)
+ self._init_dataset(dataset)
+
+ def _load_anyinstruct_audio(self, filename: str):
+ return super()._load_audio(
+ "https://storage.googleapis.com/train-anyinstruct-speechconv-v1",
+ "anyinstruct/speech",
+ filename,
+ )
+
+
+class AnyInstructAnswerDataset(AnyInstructDataset):
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ return VoiceSample(
+ self._get_answer_messages(idx, row["chat"][1]["message"]),
+ self._load_anyinstruct_audio(row["chat"][0]["speech"]),
+ audio_transcript=row["chat"][0]["message"],
+ )
+
+
+class AnyInstructInputDataset(AnyInstructDataset):
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ audio_transcript = row["chat"][0]["message"]
+ return VoiceSample(
+ self._get_transcribe_messages(idx, audio_transcript),
+ self._load_anyinstruct_audio(row["chat"][0]["speech"]),
+ audio_transcript=audio_transcript,
+ )
+
+
+class AnyInstructOutputDataset(AnyInstructDataset):
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ audio_transcript = row["chat"][1]["message"]
+ return VoiceSample(
+ self._get_transcribe_messages(idx, audio_transcript),
+ self._load_anyinstruct_audio(row["chat"][1]["speech"]),
+ audio_transcript=audio_transcript,
+ )
+
+
+class BoolQDataset(VoiceDataset):
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ assert (
+ args.split == DatasetSplit.VALIDATION
+ ), f"BoolQ is only for validation, but got split={args.split}"
+ super().__init__(args)
+ dataset = self._load_audio_dataset("fixie-ai/boolq-audio", split="train")
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ return VoiceSample(
+ self._get_answer_messages(idx, "True" if row["answer"] else "False"),
+ self._get_audio(row),
+ audio_transcript=row["question"],
+ )
+
+
+class BoolQInputDataset(BoolQDataset):
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ audio_transcript = str(row["question"])
+ return VoiceSample(
+ self._get_transcribe_messages(idx, audio_transcript),
+ self._get_audio(row),
+ )
+
+
+class LibriSpeechDataset(VoiceDataset):
+ """
+ LibriSpeech is a corpus of approximately 1000 hours of 16kHz read
+ English speech. The data is derived from read audiobooks from the
+ LibriVox project. A simple automatic procedure was used to select
+ the audio in the first two sets to be, on average, of higher
+ recording quality and with accents closer to US English.
+ https://huggingface.co/datasets/librispeech_asr
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ # TODO(juberti): convert to MDS, in a way that preserves the same
+ # concatenation of the three splits. MDS can interleave but not
+ # concatenate, it seems.
+ super().__init__(args)
+ ds: Any
+ if args.split == DatasetSplit.VALIDATION:
+ ds = self._load_audio_dataset("librispeech_asr", split="validation.clean")
+ else:
+ splits = ["train.clean.100", "train.clean.360", "train.other.500"]
+ ds = datasets.concatenate_datasets(
+ [
+ self._load_audio_dataset("librispeech_asr", split=s, shuffle=False)
+ for s in splits
+ ]
+ )
+ if self._args.shuffle:
+ ds = ds.shuffle(seed=self._args.shuffle_seed)
+ self._init_dataset(ds)
+
+ def _get_sample(self, idx: int, row: transformers.BatchFeature) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tproc=text_proc.format_asr_text)
+
+
+class GigaSpeechDataset(VoiceDataset):
+ """
+ GigaSpeech is an evolving, multi-domain English speech recognition corpus
+ with 10,000 hours of high quality labeled audio suitable for supervised training.
+ "s" split is 250 hours. Non-commercial use only.
+ https://huggingface.co/datasets/speechcolab/gigaspeech
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+ dataset = self._load_audio_dataset(
+ "speechcolab/gigaspeech", "xl", split=args.split.value
+ )
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx, row) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tproc=text_proc.format_asr_text)
+
+
+class VoxPopuliDataset(VoiceDataset):
+ """
+ VoxPopuli is a large-scale multilingual speech corpus for representation learning,
+ semi-supervised learning and interpretation.
+ "en" split is 543 hours.
+ https://huggingface.co/datasets/facebook/voxpopuli
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+ dataset = self._load_audio_dataset(
+ "facebook/voxpopuli", "en", split=args.split.value
+ )
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx, row) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tcol="raw_text")
+
+
+class CommonVoiceDataset(VoiceDataset):
+ """
+ The Common Voice dataset consists of a unique MP3 and corresponding text file
+ https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1
+ Dataset({
+ features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment', 'variant'],
+ num_rows: 1090061
+ })
+ NOTE: requires HF login
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+ dataset = self._load_audio_dataset(
+ "mozilla-foundation/common_voice_16_1", "en", split=args.split.value
+ )
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx, row) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tcol="sentence")
+
+
+class PeopleSpeechDataset(VoiceDataset):
+ """
+ The People's Speech Dataset is among the world's largest English speech
+ recognition corpus. It includes 30,000+ hours of transcribed speech in
+ English languages with a diverse set of speakers.
+ https://huggingface.co/datasets/MLCommons/peoples_speech
+ """
+
+ def __init__(self, args: VoiceDatasetArgs) -> None:
+ super().__init__(args)
+ dataset = self._load_audio_dataset(
+ "MLCommons/peoples_speech", "clean", split=args.split.value
+ )
+ self._init_dataset(dataset)
+
+ def _get_sample(self, idx, row) -> VoiceSample:
+ return self._get_transcribe_sample(idx, row, tcol="text")
+
+
+def create_dataset(name: str, args: VoiceDatasetArgs) -> data.IterableDataset:
+ DATASET_MAP: Dict[str, Any] = {
+ "anyinstruct": AnyInstructAnswerDataset,
+ "anyinstruct_in": AnyInstructInputDataset,
+ "anyinstruct_out": AnyInstructOutputDataset,
+ "boolq": BoolQDataset,
+ "boolq_in": BoolQInputDataset,
+ "gigaspeech": GigaSpeechDataset,
+ "librispeech": LibriSpeechDataset,
+ "voxpopuli": VoxPopuliDataset,
+ "commonvoice": CommonVoiceDataset,
+ "peoplespeech": PeopleSpeechDataset,
+ "dummy": LibriSpeechDummyDataset,
+ }
+ return DATASET_MAP[name](args)
+
+
+class InterleaveDataset(data.IterableDataset):
+ """Interleaves multiple IterableDataset objects."""
+
+ def __init__(
+ self, datasets: Sequence[data.IterableDataset], repeat: bool = False
+ ) -> None:
+ """
+ Args:
+ datasets: a list of IterableDataset objects
+ repeat: whether to repeat the datasets indefinitely.
+ This matters most when the datasets have different lengths.
+ Let's say you have two datasets, A and B which have 5 and 3 samples respectively.
+
+ `repeat=False`: [A0, B0, A1, B1, A2, B2, A3, A4]
+ `repeat=True` : [A0, B0, A1, B1, A2, B2, A3, B0, A4, B1, A0, ...]
+
+ NOTE: with `repeat=True`, `__iter__` never stops.
+ """
+ super().__init__()
+ self._datasets = datasets
+ self._repeat = repeat
+
+ def __iter__(self):
+ iters = [iter(ds) for ds in self._datasets]
+ iter_index = 0
+
+ while len(iters):
+ it = iters[iter_index]
+ try:
+ val = next(it)
+ iter_index = (iter_index + 1) % len(iters)
+ yield val
+ except StopIteration:
+ if not self._repeat:
+ iters.pop(iter_index)
+ iter_index %= max(1, len(iters))
+ else:
+ iters[iter_index] = iter(self._datasets[iter_index])
+
+
+class Dataproc(abc.ABC, data.IterableDataset):
+ """Base class to preprocess a dataset of VoiceSamples."""
+
+ def __init__(self, dataset: data.IterableDataset) -> None:
+ self._dataset = dataset
+
+ @abc.abstractmethod
+ def _process(self, sample: VoiceSample) -> Dict[str, Any]:
+ pass
+
+ def __iter__(self):
+ return (self._process(sample) for sample in self._dataset)
+
+
+class Range(data.IterableDataset):
+ """Limits the number of samples from another dataset."""
+
+ def __init__(
+ self, dataset: data.IterableDataset, num_samples: Optional[int] = None
+ ) -> None:
+ self._dataset = dataset
+ self._num_samples = num_samples
+
+ def __iter__(self):
+ for i, sample in enumerate(self._dataset):
+ if self._num_samples is not None and i >= self._num_samples:
+ break
+ yield sample
diff --git a/ultravox/data/datasets_test.py b/ultravox/data/datasets_test.py
new file mode 100644
index 00000000..74239145
--- /dev/null
+++ b/ultravox/data/datasets_test.py
@@ -0,0 +1,162 @@
+import itertools
+from typing import Optional
+
+import datasets as hf_datasets
+import numpy as np
+import torch
+from torch.utils import data
+from transformers.feature_extraction_utils import BatchFeature
+
+from ultravox.data import datasets
+
+
+class FakeIterableDataset(data.IterableDataset):
+ """Fake version of a PyTorch IterableDataset."""
+
+ def __init__(self, n, start=0):
+ self.data = range(start, start + n)
+
+ def __iter__(self):
+ return (i for i in self.data)
+
+
+class FakeHuggingFaceIterableDataset(hf_datasets.IterableDataset):
+ """Fake version of an ASR Hugging Face IterableDataset."""
+
+ def __init__(self, n):
+ self.data = [
+ {
+ "text": str(i),
+ "audio": {"array": np.full(256, float(i)), "sampling_rate": 16000},
+ }
+ for i in range(n)
+ ]
+
+ def __iter__(self):
+ return (i for i in self.data)
+
+
+class FakeTranscribeDataset(datasets.VoiceDataset):
+ """Fake version of our VoiceDataset using a transcribe prompt."""
+
+ def __init__(self, n: int, args: Optional[datasets.VoiceDatasetArgs] = None):
+ super().__init__(args or datasets.VoiceDatasetArgs())
+ self._init_dataset(FakeHuggingFaceIterableDataset(n))
+
+ def _get_sample(self, idx: int, row: BatchFeature) -> datasets.VoiceSample:
+ return self._get_transcribe_sample(idx, row)
+
+
+class FakeDataproc(datasets.Dataproc):
+ def __init__(self, dataset):
+ super().__init__(dataset)
+
+ def _process(self, sample):
+ return -sample
+
+
+def test_dataproc():
+ ds = FakeIterableDataset(5)
+ s = FakeDataproc(ds)
+ assert list(s) == [0, -1, -2, -3, -4]
+
+
+def test_interleaved():
+ # We put the smallest iterator last to test for that edge case.
+ ds1 = FakeIterableDataset(5)
+ s = datasets.InterleaveDataset([ds1])
+ assert list(s) == [0, 1, 2, 3, 4]
+ ds2 = FakeIterableDataset(9)
+ ds3 = FakeIterableDataset(3)
+ s = datasets.InterleaveDataset([ds1, ds2, ds3])
+ assert list(s) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 5, 6, 7, 8]
+ s = datasets.InterleaveDataset([])
+ assert list(s) == []
+
+
+def test_interleaved_repeat():
+ ds1 = FakeIterableDataset(4)
+ ds2 = FakeIterableDataset(2, start=10)
+ s = datasets.InterleaveDataset([ds1, ds2], repeat=True)
+ # repeat=True makes the dataset infinite, so we cannot safely use list()
+ assert list(itertools.islice(s, 9)) == [0, 10, 1, 11, 2, 10, 3, 11, 0]
+
+
+def test_interleaved_with_multiprocessing():
+ ds = FakeIterableDataset(5)
+ s = datasets.InterleaveDataset([ds])
+
+ dl = data.DataLoader(s, num_workers=1, batch_size=5)
+
+ batch = next(iter(dl))
+ assert torch.allclose(batch, torch.tensor([0, 1, 2, 3, 4]))
+
+
+def test_range():
+ ds = FakeIterableDataset(10)
+ s = datasets.Range(ds, 5)
+ assert list(s) == [0, 1, 2, 3, 4]
+ s = datasets.Range(ds, 100)
+ assert list(s) == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+ s = datasets.Range(ds)
+ assert list(s) == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+
+def test_transcribe_dataset():
+ ds = FakeTranscribeDataset(5)
+ sample = next(iter(ds))
+ assert isinstance(sample, datasets.VoiceSample)
+ assert sample.messages == [
+ {"role": "user", "content": "Transcribe <|audio|>"},
+ {"role": "assistant", "content": "0"},
+ ]
+ assert np.array_equal(sample.audio, np.zeros(256))
+ assert sample.sample_rate == 16000
+ assert sample.audio_transcript == "0"
+
+
+def test_num_prompts():
+ ds = FakeTranscribeDataset(5, datasets.VoiceDatasetArgs(num_prompts=3))
+ samples = list(ds)
+ assert samples[0].messages[0]["content"] == "Transcribe <|audio|>"
+ assert (
+ samples[1].messages[0]["content"]
+ == "Transcribe exactly what is said here <|audio|>"
+ )
+ assert (
+ samples[2].messages[0]["content"]
+ == "Repeat exactly what is written here: <|audio|>"
+ )
+ assert samples[3].messages[0]["content"] == "Transcribe <|audio|>"
+
+
+def _create_sine_wave(
+ freq: int = 440,
+ duration: float = 1.0,
+ sample_rate: int = 16000,
+ amplitude: float = 0.1,
+):
+ t = np.arange(sample_rate * duration, dtype=np.float32) / sample_rate
+ return amplitude * np.sin(2 * np.pi * freq * t)
+
+
+def test_create_sample():
+ # Create a PCM sine wave at 440 Hz, as int16.
+ array = _create_sine_wave()
+ sample = datasets.VoiceSample.from_prompt_and_raw(
+ "Transcribe <|audio|>", array, 16000
+ )
+ assert sample.sample_rate == 16000
+ assert len(sample.audio) == 16000
+ assert sample.audio.dtype == np.float32
+ assert sample.messages == [
+ {"role": "user", "content": "Transcribe <|audio|>"},
+ ]
+ # Serialize and deserialize the sample.
+ json = sample.to_json()
+ sample2 = datasets.VoiceSample.from_json(json)
+ assert sample2.sample_rate == sample.sample_rate
+ assert len(sample2.audio) == len(sample.audio)
+ assert sample2.audio.dtype == sample.audio.dtype
+ assert sample2.messages == sample.messages
+ assert np.allclose(sample2.audio, sample.audio, rtol=0.0001, atol=0.0001)
diff --git a/ultravox/data/text_proc.py b/ultravox/data/text_proc.py
new file mode 100644
index 00000000..ed517aac
--- /dev/null
+++ b/ultravox/data/text_proc.py
@@ -0,0 +1,72 @@
+import os
+
+import nltk # needed for truecase
+import truecase
+
+# only in master thread per node to avoid
+# other threads overwriting the downloaded .zip
+if int(os.environ.get("LOCAL_RANK", 0)) == 0:
+ try:
+ truecase.get_true_case("test")
+ except LookupError:
+ nltk.download("punkt", quiet=True)
+
+
+def format_asr_text(text: str) -> str:
+ """
+ Cleans the text for training. First one is Gigaspeech-specific, but the second one is useful for LibriSpeech as well.
+ - Convert punctuations
+ - Convert to true case
+ - This is not perfect, but it's better than nothing
+ - Strip leading/trailing spaces
+
+ Example:
+ "I SEE LOTS OF PEOPLE HAVE AH DRONES HERE AH MAVERICK AH AS WELL "
+ --> "I see lots of people have drones here, maverick as well."
+ """
+ remaining_words = []
+ for word in text.split():
+ if word in GIGASPEECH_PUNCTUATIONS:
+ word = GIGASPEECH_PUNCTUATIONS[word]
+ remaining_words.append(word)
+
+ text = " ".join(remaining_words)
+ text = truecase.get_true_case(text)
+
+ return text.strip()
+
+
+CONVERSATIONAL_FILLER = [
+ "UH",
+ "UHH",
+ "UM",
+ "EH",
+ "MM",
+ "HM",
+ "AH",
+ "HUH",
+ "HA",
+ "ER",
+ "OOF",
+ "HEE",
+ "ACH",
+ "EEE",
+ "EW",
+]
+SPECIAL_TAGS = ["", "", ""]
+GIGASPEECH_PUNCTUATIONS = {
+ "": ",",
+ "": ".",
+ "": "?",
+ "": "!",
+}
+
+# These are the words that are not scored in the Gigaspeech dataset
+# Right now we are not using them, but for testing we should use them so that our numbers match other implementations
+GIGASPEECH_GARBAGE_UTTERANCE_TAGS = ["", "", "", ""]
+NON_SCORING_WORDS = set(
+ CONVERSATIONAL_FILLER
+ + SPECIAL_TAGS
+ + list(GIGASPEECH_PUNCTUATIONS.keys())
+ + GIGASPEECH_GARBAGE_UTTERANCE_TAGS
+)
diff --git a/ultravox/data/text_proc_test.py b/ultravox/data/text_proc_test.py
new file mode 100644
index 00000000..e949c317
--- /dev/null
+++ b/ultravox/data/text_proc_test.py
@@ -0,0 +1,18 @@
+import pytest
+
+from ultravox.data import text_proc
+
+
+@pytest.mark.parametrize(
+ ("text", "expected"),
+ [
+ (
+ "I SEE LOTS OF PEOPLE HAVE DRONES HERE MAVERICK AS WELL ",
+ "I see lots of people have drones here, maverick as well.",
+ ),
+ # truecase messes with the case of special tags too, but we probably don't care about that
+ (" OH WHAT WAS THAT?", " Oh what was that?"),
+ ],
+)
+def test_no_space_punctuation(text, expected):
+ assert text_proc.format_asr_text(text) == expected
diff --git a/ultravox/evaluation/__init__.py b/ultravox/evaluation/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/evaluation/eval.py b/ultravox/evaluation/eval.py
new file mode 100644
index 00000000..7e6f2eda
--- /dev/null
+++ b/ultravox/evaluation/eval.py
@@ -0,0 +1,14 @@
+from ultravox.evaluation import eval_types
+from ultravox.evaluation import gpt_eval
+from ultravox.evaluation import wer
+
+
+def evaluate_answer(sample: eval_types.Sample, metric: str) -> eval_types.Result:
+ if metric == "asr":
+ return wer.evaluate_answer_asr(sample)
+ elif metric == "boolq":
+ return gpt_eval.evaluate_answer_boolq(sample)
+ elif metric == "instruct":
+ return gpt_eval.evaluate_answer_instruct(sample)
+ else:
+ raise ValueError(f"Unknown metric: {metric}")
diff --git a/ultravox/evaluation/eval_types.py b/ultravox/evaluation/eval_types.py
new file mode 100644
index 00000000..63788ce0
--- /dev/null
+++ b/ultravox/evaluation/eval_types.py
@@ -0,0 +1,29 @@
+import dataclasses
+from typing import Optional, Union
+
+import dataclasses_json
+
+
+@dataclasses.dataclass
+class Sample(dataclasses_json.DataClassJsonMixin):
+ question: str
+ generated_answer: str
+ expected_answer: str
+
+
+@dataclasses.dataclass
+class InstructResult:
+ """Score is a 0-1 evaluation of the accuracy of the generated answer, or None if an error occurred."""
+
+ score: Optional[float]
+ reason: str
+
+
+@dataclasses.dataclass
+class WerResult:
+ """Score is the 0-1 Word Error Rate for the generated transcript."""
+
+ score: float
+
+
+Result = Union[InstructResult, WerResult]
diff --git a/ultravox/evaluation/gpt_eval.py b/ultravox/evaluation/gpt_eval.py
new file mode 100644
index 00000000..6b29b4ef
--- /dev/null
+++ b/ultravox/evaluation/gpt_eval.py
@@ -0,0 +1,87 @@
+import dataclasses
+
+import openai
+
+from ultravox.evaluation import eval_types
+
+INSTRUCT_SYSTEM_PROMPT = f"""
+You are an expert evaluator of AI systems.
+Given a question with a specified instruction, you will be rating the correctness of an AI model's ability to follow that instruction.
+Based on the supplied answer, and exemplary (correct) answer, you will rate the model's answer as either correct or incorrect.
+Award 1 point if the model followed the instruction, and 0 points if it did not.
+For example, given a question with an instruction of "Write a sentence about pickleball",
+- if the model responds "Pickleball is a tennis-like game played with a wiffle ball.", you should award 1 point.
+- if the model responds "Pickleball is a type of fruit", you should award 0 points.
+- if the model responds with something off-topic or nonsensical, you should award 0 points.
+Your response MUST start with either 0 or 1, followed by a space, and then an explanation for why you awarded that score.
+"""
+INSTRUCT_USER_PROMPT = """
+Using the supplied correct answer as an example, evaluate the model's ability to follow the instructions in the question below:
+Question: {question}
+Model answer: {generated_answer}
+Correct answer: {expected_answer}
+"""
+
+
+BOOLQ_SYSTEM_PROMPT = f"""
+You are an expert evaluator of AI systems.
+Given a question with a known true/false answer, you will be rating the correctness of an AI model's answer to that same question.
+Based on the supplied question, answer, and expected (correct) answer, you will rate the model's answer as either correct or incorrect.
+Award 1 point if the model's answer matches the correct answer, and 0 points if the model's answer does not match, or cannot be converted to a true/false verdict.
+Model answers of the form "True", "Yes", "Yeah", etc., should be considered to match a True answer.
+Model answers of the form "False", "No", "Incorrect", etc., should be considered to match a False answer.
+Only use the supplied correct answer to make your decision; DO NOT use your own knowledge to determine correctness.
+Your response MUST start with either 0 or 1, followed by a space, and then a brief explanation for why you awarded that score.
+"""
+BOOLQ_USER_PROMPT = """
+Using the supplied correct answer as ground truth, evaluate the model's answer to the question below:
+Question: {question}
+Model answer: {generated_answer}
+Correct answer: {expected_answer}
+"""
+
+RATING_MODEL = "gpt-4o"
+
+
+def _evaluate_answer_gpt(
+ client: openai.OpenAI,
+ sys_prompt: str,
+ user_prompt: str,
+ sample: eval_types.Sample,
+):
+ response = client.chat.completions.create(
+ model=RATING_MODEL,
+ messages=[
+ {"role": "system", "content": sys_prompt},
+ {
+ "role": "user",
+ "content": user_prompt.format(**dataclasses.asdict(sample)),
+ },
+ ],
+ max_tokens=50,
+ temperature=0,
+ )
+ rating_text = response.choices[0].message.content
+ assert rating_text is not None
+ score = None
+ try:
+ rating = int(rating_text.strip()[0])
+ if rating == 0 or rating == 1:
+ score = rating
+ except ValueError:
+ pass
+
+ return eval_types.InstructResult(score=score, reason=rating_text[2:])
+
+
+CLIENT = openai.OpenAI()
+
+
+def evaluate_answer_boolq(sample: eval_types.Sample):
+ return _evaluate_answer_gpt(CLIENT, BOOLQ_SYSTEM_PROMPT, BOOLQ_USER_PROMPT, sample)
+
+
+def evaluate_answer_instruct(sample: eval_types.Sample):
+ return _evaluate_answer_gpt(
+ CLIENT, INSTRUCT_SYSTEM_PROMPT, INSTRUCT_USER_PROMPT, sample
+ )
diff --git a/ultravox/evaluation/wer.py b/ultravox/evaluation/wer.py
new file mode 100644
index 00000000..cdba9269
--- /dev/null
+++ b/ultravox/evaluation/wer.py
@@ -0,0 +1,33 @@
+# compute WER comparing a set of references with a set of hypotheses
+
+import jiwer
+
+from ultravox.evaluation import eval_types
+
+
+def compute_wer(references, hypotheses):
+ transforms = jiwer.Compose(
+ [
+ jiwer.ExpandCommonEnglishContractions(),
+ jiwer.RemoveEmptyStrings(),
+ jiwer.ToLowerCase(),
+ jiwer.RemoveMultipleSpaces(),
+ jiwer.Strip(),
+ jiwer.RemovePunctuation(),
+ jiwer.ReduceToListOfListOfWords(),
+ ]
+ )
+
+ wer = jiwer.wer(
+ references,
+ hypotheses,
+ truth_transform=transforms,
+ hypothesis_transform=transforms,
+ )
+
+ return wer
+
+
+def evaluate_answer_asr(sample: eval_types.Sample):
+ wer_rate = compute_wer([sample.expected_answer], [sample.generated_answer])
+ return eval_types.WerResult(score=min(1.0, wer_rate))
diff --git a/ultravox/inference/__init__.py b/ultravox/inference/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/inference/base.py b/ultravox/inference/base.py
new file mode 100644
index 00000000..bef8185f
--- /dev/null
+++ b/ultravox/inference/base.py
@@ -0,0 +1,52 @@
+import abc
+import dataclasses
+from typing import Generator, Optional
+
+from ultravox.data import datasets
+
+
+@dataclasses.dataclass
+class VoiceOutput:
+ text: str
+ input_tokens: int
+ output_tokens: int
+
+
+class InferenceMessage:
+ pass
+
+
+@dataclasses.dataclass
+class InferenceChunk(InferenceMessage):
+ text: str
+
+
+@dataclasses.dataclass
+class InferenceStats(InferenceMessage):
+ input_tokens: int
+ output_tokens: int
+
+
+InferenceGenerator = Generator[InferenceMessage, None, None]
+
+
+class VoiceInference(abc.ABC):
+ @abc.abstractmethod
+ def infer(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> VoiceOutput:
+ pass
+
+ def infer_stream(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> InferenceGenerator:
+ """Streaming polyfill, if not supported directly in derived classes."""
+ output = self.infer(sample, max_tokens, temperature)
+ yield InferenceChunk(output.text)
+ yield InferenceStats(output.input_tokens, output.output_tokens)
diff --git a/ultravox/inference/infer.py b/ultravox/inference/infer.py
new file mode 100644
index 00000000..4752dbfe
--- /dev/null
+++ b/ultravox/inference/infer.py
@@ -0,0 +1,96 @@
+from typing import Optional
+
+import librosa
+import numpy as np
+import torch
+import transformers
+
+from ultravox.data import datasets
+from ultravox.inference import base
+from ultravox.model import ultravox_processing
+
+SAMPLE_RATE = 16000
+MAX_TOKENS = 1024
+# Without this penalty, the model tends to repeat itself.
+REPETITION_PENALTY = 1.1
+
+
+class LocalInference(base.VoiceInference):
+ def __init__(
+ self,
+ model: transformers.PreTrainedModel,
+ processor: ultravox_processing.UltravoxProcessor,
+ tokenizer: transformers.PreTrainedTokenizer,
+ device: str,
+ dtype: torch.dtype,
+ ):
+ self.model = model.to(device).to(dtype).eval()
+ self.tokenizer = tokenizer
+ self.processor = processor
+ self.dtype = dtype
+
+ @torch.inference_mode()
+ def infer(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> base.VoiceOutput:
+ inputs = self._dataproc(sample)
+ input_len = inputs["input_ids"].shape[1]
+ temperature = temperature or None
+ do_sample = temperature is not None
+
+ terminators = [self.tokenizer.eos_token_id]
+ if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
+ terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
+
+ output = self.model.generate(
+ **inputs,
+ do_sample=do_sample,
+ max_new_tokens=max_tokens or MAX_TOKENS,
+ temperature=temperature,
+ repetition_penalty=REPETITION_PENALTY,
+ pad_token_id=self.tokenizer.eos_token_id,
+ eos_token_id=terminators,
+ )
+ output_tokens = output[0][input_len:]
+ output_text = self.tokenizer.decode(output_tokens, skip_special_tokens=True)
+ output_len = len(output_tokens)
+ return base.VoiceOutput(output_text, input_len, output_len)
+
+ def _dataproc(self, sample: datasets.VoiceSample):
+ text_input = self.tokenizer.apply_chat_template(
+ sample.messages, add_generation_prompt=True, tokenize=False
+ )
+ if sample.audio is not None:
+ audio = sample.audio
+ sample_rate = sample.sample_rate
+ # Normalize audio to float32.
+ if audio.dtype == np.int16:
+ audio = audio / np.float32(32768.0)
+ if audio.dtype not in [np.float64, np.float32]:
+ raise ValueError("Audio must be float64 or float32 or int16")
+
+ # Convert to tensor, resampling to 16kHz if needed.
+ if sample_rate != SAMPLE_RATE:
+ audio = librosa.resample(
+ audio, orig_sr=sample_rate, target_sr=SAMPLE_RATE
+ )
+ audio_input = torch.from_numpy(audio)
+ # Squeeze from [1, T] to [T] if needed.
+ if sample.audio.ndim == 2:
+ audio_input = audio_input.squeeze(0)
+ else:
+ audio_input = None
+
+ inputs = self.processor(
+ audio=audio_input,
+ text=text_input,
+ return_tensors="pt",
+ sampling_rate=SAMPLE_RATE,
+ )
+ inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+ if "audio_values" in inputs:
+ inputs["audio_values"] = inputs["audio_values"].to(dtype=self.dtype)
+ return inputs
diff --git a/ultravox/inference/infer_test.py b/ultravox/inference/infer_test.py
new file mode 100644
index 00000000..f352bc16
--- /dev/null
+++ b/ultravox/inference/infer_test.py
@@ -0,0 +1,141 @@
+from unittest import mock
+
+import numpy as np
+import pytest
+import torch
+import transformers
+
+from ultravox.data import datasets
+from ultravox.inference import base as infer_base
+from ultravox.inference import infer
+from ultravox.model import ultravox_processing
+
+
+@pytest.fixture(scope="module")
+def tokenizer():
+ return transformers.AutoTokenizer.from_pretrained(
+ "meta-llama/Meta-Llama-3-8B-Instruct"
+ )
+
+
+@pytest.fixture(scope="module")
+def audio_processor():
+ return transformers.AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
+
+
+class FakeInference(infer.LocalInference):
+ def __init__(
+ self,
+ tokenizer: transformers.PreTrainedTokenizer,
+ audio_processor: transformers.ProcessorMixin,
+ ):
+ processor = ultravox_processing.UltravoxProcessor(
+ audio_processor, tokenizer=tokenizer
+ )
+ super().__init__(
+ mock.MagicMock(),
+ processor=processor,
+ tokenizer=tokenizer,
+ device="cpu",
+ dtype=torch.float32,
+ )
+ self.model.device = "cpu"
+ self.model.generate = mock.MagicMock(return_value=[range(25)])
+
+
+EXPECTED_TOKEN_IDS_START = [128000, 128006, 882, 128007]
+EXPECTED_TOKEN_IDS_END = [128009, 128006, 78191, 128007, 271]
+
+
+def test_infer_16kHz(tokenizer, audio_processor):
+ """Ensure we handle 16kHz float32 audio properly."""
+ inference = FakeInference(tokenizer, audio_processor)
+ array = np.ones(16000, dtype=np.float32)
+ sample = datasets.VoiceSample.from_prompt_and_raw(
+ "Transcribe <|audio|>", array, 16000
+ )
+ output = inference.infer(sample)
+ assert output.input_tokens == 20
+ assert output.output_tokens == 5
+ assert output.text == "56789"
+ generate_args = inference.model.generate.call_args[1]
+ call_audio_values = generate_args["audio_values"]
+ assert call_audio_values.shape == (1, 16000)
+ call_input_ids = generate_args["input_ids"]
+ assert call_input_ids.shape == (1, 20)
+ assert call_input_ids[0, :4].tolist() == EXPECTED_TOKEN_IDS_START
+ assert call_input_ids[0, -5:].tolist() == EXPECTED_TOKEN_IDS_END
+ assert torch.all(call_input_ids[0, 8:15] == inference.tokenizer.eos_token_id)
+ assert generate_args["audio_token_len"].item() == 7
+ assert generate_args["audio_token_start_idx"].item() == 8
+
+
+def test_infer_48kHz(tokenizer, audio_processor):
+ """Ensure we resample 48KHz to 16kHz properly."""
+ inference = FakeInference(tokenizer, audio_processor)
+ array = np.ones(48000, dtype=np.float32)
+ sample = datasets.VoiceSample.from_prompt_and_raw(
+ "Transcribe <|audio|>", array, 48000
+ )
+ output = inference.infer(sample)
+ assert output.input_tokens == 20
+ assert output.output_tokens == 5
+ assert output.text == "56789"
+ generate_args = inference.model.generate.call_args[1]
+ call_audio_values = generate_args["audio_values"]
+ assert call_audio_values.shape == (1, 16000)
+ call_input_ids = generate_args["input_ids"]
+ assert call_input_ids.shape == (1, 20)
+ assert call_input_ids[0, :4].tolist() == EXPECTED_TOKEN_IDS_START
+ assert call_input_ids[0, -5:].tolist() == EXPECTED_TOKEN_IDS_END
+ assert torch.all(call_input_ids[0, 8:15] == inference.tokenizer.eos_token_id)
+ assert generate_args["audio_token_len"].item() == 7
+ assert generate_args["audio_token_start_idx"].item() == 8
+
+
+def test_infer_16kHz_stream(tokenizer, audio_processor):
+ """Ensure we handle streaming output properly."""
+ inference = FakeInference(tokenizer, audio_processor)
+ array = np.ones(16000, dtype=np.float32)
+ sample = datasets.VoiceSample.from_prompt_and_raw(
+ "Transcribe <|audio|>", array, 16000
+ )
+ gen = inference.infer_stream(sample)
+ text = ""
+ stats = None
+ for msg in gen:
+ if isinstance(msg, infer_base.InferenceChunk):
+ text += msg.text
+ elif isinstance(msg, infer_base.InferenceStats):
+ stats = msg
+ assert text == "56789"
+ assert stats.input_tokens == 20
+ assert stats.output_tokens == 5
+ generate_args = inference.model.generate.call_args[1]
+ call_audio_values = generate_args["audio_values"]
+ assert call_audio_values.shape == (1, 16000)
+ call_input_ids = generate_args["input_ids"]
+ assert call_input_ids.shape == (1, 20)
+ assert call_input_ids[0, :4].tolist() == EXPECTED_TOKEN_IDS_START
+ assert call_input_ids[0, -5:].tolist() == EXPECTED_TOKEN_IDS_END
+ assert torch.all(call_input_ids[0, 8:15] == inference.tokenizer.eos_token_id)
+ assert generate_args["audio_token_len"].item() == 7
+ assert generate_args["audio_token_start_idx"].item() == 8
+
+
+def test_infer_text_only(tokenizer, audio_processor):
+ """Ensure we handle text without audio properly."""
+ inference = FakeInference(tokenizer, audio_processor)
+ sample = datasets.VoiceSample.from_prompt("Hello?")
+ output = inference.infer(sample)
+ assert output.input_tokens == 12
+ assert output.output_tokens == 13
+ assert output.text == "-./0123456789"
+ generate_args = inference.model.generate.call_args[1]
+ assert generate_args.get("audio_values") is None
+ call_input_ids = generate_args["input_ids"]
+ assert call_input_ids.shape == (1, 12)
+ assert call_input_ids[0, :4].tolist() == EXPECTED_TOKEN_IDS_START
+ assert call_input_ids[0, -5:].tolist() == EXPECTED_TOKEN_IDS_END
+ assert generate_args.get("audio_token_len") is None
+ assert generate_args.get("audio_token_start_idx") is None
diff --git a/ultravox/inference/ultravox_infer.py b/ultravox/inference/ultravox_infer.py
new file mode 100644
index 00000000..11f203a4
--- /dev/null
+++ b/ultravox/inference/ultravox_infer.py
@@ -0,0 +1,67 @@
+from typing import Optional
+
+import transformers
+
+from ultravox.inference import infer
+from ultravox.inference import utils
+from ultravox.model import ultravox_model
+from ultravox.model import ultravox_processing
+from ultravox.model import wandb_utils
+
+
+class UltravoxInference(infer.LocalInference):
+ def __init__(
+ self,
+ model_path: str,
+ audio_processor_id: Optional[str] = None,
+ tokenizer_id: Optional[str] = None,
+ device: Optional[str] = None,
+ data_type: Optional[str] = None,
+ ):
+ """
+ Args:
+ model_path: can refer to a HF hub model_id, a local path, or a W&B artifact
+ Examples:
+ fixie-ai/ultravox
+ runs/llama2_asr_gigaspeech/checkpoint-1000/
+ wandb://fixie/ultravox/model-llama2_asr_gigaspeech:v0
+ audio_processor_id: model_id for the audio processor to use. If not provided, it will be inferred
+ tokenizer_id: model_id for the tokenizer to use. If not provided, it will be inferred
+ device: where to put the model and data
+ data_type: data type to use for the model
+ """
+ device = device or utils.default_device()
+ dtype = utils.get_dtype(data_type) if data_type else utils.default_dtype()
+ if wandb_utils.is_wandb_url(model_path):
+ model_path = wandb_utils.download_model_from_wandb(model_path)
+ model = ultravox_model.UltravoxModel.from_pretrained(
+ model_path, torch_dtype=dtype
+ )
+ model.merge_and_unload()
+
+ tokenizer_id = tokenizer_id or model_path
+ tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_id)
+
+ tokenizer.padding_side = "left"
+ tokenizer.pad_token = tokenizer.eos_token
+
+ # tincans-ai models don't set audio_model_id, instead audio_config._name_or_path has the
+ # model name. A default value is added just as a precaution, but it shouldn't be needed.
+ audio_processor = transformers.AutoProcessor.from_pretrained(
+ audio_processor_id
+ or model.config.audio_model_id
+ or model.config.audio_config._name_or_path
+ or "facebook/wav2vec2-base-960h"
+ )
+
+ processor = ultravox_processing.UltravoxProcessor(
+ audio_processor, tokenizer=tokenizer, stack_factor=model.config.stack_factor
+ )
+
+ super().__init__(
+ model=model,
+ processor=processor,
+ tokenizer=tokenizer,
+ device=device,
+ dtype=dtype,
+ )
diff --git a/ultravox/inference/utils.py b/ultravox/inference/utils.py
new file mode 100644
index 00000000..096a891f
--- /dev/null
+++ b/ultravox/inference/utils.py
@@ -0,0 +1,23 @@
+import torch
+
+
+def default_device():
+ return (
+ "cuda"
+ if torch.cuda.is_available()
+ # until https://github.com/pytorch/pytorch/issues/77764 is resolved
+ # else "mps" if torch.backends.mps.is_available() else "cpu"
+ else "cpu"
+ )
+
+
+def default_dtype():
+ return torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+
+def get_dtype(data_type: str):
+ return (
+ torch.bfloat16
+ if data_type == "bfloat16"
+ else torch.float16 if data_type == "float16" else torch.float32
+ )
diff --git a/ultravox/model/ultravox_config.py b/ultravox/model/ultravox_config.py
new file mode 100644
index 00000000..e9466694
--- /dev/null
+++ b/ultravox/model/ultravox_config.py
@@ -0,0 +1,141 @@
+import dataclasses
+from typing import Any, Dict, List, Optional
+
+import transformers
+
+
+@dataclasses.dataclass
+class LoraConfigSimplified:
+ """
+ Low Rank Approximation (LoRA) configuration.
+
+ Used for language and audio models separately.
+ """
+
+ # The rank of the approximation
+ r: int = 0
+ lora_alpha: float = 8
+ target_modules: Optional[List[str]] = dataclasses.field(
+ default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
+ )
+
+
+class UltravoxConfig(transformers.PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`UltravoxForConditionalGeneration`]. It is used to instantiate an
+ Ultravox model according to the specified arguments, defining the model architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ audio_config (`Wav2Vec2Config`, *optional*):
+ Custom audio config or dict
+ text_config (`Union[AutoConfig, dict]`, *optional*):
+ The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
+ ignore_index (`int`, *optional*, defaults to -100):
+ The ignore index for the loss function.
+ audio_token_index (`int`, *optional*, defaults to 32000):
+ The audio token index to encode the audio prompt.
+ stack_factor (`int`, *optional*, defaults to 8):
+ Audio downsampling factor for the multimodal projector.
+ norm_init (`float`, *optional*, defaults to 0.4):
+ The initialization value for the layer normalization.
+ projector_act (`str`, *optional*, defaults to `"swiglu"`):
+ The activation function used by the multimodal projector.
+ text_model_lora_config (`LoraConfigSimplified`, *optional*):
+ The LoRA configuration for finetuning the text model.
+ audio_model_lora_config (`LoraConfigSimplified`, *optional*):
+ The LoRA configuration for finetuning the audio model.
+
+
+ Example:
+
+ ```python
+ >>> from transformers import UltravoxForConditionalGeneration, Wav2Vec2Config, UltravoxConfig, LlamaConfig
+
+ >>> # Initializing an audio encoder config
+ >>> audio_config = Wav2Vec2Config()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> # Initializing a default configuration
+ >>> configuration = UltravoxConfig(audio_config, text_config)
+
+ >>> # Initializing a completely untrained model from the configuration
+ >>> model = UltravoxForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+
+ >>> # Initialize a model from pretrained checkpoints and random projector weights
+ >>> config = UltravoxConfig(audio_model_id="facebook/wav2vec2-base-960h", text_model_id="meta-llama/Llama-2-7b-chat-hf")
+ ```"""
+
+ model_type = "ultravox"
+ is_composition = False
+
+ def __init__(
+ self,
+ audio_config: Optional[Dict[str, Any]] = None,
+ text_config: Optional[Dict[str, Any]] = None,
+ audio_model_id: Optional[str] = None,
+ text_model_id: Optional[str] = None,
+ ignore_index: int = -100,
+ audio_token_index: int = 32000,
+ hidden_size: int = 4096,
+ stack_factor: int = 8,
+ norm_init: float = 0.4,
+ projector_act: str = "swiglu",
+ text_model_lora_config: Optional[LoraConfigSimplified] = None,
+ audio_model_lora_config: Optional[LoraConfigSimplified] = None,
+ **kwargs,
+ ):
+ self.ignore_index = ignore_index
+
+ self.audio_model_id = audio_model_id
+ self.text_model_id = text_model_id
+ self.audio_token_index = audio_token_index
+
+ self.hidden_size = hidden_size
+ self.stack_factor = stack_factor
+ self.norm_init = norm_init
+ self.projector_act = projector_act
+
+ if text_model_id is not None:
+ self.text_config: transformers.LlamaConfig = (
+ transformers.AutoConfig.from_pretrained(text_model_id)
+ )
+ else:
+ text_config = text_config or {}
+ self.text_config = transformers.CONFIG_MAPPING[
+ text_config.get("model_type", "llama")
+ ](**text_config)
+
+ if audio_model_id is not None:
+ self.audio_config: transformers.PretrainedConfig = (
+ transformers.AutoConfig.from_pretrained(audio_model_id)
+ )
+ else:
+ audio_config = audio_config or {}
+ self.audio_config = transformers.CONFIG_MAPPING[
+ audio_config.get("model_type", "wav2vec2")
+ ](**audio_config)
+
+ self.text_model_lora_config = (
+ text_model_lora_config
+ if isinstance(text_model_lora_config, dict)
+ else dataclasses.asdict(text_model_lora_config or LoraConfigSimplified())
+ )
+ self.audio_model_lora_config = (
+ audio_model_lora_config
+ if isinstance(audio_model_lora_config, dict)
+ else dataclasses.asdict(audio_model_lora_config or LoraConfigSimplified())
+ )
+
+ self.vocab_size = self.text_config.vocab_size
+
+ self.initializer_range = self.text_config.initializer_range
+
+ super().__init__(**kwargs)
diff --git a/ultravox/model/ultravox_model.py b/ultravox/model/ultravox_model.py
new file mode 100644
index 00000000..768026a6
--- /dev/null
+++ b/ultravox/model/ultravox_model.py
@@ -0,0 +1,392 @@
+import logging
+from typing import Any, Dict, Optional, Set, Tuple, Union
+
+import peft
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import transformers
+import transformers.activations
+import transformers.modeling_outputs
+import transformers.models
+
+from ultravox.model import ultravox_config
+
+
+class UltravoxModel(
+ transformers.LlamaPreTrainedModel,
+ transformers.GenerationMixin,
+):
+ """
+ The Ultravox model which consists of an audio encoder and a language model.
+
+ Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
+ projected to the language model's embedding space using a few linear layers.
+ The text is embedded by the language model as usual and then the audio and text embeddings are merged together.
+
+ A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.
+
+ Parameters:
+ config: Model configuration class with all the parameters of the model.
+ """
+
+ config_class = ultravox_config.UltravoxConfig
+ config: ultravox_config.UltravoxConfig # for type hinting
+ _no_split_modules = ["Wav2Vec2Model", "WhisperEncoder", "LlamaDecoderLayer"]
+
+ def __init__(self, config: ultravox_config.UltravoxConfig):
+ super().__init__(config)
+
+ self.keep_params: Set[str] = set()
+ self.vocab_size = config.vocab_size
+
+ self.audio_tower = self._create_audio_tower(config)
+ self.multi_modal_projector = UltravoxProjector(config)
+ self.language_model = self._create_language_model(config)
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def get_output_embeddings(self):
+ return self.language_model.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ self.language_model.set_output_embeddings(new_embeddings)
+
+ def set_decoder(self, decoder):
+ self.language_model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.language_model.get_decoder()
+
+ def tie_weights(self):
+ return self.language_model.tie_weights()
+
+ def _setup_cache(
+ self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
+ ):
+ self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)
+
+ def _reorder_cache(self, past_key_values, beam_idx):
+ return self.language_model._reorder_cache(past_key_values, beam_idx)
+
+ def resize_token_embeddings(
+ self,
+ new_num_tokens: Optional[int] = None,
+ pad_to_multiple_of: Optional[int] = None,
+ ) -> nn.Embedding:
+ model_embeds = self.language_model.resize_token_embeddings(
+ new_num_tokens, pad_to_multiple_of
+ )
+ # update vocab size
+ self.config.text_config.vocab_size = model_embeds.num_embeddings
+ self.config.vocab_size = model_embeds.num_embeddings
+ self.vocab_size = model_embeds.num_embeddings
+ return model_embeds
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ audio_values: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ audio_token_start_idx: Optional[torch.Tensor] = None,
+ audio_token_len: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Tuple] = None,
+ **kwargs,
+ ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
+ """
+ Forward pass for the Ultravox model.
+
+ `input_ids` are the tokenized text input. They are embedded by the language model as usual.
+ `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
+ projected to the language model's embedding space using a few linear layers.
+ The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
+ of the audio embeddings in the merged embeddings.
+
+ Args:
+ input_ids: The tokenized text input.
+ audio_values: The processed audio values.
+ inputs_embeds: The embeddings for the input tokens.
+ labels: The tokenized text labels.
+ attention_mask: The attention mask for the input.
+ position_ids: The position ids for the input.
+ past_key_values: The past key value cache for the language model attention layers.
+ **kwargs: Additional keyword arguments. Passed directly to the language model.
+ """
+ if inputs_embeds is None:
+ # B x T -> B x T x D
+ inputs_embeds = self.get_input_embeddings().forward(input_ids)
+
+ if audio_values is not None:
+ assert (
+ audio_token_start_idx is not None and audio_token_len is not None
+ ), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
+ assert (
+ len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
+ ), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."
+
+ # B x A/3200 x D
+ audio_tower_output = self.audio_tower.forward(
+ audio_values
+ ).last_hidden_state
+ audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
+
+ audio_embeds = self.multi_modal_projector.forward(audio_tower_output)
+
+ # combine audio and text embeddings
+ for i, (audio, start, length) in enumerate(
+ zip(audio_embeds, audio_token_start_idx, audio_token_len)
+ ):
+ length = min(length, audio.shape[0])
+ inputs_embeds[i, start : start + length] = audio[:length]
+
+ lm_output = self.language_model.forward(
+ inputs_embeds=inputs_embeds,
+ labels=labels,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ **kwargs,
+ )
+
+ return lm_output
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids: torch.Tensor,
+ audio_values: Optional[torch.FloatTensor] = None,
+ audio_token_start_idx: Optional[torch.Tensor] = None,
+ audio_token_len: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Tuple] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Dict[str, Any]:
+ model_input = self.language_model.prepare_inputs_for_generation(
+ input_ids=input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ **kwargs,
+ )
+
+ if past_key_values is None and audio_values is not None:
+ # We only want to use audio features in the 1st generation step
+ model_input["audio_values"] = audio_values
+ model_input["audio_token_start_idx"] = audio_token_start_idx
+ model_input["audio_token_len"] = audio_token_len
+
+ return model_input
+
+ @classmethod
+ def _create_audio_tower(cls, config: ultravox_config.UltravoxConfig) -> Union[
+ transformers.Wav2Vec2Model,
+ transformers.models.whisper.modeling_whisper.WhisperEncoder,
+ ]:
+ if config.audio_model_id is not None:
+ audio_tower = transformers.AutoModel.from_pretrained(config.audio_model_id)
+ else:
+ audio_tower = transformers.AutoModel.from_config(config.audio_config)
+
+ if isinstance(
+ audio_tower,
+ (transformers.Wav2Vec2BertModel, transformers.WhisperModel),
+ ):
+ # For these models we only need the encoder part
+ # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
+ # WhisperModel -> WhisperEncoder
+ audio_tower = audio_tower.encoder
+
+ audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
+ return audio_tower
+
+ @classmethod
+ def _create_language_model(
+ cls, config: ultravox_config.UltravoxConfig
+ ) -> transformers.LlamaForCausalLM:
+ if config.text_model_id is not None:
+ language_model = transformers.AutoModelForCausalLM.from_pretrained(
+ config.text_model_id, attn_implementation=config._attn_implementation
+ )
+ else:
+ language_model = transformers.AutoModelForCausalLM.from_config(
+ config.text_config, attn_implementation=config._attn_implementation
+ )
+
+ language_model = apply_lora(language_model, config.text_model_lora_config)
+ return language_model
+
+ def merge_and_unload(self):
+ if isinstance(self.language_model, peft.PeftModel):
+ self.language_model = self.language_model.merge_and_unload()
+ # no need to download base language model weights anymore, so we can remove the id
+ self.config.text_model_id = None
+ self.keep_params.update(
+ set(
+ [
+ f"language_model.{name}"
+ for name, _ in self.language_model.named_parameters()
+ ]
+ )
+ )
+
+ if isinstance(self.audio_tower, peft.PeftModel):
+ self.audio_tower = self.audio_tower.merge_and_unload()
+ # no need to download base audio model weights anymore, so we can remove the id
+ self.config.audio_model_id = None
+ self.keep_params.update(
+ set(
+ [
+ f"audio_tower.{name}"
+ for name, _ in self.audio_tower.named_parameters()
+ ]
+ )
+ )
+
+ for param in ["text_model_lora_config", "audio_model_lora_config"]:
+ if hasattr(self.config, param):
+ delattr(self.config, param)
+
+ def push_to_hub(self, *args, **kwargs):
+ self.merge_and_unload()
+ self.to(self.language_model.dtype)
+ return super().push_to_hub(*args, **kwargs)
+
+ def state_dict(self, *args, **kwargs):
+ named_params = dict(self.named_parameters())
+ state_dict = super().state_dict(*args, **kwargs)
+
+ state_dict = {
+ k: v
+ for k, v in state_dict.items()
+ if k in self.keep_params
+ or (k in named_params and named_params[k].requires_grad)
+ }
+ return state_dict
+
+ def load_state_dict(
+ self,
+ state_dict: Dict[str, Any],
+ *args,
+ **kwargs,
+ ):
+ self.keep_params.update(set(state_dict.keys()))
+ return super().load_state_dict(state_dict, *args, **kwargs)
+
+ def print_trainable_parameters(self):
+ """
+ Prints the number of trainable parameters in the model (reuses Peft model's method)
+ """
+ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
+
+ trainable_params, all_param = count_params(self)
+
+ logging.info(
+ f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
+ f" || trainable%: {100 * trainable_params / all_param:.1f}%"
+ )
+
+ lm_trainable_params, lm_all_params = count_params(self.language_model)
+ audio_trainable_params, audio_all_params = count_params(self.audio_tower)
+
+ projector_trainable_params = (
+ trainable_params - lm_trainable_params - audio_trainable_params
+ )
+ projector_all_params = all_param - lm_all_params - audio_all_params
+
+ logging.info(
+ f"Trainable%: "
+ f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
+ f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
+ f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
+ )
+
+
+def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
+ """
+ Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
+ """
+ lora_config = peft.LoraConfig(**lora_config or {})
+
+ if lora_config.r == 0:
+ # freeze the model entirely
+ for param in model.parameters():
+ param.requires_grad = False
+ else:
+ model = peft.get_peft_model(model, lora_config)
+
+ return model
+
+
+class StackAudioFrames(nn.Module):
+ """
+ Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.
+
+ The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
+ NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
+ we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
+ In most cases this extra padding will get removed in the model's forward function so it has no effect.
+ """
+
+ def __init__(self, stack_factor: int = 8):
+ super().__init__()
+ self.stack_factor = stack_factor
+
+ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
+ B, T, C = audio_embeds.shape
+ T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
+ audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
+ B, T, C = audio_embeds.shape
+ audio_embeds = audio_embeds.view(
+ B, T // self.stack_factor, C * self.stack_factor
+ )
+ return audio_embeds
+
+
+class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
+ def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
+ super().__init__(hidden_size=hidden_size, eps=eps)
+ self.weight.data.fill_(init)
+
+
+class SwiGLU(nn.Module):
+ def forward(self, x):
+ x, gate = x.chunk(2, dim=-1)
+ return F.silu(gate) * x
+
+
+class UltravoxProjector(nn.Sequential):
+ def __init__(self, config: ultravox_config.UltravoxConfig):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self._pad_and_stack = StackAudioFrames(config.stack_factor)
+ dim = config.audio_config.hidden_size * config.stack_factor
+ self.ln_pre = RMSNorm(dim, init=config.norm_init)
+ self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
+ dim = self.hidden_dim
+ self.act = transformers.activations.get_activation(config.projector_act)
+ dim = dim // 2 if config.projector_act == "swiglu" else dim
+ self.linear_2 = nn.Linear(dim, config.text_config.hidden_size, bias=False)
+ self.ln_post = RMSNorm(config.text_config.hidden_size, init=config.norm_init)
+
+ def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
+ audio_features = self._pad_and_stack(audio_features)
+ audio_features = self.ln_pre(audio_features)
+ hidden_states = self.linear_1(audio_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ hidden_states = self.ln_post(hidden_states)
+ return hidden_states
+
+
+transformers.AutoModelForCausalLM.register(
+ ultravox_config.UltravoxConfig, UltravoxModel
+)
+
+transformers.activations.ACT2FN["swiglu"] = SwiGLU
diff --git a/ultravox/model/ultravox_processing.py b/ultravox/model/ultravox_processing.py
new file mode 100644
index 00000000..e0f26b5e
--- /dev/null
+++ b/ultravox/model/ultravox_processing.py
@@ -0,0 +1,255 @@
+from typing import Any, Dict, Optional, Union
+
+import numpy as np
+import torch
+import transformers
+from torch.utils import data
+
+from ultravox.data import datasets
+
+
+class UltravoxProcessor(transformers.ProcessorMixin):
+ """
+ Constructs an Ultravox processor which wraps an audio processor and a tokenizer into a single processor.
+
+ Args:
+ audio_processor: The audio processor for the audio encoder.
+ tokenizer: The tokenizer for the language model.
+ """
+
+ attributes = ["audio_processor", "tokenizer"]
+ audio_processor_class = (
+ "Wav2Vec2Processor",
+ "SeamlessM4TFeatureExtractor",
+ "WhisperProcessor",
+ )
+ tokenizer_class = (
+ "PreTrainedTokenizer",
+ "PreTrainedTokenizerFast",
+ )
+
+ tokenizer: transformers.PreTrainedTokenizerBase
+ audio_processor: transformers.ProcessorMixin
+
+ def __init__(
+ self,
+ audio_processor=None,
+ tokenizer=None,
+ encoder_ds_factor: int = 320,
+ stack_factor: int = 8,
+ audio_placeholder: str = "<|audio|>",
+ ):
+ """
+ Args:
+ audio_processor: The audio processor for the audio encoder.
+ tokenizer: The tokenizer for the language model.
+ encoder_ds_factor: The downsample factor of the audio encoder.
+ stack_factor: The factor by which the audio encoder output is stacked in the multimodal projector.
+ audio_placeholder: The placeholder for the audio in the text.
+ """
+ self.encoder_ds_factor = encoder_ds_factor
+ self.stack_factor = stack_factor
+ self.audio_placeholder = audio_placeholder
+ self.audio_token_replacement = tokenizer.eos_token
+ assert (
+ self.audio_token_replacement is not None
+ ), "The tokenizer has no EOS token. Cannot recover."
+ super().__init__(audio_processor=audio_processor, tokenizer=tokenizer)
+
+ def __call__(
+ self,
+ text: Optional[str] = None,
+ audio: Optional[Union[np.ndarray, torch.Tensor]] = None,
+ sampling_rate: Optional[int] = None,
+ return_tensors: Optional[
+ Union[str, transformers.TensorType]
+ ] = transformers.TensorType.PYTORCH,
+ **kwargs,
+ ) -> transformers.BatchFeature:
+ """
+ Main method to prepare for the model one text sequence and audio. This method forwards the `text`
+ and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the audio(s), this method forwards the `audio`, `sampling_rate` and `kwargs` arguments to
+ audio processor's [`~Wav2Vec2Processor.__call__`] if `audio` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ text (`str`, `List[str]`):
+ The sequence to be encoded. Sequence can be a string or (pretokenized string).
+ audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+ The audio to be prepared. Audio can be NumPy array or PyTorch tensor. In case of a
+ NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T the
+ sample length of the audio.
+ sampling_rate (`int`, *optional*, defaults to 16000):
+ Sampling rate of the input audio. We expect 16kHz audio. Don't change this value unless you know what
+ you are doing.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **audio_values** -- Processed audio values to be fed to a model. Returned when `audio` is not `None`.
+ - **audio_token_len** -- Predicted number of audio frames: this value is guaranteed to be a close upper bound.
+ Returned when `audio` is not `None`.
+ - **audio_token_start_idx** -- The index in the tokenized text where the audio starts. Returned when `audio` is not `None`.
+ """
+ # TODO: Add support for multiple audio and text inputs.
+ data = {}
+ audio_embed_frames = 0
+ if audio is not None and len(audio) > 0:
+ audio_len = audio.shape[-1]
+ # It's guaranteed that the number of frames is less than or equal to this amount.
+ # For Whisper this is exact AFAICT, but for Wav2Vec2 it's an upper bound.
+ # Currently, StackAudioFrames makes sure an over-estimation won't cause issues by padding the audio embeddings.
+ nb_encoder_frames = int(round(audio_len / self.encoder_ds_factor + 1e-4))
+ audio_embed_frames = int(np.ceil(nb_encoder_frames / self.stack_factor))
+ data["audio_token_len"] = [audio_embed_frames]
+
+ x = self.audio_processor(
+ audio, sampling_rate=sampling_rate, padding="longest", **kwargs
+ )
+ if "input_features" in x:
+ data["audio_values"] = x.input_features
+ else:
+ data["audio_values"] = x.input_values
+
+ if text is not None:
+ assert isinstance(
+ text, str
+ ), "Text must be a string. Batch mode not supported yet."
+ if self.audio_placeholder in text:
+ if "audio_token_len" not in data:
+ raise ValueError(
+ f"audio must be provided when using audio placeholder ({self.audio_placeholder}) in text."
+ )
+
+ start_idx = len(
+ self.tokenizer.encode(
+ text[: text.index(self.audio_placeholder)],
+ add_special_tokens=False,
+ )
+ )
+ data["audio_token_start_idx"] = [start_idx]
+ text = text.replace(
+ self.audio_placeholder,
+ self.audio_token_replacement * audio_embed_frames,
+ )
+
+ # Special tokens like BOS should already have been added by the caller.
+ data.update(self.tokenizer([text], add_special_tokens=False, **kwargs))
+
+ return transformers.BatchFeature(data=data, tensor_type=return_tensors)
+
+ def batch_decode(self, *args, **kwargs):
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ audio_processor_input_names = self.audio_processor.model_input_names
+ return list(set(tokenizer_input_names + audio_processor_input_names))
+
+
+class UltravoxDataproc(datasets.Dataproc):
+ def __init__(
+ self,
+ dataset: data.IterableDataset,
+ processor: UltravoxProcessor,
+ train_on_inputs: bool = False,
+ inference_mode: bool = False,
+ ) -> None:
+ """
+ Pre-processing for the Ultravox model: applies tokenization and audio processing using the UltravoxProcessor
+ and prepares the shape of the data for being fed into the model.
+
+ Args:
+ dataset: The dataset to wrap/preprocess.
+ processor: The processor.
+ train_on_inputs: If True, the token_ids for prompt (user input) are also included in the labels,
+ so the model learns to predict the input message.
+ inference_mode: If True, only the input message is included in input_ids and labels, and the assistant
+ message is removed from the sample. This is used for inference (e.g. testing) since the model should
+ generate the assistant message. For training and validation, this should be False.
+ """
+ super().__init__(dataset)
+ self.processor = processor
+ self.train_on_inputs = train_on_inputs
+ self.inference_mode = inference_mode
+ if self.inference_mode:
+ self.train_on_inputs = True
+
+ def _process(self, sample: datasets.VoiceSample) -> Dict[str, Any]:
+ if self.inference_mode:
+ # remove the assistant message from the sample so that the model can generate it
+ sample.messages = sample.messages[:-1]
+
+ text = self.processor.tokenizer.apply_chat_template(
+ sample.messages, tokenize=False
+ )
+
+ # Process audio and text using GazelleProcessor.
+ # Audio is expanded to be a [C x M] array, although C=1 for mono audio.
+ audio = (
+ np.expand_dims(sample.audio, axis=0) if sample.audio is not None else None
+ )
+ inputs = self.processor(
+ text=text,
+ audio=audio,
+ return_tensors="pt",
+ sampling_rate=sample.sample_rate,
+ )
+
+ # Extract input_ids, attention_mask, and audio_values from the processed inputs
+ input_ids = inputs["input_ids"].squeeze(0)
+ attention_mask = inputs["attention_mask"].squeeze(0)
+ audio_values = inputs["audio_values"].squeeze(0)
+ audio_token_start_idx = inputs["audio_token_start_idx"].squeeze(0)
+ audio_token_len = inputs["audio_token_len"].squeeze(0)
+
+ # No need to shift the labels as the model does it internally
+ labels = input_ids.clone()
+
+ if not self.train_on_inputs:
+ # Mask the prompt tokens and only compute loss on the assistant message, not the prompt.
+ # The idea is that the model should only be able to predict the assistant message given the user message.
+ # One reason is that there's very little randomness in the prompt, so the model would be forced to memorize it.
+ #
+ # Example (-100 is the ignore index):
+ # Tokens: Transcribe <|audio|> Brown fox jumps over the lazy dog
+ # Labels: -100 -100 -100 -100 Brown fox jumps over the lazy dog
+ #
+ # Note: The above might look weird because I'm mixing token IDs and text, but that's just for illustration.
+ input_text = self.processor.tokenizer.apply_chat_template(
+ sample.messages[:-1], tokenize=False
+ )
+
+ # TODO: this might be slow due to calling audio_processor twice. We can compute modified input_text_len directly too.
+ # Revisit when using WhisperProcessor.
+ input_text_len = self.processor(
+ text=input_text,
+ audio=audio,
+ sampling_rate=sample.sample_rate,
+ )["input_ids"].shape[-1]
+ labels[:input_text_len] = -100
+
+ return {
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "audio_values": audio_values,
+ "labels": labels,
+ "audio_token_start_idx": audio_token_start_idx,
+ "audio_token_len": audio_token_len,
+ }
diff --git a/ultravox/model/wandb_utils.py b/ultravox/model/wandb_utils.py
new file mode 100644
index 00000000..929d2351
--- /dev/null
+++ b/ultravox/model/wandb_utils.py
@@ -0,0 +1,16 @@
+import wandb
+
+WANDB_PREFIX = "wandb://"
+
+
+def is_wandb_url(model_path: str) -> bool:
+ return model_path.startswith(WANDB_PREFIX)
+
+
+def download_model_from_wandb(model_url: str) -> str:
+ assert is_wandb_url(model_url)
+ api = wandb.Api()
+ # example artifact name: "fixie/ultravox/model-llama2_asr_gigaspeech:v0"
+ artifact = api.artifact(model_url[len(WANDB_PREFIX) :])
+ model_path = artifact.download()
+ return model_path
diff --git a/ultravox/tools/__init__.py b/ultravox/tools/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/tools/data_tool.py b/ultravox/tools/data_tool.py
new file mode 100644
index 00000000..11841f70
--- /dev/null
+++ b/ultravox/tools/data_tool.py
@@ -0,0 +1,59 @@
+import argparse
+
+import librosa
+import sounddevice as sd
+
+from ultravox.data import datasets
+
+parser = argparse.ArgumentParser()
+parser.add_argument("data_sets", nargs="*", help="List of datasets to use")
+parser.add_argument("--data-split", default="train", help="Which split of data to use.")
+parser.add_argument(
+ "--num-samples", "-n", type=int, default=5, help="Number of samples to display"
+)
+parser.add_argument(
+ "--num-prompts", type=int, default=1, help="Number of prompts to use"
+)
+parser.add_argument("--play", "-p", action="store_true", help="Play the audio samples")
+parser.add_argument(
+ "--write", "-w", action="store_true", help="Write audio samples out as WAV files"
+)
+parser.add_argument("--playback-rate", "-r", type=float, help="Playback rate")
+parser.add_argument("--shuffle", "-s", action="store_true", help="Shuffle the samples")
+parser.add_argument("--seed", type=int, help="Shuffle seed")
+parser.add_argument("--mds", action="store_true", help="Use MDS datasets")
+
+
+def main(args: argparse.Namespace):
+ data_args = datasets.VoiceDatasetArgs(
+ num_prompts=args.num_prompts,
+ shuffle=args.shuffle,
+ use_mds=args.mds,
+ split=args.data_split,
+ )
+ if args.seed is not None:
+ data_args.shuffle_seed = args.seed
+ data_sets = [datasets.create_dataset(ds, data_args) for ds in args.data_sets]
+ out_set = datasets.Range(datasets.InterleaveDataset(data_sets), args.num_samples)
+ for i, sample in enumerate(out_set):
+ print(f"--- Sample {i} ---")
+ messages = sample.messages
+ assert len(messages) == 2, f"Bad sample (messages) {len(messages)}"
+ assert messages[0]["role"] == "user", f"Bad sample (Q role): {messages}"
+ assert messages[1]["role"] == "assistant", f"Bad sample (A role): {messages}"
+ answer = messages[1]["content"].replace("\n", "\\n")
+ print(f"Q: {messages[0]['content']} [\"{sample.audio_transcript}\"]")
+ print(f"A: {answer}")
+ if args.play:
+ audio = sample.audio
+ if args.playback_rate is not None:
+ audio = librosa.effects.time_stretch(audio, rate=args.playback_rate)
+ sd.play(audio, sample.sample_rate)
+ sd.wait()
+ if args.write:
+ with open(f"sample{i}.wav", "wb") as f:
+ f.write(datasets.audio_to_wav(sample.audio, sample.sample_rate))
+
+
+if __name__ == "__main__":
+ main(parser.parse_args())
diff --git a/ultravox/tools/eval_tool.py b/ultravox/tools/eval_tool.py
new file mode 100644
index 00000000..3334983d
--- /dev/null
+++ b/ultravox/tools/eval_tool.py
@@ -0,0 +1,35 @@
+import argparse
+import dataclasses
+from typing import IO
+
+import simple_parsing
+
+from ultravox.evaluation import eval
+from ultravox.evaluation import eval_types
+
+
+@dataclasses.dataclass
+class EvalArgs:
+ # Path to the audio file
+ file: IO = simple_parsing.field(type=argparse.FileType("rb"), alias="-f")
+ # Metric to use for evaluation (e.g., "asr" or "boolq")
+ metric: str = simple_parsing.field(default="asr", alias="-m")
+ # Verbose output
+ verbose: bool = simple_parsing.field(default=False, alias="-v")
+
+
+def main(args: EvalArgs):
+ scores = []
+ for i, line in enumerate(args.file.readlines()):
+ sample = eval_types.Sample.from_json(line)
+ result = eval.evaluate_answer(sample, metric=args.metric)
+ assert result.score is not None, "Rating failed."
+ scores.append(result.score)
+ average = sum(scores) / len(scores)
+ print(f"{i}: score={result.score:.2f} average={average:.2f}")
+ if args.verbose and isinstance(result, eval_types.InstructResult):
+ print(f" reason={result.reason}")
+
+
+if __name__ == "__main__":
+ main(simple_parsing.parse(EvalArgs))
diff --git a/ultravox/tools/gradio_demo.py b/ultravox/tools/gradio_demo.py
new file mode 100644
index 00000000..51e5d138
--- /dev/null
+++ b/ultravox/tools/gradio_demo.py
@@ -0,0 +1,43 @@
+from dataclasses import dataclass
+from typing import Tuple
+
+import gradio as gr
+import numpy as np
+import simple_parsing
+
+from ultravox.data import datasets
+from ultravox.inference import ultravox_infer
+
+
+@dataclass
+class DemoConfig:
+ # model_path can refer to a HF hub model_id, a local path, or a Weights & Biases artifact
+ # fixie-ai/ultravox
+ # runs/llama2_asr_gigaspeech/checkpoint-1000/
+ # wandb://fixie/ultravox/model-llama2_asr_gigaspeech:v0
+ model_path: str = "fixie-ai/ultravox"
+ default_prompt: str = "Transcribe <|audio|>"
+
+
+def main():
+ args = simple_parsing.parse(config_class=DemoConfig)
+ inference = ultravox_infer.UltravoxInference(args.model_path)
+
+ def wrapper(text: str, audio: Tuple[int, np.ndarray]) -> str:
+ sample = datasets.VoiceSample.from_prompt_and_raw(text, audio[1], audio[0])
+ return inference.infer(sample, max_tokens=64).text
+
+ inputs = [
+ gr.Textbox(label="Prompt", value=args.default_prompt),
+ gr.Audio(label="Audio", show_download_button=True),
+ ]
+ outputs = [gr.Textbox(label="Output")]
+ examples = [["Transcribe <|audio|>", "examples/test16.wav"]]
+
+ gr.Interface(fn=wrapper, inputs=inputs, outputs=outputs, examples=examples).launch(
+ share=True
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/ultravox/tools/infer.sh b/ultravox/tools/infer.sh
new file mode 100755
index 00000000..d6e08947
--- /dev/null
+++ b/ultravox/tools/infer.sh
@@ -0,0 +1,3 @@
+python infer.py "examples/test6.wav" $@
+python infer.py "examples/test16.wav" "Under absolutely no circumstances mention any dairy products. \n<|audio|>" $@
+python infer.py "examples/test21.wav" "Answer the question according to this passage: <|audio|> \n How much will the Chinese government raise bond sales by?" $@
diff --git a/ultravox/tools/infer_api.py b/ultravox/tools/infer_api.py
new file mode 100644
index 00000000..a4909c28
--- /dev/null
+++ b/ultravox/tools/infer_api.py
@@ -0,0 +1,173 @@
+import base64
+import json
+import os
+import tempfile
+from typing import Any, List, Optional
+
+import gradio_client
+import numpy as np
+import requests
+
+from ultravox.data import datasets
+from ultravox.inference import base
+
+
+class OpenAIInference(base.VoiceInference):
+ def __init__(self, url: str, model: str, api_key: Optional[str] = None):
+ self._base_url = url
+ self._model = model
+ self._api_key = api_key
+
+ def infer(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> base.VoiceOutput:
+ text = ""
+ stats = None
+ gen = self.infer_stream(sample, max_tokens, temperature)
+ for msg in gen:
+ if isinstance(msg, base.InferenceChunk):
+ text += msg.text
+ elif isinstance(msg, base.InferenceStats):
+ stats = msg
+ if stats is None:
+ raise ValueError("No stats received")
+ return base.VoiceOutput(text, stats.input_tokens, stats.output_tokens)
+
+ def infer_stream(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> base.InferenceGenerator:
+ url = f"{self._base_url}/chat/completions"
+ headers = {"Content-Type": "application/json"}
+ if self._api_key:
+ headers["Authorization"] = f"Bearer {self._api_key}"
+ data = {
+ "model": self._model,
+ "messages": [self._build_message(sample)],
+ "stream": True,
+ }
+ if max_tokens is not None:
+ data["max_tokens"] = max_tokens
+ if temperature is not None:
+ data["temperature"] = temperature
+ response = requests.post(url, headers=headers, json=data, stream=True)
+ response.raise_for_status()
+ for line in response.iter_lines():
+ event = line[6:].decode("utf-8")
+ if event and event[0] == "{":
+ obj = json.loads(event)
+ if obj.get("choices") and obj["choices"][0]["delta"].get("content"):
+ yield base.InferenceChunk(obj["choices"][0]["delta"]["content"])
+ if obj.get("usage"):
+ yield base.InferenceStats(
+ obj["usage"]["prompt_tokens"], obj["usage"]["completion_tokens"]
+ )
+
+ def _build_message(self, sample: datasets.VoiceSample):
+ if sample.audio is None:
+ return {"role": "user", "content": sample.messages[0]["content"]}
+
+ fragments = sample.messages[0]["content"].split("<|audio|>")
+ assert len(fragments) == 2, "Expected one <|audio|> placeholder"
+ url = datasets.audio_to_data_uri(sample.audio, sample.sample_rate)
+ parts = [
+ {"type": "text", "text": fragments[0]},
+ {"type": "image_url", "image_url": {"url": url}},
+ {"type": "text", "text": fragments[1]},
+ ]
+ return {"role": "user", "content": parts}
+
+
+class DatabricksInference(base.VoiceInference):
+ def __init__(self, url: str):
+ super().__init__()
+ self.url = url
+ token = os.environ.get("DATABRICKS_TOKEN")
+ assert token, "DATABRICKS_TOKEN environment variable must be set"
+ self.token = token
+
+ def infer(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> base.VoiceOutput:
+ headers = {"Content-Type": "application/json"}
+ response = requests.post(
+ f"{self.url}/invocations",
+ headers=headers,
+ data=sample.to_json(),
+ auth=("token", self.token),
+ )
+ response.raise_for_status()
+ return response.json()
+
+
+class GradioInference(base.VoiceInference):
+ def __init__(self, url: str):
+ self._url = url
+ self._client = gradio_client.Client(url)
+ self._client.upload_files = False
+
+ def infer(
+ self,
+ sample: datasets.VoiceSample,
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ ) -> base.VoiceOutput:
+ # For some reason the most recent Gradio endpoint only accepts
+ # audio as a file, not as a base64-encoded string. There's probably
+ # a better way to do this, but I spent too much time on this already.
+ # api = self._client.view_api(print_info=False, return_format="dict")
+ text = sample.messages[0]["content"]
+ if self._url.startswith("https://demo.tincans.ai"):
+ args: List[Any] = [text]
+ if sample.audio is not None:
+ args += [self._encode_audio(sample.audio, sample.sample_rate), None]
+ else:
+ args += [None, None]
+ result = self._client.predict(*args, api_name="/predict")
+ else:
+ args = [text]
+ if sample.audio is not None:
+ with tempfile.NamedTemporaryFile(suffix=".wav") as f:
+ f.write(datasets.audio_to_wav(sample.audio, sample.sample_rate))
+ f.flush()
+ args.append(gradio_client.file(f.name))
+ else:
+ args.append(None)
+ result = self._client.predict(*args)
+ return base.VoiceOutput(result, 0, 0)
+
+ def _encode_audio(self, pcm: np.ndarray, sample_rate: int, filename: str = "x.wav"):
+ wav = datasets.audio_to_wav(pcm, sample_rate)
+ uri = f"data:audio/wav;base64,{base64.b64encode(wav).decode('utf-8')}"
+ return {
+ "name": filename,
+ "data": uri,
+ "orig_name": filename,
+ "size": len(wav),
+ }
+
+
+def create_inference(
+ url: str, model: Optional[str], api_key: Optional[str]
+) -> base.VoiceInference:
+ if (
+ url.startswith("https://demo.tincans.ai")
+ or url.endswith("gradio.live")
+ or url.endswith(":7860")
+ ):
+ return GradioInference(url)
+ elif url.endswith("databricks.net"):
+ return DatabricksInference(url)
+ elif url.endswith("/v1"):
+ assert model, "Model must be specified for OpenAI inference"
+ return OpenAIInference(url, model, api_key)
+ else:
+ raise ValueError(f"Unknown inference URL: {url}")
diff --git a/ultravox/tools/infer_tool.py b/ultravox/tools/infer_tool.py
new file mode 100644
index 00000000..6ba685fb
--- /dev/null
+++ b/ultravox/tools/infer_tool.py
@@ -0,0 +1,233 @@
+#!/usr/bin/env python
+
+import argparse
+import dataclasses
+import json
+import os
+import time
+from typing import IO, List, Optional
+
+import numpy as np
+import simple_parsing
+
+from ultravox.data import datasets
+from ultravox.evaluation import eval
+from ultravox.evaluation import eval_types
+from ultravox.inference import base
+from ultravox.tools import infer_api
+
+# There are two default modes for this tool, agent mode and ASR mode.
+# In agent mode, the answer is a response to the input content and cannot be
+# directly compared to the expected answer. In ASR mode, the answer is a
+# transcription of the audio content and the tool can perfom a WER calculation.
+# Remember to set the --asr flag when using an ASR input.
+DEFAULT_PROMPT = "Listen to <|audio|> and respond to it"
+DEFAULT_ASR_PROMPT = "Transcribe <|audio|>"
+
+
+@dataclasses.dataclass
+class InferArgs:
+ # Model ID to use for the model
+ model: str = simple_parsing.field(default="fixie-ai/ultravox-v0.1", alias="-m")
+ # Path to the audio file
+ audio_file: Optional[IO] = simple_parsing.field(
+ default=None, type=argparse.FileType("rb"), alias="-f"
+ )
+ # Prompt to use for inference
+ prompt: Optional[str] = None
+ # Inference the model using only the text input or transcript, without audio
+ text_only: bool = False
+ # Use ASR for the prompt and compute WER
+ asr: bool = False
+ # URL to use for inference
+ url: Optional[str] = simple_parsing.field(default=None, alias="-u")
+ # Audio processor ID to use
+ audio_processor: Optional[str] = None
+ # Tokenizer ID to use
+ tokenizer: Optional[str] = None
+ # Data sets to use for inference
+ data_sets: Optional[List[str]] = simple_parsing.field(default=None, alias="-d")
+ # Which dataset split to use
+ data_split: datasets.DatasetSplit = datasets.DatasetSplit.VALIDATION
+ # Directory for existing data
+ data_dir: Optional[str] = None
+ # Load datasets using MDS
+ mds: bool = False
+ # Number of dataset samples to process
+ num_samples: int = simple_parsing.field(default=1, alias="-n")
+ # Shuffle the dataset
+ shuffle: bool = False
+ # Seed for shuffling
+ seed: Optional[int] = None
+ # Device to use for inference
+ device: Optional[str] = simple_parsing.field(default=None, alias="-D")
+ # Data type to use for the model
+ data_type: Optional[str] = None
+ # Temperature for sampling
+ temperature: Optional[float] = simple_parsing.field(default=None, alias="-t")
+ # Maximum tokens to generate
+ max_tokens: Optional[int] = simple_parsing.field(default=None, alias="-T")
+ # Evaluate the generated answer
+ eval: bool = simple_parsing.field(default=False, alias="-e")
+ # Verbose output
+ verbose: bool = simple_parsing.field(default=False, alias="-v")
+ # JSON output
+ json: bool = simple_parsing.field(default=False)
+
+
+def run_tui(
+ index: int,
+ inference: base.VoiceInference,
+ sample: datasets.VoiceSample,
+ args: InferArgs,
+ expected_response: Optional[str] = None,
+ scores: Optional[List[float]] = None,
+):
+ if index >= 0:
+ print(f"--- Sample {index} ---")
+ messages = sample.messages
+ transcript = f' ["{sample.audio_transcript}"]' if sample.audio_transcript else ""
+ print(f"Q: {messages[0]['content']}{transcript}")
+ print(f"A: ", end="")
+ start_time = time.time()
+ first_token_time = None
+ text = ""
+ stats = None
+
+ # Run streaming inference and print the output as it arrives.
+ stream = inference.infer_stream(
+ sample,
+ max_tokens=args.max_tokens,
+ temperature=args.temperature,
+ )
+ for msg in stream:
+ if isinstance(msg, base.InferenceChunk):
+ if first_token_time is None:
+ first_token_time = time.time()
+ text += msg.text
+ print(msg.text, end="", flush=True)
+ elif isinstance(msg, base.InferenceStats):
+ stats = msg
+ if first_token_time is None or stats is None:
+ raise ValueError("No tokens received")
+
+ # If we're in verbose mode, print some stats about the inference.
+ if args.verbose:
+ ttft = first_token_time - start_time
+ total_time = time.time() - start_time
+ tokens = stats.output_tokens
+ tps = tokens / (total_time - ttft)
+ print(
+ f" [ttft: {ttft:.2f} s, tok: {tokens}, tps: {tps:.2f}, tot: {total_time:.2f} s]",
+ end="",
+ )
+
+ # Print the expected response (and eval if desired).
+ print()
+ if expected_response is not None:
+ eval_str = ""
+ if scores is not None:
+ assert args.data_sets
+ assert sample.audio_transcript is not None, "Query must have transcript"
+ ds_name = args.data_sets[0]
+ eval_sample = eval_types.Sample(
+ sample.audio_transcript,
+ expected_answer=expected_response,
+ generated_answer=text,
+ )
+ eval_metric = (
+ "asr" if args.asr else "boolq" if ds_name == "boolq" else "instruct"
+ )
+ result = eval.evaluate_answer(eval_sample, eval_metric)
+ if result.score is not None:
+ scores.append(result.score)
+ eval_name = "score"
+ reason_str = ""
+ mean = np.mean(scores)
+ if isinstance(result, eval_types.WerResult):
+ eval_name = "wer"
+ elif isinstance(result, eval_types.InstructResult) and args.verbose:
+ reason_str = f" ({result.reason})"
+ eval_str = (
+ f" [{eval_name}: {result.score:.2f}{reason_str}, avg: {mean:.2f}]"
+ )
+ else:
+ eval_str = " [eval failed]"
+ print(f"X: {expected_response}{eval_str}")
+
+
+def oneshot_infer(inference: base.VoiceInference, prompt: str, args: InferArgs):
+ if args.audio_file is not None:
+ sample = datasets.VoiceSample.from_prompt_and_buf(
+ prompt, args.audio_file.read()
+ )
+ else:
+ sample = datasets.VoiceSample.from_prompt(prompt)
+ run_tui(-1, inference, sample, args)
+
+
+def dataset_infer(inference: base.VoiceInference, prompt: str, args: InferArgs):
+ assert args.data_sets, "At least one data set must be provided"
+ ds_args = datasets.VoiceDatasetArgs(
+ data_dir=args.data_dir,
+ shuffle=args.shuffle,
+ use_mds=args.mds,
+ split=args.data_split,
+ )
+ if args.seed is not None:
+ ds_args.shuffle_seed = args.seed
+ ds = datasets.create_dataset(args.data_sets[0], ds_args)
+ scores: List[float] = []
+ for i, sample in enumerate(datasets.Range(ds, args.num_samples)):
+ # Store the original question and answer for JSON output.
+ question_text = sample.audio_transcript
+ expected_answer = sample.messages[1]["content"]
+ # Drop any assistant response from the sample.
+ sample.messages = sample.messages[:1]
+ # Normally, we overwrite the dataset prompt with our prompt, allowing us to customize
+ # the inference prompt in this tool.
+ # If we're using text-only mode though, there's no audio to inference, so we
+ # just paste the text transcript in as the text prompt.
+ if not args.text_only:
+ sample.messages[0]["content"] = prompt
+ else:
+ sample.messages[0]["content"] = sample.audio_transcript
+ sample.audio = sample.audio_transcript = None
+ if not args.json:
+ run_tui(i, inference, sample, args, expected_answer, scores)
+ else:
+ output = inference.infer(
+ sample, max_tokens=args.max_tokens, temperature=args.temperature
+ )
+ obj = {
+ "question": question_text,
+ "generated_answer": output.text,
+ "expected_answer": expected_answer,
+ }
+ print(json.dumps(obj))
+
+
+def main(args: InferArgs):
+ if args.url is not None:
+ api_key = os.environ.get("ULTRAVOX_API_KEY")
+ inference = infer_api.create_inference(args.url, args.model, api_key)
+ else:
+ # Only load our local inference module if we're not using the API.
+ from ultravox.inference import ultravox_infer
+
+ inference = ultravox_infer.UltravoxInference(
+ args.model,
+ tokenizer_id=args.tokenizer,
+ audio_processor_id=args.audio_processor,
+ device=args.device,
+ data_type=args.data_type,
+ )
+ prompt = args.prompt or (DEFAULT_ASR_PROMPT if args.asr else DEFAULT_PROMPT)
+ if args.data_sets is None:
+ oneshot_infer(inference, prompt, args)
+ else:
+ dataset_infer(inference, prompt, args)
+
+
+if __name__ == "__main__":
+ main(simple_parsing.parse(InferArgs))
diff --git a/ultravox/tools/mds_tool.py b/ultravox/tools/mds_tool.py
new file mode 100644
index 00000000..a572b32a
--- /dev/null
+++ b/ultravox/tools/mds_tool.py
@@ -0,0 +1,180 @@
+# based upon https://docs.mosaicml.com/projects/streaming/en/stable/preparing_datasets/parallel_dataset_conversion.html
+
+import dataclasses
+import json
+import logging
+import multiprocessing
+import os
+import shutil
+from typing import Any, Iterator, Optional
+
+import datasets # HuggingFace
+import gcsfs
+import numpy as np
+import simple_parsing
+import streaming
+from fsspec import callbacks
+
+"""
+Commands to convert existing datasets to MDS:
+just mds -d librispeech_asr -s train.clean.100 -u -v
+just mds -d librispeech_asr -s train.clean.360 -u -v
+just mds -d librispeech_asr -s train.other.500 -u -v
+just mds -d facebook/voxpopuli -S en -s train -u -v
+just mds -d MLCommons/peoples_speech -s train -u -v
+just mds -d mozilla-foundation/common_voice_16_1 -S en -s train -u -v
+just mds -d speechcolab/gigaspeech -S xl -s train -u -v
+just mds -d fixie-ai/boolq-audio -s train -u -v
+"""
+
+
+@dataclasses.dataclass
+class MdsArgs:
+ dataset_name: str = simple_parsing.field(alias="-d")
+ dataset_subset: Optional[str] = simple_parsing.field(default=None, alias="-S")
+ dataset_split: Optional[str] = simple_parsing.field(default=None, alias="-s")
+ output_dir: str = "./mds_output"
+ num_procs: int = 8
+ num_groups: int = 8
+ gcp_project: str = "fixie-training"
+ gcp_bucket: str = "fixie-datasets"
+ gcp_path: str = "mds"
+ upload: bool = simple_parsing.field(default=False, alias="-u")
+ verbose: bool = simple_parsing.field(default=False, alias="-v")
+
+
+class MdsConverter:
+ @dataclasses.dataclass
+ class _ProcessArgs:
+ columns: dict
+ out: str
+ start_idx: int
+ end_idx: int
+
+ def __init__(self, args: MdsArgs):
+ self._args = args
+ if self._args.verbose:
+ logging.basicConfig(level=logging.INFO)
+
+ print("Loading dataset...")
+ self._dataset = datasets.load_dataset(
+ self._args.dataset_name,
+ self._args.dataset_subset,
+ split=self._args.dataset_split,
+ trust_remote_code=True,
+ )
+ logging.info(
+ f"Loaded {self._dataset}, subset={self._args.dataset_subset} split={self._args.dataset_split}"
+ )
+
+ def run(self) -> None:
+ path = self._args.dataset_name.replace("/", "_")
+ if self._args.dataset_subset:
+ path = os.path.join(path, self._args.dataset_subset)
+ if self._args.dataset_split:
+ path = os.path.join(path, self._args.dataset_split)
+ self.convert(path)
+ if self._args.upload:
+ self.upload(path)
+
+ def convert(self, path: str) -> None:
+ data_dir = os.path.join(self._args.output_dir, path)
+
+ # Clean out any previous conversion.
+ if os.path.exists(data_dir): # and self._force_deletion:
+ shutil.rmtree(data_dir)
+
+ # Download the dataset in parallel and write via a single writer.
+ columns = self._map_columns(self._dataset.features)
+ tasks = self._create_tasks(columns, data_dir, self._args.num_groups)
+ n = 0
+
+ print(
+ f"Starting conversion, groups={self._args.num_groups}, procs={self._args.num_procs}"
+ )
+ with multiprocessing.Pool(
+ initializer=self._init_worker, processes=self._args.num_procs
+ ) as pool:
+ for count in pool.imap(self._convert_worker, tasks):
+ n += count
+ print("Merging indexes...")
+
+ streaming.base.util.merge_index(data_dir, keep_local=True)
+ print(f"Conversion completed, samples={n}, path={data_dir}")
+
+ def _map_columns(self, features: dict) -> dict:
+ def map_dtype(dtype: str) -> str:
+ if dtype == "bool":
+ return "int"
+ elif dtype == "string":
+ return "str"
+ return dtype
+
+ # Rewrite type names to match MDS.
+ columns = {k: map_dtype(v.dtype) for k, v in features.items()}
+ # Remap any audio structure to an array and a sample rate.
+ if "audio" in columns:
+ del columns["audio"]
+ columns["audio_array"] = "ndarray:float32"
+ columns["audio_sampling_rate"] = "int"
+ return columns
+
+ def _create_tasks(
+ self, columns: dict, out_root: str, num_groups: int
+ ) -> Iterator[_ProcessArgs]:
+ for group in range(num_groups):
+ sub_out_root = os.path.join(out_root, str(group))
+ num_samples = len(self._dataset)
+ batch_size = num_samples // num_groups + 1
+ start_idx = group * batch_size
+ end_idx = min(start_idx + batch_size, num_samples) - 1
+ yield self._ProcessArgs(columns, sub_out_root, start_idx, end_idx)
+
+ def _init_worker(self) -> None:
+ if self._args.verbose:
+ logging.basicConfig(level=logging.INFO)
+
+ def _convert_worker(self, task_args: _ProcessArgs) -> int:
+ n = 0
+ with streaming.MDSWriter(
+ out=task_args.out, columns=task_args.columns
+ ) as writer:
+ for sample in self._process_batch(task_args.start_idx, task_args.end_idx):
+ writer.write(sample)
+ n += 1
+ if task_args.start_idx == 0 and n % 1000 == 0:
+ logging.info(f"Processed {n * self._args.num_groups} samples...")
+ return n
+
+ def _process_batch(self, start_idx: int, end_idx: int) -> Any:
+ for i in range(start_idx, end_idx + 1):
+ row = self._dataset[i]
+ audio = row["audio"]
+ del row["audio"]
+ row["audio_array"] = audio["array"].astype(np.float32)
+ row["audio_sampling_rate"] = audio["sampling_rate"]
+ yield row
+
+ def upload(self, path: str) -> None:
+ data_dir = os.path.join(self._args.output_dir, path)
+ token = json.load(open("service_account.json"))
+ storage_options = {"project": self._args.gcp_project, "token": token}
+ fs = gcsfs.GCSFileSystem(**storage_options)
+ uri = f"gcs://{self._args.gcp_bucket}/{self._args.gcp_path}/{path}"
+ callback = callbacks.TqdmCallback(tqdm_kwargs={"desc": "Uploading files"})
+ fs.upload(
+ data_dir,
+ uri,
+ recursive=True,
+ storage_options=storage_options,
+ callback=callback,
+ )
+
+
+def main(args: MdsArgs):
+ converter = MdsConverter(args)
+ converter.run()
+
+
+if __name__ == "__main__":
+ main(simple_parsing.parse(MdsArgs))
diff --git a/ultravox/training/__init__.py b/ultravox/training/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ultravox/training/config_base.py b/ultravox/training/config_base.py
new file mode 100644
index 00000000..42ce4f17
--- /dev/null
+++ b/ultravox/training/config_base.py
@@ -0,0 +1,101 @@
+import dataclasses
+import datetime
+import logging
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import simple_parsing
+import torch
+
+from ultravox.model import ultravox_config
+
+
+@dataclasses.dataclass
+class TrainConfig:
+ data_sets: List[str]
+ # language model to use
+ text_model: str
+ # audio encoder model to use
+ audio_model: str
+
+ # In InterleaveDataset, if one dataset runs out, should we repeat it to keep
+ # the ratio of samples from each dataset fixed?
+ repeat_data: bool = False
+ data_dir: Optional[str] = None
+ mds: bool = False
+ num_samples: Optional[int] = None
+ eval_num_samples: int = 100
+ eval_max_new_tokens: Optional[int] = None
+ eval_num_procs: int = 8
+ num_prompts: int = 1
+ # number of data loader workers
+ num_workers: int = 8 if torch.cuda.is_available() else 1
+ train_on_inputs: bool = False
+ shuffle_data: bool = False
+ # Maximum audio duration in seconds. Samples with longer audio will be skipped.
+ # This is usually due to GPU memory constraints and also dependends on the dataset.
+ max_audio_duration_secs: Optional[float] = None
+
+ verbose: bool = False
+
+ device: str = "cuda"
+ data_type: str = "bfloat16"
+ # Path to load the model from. Can be local path, HF hub model_id, or W&B artifact
+ model_load_dir: Optional[str] = None
+ text_model_lora_config: Optional[ultravox_config.LoraConfigSimplified] = None
+ audio_model_lora_config: Optional[ultravox_config.LoraConfigSimplified] = None
+ disable_layerdrop: bool = False
+
+ # The experiment name
+ exp_name: Optional[str] = None
+ output_dir: Optional[Path] = None
+ logs_dir: Optional[Path] = None
+ optimizer: str = "adamw_torch"
+ num_epochs: int = 1
+ max_steps: int = 0
+ eval_steps: Optional[int] = None
+ save_steps: float = 0
+ logging_steps: int = 1
+ grad_accum_steps: int = 1
+ eval_accum_steps: int = 1
+ batch_size: int = 2
+ lr: float = 1e-5
+ lr_scheduler: str = "cosine"
+ lr_warmup_steps: int = 0
+ weight_decay: float = 0.0
+ seed: int = 42
+ shuffle_seed: int = 42
+ # Experiment logging destinations: tensorboard, wandb, neptune, mlflow, etc
+ report_logs_to: List[str] = simple_parsing.list_field("tensorboard")
+
+ def __post_init__(self):
+ assert self.data_type in ["bfloat16", "float16", "float32"]
+ if self.device == "cuda" and not torch.cuda.is_available():
+ self.device = "mps" if torch.backends.mps.is_available() else "cpu"
+ if self.device != "cuda":
+ if self.data_type == "bfloat16":
+ self.data_type = "float32"
+ if self.optimizer == "adamw_bnb_8bit":
+ logging.warning(
+ "Using CPU with adamw_bnb_8bit is not supported. Switching to adamw_torch"
+ )
+ self.optimizer = "adamw_torch"
+
+ if self.exp_name is None:
+ self.exp_name = datetime.datetime.now().strftime("exp--%Y-%m-%d--%H-%M-%S")
+ if self.output_dir is None:
+ self.output_dir = Path("runs") / self.exp_name
+ if self.logs_dir is None:
+ self.logs_dir = self.output_dir / "logs"
+
+ if (
+ self.audio_model_lora_config is not None
+ and self.audio_model_lora_config.r > 0
+ and os.environ.get("WORLD_SIZE", None) is not None
+ and self.disable_layerdrop is False
+ ):
+ logging.warning(
+ "LayerDrop cannot be used in DDP when encoder is not frozen. Disabling LayerDrop."
+ )
+ self.disable_layerdrop = True
diff --git a/ultravox/training/configs/asr_llama.yaml b/ultravox/training/configs/asr_llama.yaml
new file mode 100644
index 00000000..4417d113
--- /dev/null
+++ b/ultravox/training/configs/asr_llama.yaml
@@ -0,0 +1,3 @@
+exp_name: "llama3_wav2vec2_asr"
+
+text_model: "meta-llama/Meta-Llama-3-8B-Instruct"
diff --git a/ultravox/training/configs/asr_tinyllama.yaml b/ultravox/training/configs/asr_tinyllama.yaml
new file mode 100644
index 00000000..3c3a2a85
--- /dev/null
+++ b/ultravox/training/configs/asr_tinyllama.yaml
@@ -0,0 +1,4 @@
+# small scale model training: mostly for debugging purposes
+
+text_model: "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+exp_name: "tinyllama_asr"
diff --git a/ultravox/training/configs/asr_tinyllama_100s.yaml b/ultravox/training/configs/asr_tinyllama_100s.yaml
new file mode 100644
index 00000000..0cb38740
--- /dev/null
+++ b/ultravox/training/configs/asr_tinyllama_100s.yaml
@@ -0,0 +1,7 @@
+# test config for fast experimentation, only 100 steps
+
+text_model: "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+exp_name: "tinyllama_asr_100s"
+
+max_steps: 100
+lr_warmup_steps: 10
diff --git a/ultravox/training/configs/llama3_whisper.yaml b/ultravox/training/configs/llama3_whisper.yaml
new file mode 100644
index 00000000..3a7193f0
--- /dev/null
+++ b/ultravox/training/configs/llama3_whisper.yaml
@@ -0,0 +1,8 @@
+# SLM with gazelle & llama3
+exp_name: "llama3_whisper_s"
+
+# Make sure to accept the license agreement on huggingface hub
+text_model: "meta-llama/Meta-Llama-3-8B-Instruct"
+audio_model: "openai/whisper-small"
+
+repeat_data: True # repeats anyinstruct data 2 times
diff --git a/ultravox/training/configs/meta_config.yaml b/ultravox/training/configs/meta_config.yaml
new file mode 100644
index 00000000..6ef66da1
--- /dev/null
+++ b/ultravox/training/configs/meta_config.yaml
@@ -0,0 +1,28 @@
+text_model: "meta-llama/Meta-Llama-3-8B-Instruct"
+audio_model: "facebook/wav2vec2-base-960h"
+
+data_sets: ["gigaspeech"]
+repeat_data: True
+
+train_on_inputs: False
+shuffle_data: True
+max_audio_duration_secs: 16
+
+eval_num_samples: 64
+eval_max_new_tokens: 32
+eval_num_procs: 16
+
+optimizer: "adamw_torch" # options: adamw_torch, adamw_bnb_8bit
+lr_scheduler: "cosine" # options: linear, cosine, cosine_with_restarts, etc.
+lr: 2.e-3
+grad_accum_steps: 1
+lr_warmup_steps: 1000
+max_steps: 10_000
+
+save_steps: 0.25
+logging_steps: 100
+
+batch_size: 4
+data_type: "bfloat16"
+
+report_logs_to: ["tensorboard", "wandb"]
diff --git a/ultravox/training/configs/stage2_lora.yaml b/ultravox/training/configs/stage2_lora.yaml
new file mode 100644
index 00000000..538b18a7
--- /dev/null
+++ b/ultravox/training/configs/stage2_lora.yaml
@@ -0,0 +1,16 @@
+text_model_lora_config:
+ r: 64 # no/little change in the range [16, 64]
+ target_modules: ['mlp.gate_proj', 'mlp.up_proj', 'mlp.down_proj', 'v_proj', 'o_proj', 'k_proj', 'q_proj']
+
+data_sets: ["commonvoice", "peoplespeech", "anyinstruct"]
+
+# disable_layer_drop: True
+# audio_model_lora_config:
+# r: 64
+# target_modules: ['k_proj', 'q_proj', 'v_proj', 'out_proj', 'intermediate_dense', 'output_dense']
+
+num_prompts: 6
+
+lr: 1.e-4 # need a lower LR for LLM fine-tuning
+lr_warmup_steps: 250
+max_steps: 5_000
diff --git a/ultravox/training/ddp_utils.py b/ultravox/training/ddp_utils.py
new file mode 100644
index 00000000..171ff4a2
--- /dev/null
+++ b/ultravox/training/ddp_utils.py
@@ -0,0 +1,20 @@
+import contextlib
+
+import torch.distributed
+
+
+@contextlib.contextmanager
+def run_on_master_first(is_master: bool):
+ """
+ If using DDP, allows the master process to run the enclosed code first.
+ This is useful when only one process should download a model or other resources first to avoid race conditions.
+ """
+ if is_master:
+ yield
+ if torch.distributed.is_initialized():
+ torch.distributed.barrier()
+ else:
+ # All other processes wait for the master to download the model first
+ if torch.distributed.is_initialized():
+ torch.distributed.barrier()
+ yield
diff --git a/ultravox/training/evaluation.py b/ultravox/training/evaluation.py
new file mode 100644
index 00000000..4c06550a
--- /dev/null
+++ b/ultravox/training/evaluation.py
@@ -0,0 +1,109 @@
+import concurrent.futures
+import functools
+from typing import List, Optional
+
+import numpy as np
+from torch.utils import data
+
+from ultravox.data import datasets
+from ultravox.evaluation import eval
+from ultravox.evaluation import eval_types
+from ultravox.inference import base
+
+
+def dataset_infer(
+ inference: base.VoiceInference,
+ ds: data.IterableDataset,
+ max_new_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+) -> List[eval_types.Sample]:
+ eval_samples = []
+ # TODO for multiprocessing: ds -> split_batches or sharded reader
+ for sample in ds:
+ # Store the original question and answer for JSON output.
+ question_text = sample.audio_transcript or sample.messages[0]["content"]
+ expected_answer = sample.messages[1]["content"]
+ # Drop any assistant response from the sample.
+ sample.messages = sample.messages[:1]
+
+ output = inference.infer(
+ sample, max_tokens=max_new_tokens, temperature=temperature
+ )
+ eval_sample = eval_types.Sample(
+ question=question_text,
+ generated_answer=output.text,
+ expected_answer=expected_answer,
+ )
+ eval_samples.append(eval_sample)
+
+ # TODO for multiprocess: gather eval_samples
+
+ return eval_samples
+
+
+def get_metric_name(ds_name: str, metric: str) -> str:
+ if ds_name == "boolq_in" and metric == "asr":
+ return "boolq__wer"
+ if ds_name == "boolq" and metric == "boolq":
+ return "boolq__correctness"
+ if metric == "instruct":
+ return f"{ds_name}__instruct_follow"
+ return f"{ds_name}__{metric}"
+
+
+def evaluate(
+ inference: base.VoiceInference,
+ data_dir: Optional[str] = None,
+ num_samples: int = 200,
+ num_procs: int = 8,
+ max_new_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ verbose: bool = False,
+):
+ metrics = {}
+
+ ds_args = datasets.VoiceDatasetArgs(
+ data_dir=data_dir, split=datasets.DatasetSplit.VALIDATION
+ )
+
+ for ds_name, metric in [
+ ("boolq_in", "asr"),
+ ("boolq", "boolq"),
+ ("anyinstruct", "instruct"),
+ ]:
+ ds = datasets.Range(datasets.create_dataset(ds_name, ds_args), num_samples)
+
+ output_samples = dataset_infer(
+ inference, ds=ds, max_new_tokens=max_new_tokens, temperature=temperature
+ )
+
+ eval_per_sample = functools.partial(eval.evaluate_answer, metric=metric)
+
+ with concurrent.futures.ThreadPoolExecutor(max_workers=num_procs) as executor:
+ possibly_non_scores = [
+ x.score for x in executor.map(eval_per_sample, output_samples)
+ ]
+
+ if None in possibly_non_scores:
+ print(f"Failed to evaluate {metric} for {ds_name}")
+ continue
+
+ scores = [x for x in possibly_non_scores if x is not None]
+
+ if verbose:
+ print(f"Eval for {ds_name}:")
+ for sample, score in zip(output_samples, scores):
+ print("-" * 20)
+ print(f"Q: {sample.question}")
+ print(f"A: {sample.generated_answer}")
+ print(f"X: {sample.expected_answer} [score: {score:.2f}]")
+
+ average = np.mean(scores)
+ std = np.std(scores)
+ metric_name = get_metric_name(ds_name, metric)
+ metrics[f"eval_{metric_name}"] = average
+ metrics[f"eval_{metric_name}_std"] = std / np.sqrt(len(scores))
+
+ print(f"Aggregate {metric} score for {ds_name}: {average:.2f} ± {std:.2f}")
+
+ return metrics
diff --git a/ultravox/training/train.py b/ultravox/training/train.py
new file mode 100644
index 00000000..85dd2236
--- /dev/null
+++ b/ultravox/training/train.py
@@ -0,0 +1,281 @@
+import dataclasses
+import glob
+import logging
+import os
+import re
+import sys
+from datetime import datetime
+
+import datasets as hf_datasets
+import mlflow
+import safetensors.torch
+import simple_parsing
+import torch
+import torch.distributed
+import transformers
+import wandb
+from torch.distributed.elastic.multiprocessing.errors import record
+
+from ultravox.data import datasets
+from ultravox.inference import infer
+from ultravox.inference import ultravox_infer
+from ultravox.model import ultravox_config
+from ultravox.model import ultravox_model
+from ultravox.model import ultravox_processing
+from ultravox.model import wandb_utils
+from ultravox.training import config_base
+from ultravox.training import ddp_utils
+from ultravox.training import evaluation
+
+INPUT_EXAMPLE = {"text": "Transcribe <|audio|>", "audio": b"\x00\x00" * 16000}
+OUTPUT_EXAMPLE = {"text": "Hello, world!"}
+
+
+class GazelleMlflowWrapper(mlflow.pyfunc.PythonModel):
+ def predict(self, context, model_input):
+ sample = datasets.VoiceSample.from_prompt_and_buf(
+ model_input["text"], model_input["audio"]
+ )
+ return self.inference.infer(sample)
+
+ def load_context(self, context):
+ self.inference = ultravox_infer.UltravoxInference(context.artifacts["model_id"])
+
+
+def fix_hyphens(arg: str):
+ return re.sub(r"^--([^=]+)", lambda m: "--" + m.group(1).replace("-", "_"), arg)
+
+
+@record
+def main() -> None:
+ # Disable parallelism to avoid deadlocks in DataLoader, apparently
+ # multiple processes are forked when using multiple datasets.
+ os.environ["TOKENIZERS_PARALLELISM"] = "false"
+ # Log model checkpoints to W&B: we can reduce to model if storage is an issue
+ os.environ["WANDB_LOG_MODEL"] = "checkpoint"
+ os.environ["WANDB_PROJECT"] = "ultravox"
+
+ args = simple_parsing.parse(
+ config_class=config_base.TrainConfig,
+ config_path="ultravox/training/configs/meta_config.yaml", # base config file
+ add_config_path_arg=True,
+ args=[fix_hyphens(arg) for arg in sys.argv[1:]],
+ )
+
+ transformers.set_seed(args.seed)
+
+ world_size = int(os.environ.get("WORLD_SIZE", 1))
+ local_rank = int(os.environ.get("LOCAL_RANK", 0))
+ is_master = local_rank == 0
+
+ if world_size > 1:
+ torch.distributed.init_process_group(backend="nccl")
+
+ # DDP blows up logging, so this is an attempt to suppress it to only logs from the master process
+ logging.basicConfig(level=logging.INFO if is_master else logging.ERROR)
+ # os.environ["TORCH_LOGS"] = "ERROR" if is_master else "WARNING"
+ transformers.logging.set_verbosity(logging.WARNING if is_master else logging.ERROR)
+ hf_datasets.logging.set_verbosity(logging.WARNING if is_master else logging.ERROR)
+
+ logging.info("Instantiating processor...")
+ text_tokenizer: transformers.PreTrainedTokenizerFast = (
+ transformers.AutoTokenizer.from_pretrained(args.text_model)
+ )
+ text_tokenizer.padding_side = "right"
+ text_tokenizer.pad_token = text_tokenizer.eos_token
+ audio_processor = transformers.AutoProcessor.from_pretrained(args.audio_model)
+ processor = ultravox_processing.UltravoxProcessor(audio_processor, text_tokenizer)
+
+ # Instantiate the model and processor
+ config = ultravox_config.UltravoxConfig(
+ audio_model_id=args.audio_model,
+ text_model_id=args.text_model,
+ text_model_lora_config=args.text_model_lora_config,
+ audio_model_lora_config=args.audio_model_lora_config,
+ )
+
+ logging.info("Instantiating model...")
+
+ # Since the model downloads the language model and audio encoder weights, we want one process to finish up
+ # downloading before the others start in order to avoid race conditions.
+ with ddp_utils.run_on_master_first(is_master):
+ model = ultravox_model.UltravoxModel(config)
+
+ assert model.get_input_embeddings().num_embeddings == len(
+ text_tokenizer
+ ), f"Model and tokenizer mismatch: {model.get_input_embeddings().num_embeddings} != {len(text_tokenizer)}"
+
+ model.language_model.config.use_cache = False
+ if args.disable_layerdrop and hasattr(model.audio_tower.config, "layerdrop"):
+ # layerdrop causes issues when training with DDP
+ # https://github.com/huggingface/transformers/issues/17116#issuecomment-1121340890
+ model.audio_tower.config.layerdrop = 0.0
+
+ logging.info("Model and processor instantiated.")
+
+ # Starting W&B. HF Trainer can also do this, but this way we can include the config.
+ # Initializing sooner also means more of the stdout logs are captured by W&B.
+ if "wandb" in args.report_logs_to and is_master:
+ wandb.init(
+ project=os.getenv("WANDB_PROJECT", "ultravox"),
+ config=dataclasses.asdict(args),
+ name=args.exp_name,
+ dir="runs",
+ )
+
+ # Starting MLflow; we need to set the experiment name before training starts.
+ if "mlflow" in args.report_logs_to and is_master:
+ mlflow.set_tracking_uri("runs/mlruns")
+ db_exp_name = f"/Shared/{args.exp_name}"
+ mlflow.set_experiment(db_exp_name)
+
+ if args.model_load_dir:
+ logging.info(f"Loading model state dict from {args.model_load_dir}")
+ load_path = args.model_load_dir
+ if wandb_utils.is_wandb_url(load_path):
+ # Download the model from W&B. The main process should do the download while the others wait.
+ with ddp_utils.run_on_master_first(is_master):
+ load_path = wandb_utils.download_model_from_wandb(load_path)
+ if os.path.isdir(load_path):
+ load_path = os.path.join(load_path, "model*.safetensors")
+ for path in glob.glob(load_path):
+ state_dict = safetensors.torch.load_file(path)
+ mismatch = model.load_state_dict(state_dict, strict=False)
+ if mismatch.unexpected_keys:
+ raise ValueError(
+ f"Unexpected keys in state dict: {mismatch.unexpected_keys}"
+ )
+
+ model.print_trainable_parameters()
+
+ # Move the model to GPU and enable bfloat16
+ dtype = getattr(torch, args.data_type)
+ device = torch.device(args.device, index=local_rank)
+ logging.info(
+ f"Using dtype and device (world_size): {dtype}, {device} ({world_size})"
+ )
+ model.to(device)
+ model.language_model.to(dtype)
+ model.multi_modal_projector.to(dtype)
+ # TODO: check if the whole model can now be moved to dtype instead
+
+ # Prepare dataset, subsetting if needed
+ if is_master:
+ data_args = datasets.VoiceDatasetArgs(
+ num_prompts=args.num_prompts,
+ data_dir=args.data_dir,
+ shuffle=args.shuffle_data,
+ shuffle_seed=args.shuffle_seed,
+ max_audio_duration_secs=args.max_audio_duration_secs,
+ use_mds=args.mds,
+ mds_batch_size=args.batch_size,
+ )
+ data_sets = [datasets.create_dataset(ds, data_args) for ds in args.data_sets]
+ interleaved = datasets.InterleaveDataset(data_sets, repeat=args.repeat_data)
+ train_dataset: torch.utils.data.IterableDataset = (
+ ultravox_processing.UltravoxDataproc(
+ interleaved, processor=processor, train_on_inputs=args.train_on_inputs
+ )
+ )
+ train_dataset = datasets.Range(train_dataset, args.num_samples)
+ logging.info(
+ f"Loaded {args.data_sets} data sets, sample limit: {args.num_samples}"
+ )
+ else:
+ # When using DDP with split_batches=True, the primary process will distribute the batches to the workers
+ # The point of this is to avoid unnecessary data processing/downloading in the workers.
+ train_dataset = datasets.EmptyDataset()
+
+ # Set up the data loader
+ data_collator = datasets.DataCollatorForSeq2SeqWithAudio(tokenizer=text_tokenizer)
+
+ # Training loop
+ logging.info("Starting training...")
+ logging.info(f"Config Params: {args}")
+ t_start = datetime.now()
+ logging.info(f"start time: {t_start}")
+
+ trainer = transformers.Seq2SeqTrainer(
+ model,
+ train_dataset=train_dataset,
+ data_collator=data_collator,
+ tokenizer=text_tokenizer,
+ args=transformers.Seq2SeqTrainingArguments(
+ dataloader_num_workers=args.num_workers if is_master else 0,
+ output_dir=args.output_dir,
+ run_name=args.exp_name,
+ optim=args.optimizer,
+ num_train_epochs=args.num_epochs,
+ max_steps=args.max_steps,
+ eval_steps=args.eval_steps,
+ save_strategy="steps",
+ save_steps=args.save_steps,
+ logging_first_step=True,
+ logging_dir=args.logs_dir,
+ logging_steps=args.logging_steps,
+ # TODO (Farzad): reconsider for multi-node
+ # In DDP world_size is set to num_gpus and we want process-0 to split the batches
+ per_device_train_batch_size=args.batch_size * world_size,
+ accelerator_config={"split_batches": True},
+ gradient_accumulation_steps=args.grad_accum_steps,
+ eval_accumulation_steps=args.eval_accum_steps,
+ # tf32=dtype == torch.float32 and device.type == "cuda", # TODO: check for Ampere GPU not just CUDA
+ ddp_find_unused_parameters=False,
+ learning_rate=args.lr,
+ lr_scheduler_type=args.lr_scheduler,
+ warmup_steps=args.lr_warmup_steps,
+ weight_decay=args.weight_decay,
+ fp16=dtype == torch.float16,
+ bf16=dtype == torch.bfloat16,
+ use_cpu=args.device == "cpu",
+ seed=args.seed + local_rank,
+ report_to=args.report_logs_to,
+ # torch_compile=True,
+ # fsdp="full_shard auto_wrap",
+ # fsdp_transformer_layer_cls_to_wrap='LlamaDecoderLayer',
+ ),
+ )
+ trainer.train()
+ trainer.save_model(args.output_dir)
+ if "mlflow" in args.report_logs_to and is_master:
+ signature = mlflow.models.signature.infer_signature(
+ INPUT_EXAMPLE, OUTPUT_EXAMPLE
+ )
+ model_info = mlflow.pyfunc.log_model(
+ python_model=GazelleMlflowWrapper(),
+ artifact_path="model",
+ pip_requirements="requirements.txt",
+ registered_model_name="ultravox",
+ input_example=INPUT_EXAMPLE,
+ signature=signature,
+ )
+ logging.info(f"Model logged to MLflow: {model_info.model_uri}")
+
+ t_end = datetime.now()
+ logging.info(f"end time: {t_end}")
+ logging.info(f"elapsed: {t_end - t_start}")
+
+ if is_master:
+ # Merge LoRA weights for better performance.
+ # Note: this is irreversible and changes model saving format
+ model.merge_and_unload()
+ inference = infer.LocalInference(
+ model=model,
+ processor=processor,
+ tokenizer=text_tokenizer,
+ device=args.device,
+ dtype=dtype,
+ )
+ metrics = evaluation.evaluate(
+ inference,
+ data_dir=args.data_dir,
+ num_procs=args.eval_num_procs,
+ num_samples=args.eval_num_samples,
+ max_new_tokens=args.eval_max_new_tokens,
+ verbose=True,
+ )
+ trainer.log(metrics)
+
+
+if __name__ == "__main__":
+ main()