Add files using upload-large-folder tool

.gitattributes

@@ -50,3 +50,4 @@ cudnn-windows-x86_64-8.9.5.30_cuda11-archive/bin/cudnn_ops_train64_8.dll filter=
 pythonProject/.venv/Lib/site-packages/diffusers/loaders/__pycache__/lora_pipeline.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 pythonProject/.venv/Lib/site-packages/diffusers/utils/__pycache__/dummy_torch_and_transformers_objects.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 pythonProject/.venv/Lib/site-packages/functorch/_C.cp310-win_amd64.pyd filter=lfs diff=lfs merge=lfs -text
+pythonProject/.venv/Lib/site-packages/huggingface_hub/inference/__pycache__/_client.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/LICENSE.rst

@@ -0,0 +1,28 @@
+Copyright 2007 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: Jinja2
+Version: 3.1.3
+Summary: A very fast and expressive template engine.
+Home-page: https://palletsprojects.com/p/jinja/
+Maintainer: Pallets
+Maintainer-email: contact@palletsprojects.com
+License: BSD-3-Clause
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Documentation, https://jinja.palletsprojects.com/
+Project-URL: Changes, https://jinja.palletsprojects.com/changes/
+Project-URL: Source Code, https://github.com/pallets/jinja/
+Project-URL: Issue Tracker, https://github.com/pallets/jinja/issues/
+Project-URL: Chat, https://discord.gg/pallets
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.rst
+Requires-Dist: MarkupSafe >=2.0
+Provides-Extra: i18n
+Requires-Dist: Babel >=2.7 ; extra == 'i18n'
+
+Jinja
+=====
+
+Jinja is a fast, expressive, extensible templating engine. Special
+placeholders in the template allow writing code similar to Python
+syntax. Then the template is passed data to render the final document.
+
+It includes:
+
+-   Template inheritance and inclusion.
+-   Define and import macros within templates.
+-   HTML templates can use autoescaping to prevent XSS from untrusted
+    user input.
+-   A sandboxed environment can safely render untrusted templates.
+-   AsyncIO support for generating templates and calling async
+    functions.
+-   I18N support with Babel.
+-   Templates are compiled to optimized Python code just-in-time and
+    cached, or can be compiled ahead-of-time.
+-   Exceptions point to the correct line in templates to make debugging
+    easier.
+-   Extensible filters, tests, functions, and even syntax.
+
+Jinja's philosophy is that while application logic belongs in Python if
+possible, it shouldn't make the template designer's job difficult by
+restricting functionality too much.
+
+
+Installing
+----------
+
+Install and update using `pip`_:
+
+.. code-block:: text
+
+    $ pip install -U Jinja2
+
+.. _pip: https://pip.pypa.io/en/stable/getting-started/
+
+
+In A Nutshell
+-------------
+
+.. code-block:: jinja
+
+    {% extends "base.html" %}
+    {% block title %}Members{% endblock %}
+    {% block content %}
+      <ul>
+      {% for user in users %}
+        <li><a href="{{ user.url }}">{{ user.username }}</a></li>
+      {% endfor %}
+      </ul>
+    {% endblock %}
+
+
+Donate
+------
+
+The Pallets organization develops and supports Jinja and other popular
+packages. In order to grow the community of contributors and users, and
+allow the maintainers to devote more time to the projects, `please
+donate today`_.
+
+.. _please donate today: https://palletsprojects.com/donate
+
+
+Links
+-----
+
+-   Documentation: https://jinja.palletsprojects.com/
+-   Changes: https://jinja.palletsprojects.com/changes/
+-   PyPI Releases: https://pypi.org/project/Jinja2/
+-   Source Code: https://github.com/pallets/jinja/
+-   Issue Tracker: https://github.com/pallets/jinja/issues/
+-   Chat: https://discord.gg/pallets

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pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/WHEEL

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+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[babel.extractors]
+jinja2 = jinja2.ext:babel_extract[i18n]

pythonProject/.venv/Lib/site-packages/Jinja2-3.1.3.dist-info/top_level.txt

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+jinja2

pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/LICENSE

@@ -0,0 +1,201 @@
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pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/METADATA

@@ -0,0 +1,331 @@
+Metadata-Version: 2.1
+Name: huggingface-hub
+Version: 0.34.4
+Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub
+Home-page: https://github.com/huggingface/huggingface_hub
+Author: Hugging Face, Inc.
+Author-email: julien@huggingface.co
+License: Apache
+Keywords: model-hub machine-learning models natural-language-processing deep-learning pytorch pretrained-models
+Platform: UNKNOWN
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
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+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.8.0
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+License-File: LICENSE
+Requires-Dist: filelock
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+Requires-Dist: hf-xet<2.0.0,>=1.1.3; platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64"
+Provides-Extra: all
+Requires-Dist: InquirerPy==0.3.4; extra == "all"
+Requires-Dist: aiohttp; extra == "all"
+Requires-Dist: authlib>=1.3.2; extra == "all"
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+Requires-Dist: httpx; extra == "all"
+Requires-Dist: itsdangerous; extra == "all"
+Requires-Dist: jedi; extra == "all"
+Requires-Dist: Jinja2; extra == "all"
+Requires-Dist: pytest<8.2.2,>=8.1.1; extra == "all"
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+Provides-Extra: torch
+Requires-Dist: torch; extra == "torch"
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+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub-dark.svg">
+    <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg">
+    <img alt="huggingface_hub library logo" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/huggingface_hub.svg" width="352" height="59" style="max-width: 100%;">
+  </picture>
+  <br/>
+  <br/>
+</p> 
+
+<p align="center">
+    <i>The official Python client for the Huggingface Hub.</i>
+</p>
+
+<p align="center">
+    <a href="https://huggingface.co/docs/huggingface_hub/en/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/huggingface_hub/index.svg?down_color=red&down_message=offline&up_message=online&label=doc"></a>
+    <a href="https://github.com/huggingface/huggingface_hub/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/huggingface_hub.svg"></a>
+    <a href="https://github.com/huggingface/huggingface_hub"><img alt="PyPi version" src="https://img.shields.io/pypi/pyversions/huggingface_hub.svg"></a>
+    <a href="https://pypi.org/project/huggingface-hub"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/huggingface_hub"></a>
+    <a href="https://codecov.io/gh/huggingface/huggingface_hub"><img alt="Code coverage" src="https://codecov.io/gh/huggingface/huggingface_hub/branch/main/graph/badge.svg?token=RXP95LE2XL"></a>
+</p>
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_de.md">Deutsch</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_hi.md">हिंदी</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_ko.md">한국어</a> |
+        <a href="https://github.com/huggingface/huggingface_hub/blob/main/i18n/README_cn.md">中文（简体）</a>
+    <p>
+</h4>
+
+---
+
+**Documentation**: <a href="https://hf.co/docs/huggingface_hub" target="_blank">https://hf.co/docs/huggingface_hub</a>
+
+**Source Code**: <a href="https://github.com/huggingface/huggingface_hub" target="_blank">https://github.com/huggingface/huggingface_hub</a>
+
+---
+
+## Welcome to the huggingface_hub library
+
+The `huggingface_hub` library allows you to interact with the [Hugging Face Hub](https://huggingface.co/), a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The `huggingface_hub` library provides a simple way to do all these things with Python.
+
+## Key features
+
+- [Download files](https://huggingface.co/docs/huggingface_hub/en/guides/download) from the Hub.
+- [Upload files](https://huggingface.co/docs/huggingface_hub/en/guides/upload) to the Hub.
+- [Manage your repositories](https://huggingface.co/docs/huggingface_hub/en/guides/repository).
+- [Run Inference](https://huggingface.co/docs/huggingface_hub/en/guides/inference) on deployed models.
+- [Search](https://huggingface.co/docs/huggingface_hub/en/guides/search) for models, datasets and Spaces.
+- [Share Model Cards](https://huggingface.co/docs/huggingface_hub/en/guides/model-cards) to document your models.
+- [Engage with the community](https://huggingface.co/docs/huggingface_hub/en/guides/community) through PRs and comments.
+
+## Installation
+
+Install the `huggingface_hub` package with [pip](https://pypi.org/project/huggingface-hub/):
+
+```bash
+pip install huggingface_hub
+```
+
+If you prefer, you can also install it with [conda](https://huggingface.co/docs/huggingface_hub/en/installation#install-with-conda).
+
+In order to keep the package minimal by default, `huggingface_hub` comes with optional dependencies useful for some use cases. For example, if you want have a complete experience for Inference, run:
+
+```bash
+pip install huggingface_hub[inference]
+```
+
+To learn more installation and optional dependencies, check out the [installation guide](https://huggingface.co/docs/huggingface_hub/en/installation).
+
+## Quick start
+
+### Download files
+
+Download a single file
+
+```py
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="tiiuae/falcon-7b-instruct", filename="config.json")
+```
+
+Or an entire repository
+
+```py
+from huggingface_hub import snapshot_download
+
+snapshot_download("stabilityai/stable-diffusion-2-1")
+```
+
+Files will be downloaded in a local cache folder. More details in [this guide](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache).
+
+### Login
+
+The Hugging Face Hub uses tokens to authenticate applications (see [docs](https://huggingface.co/docs/hub/security-tokens)). To log in your machine, run the following CLI:
+
+```bash
+hf auth login
+# or using an environment variable
+hf auth login --token $HUGGINGFACE_TOKEN
+```
+
+### Create a repository
+
+```py
+from huggingface_hub import create_repo
+
+create_repo(repo_id="super-cool-model")
+```
+
+### Upload files
+
+Upload a single file
+
+```py
+from huggingface_hub import upload_file
+
+upload_file(
+    path_or_fileobj="/home/lysandre/dummy-test/README.md",
+    path_in_repo="README.md",
+    repo_id="lysandre/test-model",
+)
+```
+
+Or an entire folder
+
+```py
+from huggingface_hub import upload_folder
+
+upload_folder(
+    folder_path="/path/to/local/space",
+    repo_id="username/my-cool-space",
+    repo_type="space",
+)
+```
+
+For details in the [upload guide](https://huggingface.co/docs/huggingface_hub/en/guides/upload).
+
+## Integrating to the Hub.
+
+We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations [here](https://huggingface.co/docs/hub/libraries).
+
+The advantages are:
+
+- Free model or dataset hosting for libraries and their users.
+- Built-in file versioning, even with very large files, thanks to a git-based approach.
+- In-browser widgets to play with the uploaded models.
+- Anyone can upload a new model for your library, they just need to add the corresponding tag for the model to be discoverable.
+- Fast downloads! We use Cloudfront (a CDN) to geo-replicate downloads so they're blazing fast from anywhere on the globe.
+- Usage stats and more features to come.
+
+If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a [step-by-step guide](https://huggingface.co/docs/hub/adding-a-library) with ❤️ showing how to do this integration.
+
+## Contributions (feature requests, bugs, etc.) are super welcome 💙💚💛💜🧡❤️
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community.
+Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.
+We wrote a [contribution guide](https://github.com/huggingface/huggingface_hub/blob/main/CONTRIBUTING.md) to summarize
+how to get started to contribute to this repository.
+
+

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pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.45.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/entry_points.txt

@@ -0,0 +1,8 @@
+[console_scripts]
+hf = huggingface_hub.cli.hf:main
+huggingface-cli = huggingface_hub.commands.huggingface_cli:main
+tiny-agents = huggingface_hub.inference._mcp.cli:app
+
+[fsspec.specs]
+hf=huggingface_hub.HfFileSystem
+

pythonProject/.venv/Lib/site-packages/huggingface_hub-0.34.4.dist-info/top_level.txt

@@ -0,0 +1 @@
+huggingface_hub

pythonProject/.venv/Lib/site-packages/huggingface_hub/inference/__pycache__/_client.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fcedb6fe5ff0f30068953849983fc2d2bc0163e249770936db741589d32fc62
+size 137799

pythonProject/.venv/Lib/site-packages/huggingface_hub/serialization/__init__.py

@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ruff: noqa: F401
+"""Contains helpers to serialize tensors."""
+
+from ._base import StateDictSplit, split_state_dict_into_shards_factory
+from ._tensorflow import get_tf_storage_size, split_tf_state_dict_into_shards
+from ._torch import (
+    get_torch_storage_id,
+    get_torch_storage_size,
+    load_state_dict_from_file,
+    load_torch_model,
+    save_torch_model,
+    save_torch_state_dict,
+    split_torch_state_dict_into_shards,
+)

pythonProject/.venv/Lib/site-packages/huggingface_hub/serialization/_base.py

@@ -0,0 +1,210 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains helpers to split tensors into shards."""
+
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, TypeVar, Union
+
+from .. import logging
+
+
+TensorT = TypeVar("TensorT")
+TensorSizeFn_T = Callable[[TensorT], int]
+StorageIDFn_T = Callable[[TensorT], Optional[Any]]
+
+MAX_SHARD_SIZE = "5GB"
+SIZE_UNITS = {
+    "TB": 10**12,
+    "GB": 10**9,
+    "MB": 10**6,
+    "KB": 10**3,
+}
+
+
+logger = logging.get_logger(__file__)
+
+
+@dataclass
+class StateDictSplit:
+    is_sharded: bool = field(init=False)
+    metadata: Dict[str, Any]
+    filename_to_tensors: Dict[str, List[str]]
+    tensor_to_filename: Dict[str, str]
+
+    def __post_init__(self):
+        self.is_sharded = len(self.filename_to_tensors) > 1
+
+
+def split_state_dict_into_shards_factory(
+    state_dict: Dict[str, TensorT],
+    *,
+    get_storage_size: TensorSizeFn_T,
+    filename_pattern: str,
+    get_storage_id: StorageIDFn_T = lambda tensor: None,
+    max_shard_size: Union[int, str] = MAX_SHARD_SIZE,
+) -> StateDictSplit:
+    """
+    Split a model state dictionary in shards so that each shard is smaller than a given size.
+
+    The shards are determined by iterating through the `state_dict` in the order of its keys. There is no optimization
+    made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
+    have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
+    [6+2+2GB], [6+2GB], [6GB].
+
+    <Tip warning={true}>
+
+    If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+    size greater than `max_shard_size`.
+
+    </Tip>
+
+    Args:
+        state_dict (`Dict[str, Tensor]`):
+            The state dictionary to save.
+        get_storage_size (`Callable[[Tensor], int]`):
+            A function that returns the size of a tensor when saved on disk in bytes.
+        get_storage_id (`Callable[[Tensor], Optional[Any]]`, *optional*):
+            A function that returns a unique identifier to a tensor storage. Multiple different tensors can share the
+            same underlying storage. This identifier is guaranteed to be unique and constant for this tensor's storage
+            during its lifetime. Two tensor storages with non-overlapping lifetimes may have the same id.
+        filename_pattern (`str`, *optional*):
+            The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+            can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+        max_shard_size (`int` or `str`, *optional*):
+            The maximum size of each shard, in bytes. Defaults to 5GB.
+
+    Returns:
+        [`StateDictSplit`]: A `StateDictSplit` object containing the shards and the index to retrieve them.
+    """
+    storage_id_to_tensors: Dict[Any, List[str]] = {}
+
+    shard_list: List[Dict[str, TensorT]] = []
+    current_shard: Dict[str, TensorT] = {}
+    current_shard_size = 0
+    total_size = 0
+
+    if isinstance(max_shard_size, str):
+        max_shard_size = parse_size_to_int(max_shard_size)
+
+    for key, tensor in state_dict.items():
+        # when bnb serialization is used the weights in the state dict can be strings
+        # check: https://github.com/huggingface/transformers/pull/24416 for more details
+        if isinstance(tensor, str):
+            logger.info("Skipping tensor %s as it is a string (bnb serialization)", key)
+            continue
+
+        # If a `tensor` shares the same underlying storage as another tensor, we put `tensor` in the same `block`
+        storage_id = get_storage_id(tensor)
+        if storage_id is not None:
+            if storage_id in storage_id_to_tensors:
+                # We skip this tensor for now and will reassign to correct shard later
+                storage_id_to_tensors[storage_id].append(key)
+                continue
+            else:
+                # This is the first tensor with this storage_id, we create a new entry
+                # in the storage_id_to_tensors dict => we will assign the shard id later
+                storage_id_to_tensors[storage_id] = [key]
+
+        # Compute tensor size
+        tensor_size = get_storage_size(tensor)
+
+        # If this tensor is bigger than the maximal size, we put it in its own shard
+        if tensor_size > max_shard_size:
+            total_size += tensor_size
+            shard_list.append({key: tensor})
+            continue
+
+        # If this tensor is going to tip up over the maximal size, we split.
+        # Current shard already has some tensors, we add it to the list of shards and create a new one.
+        if current_shard_size + tensor_size > max_shard_size:
+            shard_list.append(current_shard)
+            current_shard = {}
+            current_shard_size = 0
+
+        # Add the tensor to the current shard
+        current_shard[key] = tensor
+        current_shard_size += tensor_size
+        total_size += tensor_size
+
+    # Add the last shard
+    if len(current_shard) > 0:
+        shard_list.append(current_shard)
+    nb_shards = len(shard_list)
+
+    # Loop over the tensors that share the same storage and assign them together
+    for storage_id, keys in storage_id_to_tensors.items():
+        # Let's try to find the shard where the first tensor of this storage is and put all tensors in the same shard
+        for shard in shard_list:
+            if keys[0] in shard:
+                for key in keys:
+                    shard[key] = state_dict[key]
+                break
+
+    # If we only have one shard, we return it => no need to build the index
+    if nb_shards == 1:
+        filename = filename_pattern.format(suffix="")
+        return StateDictSplit(
+            metadata={"total_size": total_size},
+            filename_to_tensors={filename: list(state_dict.keys())},
+            tensor_to_filename={key: filename for key in state_dict.keys()},
+        )
+
+    # Now that each tensor is assigned to a shard, let's assign a filename to each shard
+    tensor_name_to_filename = {}
+    filename_to_tensors = {}
+    for idx, shard in enumerate(shard_list):
+        filename = filename_pattern.format(suffix=f"-{idx + 1:05d}-of-{nb_shards:05d}")
+        for key in shard:
+            tensor_name_to_filename[key] = filename
+        filename_to_tensors[filename] = list(shard.keys())
+
+    # Build the index and return
+    return StateDictSplit(
+        metadata={"total_size": total_size},
+        filename_to_tensors=filename_to_tensors,
+        tensor_to_filename=tensor_name_to_filename,
+    )
+
+
+def parse_size_to_int(size_as_str: str) -> int:
+    """
+    Parse a size expressed as a string with digits and unit (like `"5MB"`) to an integer (in bytes).
+
+    Supported units are "TB", "GB", "MB", "KB".
+
+    Args:
+        size_as_str (`str`): The size to convert. Will be directly returned if an `int`.
+
+    Example:
+
+    ```py
+    >>> parse_size_to_int("5MB")
+    5000000
+    ```
+    """
+    size_as_str = size_as_str.strip()
+
+    # Parse unit
+    unit = size_as_str[-2:].upper()
+    if unit not in SIZE_UNITS:
+        raise ValueError(f"Unit '{unit}' not supported. Supported units are TB, GB, MB, KB. Got '{size_as_str}'.")
+    multiplier = SIZE_UNITS[unit]
+
+    # Parse value
+    try:
+        value = float(size_as_str[:-2].strip())
+    except ValueError as e:
+        raise ValueError(f"Could not parse the size value from '{size_as_str}': {e}") from e
+
+    return int(value * multiplier)

pythonProject/.venv/Lib/site-packages/huggingface_hub/serialization/_dduf.py

@@ -0,0 +1,387 @@
+import json
+import logging
+import mmap
+import os
+import shutil
+import zipfile
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, Generator, Iterable, Tuple, Union
+
+from ..errors import DDUFCorruptedFileError, DDUFExportError, DDUFInvalidEntryNameError
+
+
+logger = logging.getLogger(__name__)
+
+DDUF_ALLOWED_ENTRIES = {
+    # Allowed file extensions in a DDUF file
+    ".json",
+    ".model",
+    ".safetensors",
+    ".txt",
+}
+
+DDUF_FOLDER_REQUIRED_ENTRIES = {
+    # Each folder must contain at least one of these entries
+    "config.json",
+    "tokenizer_config.json",
+    "preprocessor_config.json",
+    "scheduler_config.json",
+}
+
+
+@dataclass
+class DDUFEntry:
+    """Object representing a file entry in a DDUF file.
+
+    See [`read_dduf_file`] for how to read a DDUF file.
+
+    Attributes:
+        filename (str):
+            The name of the file in the DDUF archive.
+        offset (int):
+            The offset of the file in the DDUF archive.
+        length (int):
+            The length of the file in the DDUF archive.
+        dduf_path (str):
+            The path to the DDUF archive (for internal use).
+    """
+
+    filename: str
+    length: int
+    offset: int
+
+    dduf_path: Path = field(repr=False)
+
+    @contextmanager
+    def as_mmap(self) -> Generator[bytes, None, None]:
+        """Open the file as a memory-mapped file.
+
+        Useful to load safetensors directly from the file.
+
+        Example:
+            ```py
+            >>> import safetensors.torch
+            >>> with entry.as_mmap() as mm:
+            ...     tensors = safetensors.torch.load(mm)
+            ```
+        """
+        with self.dduf_path.open("rb") as f:
+            with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
+                yield mm[self.offset : self.offset + self.length]
+
+    def read_text(self, encoding: str = "utf-8") -> str:
+        """Read the file as text.
+
+        Useful for '.txt' and '.json' entries.
+
+        Example:
+            ```py
+            >>> import json
+            >>> index = json.loads(entry.read_text())
+            ```
+        """
+        with self.dduf_path.open("rb") as f:
+            f.seek(self.offset)
+            return f.read(self.length).decode(encoding=encoding)
+
+
+def read_dduf_file(dduf_path: Union[os.PathLike, str]) -> Dict[str, DDUFEntry]:
+    """
+    Read a DDUF file and return a dictionary of entries.
+
+    Only the metadata is read, the data is not loaded in memory.
+
+    Args:
+        dduf_path (`str` or `os.PathLike`):
+            The path to the DDUF file to read.
+
+    Returns:
+        `Dict[str, DDUFEntry]`:
+            A dictionary of [`DDUFEntry`] indexed by filename.
+
+    Raises:
+        - [`DDUFCorruptedFileError`]: If the DDUF file is corrupted (i.e. doesn't follow the DDUF format).
+
+    Example:
+        ```python
+        >>> import json
+        >>> import safetensors.torch
+        >>> from huggingface_hub import read_dduf_file
+
+        # Read DDUF metadata
+        >>> dduf_entries = read_dduf_file("FLUX.1-dev.dduf")
+
+        # Returns a mapping filename <> DDUFEntry
+        >>> dduf_entries["model_index.json"]
+        DDUFEntry(filename='model_index.json', offset=66, length=587)
+
+        # Load model index as JSON
+        >>> json.loads(dduf_entries["model_index.json"].read_text())
+        {'_class_name': 'FluxPipeline', '_diffusers_version': '0.32.0.dev0', '_name_or_path': 'black-forest-labs/FLUX.1-dev', ...
+
+        # Load VAE weights using safetensors
+        >>> with dduf_entries["vae/diffusion_pytorch_model.safetensors"].as_mmap() as mm:
+        ...     state_dict = safetensors.torch.load(mm)
+        ```
+    """
+    entries = {}
+    dduf_path = Path(dduf_path)
+    logger.info(f"Reading DDUF file {dduf_path}")
+    with zipfile.ZipFile(str(dduf_path), "r") as zf:
+        for info in zf.infolist():
+            logger.debug(f"Reading entry {info.filename}")
+            if info.compress_type != zipfile.ZIP_STORED:
+                raise DDUFCorruptedFileError("Data must not be compressed in DDUF file.")
+
+            try:
+                _validate_dduf_entry_name(info.filename)
+            except DDUFInvalidEntryNameError as e:
+                raise DDUFCorruptedFileError(f"Invalid entry name in DDUF file: {info.filename}") from e
+
+            offset = _get_data_offset(zf, info)
+
+            entries[info.filename] = DDUFEntry(
+                filename=info.filename, offset=offset, length=info.file_size, dduf_path=dduf_path
+            )
+
+    # Consistency checks on the DDUF file
+    if "model_index.json" not in entries:
+        raise DDUFCorruptedFileError("Missing required 'model_index.json' entry in DDUF file.")
+    index = json.loads(entries["model_index.json"].read_text())
+    _validate_dduf_structure(index, entries.keys())
+
+    logger.info(f"Done reading DDUF file {dduf_path}. Found {len(entries)} entries")
+    return entries
+
+
+def export_entries_as_dduf(
+    dduf_path: Union[str, os.PathLike], entries: Iterable[Tuple[str, Union[str, Path, bytes]]]
+) -> None:
+    """Write a DDUF file from an iterable of entries.
+
+    This is a lower-level helper than [`export_folder_as_dduf`] that allows more flexibility when serializing data.
+    In particular, you don't need to save the data on disk before exporting it in the DDUF file.
+
+    Args:
+        dduf_path (`str` or `os.PathLike`):
+            The path to the DDUF file to write.
+        entries (`Iterable[Tuple[str, Union[str, Path, bytes]]]`):
+            An iterable of entries to write in the DDUF file. Each entry is a tuple with the filename and the content.
+            The filename should be the path to the file in the DDUF archive.
+            The content can be a string or a pathlib.Path representing a path to a file on the local disk or directly the content as bytes.
+
+    Raises:
+        - [`DDUFExportError`]: If anything goes wrong during the export (e.g. invalid entry name, missing 'model_index.json', etc.).
+
+    Example:
+        ```python
+        # Export specific files from the local disk.
+        >>> from huggingface_hub import export_entries_as_dduf
+        >>> export_entries_as_dduf(
+        ...     dduf_path="stable-diffusion-v1-4-FP16.dduf",
+        ...     entries=[ # List entries to add to the DDUF file (here, only FP16 weights)
+        ...         ("model_index.json", "path/to/model_index.json"),
+        ...         ("vae/config.json", "path/to/vae/config.json"),
+        ...         ("vae/diffusion_pytorch_model.fp16.safetensors", "path/to/vae/diffusion_pytorch_model.fp16.safetensors"),
+        ...         ("text_encoder/config.json", "path/to/text_encoder/config.json"),
+        ...         ("text_encoder/model.fp16.safetensors", "path/to/text_encoder/model.fp16.safetensors"),
+        ...         # ... add more entries here
+        ...     ]
+        ... )
+        ```
+
+        ```python
+        # Export state_dicts one by one from a loaded pipeline
+        >>> from diffusers import DiffusionPipeline
+        >>> from typing import Generator, Tuple
+        >>> import safetensors.torch
+        >>> from huggingface_hub import export_entries_as_dduf
+        >>> pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+        ... # ... do some work with the pipeline
+
+        >>> def as_entries(pipe: DiffusionPipeline) -> Generator[Tuple[str, bytes], None, None]:
+        ...     # Build an generator that yields the entries to add to the DDUF file.
+        ...     # The first element of the tuple is the filename in the DDUF archive (must use UNIX separator!). The second element is the content of the file.
+        ...     # Entries will be evaluated lazily when the DDUF file is created (only 1 entry is loaded in memory at a time)
+        ...     yield "vae/config.json", pipe.vae.to_json_string().encode()
+        ...     yield "vae/diffusion_pytorch_model.safetensors", safetensors.torch.save(pipe.vae.state_dict())
+        ...     yield "text_encoder/config.json", pipe.text_encoder.config.to_json_string().encode()
+        ...     yield "text_encoder/model.safetensors", safetensors.torch.save(pipe.text_encoder.state_dict())
+        ...     # ... add more entries here
+
+        >>> export_entries_as_dduf(dduf_path="stable-diffusion-v1-4.dduf", entries=as_entries(pipe))
+        ```
+    """
+    logger.info(f"Exporting DDUF file '{dduf_path}'")
+    filenames = set()
+    index = None
+    with zipfile.ZipFile(str(dduf_path), "w", zipfile.ZIP_STORED) as archive:
+        for filename, content in entries:
+            if filename in filenames:
+                raise DDUFExportError(f"Can't add duplicate entry: {filename}")
+            filenames.add(filename)
+
+            if filename == "model_index.json":
+                try:
+                    index = json.loads(_load_content(content).decode())
+                except json.JSONDecodeError as e:
+                    raise DDUFExportError("Failed to parse 'model_index.json'.") from e
+
+            try:
+                filename = _validate_dduf_entry_name(filename)
+            except DDUFInvalidEntryNameError as e:
+                raise DDUFExportError(f"Invalid entry name: {filename}") from e
+            logger.debug(f"Adding entry '{filename}' to DDUF file")
+            _dump_content_in_archive(archive, filename, content)
+
+    # Consistency checks on the DDUF file
+    if index is None:
+        raise DDUFExportError("Missing required 'model_index.json' entry in DDUF file.")
+    try:
+        _validate_dduf_structure(index, filenames)
+    except DDUFCorruptedFileError as e:
+        raise DDUFExportError("Invalid DDUF file structure.") from e
+
+    logger.info(f"Done writing DDUF file {dduf_path}")
+
+
+def export_folder_as_dduf(dduf_path: Union[str, os.PathLike], folder_path: Union[str, os.PathLike]) -> None:
+    """
+    Export a folder as a DDUF file.
+
+    AUses [`export_entries_as_dduf`] under the hood.
+
+    Args:
+        dduf_path (`str` or `os.PathLike`):
+            The path to the DDUF file to write.
+        folder_path (`str` or `os.PathLike`):
+            The path to the folder containing the diffusion model.
+
+    Example:
+        ```python
+        >>> from huggingface_hub import export_folder_as_dduf
+        >>> export_folder_as_dduf(dduf_path="FLUX.1-dev.dduf", folder_path="path/to/FLUX.1-dev")
+        ```
+    """
+    folder_path = Path(folder_path)
+
+    def _iterate_over_folder() -> Iterable[Tuple[str, Path]]:
+        for path in Path(folder_path).glob("**/*"):
+            if not path.is_file():
+                continue
+            if path.suffix not in DDUF_ALLOWED_ENTRIES:
+                logger.debug(f"Skipping file '{path}' (file type not allowed)")
+                continue
+            path_in_archive = path.relative_to(folder_path)
+            if len(path_in_archive.parts) >= 3:
+                logger.debug(f"Skipping file '{path}' (nested directories not allowed)")
+                continue
+            yield path_in_archive.as_posix(), path
+
+    export_entries_as_dduf(dduf_path, _iterate_over_folder())
+
+
+def _dump_content_in_archive(archive: zipfile.ZipFile, filename: str, content: Union[str, os.PathLike, bytes]) -> None:
+    with archive.open(filename, "w", force_zip64=True) as archive_fh:
+        if isinstance(content, (str, Path)):
+            content_path = Path(content)
+            with content_path.open("rb") as content_fh:
+                shutil.copyfileobj(content_fh, archive_fh, 1024 * 1024 * 8)  # type: ignore[misc]
+        elif isinstance(content, bytes):
+            archive_fh.write(content)
+        else:
+            raise DDUFExportError(f"Invalid content type for {filename}. Must be str, Path or bytes.")
+
+
+def _load_content(content: Union[str, Path, bytes]) -> bytes:
+    """Load the content of an entry as bytes.
+
+    Used only for small checks (not to dump content into archive).
+    """
+    if isinstance(content, (str, Path)):
+        return Path(content).read_bytes()
+    elif isinstance(content, bytes):
+        return content
+    else:
+        raise DDUFExportError(f"Invalid content type. Must be str, Path or bytes. Got {type(content)}.")
+
+
+def _validate_dduf_entry_name(entry_name: str) -> str:
+    if "." + entry_name.split(".")[-1] not in DDUF_ALLOWED_ENTRIES:
+        raise DDUFInvalidEntryNameError(f"File type not allowed: {entry_name}")
+    if "\\" in entry_name:
+        raise DDUFInvalidEntryNameError(f"Entry names must use UNIX separators ('/'). Got {entry_name}.")
+    entry_name = entry_name.strip("/")
+    if entry_name.count("/") > 1:
+        raise DDUFInvalidEntryNameError(f"DDUF only supports 1 level of directory. Got {entry_name}.")
+    return entry_name
+
+
+def _validate_dduf_structure(index: Any, entry_names: Iterable[str]) -> None:
+    """
+    Consistency checks on the DDUF file structure.
+
+    Rules:
+    - The 'model_index.json' entry is required and must contain a dictionary.
+    - Each folder name must correspond to an entry in 'model_index.json'.
+    - Each folder must contain at least a config file ('config.json', 'tokenizer_config.json', 'preprocessor_config.json', 'scheduler_config.json').
+
+    Args:
+        index (Any):
+            The content of the 'model_index.json' entry.
+        entry_names (Iterable[str]):
+            The list of entry names in the DDUF file.
+
+    Raises:
+        - [`DDUFCorruptedFileError`]: If the DDUF file is corrupted (i.e. doesn't follow the DDUF format).
+    """
+    if not isinstance(index, dict):
+        raise DDUFCorruptedFileError(f"Invalid 'model_index.json' content. Must be a dictionary. Got {type(index)}.")
+
+    dduf_folders = {entry.split("/")[0] for entry in entry_names if "/" in entry}
+    for folder in dduf_folders:
+        if folder not in index:
+            raise DDUFCorruptedFileError(f"Missing required entry '{folder}' in 'model_index.json'.")
+        if not any(f"{folder}/{required_entry}" in entry_names for required_entry in DDUF_FOLDER_REQUIRED_ENTRIES):
+            raise DDUFCorruptedFileError(
+                f"Missing required file in folder '{folder}'. Must contains at least one of {DDUF_FOLDER_REQUIRED_ENTRIES}."
+            )
+
+
+def _get_data_offset(zf: zipfile.ZipFile, info: zipfile.ZipInfo) -> int:
+    """
+    Calculate the data offset for a file in a ZIP archive.
+
+    Args:
+        zf (`zipfile.ZipFile`):
+            The opened ZIP file. Must be opened in read mode.
+        info (`zipfile.ZipInfo`):
+            The file info.
+
+    Returns:
+        int: The offset of the file data in the ZIP archive.
+    """
+    if zf.fp is None:
+        raise DDUFCorruptedFileError("ZipFile object must be opened in read mode.")
+
+    # Step 1: Get the local file header offset
+    header_offset = info.header_offset
+
+    # Step 2: Read the local file header
+    zf.fp.seek(header_offset)
+    local_file_header = zf.fp.read(30)  # Fixed-size part of the local header
+
+    if len(local_file_header) < 30:
+        raise DDUFCorruptedFileError("Incomplete local file header.")
+
+    # Step 3: Parse the header fields to calculate the start of file data
+    # Local file header: https://en.wikipedia.org/wiki/ZIP_(file_format)#File_headers
+    filename_len = int.from_bytes(local_file_header[26:28], "little")
+    extra_field_len = int.from_bytes(local_file_header[28:30], "little")
+
+    # Data offset is after the fixed header, filename, and extra fields
+    data_offset = header_offset + 30 + filename_len + extra_field_len
+
+    return data_offset

pythonProject/.venv/Lib/site-packages/huggingface_hub/serialization/_tensorflow.py

@@ -0,0 +1,95 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains tensorflow-specific helpers."""
+
+import math
+import re
+from typing import TYPE_CHECKING, Dict, Union
+
+from .. import constants
+from ._base import MAX_SHARD_SIZE, StateDictSplit, split_state_dict_into_shards_factory
+
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+
+def split_tf_state_dict_into_shards(
+    state_dict: Dict[str, "tf.Tensor"],
+    *,
+    filename_pattern: str = constants.TF2_WEIGHTS_FILE_PATTERN,
+    max_shard_size: Union[int, str] = MAX_SHARD_SIZE,
+) -> StateDictSplit:
+    """
+    Split a model state dictionary in shards so that each shard is smaller than a given size.
+
+    The shards are determined by iterating through the `state_dict` in the order of its keys. There is no optimization
+    made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
+    have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
+    [6+2+2GB], [6+2GB], [6GB].
+
+    <Tip warning={true}>
+
+    If one of the model's tensor is bigger than `max_shard_size`, it will end up in its own shard which will have a
+    size greater than `max_shard_size`.
+
+    </Tip>
+
+    Args:
+        state_dict (`Dict[str, Tensor]`):
+            The state dictionary to save.
+        filename_pattern (`str`, *optional*):
+            The pattern to generate the files names in which the model will be saved. Pattern must be a string that
+            can be formatted with `filename_pattern.format(suffix=...)` and must contain the keyword `suffix`
+            Defaults to `"tf_model{suffix}.h5"`.
+        max_shard_size (`int` or `str`, *optional*):
+            The maximum size of each shard, in bytes. Defaults to 5GB.
+
+    Returns:
+        [`StateDictSplit`]: A `StateDictSplit` object containing the shards and the index to retrieve them.
+    """
+    return split_state_dict_into_shards_factory(
+        state_dict,
+        max_shard_size=max_shard_size,
+        filename_pattern=filename_pattern,
+        get_storage_size=get_tf_storage_size,
+    )
+
+
+def get_tf_storage_size(tensor: "tf.Tensor") -> int:
+    # Return `math.ceil` since dtype byte size can be a float (e.g., 0.125 for tf.bool).
+    # Better to overestimate than underestimate.
+    return math.ceil(tensor.numpy().size * _dtype_byte_size_tf(tensor.dtype))
+
+
+def _dtype_byte_size_tf(dtype) -> float:
+    """
+    Returns the size (in bytes) occupied by one parameter of type `dtype`.
+    Taken from https://github.com/huggingface/transformers/blob/74d9d0cebb0263a3f8ab9c280569170cc74651d0/src/transformers/modeling_tf_utils.py#L608.
+    NOTE: why not `tensor.numpy().nbytes`?
+    Example:
+    ```py
+    >>> _dtype_byte_size(tf.float32)
+    4
+    ```
+    """
+    import tensorflow as tf
+
+    if dtype == tf.bool:
+        return 1 / 8
+    bit_search = re.search(r"[^\d](\d+)$", dtype.name)
+    if bit_search is None:
+        raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
+    bit_size = int(bit_search.groups()[0])
+    return bit_size // 8