diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/INSTALLER b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/LICENSE b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/METADATA b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..2c71bba14729035fddd1ba73fa7fc633e31fbb9c
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/METADATA
@@ -0,0 +1,380 @@
+Metadata-Version: 2.1
+Name: accelerate
+Version: 0.30.0
+Summary: Accelerate
+Home-page: https://github.com/huggingface/accelerate
+Author: The HuggingFace team
+Author-email: zach.mueller@huggingface.co
+License: Apache
+Keywords: deep learning
+Classifier: Development Status :: 5 - Production/Stable
+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 :: 3.8
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.8.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy (>=1.17)
+Requires-Dist: packaging (>=20.0)
+Requires-Dist: psutil
+Requires-Dist: pyyaml
+Requires-Dist: torch (>=1.10.0)
+Requires-Dist: huggingface-hub
+Requires-Dist: safetensors (>=0.3.1)
+Provides-Extra: deepspeed
+Requires-Dist: deepspeed (<=0.14.0) ; extra == 'deepspeed'
+Provides-Extra: dev
+Requires-Dist: black (~=23.1) ; extra == 'dev'
+Requires-Dist: hf-doc-builder (>=0.3.0) ; extra == 'dev'
+Requires-Dist: ruff (~=0.2.1) ; extra == 'dev'
+Requires-Dist: pytest (<=8.0.0,>=7.2.0) ; extra == 'dev'
+Requires-Dist: pytest-xdist ; extra == 'dev'
+Requires-Dist: pytest-subtests ; extra == 'dev'
+Requires-Dist: parameterized ; extra == 'dev'
+Requires-Dist: datasets ; extra == 'dev'
+Requires-Dist: diffusers ; extra == 'dev'
+Requires-Dist: evaluate ; extra == 'dev'
+Requires-Dist: torchpippy (>=0.2.0) ; extra == 'dev'
+Requires-Dist: transformers ; extra == 'dev'
+Requires-Dist: scipy ; extra == 'dev'
+Requires-Dist: scikit-learn ; extra == 'dev'
+Requires-Dist: tqdm ; extra == 'dev'
+Requires-Dist: bitsandbytes ; extra == 'dev'
+Requires-Dist: timm ; extra == 'dev'
+Requires-Dist: rich ; extra == 'dev'
+Provides-Extra: docs
+Provides-Extra: quality
+Requires-Dist: black (~=23.1) ; extra == 'quality'
+Requires-Dist: hf-doc-builder (>=0.3.0) ; extra == 'quality'
+Requires-Dist: ruff (~=0.2.1) ; extra == 'quality'
+Provides-Extra: rich
+Requires-Dist: rich ; extra == 'rich'
+Provides-Extra: sagemaker
+Requires-Dist: sagemaker ; extra == 'sagemaker'
+Provides-Extra: test_dev
+Requires-Dist: datasets ; extra == 'test_dev'
+Requires-Dist: diffusers ; extra == 'test_dev'
+Requires-Dist: evaluate ; extra == 'test_dev'
+Requires-Dist: torchpippy (>=0.2.0) ; extra == 'test_dev'
+Requires-Dist: transformers ; extra == 'test_dev'
+Requires-Dist: scipy ; extra == 'test_dev'
+Requires-Dist: scikit-learn ; extra == 'test_dev'
+Requires-Dist: tqdm ; extra == 'test_dev'
+Requires-Dist: bitsandbytes ; extra == 'test_dev'
+Requires-Dist: timm ; extra == 'test_dev'
+Provides-Extra: test_prod
+Requires-Dist: pytest (<=8.0.0,>=7.2.0) ; extra == 'test_prod'
+Requires-Dist: pytest-xdist ; extra == 'test_prod'
+Requires-Dist: pytest-subtests ; extra == 'test_prod'
+Requires-Dist: parameterized ; extra == 'test_prod'
+Provides-Extra: test_trackers
+Requires-Dist: wandb ; extra == 'test_trackers'
+Requires-Dist: comet-ml ; extra == 'test_trackers'
+Requires-Dist: tensorboard ; extra == 'test_trackers'
+Requires-Dist: dvclive ; extra == 'test_trackers'
+Provides-Extra: testing
+Requires-Dist: pytest (<=8.0.0,>=7.2.0) ; extra == 'testing'
+Requires-Dist: pytest-xdist ; extra == 'testing'
+Requires-Dist: pytest-subtests ; extra == 'testing'
+Requires-Dist: parameterized ; extra == 'testing'
+Requires-Dist: datasets ; extra == 'testing'
+Requires-Dist: diffusers ; extra == 'testing'
+Requires-Dist: evaluate ; extra == 'testing'
+Requires-Dist: torchpippy (>=0.2.0) ; extra == 'testing'
+Requires-Dist: transformers ; extra == 'testing'
+Requires-Dist: scipy ; extra == 'testing'
+Requires-Dist: scikit-learn ; extra == 'testing'
+Requires-Dist: tqdm ; extra == 'testing'
+Requires-Dist: bitsandbytes ; extra == 'testing'
+Requires-Dist: timm ; extra == 'testing'
+
+
+
+
+
+ 
+
+
+
+
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+
+
+
Run your *raw* PyTorch training script on any kind of device
+
+
+
+ 
+
+
+## Easy to integrate
+
+🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16.
+
+🤗 Accelerate abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.
+
+Here is an example:
+
+```diff
+ import torch
+ import torch.nn.functional as F
+ from datasets import load_dataset
++ from accelerate import Accelerator
+
++ accelerator = Accelerator()
+- device = 'cpu'
++ device = accelerator.device
+
+ model = torch.nn.Transformer().to(device)
+ optimizer = torch.optim.Adam(model.parameters())
+
+ dataset = load_dataset('my_dataset')
+ data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+ model.train()
+ for epoch in range(10):
+ for source, targets in data:
+ source = source.to(device)
+ targets = targets.to(device)
+
+ optimizer.zero_grad()
+
+ output = model(source)
+ loss = F.cross_entropy(output, targets)
+
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+```
+
+As you can see in this example, by adding 5-lines to any standard PyTorch training script you can now run on any kind of single or distributed node setting (single CPU, single GPU, multi-GPUs and TPUs) as well as with or without mixed precision (fp8, fp16, bf16).
+
+In particular, the same code can then be run without modification on your local machine for debugging or your training environment.
+
+🤗 Accelerate even handles the device placement for you (which requires a few more changes to your code, but is safer in general), so you can even simplify your training loop further:
+
+```diff
+ import torch
+ import torch.nn.functional as F
+ from datasets import load_dataset
++ from accelerate import Accelerator
+
+- device = 'cpu'
++ accelerator = Accelerator()
+
+- model = torch.nn.Transformer().to(device)
++ model = torch.nn.Transformer()
+ optimizer = torch.optim.Adam(model.parameters())
+
+ dataset = load_dataset('my_dataset')
+ data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+ model.train()
+ for epoch in range(10):
+ for source, targets in data:
+- source = source.to(device)
+- targets = targets.to(device)
+
+ optimizer.zero_grad()
+
+ output = model(source)
+ loss = F.cross_entropy(output, targets)
+
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+```
+
+Want to learn more? Check out the [documentation](https://huggingface.co/docs/accelerate) or have a look at our [examples](https://github.com/huggingface/accelerate/tree/main/examples).
+
+## Launching script
+
+🤗 Accelerate also provides an optional CLI tool that allows you to quickly configure and test your training environment before launching the scripts. No need to remember how to use `torch.distributed.run` or to write a specific launcher for TPU training!
+On your machine(s) just run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. This will generate a config file that will be used automatically to properly set the default options when doing
+
+```bash
+accelerate launch my_script.py --args_to_my_script
+```
+
+For instance, here is how you would run the GLUE example on the MRPC task (from the root of the repo):
+
+```bash
+accelerate launch examples/nlp_example.py
+```
+
+This CLI tool is **optional**, and you can still use `python my_script.py` or `python -m torchrun my_script.py` at your convenience.
+
+You can also directly pass in the arguments you would to `torchrun` as arguments to `accelerate launch` if you wish to not run` accelerate config`.
+
+For example, here is how to launch on two GPUs:
+
+```bash
+accelerate launch --multi_gpu --num_processes 2 examples/nlp_example.py
+```
+
+To learn more, check the CLI documentation available [here](https://huggingface.co/docs/accelerate/package_reference/cli).
+
+## Launching multi-CPU run using MPI
+
+🤗 Here is another way to launch multi-CPU run using MPI. You can learn how to install Open MPI on [this page](https://www.open-mpi.org/faq/?category=building#easy-build). You can use Intel MPI or MVAPICH as well.
+Once you have MPI setup on your cluster, just run:
+```bash
+accelerate config
+```
+Answer the questions that are asked, selecting to run using multi-CPU, and answer "yes" when asked if you want accelerate to launch mpirun.
+Then, use `accelerate launch` with your script like:
+```bash
+accelerate launch examples/nlp_example.py
+```
+Alternatively, you can use mpirun directly, without using the CLI like:
+```bash
+mpirun -np 2 python examples/nlp_example.py
+```
+
+## Launching training using DeepSpeed
+
+🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. To use it, you don't need to change anything in your training code; you can set everything using just `accelerate config`. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide you the `DeepSpeedPlugin`.
+
+```python
+from accelerate import Accelerator, DeepSpeedPlugin
+
+# deepspeed needs to know your gradient accumulation steps beforehand, so don't forget to pass it
+# Remember you still need to do gradient accumulation by yourself, just like you would have done without deepspeed
+deepspeed_plugin = DeepSpeedPlugin(zero_stage=2, gradient_accumulation_steps=2)
+accelerator = Accelerator(mixed_precision='fp16', deepspeed_plugin=deepspeed_plugin)
+
+# How to save your 🤗 Transformer?
+accelerator.wait_for_everyone()
+unwrapped_model = accelerator.unwrap_model(model)
+unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
+```
+
+Note: DeepSpeed support is experimental for now. In case you get into some problem, please open an issue.
+
+## Launching your training from a notebook
+
+🤗 Accelerate also provides a `notebook_launcher` function you can use in a notebook to launch a distributed training. This is especially useful for Colab or Kaggle notebooks with a TPU backend. Just define your training loop in a `training_function` then in your last cell, add:
+
+```python
+from accelerate import notebook_launcher
+
+notebook_launcher(training_function)
+```
+
+An example can be found in [this notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb). [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb)
+
+## Why should I use 🤗 Accelerate?
+
+You should use 🤗 Accelerate when you want to easily run your training scripts in a distributed environment without having to renounce full control over your training loop. This is not a high-level framework above PyTorch, just a thin wrapper so you don't have to learn a new library. In fact, the whole API of 🤗 Accelerate is in one class, the `Accelerator` object.
+
+## Why shouldn't I use 🤗 Accelerate?
+
+You shouldn't use 🤗 Accelerate if you don't want to write a training loop yourself. There are plenty of high-level libraries above PyTorch that will offer you that, 🤗 Accelerate is not one of them.
+
+## Frameworks using 🤗 Accelerate
+
+If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around its capabilities, some frameworks and libraries that are built on top of 🤗 Accelerate are listed below:
+
+* [Amphion](https://github.com/open-mmlab/Amphion) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
+* [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76).
+* [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model training, and inference logic.
+* [fastai](https://github.com/fastai/fastai#installing) is a PyTorch framework for Deep Learning that simplifies training fast and accurate neural nets using modern best practices. fastai provides a [Learner](https://docs.fast.ai/learner.html#Learner) to handle the training, fine-tuning, and inference of deep learning algorithms.
+* [Finetuner](https://github.com/jina-ai/finetuner) is a service that enables models to create higher-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses.
+* [InvokeAI](https://github.com/invoke-ai/InvokeAI) is a creative engine for Stable Diffusion models, offering industry-leading WebUI, terminal usage support, and serves as the foundation for many commercial products.
+* [Kornia](https://kornia.readthedocs.io/en/latest/get-started/introduction.html) is a differentiable library that allows classical computer vision to be integrated into deep learning models. Kornia provides a [Trainer](https://kornia.readthedocs.io/en/latest/x.html#kornia.x.Trainer) with the specific purpose to train and fine-tune the supported deep learning algorithms within the library.
+* [Open Assistant](https://projects.laion.ai/Open-Assistant/) is a chat-based assistant that understands tasks, can interact with their party systems, and retrieve information dynamically to do so.
+* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centered around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves!
+* [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is an open-source browser-based easy-to-use interface based on the Gradio library for Stable Diffusion.
+* [torchkeras](https://github.com/lyhue1991/torchkeras) is a simple tool for training pytorch model just in a keras style, a dynamic and beautiful plot is provided in notebook to monitor your loss or metric.
+* [transformers](https://github.com/huggingface/transformers) as a tool for helping train state-of-the-art machine learning models in PyTorch, Tensorflow, and JAX. (Accelerate is the backend for the PyTorch side).
+
+
+## Installation
+
+This repository is tested on Python 3.8+ and PyTorch 1.10.0+
+
+You should install 🤗 Accelerate in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+First, create a virtual environment with the version of Python you're going to use and activate it.
+
+Then, you will need to install PyTorch: refer to the [official installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Then 🤗 Accelerate can be installed using pip as follows:
+
+```bash
+pip install accelerate
+```
+
+## Supported integrations
+
+- CPU only
+- multi-CPU on one node (machine)
+- multi-CPU on several nodes (machines)
+- single GPU
+- multi-GPU on one node (machine)
+- multi-GPU on several nodes (machines)
+- TPU
+- FP16/BFloat16 mixed precision
+- FP8 mixed precision with [Transformer Engine](https://github.com/NVIDIA/TransformerEngine)
+- DeepSpeed support (Experimental)
+- PyTorch Fully Sharded Data Parallel (FSDP) support (Experimental)
+- Megatron-LM support (Experimental)
+
+## Citing 🤗 Accelerate
+
+If you use 🤗 Accelerate in your publication, please cite it by using the following BibTeX entry.
+
+```bibtex
+@Misc{accelerate,
+ title = {Accelerate: Training and inference at scale made simple, efficient and adaptable.},
+ author = {Sylvain Gugger and Lysandre Debut and Thomas Wolf and Philipp Schmid and Zachary Mueller and Sourab Mangrulkar and Marc Sun and Benjamin Bossan},
+ howpublished = {\url{https://github.com/huggingface/accelerate}},
+ year = {2022}
+}
+```
diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/RECORD b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..dac316f2b788c9f3bbecd63115ba6734fc62dabf
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/RECORD
@@ -0,0 +1,163 @@
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+../../../bin/accelerate-launch,sha256=ZN1YBjUMnzCsnepNVaGugrohZN8fsJ4bqY_bToNONZs,259
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diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/WHEEL b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/entry_points.txt b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db491c83cf468945b19a098b171fcbfa7d7db1d1
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/entry_points.txt
@@ -0,0 +1,5 @@
+[console_scripts]
+accelerate = accelerate.commands.accelerate_cli:main
+accelerate-config = accelerate.commands.config:main
+accelerate-estimate-memory = accelerate.commands.estimate:main
+accelerate-launch = accelerate.commands.launch:main
diff --git a/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/top_level.txt b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a9368375be0e0e13fdad0eea4b92541bd9e1f594
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+accelerate
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diff --git a/llmeval-env/lib/python3.10/site-packages/jinja2/lexer.py b/llmeval-env/lib/python3.10/site-packages/jinja2/lexer.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b0471a3a6a37c9b17d5042f606f3fa64009d6a
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/jinja2/lexer.py
@@ -0,0 +1,868 @@
+"""Implements a Jinja / Python combination lexer. The ``Lexer`` class
+is used to do some preprocessing. It filters out invalid operators like
+the bitshift operators we don't allow in templates. It separates
+template code and python code in expressions.
+"""
+
+import re
+import typing as t
+from ast import literal_eval
+from collections import deque
+from sys import intern
+
+from ._identifier import pattern as name_re
+from .exceptions import TemplateSyntaxError
+from .utils import LRUCache
+
+if t.TYPE_CHECKING:
+ import typing_extensions as te
+
+ from .environment import Environment
+
+# cache for the lexers. Exists in order to be able to have multiple
+# environments with the same lexer
+_lexer_cache: t.MutableMapping[t.Tuple, "Lexer"] = LRUCache(50) # type: ignore
+
+# static regular expressions
+whitespace_re = re.compile(r"\s+")
+newline_re = re.compile(r"(\r\n|\r|\n)")
+string_re = re.compile(
+ r"('([^'\\]*(?:\\.[^'\\]*)*)'" r'|"([^"\\]*(?:\\.[^"\\]*)*)")', re.S
+)
+integer_re = re.compile(
+ r"""
+ (
+ 0b(_?[0-1])+ # binary
+ |
+ 0o(_?[0-7])+ # octal
+ |
+ 0x(_?[\da-f])+ # hex
+ |
+ [1-9](_?\d)* # decimal
+ |
+ 0(_?0)* # decimal zero
+ )
+ """,
+ re.IGNORECASE | re.VERBOSE,
+)
+float_re = re.compile(
+ r"""
+ (?": TOKEN_GT,
+ ">=": TOKEN_GTEQ,
+ "<": TOKEN_LT,
+ "<=": TOKEN_LTEQ,
+ "=": TOKEN_ASSIGN,
+ ".": TOKEN_DOT,
+ ":": TOKEN_COLON,
+ "|": TOKEN_PIPE,
+ ",": TOKEN_COMMA,
+ ";": TOKEN_SEMICOLON,
+}
+
+reverse_operators = {v: k for k, v in operators.items()}
+assert len(operators) == len(reverse_operators), "operators dropped"
+operator_re = re.compile(
+ f"({'|'.join(re.escape(x) for x in sorted(operators, key=lambda x: -len(x)))})"
+)
+
+ignored_tokens = frozenset(
+ [
+ TOKEN_COMMENT_BEGIN,
+ TOKEN_COMMENT,
+ TOKEN_COMMENT_END,
+ TOKEN_WHITESPACE,
+ TOKEN_LINECOMMENT_BEGIN,
+ TOKEN_LINECOMMENT_END,
+ TOKEN_LINECOMMENT,
+ ]
+)
+ignore_if_empty = frozenset(
+ [TOKEN_WHITESPACE, TOKEN_DATA, TOKEN_COMMENT, TOKEN_LINECOMMENT]
+)
+
+
+def _describe_token_type(token_type: str) -> str:
+ if token_type in reverse_operators:
+ return reverse_operators[token_type]
+
+ return {
+ TOKEN_COMMENT_BEGIN: "begin of comment",
+ TOKEN_COMMENT_END: "end of comment",
+ TOKEN_COMMENT: "comment",
+ TOKEN_LINECOMMENT: "comment",
+ TOKEN_BLOCK_BEGIN: "begin of statement block",
+ TOKEN_BLOCK_END: "end of statement block",
+ TOKEN_VARIABLE_BEGIN: "begin of print statement",
+ TOKEN_VARIABLE_END: "end of print statement",
+ TOKEN_LINESTATEMENT_BEGIN: "begin of line statement",
+ TOKEN_LINESTATEMENT_END: "end of line statement",
+ TOKEN_DATA: "template data / text",
+ TOKEN_EOF: "end of template",
+ }.get(token_type, token_type)
+
+
+def describe_token(token: "Token") -> str:
+ """Returns a description of the token."""
+ if token.type == TOKEN_NAME:
+ return token.value
+
+ return _describe_token_type(token.type)
+
+
+def describe_token_expr(expr: str) -> str:
+ """Like `describe_token` but for token expressions."""
+ if ":" in expr:
+ type, value = expr.split(":", 1)
+
+ if type == TOKEN_NAME:
+ return value
+ else:
+ type = expr
+
+ return _describe_token_type(type)
+
+
+def count_newlines(value: str) -> int:
+ """Count the number of newline characters in the string. This is
+ useful for extensions that filter a stream.
+ """
+ return len(newline_re.findall(value))
+
+
+def compile_rules(environment: "Environment") -> t.List[t.Tuple[str, str]]:
+ """Compiles all the rules from the environment into a list of rules."""
+ e = re.escape
+ rules = [
+ (
+ len(environment.comment_start_string),
+ TOKEN_COMMENT_BEGIN,
+ e(environment.comment_start_string),
+ ),
+ (
+ len(environment.block_start_string),
+ TOKEN_BLOCK_BEGIN,
+ e(environment.block_start_string),
+ ),
+ (
+ len(environment.variable_start_string),
+ TOKEN_VARIABLE_BEGIN,
+ e(environment.variable_start_string),
+ ),
+ ]
+
+ if environment.line_statement_prefix is not None:
+ rules.append(
+ (
+ len(environment.line_statement_prefix),
+ TOKEN_LINESTATEMENT_BEGIN,
+ r"^[ \t\v]*" + e(environment.line_statement_prefix),
+ )
+ )
+ if environment.line_comment_prefix is not None:
+ rules.append(
+ (
+ len(environment.line_comment_prefix),
+ TOKEN_LINECOMMENT_BEGIN,
+ r"(?:^|(?<=\S))[^\S\r\n]*" + e(environment.line_comment_prefix),
+ )
+ )
+
+ return [x[1:] for x in sorted(rules, reverse=True)]
+
+
+class Failure:
+ """Class that raises a `TemplateSyntaxError` if called.
+ Used by the `Lexer` to specify known errors.
+ """
+
+ def __init__(
+ self, message: str, cls: t.Type[TemplateSyntaxError] = TemplateSyntaxError
+ ) -> None:
+ self.message = message
+ self.error_class = cls
+
+ def __call__(self, lineno: int, filename: str) -> "te.NoReturn":
+ raise self.error_class(self.message, lineno, filename)
+
+
+class Token(t.NamedTuple):
+ lineno: int
+ type: str
+ value: str
+
+ def __str__(self) -> str:
+ return describe_token(self)
+
+ def test(self, expr: str) -> bool:
+ """Test a token against a token expression. This can either be a
+ token type or ``'token_type:token_value'``. This can only test
+ against string values and types.
+ """
+ # here we do a regular string equality check as test_any is usually
+ # passed an iterable of not interned strings.
+ if self.type == expr:
+ return True
+
+ if ":" in expr:
+ return expr.split(":", 1) == [self.type, self.value]
+
+ return False
+
+ def test_any(self, *iterable: str) -> bool:
+ """Test against multiple token expressions."""
+ return any(self.test(expr) for expr in iterable)
+
+
+class TokenStreamIterator:
+ """The iterator for tokenstreams. Iterate over the stream
+ until the eof token is reached.
+ """
+
+ def __init__(self, stream: "TokenStream") -> None:
+ self.stream = stream
+
+ def __iter__(self) -> "TokenStreamIterator":
+ return self
+
+ def __next__(self) -> Token:
+ token = self.stream.current
+
+ if token.type is TOKEN_EOF:
+ self.stream.close()
+ raise StopIteration
+
+ next(self.stream)
+ return token
+
+
+class TokenStream:
+ """A token stream is an iterable that yields :class:`Token`\\s. The
+ parser however does not iterate over it but calls :meth:`next` to go
+ one token ahead. The current active token is stored as :attr:`current`.
+ """
+
+ def __init__(
+ self,
+ generator: t.Iterable[Token],
+ name: t.Optional[str],
+ filename: t.Optional[str],
+ ):
+ self._iter = iter(generator)
+ self._pushed: "te.Deque[Token]" = deque()
+ self.name = name
+ self.filename = filename
+ self.closed = False
+ self.current = Token(1, TOKEN_INITIAL, "")
+ next(self)
+
+ def __iter__(self) -> TokenStreamIterator:
+ return TokenStreamIterator(self)
+
+ def __bool__(self) -> bool:
+ return bool(self._pushed) or self.current.type is not TOKEN_EOF
+
+ @property
+ def eos(self) -> bool:
+ """Are we at the end of the stream?"""
+ return not self
+
+ def push(self, token: Token) -> None:
+ """Push a token back to the stream."""
+ self._pushed.append(token)
+
+ def look(self) -> Token:
+ """Look at the next token."""
+ old_token = next(self)
+ result = self.current
+ self.push(result)
+ self.current = old_token
+ return result
+
+ def skip(self, n: int = 1) -> None:
+ """Got n tokens ahead."""
+ for _ in range(n):
+ next(self)
+
+ def next_if(self, expr: str) -> t.Optional[Token]:
+ """Perform the token test and return the token if it matched.
+ Otherwise the return value is `None`.
+ """
+ if self.current.test(expr):
+ return next(self)
+
+ return None
+
+ def skip_if(self, expr: str) -> bool:
+ """Like :meth:`next_if` but only returns `True` or `False`."""
+ return self.next_if(expr) is not None
+
+ def __next__(self) -> Token:
+ """Go one token ahead and return the old one.
+
+ Use the built-in :func:`next` instead of calling this directly.
+ """
+ rv = self.current
+
+ if self._pushed:
+ self.current = self._pushed.popleft()
+ elif self.current.type is not TOKEN_EOF:
+ try:
+ self.current = next(self._iter)
+ except StopIteration:
+ self.close()
+
+ return rv
+
+ def close(self) -> None:
+ """Close the stream."""
+ self.current = Token(self.current.lineno, TOKEN_EOF, "")
+ self._iter = iter(())
+ self.closed = True
+
+ def expect(self, expr: str) -> Token:
+ """Expect a given token type and return it. This accepts the same
+ argument as :meth:`jinja2.lexer.Token.test`.
+ """
+ if not self.current.test(expr):
+ expr = describe_token_expr(expr)
+
+ if self.current.type is TOKEN_EOF:
+ raise TemplateSyntaxError(
+ f"unexpected end of template, expected {expr!r}.",
+ self.current.lineno,
+ self.name,
+ self.filename,
+ )
+
+ raise TemplateSyntaxError(
+ f"expected token {expr!r}, got {describe_token(self.current)!r}",
+ self.current.lineno,
+ self.name,
+ self.filename,
+ )
+
+ return next(self)
+
+
+def get_lexer(environment: "Environment") -> "Lexer":
+ """Return a lexer which is probably cached."""
+ key = (
+ environment.block_start_string,
+ environment.block_end_string,
+ environment.variable_start_string,
+ environment.variable_end_string,
+ environment.comment_start_string,
+ environment.comment_end_string,
+ environment.line_statement_prefix,
+ environment.line_comment_prefix,
+ environment.trim_blocks,
+ environment.lstrip_blocks,
+ environment.newline_sequence,
+ environment.keep_trailing_newline,
+ )
+ lexer = _lexer_cache.get(key)
+
+ if lexer is None:
+ _lexer_cache[key] = lexer = Lexer(environment)
+
+ return lexer
+
+
+class OptionalLStrip(tuple): # type: ignore[type-arg]
+ """A special tuple for marking a point in the state that can have
+ lstrip applied.
+ """
+
+ __slots__ = ()
+
+ # Even though it looks like a no-op, creating instances fails
+ # without this.
+ def __new__(cls, *members, **kwargs): # type: ignore
+ return super().__new__(cls, members)
+
+
+class _Rule(t.NamedTuple):
+ pattern: t.Pattern[str]
+ tokens: t.Union[str, t.Tuple[str, ...], t.Tuple[Failure]]
+ command: t.Optional[str]
+
+
+class Lexer:
+ """Class that implements a lexer for a given environment. Automatically
+ created by the environment class, usually you don't have to do that.
+
+ Note that the lexer is not automatically bound to an environment.
+ Multiple environments can share the same lexer.
+ """
+
+ def __init__(self, environment: "Environment") -> None:
+ # shortcuts
+ e = re.escape
+
+ def c(x: str) -> t.Pattern[str]:
+ return re.compile(x, re.M | re.S)
+
+ # lexing rules for tags
+ tag_rules: t.List[_Rule] = [
+ _Rule(whitespace_re, TOKEN_WHITESPACE, None),
+ _Rule(float_re, TOKEN_FLOAT, None),
+ _Rule(integer_re, TOKEN_INTEGER, None),
+ _Rule(name_re, TOKEN_NAME, None),
+ _Rule(string_re, TOKEN_STRING, None),
+ _Rule(operator_re, TOKEN_OPERATOR, None),
+ ]
+
+ # assemble the root lexing rule. because "|" is ungreedy
+ # we have to sort by length so that the lexer continues working
+ # as expected when we have parsing rules like <% for block and
+ # <%= for variables. (if someone wants asp like syntax)
+ # variables are just part of the rules if variable processing
+ # is required.
+ root_tag_rules = compile_rules(environment)
+
+ block_start_re = e(environment.block_start_string)
+ block_end_re = e(environment.block_end_string)
+ comment_end_re = e(environment.comment_end_string)
+ variable_end_re = e(environment.variable_end_string)
+
+ # block suffix if trimming is enabled
+ block_suffix_re = "\\n?" if environment.trim_blocks else ""
+
+ self.lstrip_blocks = environment.lstrip_blocks
+
+ self.newline_sequence = environment.newline_sequence
+ self.keep_trailing_newline = environment.keep_trailing_newline
+
+ root_raw_re = (
+ rf"(?P{block_start_re}(\-|\+|)\s*raw\s*"
+ rf"(?:\-{block_end_re}\s*|{block_end_re}))"
+ )
+ root_parts_re = "|".join(
+ [root_raw_re] + [rf"(?P<{n}>{r}(\-|\+|))" for n, r in root_tag_rules]
+ )
+
+ # global lexing rules
+ self.rules: t.Dict[str, t.List[_Rule]] = {
+ "root": [
+ # directives
+ _Rule(
+ c(rf"(.*?)(?:{root_parts_re})"),
+ OptionalLStrip(TOKEN_DATA, "#bygroup"), # type: ignore
+ "#bygroup",
+ ),
+ # data
+ _Rule(c(".+"), TOKEN_DATA, None),
+ ],
+ # comments
+ TOKEN_COMMENT_BEGIN: [
+ _Rule(
+ c(
+ rf"(.*?)((?:\+{comment_end_re}|\-{comment_end_re}\s*"
+ rf"|{comment_end_re}{block_suffix_re}))"
+ ),
+ (TOKEN_COMMENT, TOKEN_COMMENT_END),
+ "#pop",
+ ),
+ _Rule(c(r"(.)"), (Failure("Missing end of comment tag"),), None),
+ ],
+ # blocks
+ TOKEN_BLOCK_BEGIN: [
+ _Rule(
+ c(
+ rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
+ rf"|{block_end_re}{block_suffix_re})"
+ ),
+ TOKEN_BLOCK_END,
+ "#pop",
+ ),
+ ]
+ + tag_rules,
+ # variables
+ TOKEN_VARIABLE_BEGIN: [
+ _Rule(
+ c(rf"\-{variable_end_re}\s*|{variable_end_re}"),
+ TOKEN_VARIABLE_END,
+ "#pop",
+ )
+ ]
+ + tag_rules,
+ # raw block
+ TOKEN_RAW_BEGIN: [
+ _Rule(
+ c(
+ rf"(.*?)((?:{block_start_re}(\-|\+|))\s*endraw\s*"
+ rf"(?:\+{block_end_re}|\-{block_end_re}\s*"
+ rf"|{block_end_re}{block_suffix_re}))"
+ ),
+ OptionalLStrip(TOKEN_DATA, TOKEN_RAW_END), # type: ignore
+ "#pop",
+ ),
+ _Rule(c(r"(.)"), (Failure("Missing end of raw directive"),), None),
+ ],
+ # line statements
+ TOKEN_LINESTATEMENT_BEGIN: [
+ _Rule(c(r"\s*(\n|$)"), TOKEN_LINESTATEMENT_END, "#pop")
+ ]
+ + tag_rules,
+ # line comments
+ TOKEN_LINECOMMENT_BEGIN: [
+ _Rule(
+ c(r"(.*?)()(?=\n|$)"),
+ (TOKEN_LINECOMMENT, TOKEN_LINECOMMENT_END),
+ "#pop",
+ )
+ ],
+ }
+
+ def _normalize_newlines(self, value: str) -> str:
+ """Replace all newlines with the configured sequence in strings
+ and template data.
+ """
+ return newline_re.sub(self.newline_sequence, value)
+
+ def tokenize(
+ self,
+ source: str,
+ name: t.Optional[str] = None,
+ filename: t.Optional[str] = None,
+ state: t.Optional[str] = None,
+ ) -> TokenStream:
+ """Calls tokeniter + tokenize and wraps it in a token stream."""
+ stream = self.tokeniter(source, name, filename, state)
+ return TokenStream(self.wrap(stream, name, filename), name, filename)
+
+ def wrap(
+ self,
+ stream: t.Iterable[t.Tuple[int, str, str]],
+ name: t.Optional[str] = None,
+ filename: t.Optional[str] = None,
+ ) -> t.Iterator[Token]:
+ """This is called with the stream as returned by `tokenize` and wraps
+ every token in a :class:`Token` and converts the value.
+ """
+ for lineno, token, value_str in stream:
+ if token in ignored_tokens:
+ continue
+
+ value: t.Any = value_str
+
+ if token == TOKEN_LINESTATEMENT_BEGIN:
+ token = TOKEN_BLOCK_BEGIN
+ elif token == TOKEN_LINESTATEMENT_END:
+ token = TOKEN_BLOCK_END
+ # we are not interested in those tokens in the parser
+ elif token in (TOKEN_RAW_BEGIN, TOKEN_RAW_END):
+ continue
+ elif token == TOKEN_DATA:
+ value = self._normalize_newlines(value_str)
+ elif token == "keyword":
+ token = value_str
+ elif token == TOKEN_NAME:
+ value = value_str
+
+ if not value.isidentifier():
+ raise TemplateSyntaxError(
+ "Invalid character in identifier", lineno, name, filename
+ )
+ elif token == TOKEN_STRING:
+ # try to unescape string
+ try:
+ value = (
+ self._normalize_newlines(value_str[1:-1])
+ .encode("ascii", "backslashreplace")
+ .decode("unicode-escape")
+ )
+ except Exception as e:
+ msg = str(e).split(":")[-1].strip()
+ raise TemplateSyntaxError(msg, lineno, name, filename) from e
+ elif token == TOKEN_INTEGER:
+ value = int(value_str.replace("_", ""), 0)
+ elif token == TOKEN_FLOAT:
+ # remove all "_" first to support more Python versions
+ value = literal_eval(value_str.replace("_", ""))
+ elif token == TOKEN_OPERATOR:
+ token = operators[value_str]
+
+ yield Token(lineno, token, value)
+
+ def tokeniter(
+ self,
+ source: str,
+ name: t.Optional[str],
+ filename: t.Optional[str] = None,
+ state: t.Optional[str] = None,
+ ) -> t.Iterator[t.Tuple[int, str, str]]:
+ """This method tokenizes the text and returns the tokens in a
+ generator. Use this method if you just want to tokenize a template.
+
+ .. versionchanged:: 3.0
+ Only ``\\n``, ``\\r\\n`` and ``\\r`` are treated as line
+ breaks.
+ """
+ lines = newline_re.split(source)[::2]
+
+ if not self.keep_trailing_newline and lines[-1] == "":
+ del lines[-1]
+
+ source = "\n".join(lines)
+ pos = 0
+ lineno = 1
+ stack = ["root"]
+
+ if state is not None and state != "root":
+ assert state in ("variable", "block"), "invalid state"
+ stack.append(state + "_begin")
+
+ statetokens = self.rules[stack[-1]]
+ source_length = len(source)
+ balancing_stack: t.List[str] = []
+ newlines_stripped = 0
+ line_starting = True
+
+ while True:
+ # tokenizer loop
+ for regex, tokens, new_state in statetokens:
+ m = regex.match(source, pos)
+
+ # if no match we try again with the next rule
+ if m is None:
+ continue
+
+ # we only match blocks and variables if braces / parentheses
+ # are balanced. continue parsing with the lower rule which
+ # is the operator rule. do this only if the end tags look
+ # like operators
+ if balancing_stack and tokens in (
+ TOKEN_VARIABLE_END,
+ TOKEN_BLOCK_END,
+ TOKEN_LINESTATEMENT_END,
+ ):
+ continue
+
+ # tuples support more options
+ if isinstance(tokens, tuple):
+ groups: t.Sequence[str] = m.groups()
+
+ if isinstance(tokens, OptionalLStrip):
+ # Rule supports lstrip. Match will look like
+ # text, block type, whitespace control, type, control, ...
+ text = groups[0]
+ # Skipping the text and first type, every other group is the
+ # whitespace control for each type. One of the groups will be
+ # -, +, or empty string instead of None.
+ strip_sign = next(g for g in groups[2::2] if g is not None)
+
+ if strip_sign == "-":
+ # Strip all whitespace between the text and the tag.
+ stripped = text.rstrip()
+ newlines_stripped = text[len(stripped) :].count("\n")
+ groups = [stripped, *groups[1:]]
+ elif (
+ # Not marked for preserving whitespace.
+ strip_sign != "+"
+ # lstrip is enabled.
+ and self.lstrip_blocks
+ # Not a variable expression.
+ and not m.groupdict().get(TOKEN_VARIABLE_BEGIN)
+ ):
+ # The start of text between the last newline and the tag.
+ l_pos = text.rfind("\n") + 1
+
+ if l_pos > 0 or line_starting:
+ # If there's only whitespace between the newline and the
+ # tag, strip it.
+ if whitespace_re.fullmatch(text, l_pos):
+ groups = [text[:l_pos], *groups[1:]]
+
+ for idx, token in enumerate(tokens):
+ # failure group
+ if token.__class__ is Failure:
+ raise token(lineno, filename)
+ # bygroup is a bit more complex, in that case we
+ # yield for the current token the first named
+ # group that matched
+ elif token == "#bygroup":
+ for key, value in m.groupdict().items():
+ if value is not None:
+ yield lineno, key, value
+ lineno += value.count("\n")
+ break
+ else:
+ raise RuntimeError(
+ f"{regex!r} wanted to resolve the token dynamically"
+ " but no group matched"
+ )
+ # normal group
+ else:
+ data = groups[idx]
+
+ if data or token not in ignore_if_empty:
+ yield lineno, token, data
+
+ lineno += data.count("\n") + newlines_stripped
+ newlines_stripped = 0
+
+ # strings as token just are yielded as it.
+ else:
+ data = m.group()
+
+ # update brace/parentheses balance
+ if tokens == TOKEN_OPERATOR:
+ if data == "{":
+ balancing_stack.append("}")
+ elif data == "(":
+ balancing_stack.append(")")
+ elif data == "[":
+ balancing_stack.append("]")
+ elif data in ("}", ")", "]"):
+ if not balancing_stack:
+ raise TemplateSyntaxError(
+ f"unexpected '{data}'", lineno, name, filename
+ )
+
+ expected_op = balancing_stack.pop()
+
+ if expected_op != data:
+ raise TemplateSyntaxError(
+ f"unexpected '{data}', expected '{expected_op}'",
+ lineno,
+ name,
+ filename,
+ )
+
+ # yield items
+ if data or tokens not in ignore_if_empty:
+ yield lineno, tokens, data
+
+ lineno += data.count("\n")
+
+ line_starting = m.group()[-1:] == "\n"
+ # fetch new position into new variable so that we can check
+ # if there is a internal parsing error which would result
+ # in an infinite loop
+ pos2 = m.end()
+
+ # handle state changes
+ if new_state is not None:
+ # remove the uppermost state
+ if new_state == "#pop":
+ stack.pop()
+ # resolve the new state by group checking
+ elif new_state == "#bygroup":
+ for key, value in m.groupdict().items():
+ if value is not None:
+ stack.append(key)
+ break
+ else:
+ raise RuntimeError(
+ f"{regex!r} wanted to resolve the new state dynamically"
+ f" but no group matched"
+ )
+ # direct state name given
+ else:
+ stack.append(new_state)
+
+ statetokens = self.rules[stack[-1]]
+ # we are still at the same position and no stack change.
+ # this means a loop without break condition, avoid that and
+ # raise error
+ elif pos2 == pos:
+ raise RuntimeError(
+ f"{regex!r} yielded empty string without stack change"
+ )
+
+ # publish new function and start again
+ pos = pos2
+ break
+ # if loop terminated without break we haven't found a single match
+ # either we are at the end of the file or we have a problem
+ else:
+ # end of text
+ if pos >= source_length:
+ return
+
+ # something went wrong
+ raise TemplateSyntaxError(
+ f"unexpected char {source[pos]!r} at {pos}", lineno, name, filename
+ )
diff --git a/llmeval-env/lib/python3.10/site-packages/jinja2/sandbox.py b/llmeval-env/lib/python3.10/site-packages/jinja2/sandbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b4fc12d3479772f15c20ad394aab575fd2a4975
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/jinja2/sandbox.py
@@ -0,0 +1,429 @@
+"""A sandbox layer that ensures unsafe operations cannot be performed.
+Useful when the template itself comes from an untrusted source.
+"""
+
+import operator
+import types
+import typing as t
+from collections import abc
+from collections import deque
+from string import Formatter
+
+from _string import formatter_field_name_split # type: ignore
+from markupsafe import EscapeFormatter
+from markupsafe import Markup
+
+from .environment import Environment
+from .exceptions import SecurityError
+from .runtime import Context
+from .runtime import Undefined
+
+F = t.TypeVar("F", bound=t.Callable[..., t.Any])
+
+#: maximum number of items a range may produce
+MAX_RANGE = 100000
+
+#: Unsafe function attributes.
+UNSAFE_FUNCTION_ATTRIBUTES: t.Set[str] = set()
+
+#: Unsafe method attributes. Function attributes are unsafe for methods too.
+UNSAFE_METHOD_ATTRIBUTES: t.Set[str] = set()
+
+#: unsafe generator attributes.
+UNSAFE_GENERATOR_ATTRIBUTES = {"gi_frame", "gi_code"}
+
+#: unsafe attributes on coroutines
+UNSAFE_COROUTINE_ATTRIBUTES = {"cr_frame", "cr_code"}
+
+#: unsafe attributes on async generators
+UNSAFE_ASYNC_GENERATOR_ATTRIBUTES = {"ag_code", "ag_frame"}
+
+_mutable_spec: t.Tuple[t.Tuple[t.Type[t.Any], t.FrozenSet[str]], ...] = (
+ (
+ abc.MutableSet,
+ frozenset(
+ [
+ "add",
+ "clear",
+ "difference_update",
+ "discard",
+ "pop",
+ "remove",
+ "symmetric_difference_update",
+ "update",
+ ]
+ ),
+ ),
+ (
+ abc.MutableMapping,
+ frozenset(["clear", "pop", "popitem", "setdefault", "update"]),
+ ),
+ (
+ abc.MutableSequence,
+ frozenset(["append", "reverse", "insert", "sort", "extend", "remove"]),
+ ),
+ (
+ deque,
+ frozenset(
+ [
+ "append",
+ "appendleft",
+ "clear",
+ "extend",
+ "extendleft",
+ "pop",
+ "popleft",
+ "remove",
+ "rotate",
+ ]
+ ),
+ ),
+)
+
+
+def inspect_format_method(callable: t.Callable[..., t.Any]) -> t.Optional[str]:
+ if not isinstance(
+ callable, (types.MethodType, types.BuiltinMethodType)
+ ) or callable.__name__ not in ("format", "format_map"):
+ return None
+
+ obj = callable.__self__
+
+ if isinstance(obj, str):
+ return obj
+
+ return None
+
+
+def safe_range(*args: int) -> range:
+ """A range that can't generate ranges with a length of more than
+ MAX_RANGE items.
+ """
+ rng = range(*args)
+
+ if len(rng) > MAX_RANGE:
+ raise OverflowError(
+ "Range too big. The sandbox blocks ranges larger than"
+ f" MAX_RANGE ({MAX_RANGE})."
+ )
+
+ return rng
+
+
+def unsafe(f: F) -> F:
+ """Marks a function or method as unsafe.
+
+ .. code-block: python
+
+ @unsafe
+ def delete(self):
+ pass
+ """
+ f.unsafe_callable = True # type: ignore
+ return f
+
+
+def is_internal_attribute(obj: t.Any, attr: str) -> bool:
+ """Test if the attribute given is an internal python attribute. For
+ example this function returns `True` for the `func_code` attribute of
+ python objects. This is useful if the environment method
+ :meth:`~SandboxedEnvironment.is_safe_attribute` is overridden.
+
+ >>> from jinja2.sandbox import is_internal_attribute
+ >>> is_internal_attribute(str, "mro")
+ True
+ >>> is_internal_attribute(str, "upper")
+ False
+ """
+ if isinstance(obj, types.FunctionType):
+ if attr in UNSAFE_FUNCTION_ATTRIBUTES:
+ return True
+ elif isinstance(obj, types.MethodType):
+ if attr in UNSAFE_FUNCTION_ATTRIBUTES or attr in UNSAFE_METHOD_ATTRIBUTES:
+ return True
+ elif isinstance(obj, type):
+ if attr == "mro":
+ return True
+ elif isinstance(obj, (types.CodeType, types.TracebackType, types.FrameType)):
+ return True
+ elif isinstance(obj, types.GeneratorType):
+ if attr in UNSAFE_GENERATOR_ATTRIBUTES:
+ return True
+ elif hasattr(types, "CoroutineType") and isinstance(obj, types.CoroutineType):
+ if attr in UNSAFE_COROUTINE_ATTRIBUTES:
+ return True
+ elif hasattr(types, "AsyncGeneratorType") and isinstance(
+ obj, types.AsyncGeneratorType
+ ):
+ if attr in UNSAFE_ASYNC_GENERATOR_ATTRIBUTES:
+ return True
+ return attr.startswith("__")
+
+
+def modifies_known_mutable(obj: t.Any, attr: str) -> bool:
+ """This function checks if an attribute on a builtin mutable object
+ (list, dict, set or deque) or the corresponding ABCs would modify it
+ if called.
+
+ >>> modifies_known_mutable({}, "clear")
+ True
+ >>> modifies_known_mutable({}, "keys")
+ False
+ >>> modifies_known_mutable([], "append")
+ True
+ >>> modifies_known_mutable([], "index")
+ False
+
+ If called with an unsupported object, ``False`` is returned.
+
+ >>> modifies_known_mutable("foo", "upper")
+ False
+ """
+ for typespec, unsafe in _mutable_spec:
+ if isinstance(obj, typespec):
+ return attr in unsafe
+ return False
+
+
+class SandboxedEnvironment(Environment):
+ """The sandboxed environment. It works like the regular environment but
+ tells the compiler to generate sandboxed code. Additionally subclasses of
+ this environment may override the methods that tell the runtime what
+ attributes or functions are safe to access.
+
+ If the template tries to access insecure code a :exc:`SecurityError` is
+ raised. However also other exceptions may occur during the rendering so
+ the caller has to ensure that all exceptions are caught.
+ """
+
+ sandboxed = True
+
+ #: default callback table for the binary operators. A copy of this is
+ #: available on each instance of a sandboxed environment as
+ #: :attr:`binop_table`
+ default_binop_table: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = {
+ "+": operator.add,
+ "-": operator.sub,
+ "*": operator.mul,
+ "/": operator.truediv,
+ "//": operator.floordiv,
+ "**": operator.pow,
+ "%": operator.mod,
+ }
+
+ #: default callback table for the unary operators. A copy of this is
+ #: available on each instance of a sandboxed environment as
+ #: :attr:`unop_table`
+ default_unop_table: t.Dict[str, t.Callable[[t.Any], t.Any]] = {
+ "+": operator.pos,
+ "-": operator.neg,
+ }
+
+ #: a set of binary operators that should be intercepted. Each operator
+ #: that is added to this set (empty by default) is delegated to the
+ #: :meth:`call_binop` method that will perform the operator. The default
+ #: operator callback is specified by :attr:`binop_table`.
+ #:
+ #: The following binary operators are interceptable:
+ #: ``//``, ``%``, ``+``, ``*``, ``-``, ``/``, and ``**``
+ #:
+ #: The default operation form the operator table corresponds to the
+ #: builtin function. Intercepted calls are always slower than the native
+ #: operator call, so make sure only to intercept the ones you are
+ #: interested in.
+ #:
+ #: .. versionadded:: 2.6
+ intercepted_binops: t.FrozenSet[str] = frozenset()
+
+ #: a set of unary operators that should be intercepted. Each operator
+ #: that is added to this set (empty by default) is delegated to the
+ #: :meth:`call_unop` method that will perform the operator. The default
+ #: operator callback is specified by :attr:`unop_table`.
+ #:
+ #: The following unary operators are interceptable: ``+``, ``-``
+ #:
+ #: The default operation form the operator table corresponds to the
+ #: builtin function. Intercepted calls are always slower than the native
+ #: operator call, so make sure only to intercept the ones you are
+ #: interested in.
+ #:
+ #: .. versionadded:: 2.6
+ intercepted_unops: t.FrozenSet[str] = frozenset()
+
+ def __init__(self, *args: t.Any, **kwargs: t.Any) -> None:
+ super().__init__(*args, **kwargs)
+ self.globals["range"] = safe_range
+ self.binop_table = self.default_binop_table.copy()
+ self.unop_table = self.default_unop_table.copy()
+
+ def is_safe_attribute(self, obj: t.Any, attr: str, value: t.Any) -> bool:
+ """The sandboxed environment will call this method to check if the
+ attribute of an object is safe to access. Per default all attributes
+ starting with an underscore are considered private as well as the
+ special attributes of internal python objects as returned by the
+ :func:`is_internal_attribute` function.
+ """
+ return not (attr.startswith("_") or is_internal_attribute(obj, attr))
+
+ def is_safe_callable(self, obj: t.Any) -> bool:
+ """Check if an object is safely callable. By default callables
+ are considered safe unless decorated with :func:`unsafe`.
+
+ This also recognizes the Django convention of setting
+ ``func.alters_data = True``.
+ """
+ return not (
+ getattr(obj, "unsafe_callable", False) or getattr(obj, "alters_data", False)
+ )
+
+ def call_binop(
+ self, context: Context, operator: str, left: t.Any, right: t.Any
+ ) -> t.Any:
+ """For intercepted binary operator calls (:meth:`intercepted_binops`)
+ this function is executed instead of the builtin operator. This can
+ be used to fine tune the behavior of certain operators.
+
+ .. versionadded:: 2.6
+ """
+ return self.binop_table[operator](left, right)
+
+ def call_unop(self, context: Context, operator: str, arg: t.Any) -> t.Any:
+ """For intercepted unary operator calls (:meth:`intercepted_unops`)
+ this function is executed instead of the builtin operator. This can
+ be used to fine tune the behavior of certain operators.
+
+ .. versionadded:: 2.6
+ """
+ return self.unop_table[operator](arg)
+
+ def getitem(
+ self, obj: t.Any, argument: t.Union[str, t.Any]
+ ) -> t.Union[t.Any, Undefined]:
+ """Subscribe an object from sandboxed code."""
+ try:
+ return obj[argument]
+ except (TypeError, LookupError):
+ if isinstance(argument, str):
+ try:
+ attr = str(argument)
+ except Exception:
+ pass
+ else:
+ try:
+ value = getattr(obj, attr)
+ except AttributeError:
+ pass
+ else:
+ if self.is_safe_attribute(obj, argument, value):
+ return value
+ return self.unsafe_undefined(obj, argument)
+ return self.undefined(obj=obj, name=argument)
+
+ def getattr(self, obj: t.Any, attribute: str) -> t.Union[t.Any, Undefined]:
+ """Subscribe an object from sandboxed code and prefer the
+ attribute. The attribute passed *must* be a bytestring.
+ """
+ try:
+ value = getattr(obj, attribute)
+ except AttributeError:
+ try:
+ return obj[attribute]
+ except (TypeError, LookupError):
+ pass
+ else:
+ if self.is_safe_attribute(obj, attribute, value):
+ return value
+ return self.unsafe_undefined(obj, attribute)
+ return self.undefined(obj=obj, name=attribute)
+
+ def unsafe_undefined(self, obj: t.Any, attribute: str) -> Undefined:
+ """Return an undefined object for unsafe attributes."""
+ return self.undefined(
+ f"access to attribute {attribute!r} of"
+ f" {type(obj).__name__!r} object is unsafe.",
+ name=attribute,
+ obj=obj,
+ exc=SecurityError,
+ )
+
+ def format_string(
+ self,
+ s: str,
+ args: t.Tuple[t.Any, ...],
+ kwargs: t.Dict[str, t.Any],
+ format_func: t.Optional[t.Callable[..., t.Any]] = None,
+ ) -> str:
+ """If a format call is detected, then this is routed through this
+ method so that our safety sandbox can be used for it.
+ """
+ formatter: SandboxedFormatter
+ if isinstance(s, Markup):
+ formatter = SandboxedEscapeFormatter(self, escape=s.escape)
+ else:
+ formatter = SandboxedFormatter(self)
+
+ if format_func is not None and format_func.__name__ == "format_map":
+ if len(args) != 1 or kwargs:
+ raise TypeError(
+ "format_map() takes exactly one argument"
+ f" {len(args) + (kwargs is not None)} given"
+ )
+
+ kwargs = args[0]
+ args = ()
+
+ rv = formatter.vformat(s, args, kwargs)
+ return type(s)(rv)
+
+ def call(
+ __self, # noqa: B902
+ __context: Context,
+ __obj: t.Any,
+ *args: t.Any,
+ **kwargs: t.Any,
+ ) -> t.Any:
+ """Call an object from sandboxed code."""
+ fmt = inspect_format_method(__obj)
+ if fmt is not None:
+ return __self.format_string(fmt, args, kwargs, __obj)
+
+ # the double prefixes are to avoid double keyword argument
+ # errors when proxying the call.
+ if not __self.is_safe_callable(__obj):
+ raise SecurityError(f"{__obj!r} is not safely callable")
+ return __context.call(__obj, *args, **kwargs)
+
+
+class ImmutableSandboxedEnvironment(SandboxedEnvironment):
+ """Works exactly like the regular `SandboxedEnvironment` but does not
+ permit modifications on the builtin mutable objects `list`, `set`, and
+ `dict` by using the :func:`modifies_known_mutable` function.
+ """
+
+ def is_safe_attribute(self, obj: t.Any, attr: str, value: t.Any) -> bool:
+ if not super().is_safe_attribute(obj, attr, value):
+ return False
+
+ return not modifies_known_mutable(obj, attr)
+
+
+class SandboxedFormatter(Formatter):
+ def __init__(self, env: Environment, **kwargs: t.Any) -> None:
+ self._env = env
+ super().__init__(**kwargs)
+
+ def get_field(
+ self, field_name: str, args: t.Sequence[t.Any], kwargs: t.Mapping[str, t.Any]
+ ) -> t.Tuple[t.Any, str]:
+ first, rest = formatter_field_name_split(field_name)
+ obj = self.get_value(first, args, kwargs)
+ for is_attr, i in rest:
+ if is_attr:
+ obj = self._env.getattr(obj, i)
+ else:
+ obj = self._env.getitem(obj, i)
+ return obj, first
+
+
+class SandboxedEscapeFormatter(SandboxedFormatter, EscapeFormatter):
+ pass
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/INSTALLER b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/LICENSE.md b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..12e6063183935e876e232db276568baf4954b492
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/LICENSE.md
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 EleutherAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/METADATA b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..099b22d41332a805b8a2ad11a66212cbb5c61ef0
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/METADATA
@@ -0,0 +1,542 @@
+Metadata-Version: 2.1
+Name: lm_eval
+Version: 0.4.2
+Summary: A framework for evaluating language models
+Author-email: EleutherAI
+License: MIT
+Project-URL: Homepage, https://github.com/EleutherAI/lm-evaluation-harness
+Project-URL: Repository, https://github.com/EleutherAI/lm-evaluation-harness
+Classifier: Development Status :: 3 - Alpha
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License-File: LICENSE.md
+Requires-Dist: accelerate >=0.21.0
+Requires-Dist: evaluate
+Requires-Dist: datasets >=2.16.0
+Requires-Dist: evaluate >=0.4.0
+Requires-Dist: jsonlines
+Requires-Dist: numexpr
+Requires-Dist: peft >=0.2.0
+Requires-Dist: pybind11 >=2.6.2
+Requires-Dist: pytablewriter
+Requires-Dist: rouge-score >=0.0.4
+Requires-Dist: sacrebleu >=1.5.0
+Requires-Dist: scikit-learn >=0.24.1
+Requires-Dist: sqlitedict
+Requires-Dist: torch >=1.8
+Requires-Dist: tqdm-multiprocess
+Requires-Dist: transformers >=4.1
+Requires-Dist: zstandard
+Requires-Dist: dill
+Requires-Dist: word2number
+Requires-Dist: more-itertools
+Provides-Extra: all
+Requires-Dist: lm-eval[anthropic] ; extra == 'all'
+Requires-Dist: lm-eval[dev] ; extra == 'all'
+Requires-Dist: lm-eval[gptq] ; extra == 'all'
+Requires-Dist: lm-eval[hf_transfer] ; extra == 'all'
+Requires-Dist: lm-eval[ifeval] ; extra == 'all'
+Requires-Dist: lm-eval[mamba] ; extra == 'all'
+Requires-Dist: lm-eval[math] ; extra == 'all'
+Requires-Dist: lm-eval[multilingual] ; extra == 'all'
+Requires-Dist: lm-eval[openai] ; extra == 'all'
+Requires-Dist: lm-eval[promptsource] ; extra == 'all'
+Requires-Dist: lm-eval[sentencepiece] ; extra == 'all'
+Requires-Dist: lm-eval[testing] ; extra == 'all'
+Requires-Dist: lm-eval[vllm] ; extra == 'all'
+Requires-Dist: lm-eval[zeno] ; extra == 'all'
+Requires-Dist: lm-eval[wandb] ; extra == 'all'
+Provides-Extra: anthropic
+Requires-Dist: anthropic ; extra == 'anthropic'
+Provides-Extra: dev
+Requires-Dist: pytest ; extra == 'dev'
+Requires-Dist: pytest-cov ; extra == 'dev'
+Requires-Dist: pytest-xdist ; extra == 'dev'
+Requires-Dist: pre-commit ; extra == 'dev'
+Requires-Dist: mypy ; extra == 'dev'
+Provides-Extra: gptq
+Requires-Dist: auto-gptq[triton] >=0.6.0 ; extra == 'gptq'
+Provides-Extra: hf_transfer
+Requires-Dist: hf-transfer ; extra == 'hf_transfer'
+Provides-Extra: ifeval
+Requires-Dist: langdetect ; extra == 'ifeval'
+Requires-Dist: immutabledict ; extra == 'ifeval'
+Provides-Extra: mamba
+Requires-Dist: mamba-ssm ; extra == 'mamba'
+Requires-Dist: causal-conv1d ==1.0.2 ; extra == 'mamba'
+Provides-Extra: math
+Requires-Dist: sympy >=1.12 ; extra == 'math'
+Requires-Dist: antlr4-python3-runtime ==4.11 ; extra == 'math'
+Provides-Extra: multilingual
+Requires-Dist: nagisa >=0.2.7 ; extra == 'multilingual'
+Requires-Dist: jieba >=0.42.1 ; extra == 'multilingual'
+Requires-Dist: pycountry ; extra == 'multilingual'
+Provides-Extra: neuronx
+Requires-Dist: optimum[neuronx] ; extra == 'neuronx'
+Provides-Extra: openai
+Requires-Dist: openai ==1.3.9 ; extra == 'openai'
+Requires-Dist: tiktoken ; extra == 'openai'
+Provides-Extra: optimum
+Requires-Dist: optimum[openvino] ; extra == 'optimum'
+Provides-Extra: promptsource
+Requires-Dist: promptsource >=0.2.3 ; extra == 'promptsource'
+Provides-Extra: sentencepiece
+Requires-Dist: sentencepiece >=0.1.98 ; extra == 'sentencepiece'
+Requires-Dist: protobuf >=4.22.1 ; extra == 'sentencepiece'
+Provides-Extra: testing
+Requires-Dist: pytest ; extra == 'testing'
+Requires-Dist: pytest-cov ; extra == 'testing'
+Requires-Dist: pytest-xdist ; extra == 'testing'
+Provides-Extra: vllm
+Requires-Dist: vllm ==0.3.2 ; extra == 'vllm'
+Provides-Extra: wandb
+Requires-Dist: wandb >=0.16.3 ; extra == 'wandb'
+Requires-Dist: pandas ; extra == 'wandb'
+Requires-Dist: numpy ; extra == 'wandb'
+Provides-Extra: zeno
+Requires-Dist: pandas ; extra == 'zeno'
+Requires-Dist: zeno-client ; extra == 'zeno'
+
+# Language Model Evaluation Harness
+
+[](https://doi.org/10.5281/zenodo.10256836)
+
+## Announcement
+**A new v0.4.0 release of lm-evaluation-harness is available** !
+
+New updates and features include:
+
+- Internal refactoring
+- Config-based task creation and configuration
+- Easier import and sharing of externally-defined task config YAMLs
+- Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource
+- More advanced configuration options, including output post-processing, answer extraction, and multiple LM generations per document, configurable fewshot settings, and more
+- Speedups and new modeling libraries supported, including: faster data-parallel HF model usage, vLLM support, MPS support with HuggingFace, and more
+- Logging and usability changes
+- New tasks including CoT BIG-Bench-Hard, Belebele, user-defined task groupings, and more
+
+Please see our updated documentation pages in `docs/` for more details.
+
+Development will be continuing on the `main` branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub, or in the [EleutherAI discord](https://discord.gg/eleutherai)!
+
+## Overview
+
+This project provides a unified framework to test generative language models on a large number of different evaluation tasks.
+
+**Features:**
+- Over 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants implemented.
+- Support for models loaded via [transformers](https://github.com/huggingface/transformers/) (including quantization via [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)), [GPT-NeoX](https://github.com/EleutherAI/gpt-neox), and [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/), with a flexible tokenization-agnostic interface.
+- Support for fast and memory-efficient inference with [vLLM](https://github.com/vllm-project/vllm).
+- Support for commercial APIs including [OpenAI](https://openai.com), and [TextSynth](https://textsynth.com/).
+- Support for evaluation on adapters (e.g. LoRA) supported in [HuggingFace's PEFT library](https://github.com/huggingface/peft).
+- Support for local models and benchmarks.
+- Evaluation with publicly available prompts ensures reproducibility and comparability between papers.
+- Easy support for custom prompts and evaluation metrics.
+
+The Language Model Evaluation Harness is the backend for 🤗 Hugging Face's popular [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), has been used in [hundreds of papers](https://scholar.google.com/scholar?oi=bibs&hl=en&authuser=2&cites=15052937328817631261,4097184744846514103,1520777361382155671,17476825572045927382,18443729326628441434,14801318227356878622,7890865700763267262,12854182577605049984,15641002901115500560,5104500764547628290), and is used internally by dozens of organizations including NVIDIA, Cohere, BigScience, BigCode, Nous Research, and Mosaic ML.
+
+## Install
+
+To install the `lm-eval` package from the github repository, run:
+
+```bash
+git clone https://github.com/EleutherAI/lm-evaluation-harness
+cd lm-evaluation-harness
+pip install -e .
+```
+
+We also provide a number of optional dependencies for extended functionality. A detailed table is available at the end of this document.
+
+## Basic Usage
+
+### Hugging Face `transformers`
+
+To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. GPT-J-6B) on `hellaswag` you can use the following command (this assumes you are using a CUDA-compatible GPU):
+
+```bash
+lm_eval --model hf \
+ --model_args pretrained=EleutherAI/gpt-j-6B \
+ --tasks hellaswag \
+ --device cuda:0 \
+ --batch_size 8
+```
+
+Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
+
+```bash
+lm_eval --model hf \
+ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype="float" \
+ --tasks lambada_openai,hellaswag \
+ --device cuda:0 \
+ --batch_size 8
+```
+
+Models that are loaded via both `transformers.AutoModelForCausalLM` (autoregressive, decoder-only GPT style models) and `transformers.AutoModelForSeq2SeqLM` (such as encoder-decoder models like T5) in Huggingface are supported.
+
+Batch size selection can be automated by setting the ```--batch_size``` flag to ```auto```. This will perform automatic detection of the largest batch size that will fit on your device. On tasks where there is a large difference between the longest and shortest example, it can be helpful to periodically recompute the largest batch size, to gain a further speedup. To do this, append ```:N``` to above flag to automatically recompute the largest batch size ```N``` times. For example, to recompute the batch size 4 times, the command would be:
+
+```bash
+lm_eval --model hf \
+ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype="float" \
+ --tasks lambada_openai,hellaswag \
+ --device cuda:0 \
+ --batch_size auto:4
+```
+
+The full list of supported arguments are provided [here](./docs/interface.md), and on the terminal by calling `lm_eval -h`. Alternatively, you can use `lm-eval` instead of `lm_eval`.
+
+> [!Note]
+> Just like you can provide a local path to `transformers.AutoModel`, you can also provide a local path to `lm_eval` via `--model_args pretrained=/path/to/model`
+
+#### Multi-GPU Evaluation with Hugging Face `accelerate`
+
+We support two main ways of using Hugging Face's [accelerate 🚀](https://github.com/huggingface/accelerate) library for multi-GPU evaluation.
+
+To perform *data-parallel evaluation* (where each GPU loads a **separate full copy** of the model), we leverage the `accelerate` launcher as follows:
+
+```
+accelerate launch -m lm_eval --model hf \
+ --tasks lambada_openai,arc_easy \
+ --batch_size 16
+```
+(or via `accelerate launch --no-python lm_eval`).
+
+For cases where your model can fit on a single GPU, this allows you to evaluate on K GPUs K times faster than on one.
+
+**WARNING**: This setup does not work with FSDP model sharding, so in `accelerate config` FSDP must be disabled, or the NO_SHARD FSDP option must be used.
+
+The second way of using `accelerate` for multi-GPU evaluation is when your model is *too large to fit on a single GPU.*
+
+In this setting, run the library *outside of the `accelerate` launcher*, but passing `parallelize=True` to `--model_args` as follows:
+
+```
+lm_eval --model hf \
+ --tasks lambada_openai,arc_easy \
+ --model_args parallelize=True \
+ --batch_size 16
+```
+
+This means that your model's weights will be split across all available GPUs.
+
+For more advanced users or even larger models, we allow for the following arguments when `parallelize=True` as well:
+- `device_map_option`: How to split model weights across available GPUs. defaults to "auto".
+- `max_memory_per_gpu`: the max GPU memory to use per GPU in loading the model.
+- `max_cpu_memory`: the max amount of CPU memory to use when offloading the model weights to RAM.
+- `offload_folder`: a folder where model weights will be offloaded to disk if needed.
+
+These two options (`accelerate launch` and `parallelize=True`) are mutually exclusive.
+
+**Note: we do not currently support multi-node evaluations natively, and advise using either an externally hosted server to run inference requests against, or creating a custom integration with your distributed framework [as is done for the GPT-NeoX library](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py).**
+
+### NVIDIA `nemo` models
+
+[NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo) is a generative AI framework built for researchers and pytorch developers working on language models.
+
+To evaluate a `nemo` model, start by installing NeMo following [the documentation](https://github.com/NVIDIA/NeMo?tab=readme-ov-file#installation). We highly recommended to use the NVIDIA PyTorch or NeMo container, especially if having issues installing Apex or any other dependencies (see [latest released containers](https://github.com/NVIDIA/NeMo/releases)). Please also install the lm evaluation harness library following the instructions in [the Install section](https://github.com/EleutherAI/lm-evaluation-harness/tree/main?tab=readme-ov-file#install).
+
+NeMo models can be obtained through [NVIDIA NGC Catalog](https://catalog.ngc.nvidia.com/models) or in [NVIDIA's Hugging Face page](https://huggingface.co/nvidia). In [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo/tree/main/scripts/nlp_language_modeling) there are conversion scripts to convert the `hf` checkpoints of popular models like llama, falcon, mixtral or mpt to `nemo`.
+
+Run a `nemo` model on one GPU:
+```bash
+lm_eval --model nemo_lm \
+ --model_args path= \
+ --tasks hellaswag \
+ --batch_size 32
+```
+
+It is recommended to unpack the `nemo` model to avoid the unpacking inside the docker container - it may overflow disk space. For that you can run:
+
+```
+mkdir MY_MODEL
+tar -xvf MY_MODEL.nemo -c MY_MODEL
+```
+
+#### Multi-GPU evaluation with NVIDIA `nemo` models
+
+By default, only one GPU is used. But we do support either data replication or tensor/pipeline parallelism during evaluation, on one node.
+
+1) To enable data replication, set the `model_args` of `devices` to the number of data replicas to run. For example, the command to run 8 data replicas over 8 GPUs is:
+```bash
+torchrun --nproc-per-node=8 --no-python lm_eval \
+ --model nemo_lm \
+ --model_args path=,devices=8 \
+ --tasks hellaswag \
+ --batch_size 32
+```
+
+2) To enable tensor and/or pipeline parallelism, set the `model_args` of `tensor_model_parallel_size` and/or `pipeline_model_parallel_size`. In addition, you also have to set up `devices` to be equal to the product of `tensor_model_parallel_size` and/or `pipeline_model_parallel_size`. For example, the command to use one node of 4 GPUs with tensor parallelism of 2 and pipeline parallelism of 2 is:
+```bash
+torchrun --nproc-per-node=4 --no-python lm_eval \
+ --model nemo_lm \
+ --model_args path=,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 \
+ --tasks hellaswag \
+ --batch_size 32
+```
+Note that it is recommended to substitute the `python` command by `torchrun --nproc-per-node= --no-python` to facilitate loading the model into the GPUs. This is especially important for large checkpoints loaded into multiple GPUs.
+
+Not supported yet: multi-node evaluation and combinations of data replication with tensor or pipeline parallelism.
+
+### Tensor + Data Parallel and Optimized Inference with `vLLM`
+
+We also support vLLM for faster inference on [supported model types](https://docs.vllm.ai/en/latest/models/supported_models.html), especially faster when splitting a model across multiple GPUs. For single-GPU or multi-GPU — tensor parallel, data parallel, or a combination of both — inference, for example:
+
+```bash
+lm_eval --model vllm \
+ --model_args pretrained={model_name},tensor_parallel_size={GPUs_per_model},dtype=auto,gpu_memory_utilization=0.8,data_parallel_size={model_replicas} \
+ --tasks lambada_openai \
+ --batch_size auto
+```
+To use vllm, do `pip install lm_eval[vllm]`. For a full list of supported vLLM configurations, please reference our [vLLM integration](https://github.com/EleutherAI/lm-evaluation-harness/blob/e74ec966556253fbe3d8ecba9de675c77c075bce/lm_eval/models/vllm_causallms.py) and the vLLM documentation.
+
+vLLM occasionally differs in output from Huggingface. We treat Huggingface as the reference implementation, and provide a [script](./scripts/model_comparator.py) for checking the validity of vllm results against HF.
+
+> [!Tip]
+> For fastest performance, we recommend using `--batch_size auto` for vLLM whenever possible, to leverage its continuous batching functionality!
+
+> [!Tip]
+> Passing `max_model_len=4096` or some other reasonable default to vLLM through model args may cause speedups or prevent out-of-memory errors when trying to use auto batch size, such as for Mistral-7B-v0.1 which defaults to a maximum length of 32k.
+
+### Model APIs and Inference Servers
+
+Our library also supports the evaluation of models served via several commercial APIs, and we hope to implement support for the most commonly used performant local/self-hosted inference servers.
+
+To call a hosted model, use:
+
+```bash
+export OPENAI_API_KEY=YOUR_KEY_HERE
+lm_eval --model openai-completions \
+ --model_args model=davinci \
+ --tasks lambada_openai,hellaswag
+```
+
+We also support using your own local inference server with servers that mirror the OpenAI Completions and ChatCompletions APIs.
+
+```bash
+lm_eval --model local-chat-completions --tasks gsm8k --model_args model=facebook/opt-125m,base_url=http://{yourip}:8000/v1
+```
+Note that for externally hosted models, configs such as `--device` and `--batch_size` should not be used and do not function. Just like you can use `--model_args` to pass arbitrary arguments to the model constructor for local models, you can use it to pass arbitrary arguments to the model API for hosted models. See the documentation of the hosting service for information on what arguments they support.
+
+| API or Inference Server | Implemented? | `--model ` name | Models supported: | Request Types: |
+|---------------------------------------------------------------------------------------------------------------------------|---------------------------------|---------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|------------------------------------------------------------|
+| OpenAI Completions | :heavy_check_mark: | `openai-completions`, `local-completions` | All OpenAI Completions API models | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
+| OpenAI ChatCompletions | :heavy_check_mark: | `openai-chat-completions`, `local-chat-completions` | [All ChatCompletions API models](https://platform.openai.com/docs/guides/gpt) | `generate_until` (no logprobs) |
+| Anthropic | :heavy_check_mark: | `anthropic` | [Supported Anthropic Engines](https://docs.anthropic.com/claude/reference/selecting-a-model) | `generate_until` (no logprobs) |
+| Anthropic Chat | :heavy_check_mark: | `anthropic-chat`, `anthropic-chat-completions` | [Supported Anthropic Engines](https://docs.anthropic.com/claude/docs/models-overview) | `generate_until` (no logprobs) |
+| Textsynth | :heavy_check_mark: | `textsynth` | [All supported engines](https://textsynth.com/documentation.html#engines) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
+| Cohere | [:hourglass: - blocked on Cohere API bug](https://github.com/EleutherAI/lm-evaluation-harness/pull/395) | N/A | [All `cohere.generate()` engines](https://docs.cohere.com/docs/models) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
+| [Llama.cpp](https://github.com/ggerganov/llama.cpp) (via [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)) | :heavy_check_mark: | `gguf`, `ggml` | [All models supported by llama.cpp](https://github.com/ggerganov/llama.cpp) | `generate_until`, `loglikelihood`, (perplexity evaluation not yet implemented) |
+| vLLM | :heavy_check_mark: | `vllm` | [Most HF Causal Language Models](https://docs.vllm.ai/en/latest/models/supported_models.html) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
+| Mamba | :heavy_check_mark: | `mamba_ssm` | [Mamba architecture Language Models via the `mamba_ssm` package](https://huggingface.co/state-spaces) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
+| Huggingface Optimum (Causal LMs) | ✔️ | `openvino` | Any decoder-only AutoModelForCausalLM converted with Huggingface Optimum into OpenVINO™ Intermediate Representation (IR) format | `generate_until`, `loglikelihood`, `loglikelihood_rolling` | ... |
+| Neuron via AWS Inf2 (Causal LMs) | ✔️ | `neuronx` | Any decoder-only AutoModelForCausalLM supported to run on [huggingface-ami image for inferentia2](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2) | `generate_until`, `loglikelihood`, `loglikelihood_rolling` | ... |
+| Your local inference server! | :heavy_check_mark: | `local-completions` or `local-chat-completions` (using `openai-chat-completions` model type) | Any server address that accepts GET requests using HF models and mirror's OpenAI's Completions or ChatCompletions interface | `generate_until` | | ... |
+
+Models which do not supply logits or logprobs can be used with tasks of type `generate_until` only, while local models, or APIs that supply logprobs/logits of their prompts, can be run on all task types: `generate_until`, `loglikelihood`, `loglikelihood_rolling`, and `multiple_choice`.
+
+For more information on the different task `output_types` and model request types, see [our documentation](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/model_guide.md#interface).
+
+> [!Note]
+> For best performance with closed chat model APIs such as Anthropic Claude 3 and GPT-4, we recommend carefully looking at a few sample outputs using `--limit 10` first to confirm answer extraction and scoring on generative tasks is performing as expected. providing `system=""` within `--model_args` for anthropic-chat-completions, to instruct the model what format to respond in, may be useful.
+
+
+### Other Frameworks
+
+A number of other libraries contain scripts for calling the eval harness through their library. These include [GPT-NeoX](https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py), [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md), and [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py).
+
+To create your own custom integration you can follow instructions from [this tutorial](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage).
+
+### Additional Features
+> [!Note]
+> For tasks unsuitable for direct evaluation — either due risks associated with executing untrusted code or complexities in the evaluation process — the `--predict_only` flag is available to obtain decoded generations for post-hoc evaluation.
+
+If you have a Metal compatible Mac, you can run the eval harness using the MPS back-end by replacing `--device cuda:0` with `--device mps` (requires PyTorch version 2.1 or higher).
+
+> [!Note]
+> You can inspect what the LM inputs look like by running the following command:
+> ```bash
+> python write_out.py \
+> --tasks \
+> --num_fewshot 5 \
+> --num_examples 10 \
+> --output_base_path /path/to/output/folder
+> ```
+> This will write out one text file for each task.
+
+To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the `--check_integrity` flag:
+
+```bash
+lm_eval --model openai \
+ --model_args engine=davinci \
+ --tasks lambada_openai,hellaswag \
+ --check_integrity
+```
+
+## Advanced Usage Tips
+
+For models loaded with the HuggingFace `transformers` library, any arguments provided via `--model_args` get passed to the relevant constructor directly. This means that anything you can do with `AutoModel` can be done with our library. For example, you can pass a local path via `pretrained=` or use models finetuned with [PEFT](https://github.com/huggingface/peft) by taking the call you would run to evaluate the base model and add `,peft=PATH` to the `model_args` argument:
+```bash
+lm_eval --model hf \
+ --model_args pretrained=EleutherAI/gpt-j-6b,parallelize=True,load_in_4bit=True,peft=nomic-ai/gpt4all-j-lora \
+ --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \
+ --device cuda:0
+```
+
+[GPTQ](https://github.com/PanQiWei/AutoGPTQ) quantized models can be loaded by specifying their file names in `,autogptq=NAME` (or `,autogptq=True` for default names) in the `model_args` argument:
+
+```bash
+lm_eval --model hf \
+ --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \
+ --tasks hellaswag
+```
+
+We support wildcards in task names, for example you can run all of the machine-translated lambada tasks via `--task lambada_openai_mt_*`.
+
+To save evaluation results provide an `--output_path`. We also support logging model responses with the `--log_samples` flag for post-hoc analysis.
+
+Additionally, one can provide a directory with `--use_cache` to cache the results of prior runs. This allows you to avoid repeated execution of the same (model, task) pairs for re-scoring.
+
+For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md) guide in our documentation!
+
+## Visualizing Results
+
+You can seamlessly visualize and analyze the results of your evaluation harness runs using both Weights & Biases (W&B) and Zeno.
+
+### Zeno
+
+You can use [Zeno](https://zenoml.com) to visualize the results of your eval harness runs.
+
+First, head to [hub.zenoml.com](https://hub.zenoml.com) to create an account and get an API key [on your account page](https://hub.zenoml.com/account).
+Add this key as an environment variable:
+
+```bash
+export ZENO_API_KEY=[your api key]
+```
+
+You'll also need to install the `lm_eval[zeno]` package extra.
+
+To visualize the results, run the eval harness with the `log_samples` and `output_path` flags.
+We expect `output_path` to contain multiple folders that represent individual model names.
+You can thus run your evaluation on any number of tasks and models and upload all of the results as projects on Zeno.
+
+```bash
+lm_eval \
+ --model hf \
+ --model_args pretrained=EleutherAI/gpt-j-6B \
+ --tasks hellaswag \
+ --device cuda:0 \
+ --batch_size 8 \
+ --log_samples \
+ --output_path output/gpt-j-6B
+```
+
+Then, you can upload the resulting data using the `zeno_visualize` script:
+
+```bash
+python scripts/zeno_visualize.py \
+ --data_path output \
+ --project_name "Eleuther Project"
+```
+
+This will use all subfolders in `data_path` as different models and upload all tasks within these model folders to Zeno.
+If you run the eval harness on multiple tasks, the `project_name` will be used as a prefix and one project will be created per task.
+
+You can find an example of this workflow in [examples/visualize-zeno.ipynb](examples/visualize-zeno.ipynb).
+
+### Weights and Biases
+
+With the [Weights and Biases](https://wandb.ai/site) integration, you can now spend more time extracting deeper insights into your evaluation results. The integration is designed to streamline the process of logging and visualizing experiment results using the Weights & Biases (W&B) platform.
+
+The integration provide functionalities
+
+- to automatically log the evaluation results,
+- log the samples as W&B Tables for easy visualization,
+- log the `results.json` file as an artifact for version control,
+- log the `_eval_samples.json` file if the samples are logged,
+- generate a comprehensive report for analysis and visualization with all the important metric,
+- log task and cli specific configs,
+- and more out of the box like the command used to run the evaluation, GPU/CPU counts, timestamp, etc.
+
+First you'll need to install the lm_eval[wandb] package extra. Do `pip install lm_eval[wandb]`.
+
+Authenticate your machine with an your unique W&B token. Visit https://wandb.ai/authorize to get one. Do `wandb login` in your command line terminal.
+
+Run eval harness as usual with a `wandb_args` flag. Use this flag to provide arguments for initializing a wandb run ([wandb.init](https://docs.wandb.ai/ref/python/init)) as comma separated string arguments.
+
+```bash
+lm_eval \
+ --model hf \
+ --model_args pretrained=microsoft/phi-2,trust_remote_code=True \
+ --tasks hellaswag,mmlu_abstract_algebra \
+ --device cuda:0 \
+ --batch_size 8 \
+ --output_path output/phi-2 \
+ --limit 10 \
+ --wandb_args project=lm-eval-harness-integration \
+ --log_samples
+```
+
+In the stdout, you will find the link to the W&B run page as well as link to the generated report. You can find an example of this workflow in [examples/visualize-wandb.ipynb](examples/visualize-wandb.ipynb), and an example of how to integrate it beyond the CLI.
+
+## How to Contribute or Learn More?
+
+For more information on the library and how everything fits together, check out all of our [documentation pages](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs)! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.
+
+### Implementing new tasks
+
+To implement a new task in the eval harness, see [this guide](./docs/new_task_guide.md).
+
+In general, we follow this priority list for addressing concerns about prompting and other eval details:
+1. If there is widespread agreement among people who train LLMs, use the agreed upon procedure.
+2. If there is a clear and unambiguous official implementation, use that procedure.
+3. If there is widespread agreement among people who evaluate LLMs, use the agreed upon procedure.
+4. If there are multiple common implementations but not universal or widespread agreement, use our preferred option among the common implementations. As before, prioritize choosing from among the implementations found in LLM training papers.
+
+These are guidelines and not rules, and can be overruled in special circumstances.
+
+We try to prioritize agreement with the procedures used by other groups to decrease the harm when people inevitably compare runs across different papers despite our discouragement of the practice. Historically, we also prioritized the implementation from [Language Models are Few Shot Learners](https://arxiv.org/abs/2005.14165) as our original goal was specifically to compare results with that paper.
+
+### Support
+
+The best way to get support is to open an issue on this repo or join the [EleutherAI Discord server](https://discord.gg/eleutherai). The `#lm-thunderdome` channel is dedicated to developing this project and the `#release-discussion` channel is for receiving support for our releases. If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!
+
+## Optional Extras
+Extras dependencies can be installed via `pip install -e ".[NAME]"`
+
+| Name | Use |
+|---------------|---------------------------------------|
+| anthropic | For using Anthropic's models |
+| dev | For linting PRs and contributions |
+| gptq | For loading models with GPTQ |
+| hf_transfer | For speeding up HF Hub file downloads |
+| ifeval | For running the IFEval task |
+| neuronx | For running on AWS inf2 instances |
+| mamba | For loading Mamba SSM models |
+| math | For running math task answer checking |
+| multilingual | For multilingual tokenizers |
+| openai | For using OpenAI's models |
+| optimum | For running Intel OpenVINO models |
+| promptsource | For using PromptSource prompts |
+| sentencepiece | For using the sentencepiece tokenizer |
+| testing | For running library test suite |
+| vllm | For loading models with vLLM |
+| zeno | For visualizing results with Zeno |
+|---------------|---------------------------------------|
+| all | Loads all extras (not recommended) |
+
+## Cite as
+
+```
+@misc{eval-harness,
+ author = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},
+ title = {A framework for few-shot language model evaluation},
+ month = 12,
+ year = 2023,
+ publisher = {Zenodo},
+ version = {v0.4.0},
+ doi = {10.5281/zenodo.10256836},
+ url = {https://zenodo.org/records/10256836}
+}
+```
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/RECORD b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..f63b3a8c94e0849811d966337fe7b449a29fdc66
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/RECORD
@@ -0,0 +1,14 @@
+../../../bin/lm-eval,sha256=YCHseol5Es5aPiLOSeK31BN_idM_07-Sqm_iLUi8yzY,265
+../../../bin/lm_eval,sha256=YCHseol5Es5aPiLOSeK31BN_idM_07-Sqm_iLUi8yzY,265
+__editable__.lm_eval-0.4.2.pth,sha256=C4fSS19B6d-idgvdOOiL7g2yPVs0ZZ0_9S0BPtrZ1ew,85
+__editable___lm_eval_0_4_2_finder.py,sha256=-cpF5qs4x6nwbtS29Q_gV7PPIQt_IqmyNt80QicpZmg,17818
+__pycache__/__editable___lm_eval_0_4_2_finder.cpython-310.pyc,,
+lm_eval-0.4.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+lm_eval-0.4.2.dist-info/LICENSE.md,sha256=qAbkJUdiDf-8LsAzMyLIs1I7SvEeBZvhTvgapbGuAh8,1067
+lm_eval-0.4.2.dist-info/METADATA,sha256=2a20uOIwe3xQS75A3LIJO_yggs1dNcrTgoks_SlteXs,35028
+lm_eval-0.4.2.dist-info/RECORD,,
+lm_eval-0.4.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+lm_eval-0.4.2.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+lm_eval-0.4.2.dist-info/direct_url.json,sha256=HrW0qr1d7__nbpB6c0bgoXiA3UrHaXOl999Hgj6kRvs,90
+lm_eval-0.4.2.dist-info/entry_points.txt,sha256=mMr4hFXQwSHYb924AKGzP6dbbBAWcXiyBO-AfXrV72E,98
+lm_eval-0.4.2.dist-info/top_level.txt,sha256=risQwf3pU3EoXaYL_bKYFzW6EtBOvJxJotKdDz605Dk,8
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/REQUESTED b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/WHEEL b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/direct_url.json b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..210b3385b3bb454c2298c3b4ee212fc1d25698b5
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {"editable": true}, "url": "file:///mnt/weka/peacock/llm_eval/lm-evaluation"}
\ No newline at end of file
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/entry_points.txt b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0cfb793897d6f65b9e7ba85d6ed10d57d52d0821
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/entry_points.txt
@@ -0,0 +1,3 @@
+[console_scripts]
+lm-eval = lm_eval.__main__:cli_evaluate
+lm_eval = lm_eval.__main__:cli_evaluate
diff --git a/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/top_level.txt b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b3d57e8da16b93f8501d104cad0591ca57995369
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/top_level.txt
@@ -0,0 +1 @@
+lm_eval
diff --git a/llmeval-env/lib/python3.10/site-packages/networkx/classes/tests/__pycache__/test_digraph_historical.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/networkx/classes/tests/__pycache__/test_digraph_historical.cpython-310.pyc
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diff --git a/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_all_random_functions.py b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_all_random_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3f3724c6af9d1eedc376f9de0ffce2c3c91ab25
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_all_random_functions.py
@@ -0,0 +1,251 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+import random
+
+import networkx as nx
+from networkx.algorithms import approximation as approx
+from networkx.algorithms import threshold
+
+progress = 0
+
+# store the random numbers after setting a global seed
+np.random.seed(42)
+np_rv = np.random.rand()
+random.seed(42)
+py_rv = random.random()
+
+
+def t(f, *args, **kwds):
+ """call one function and check if global RNG changed"""
+ global progress
+ progress += 1
+ print(progress, ",", end="")
+
+ f(*args, **kwds)
+
+ after_np_rv = np.random.rand()
+ # if np_rv != after_np_rv:
+ # print(np_rv, after_np_rv, "don't match np!")
+ assert np_rv == after_np_rv
+ np.random.seed(42)
+
+ after_py_rv = random.random()
+ # if py_rv != after_py_rv:
+ # print(py_rv, after_py_rv, "don't match py!")
+ assert py_rv == after_py_rv
+ random.seed(42)
+
+
+def run_all_random_functions(seed):
+ n = 20
+ m = 10
+ k = l = 2
+ s = v = 10
+ p = q = p1 = p2 = p_in = p_out = 0.4
+ alpha = radius = theta = 0.75
+ sizes = (20, 20, 10)
+ colors = [1, 2, 3]
+ G = nx.barbell_graph(12, 20)
+ H = nx.cycle_graph(3)
+ H.add_weighted_edges_from((u, v, 0.2) for u, v in H.edges)
+ deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1]
+ in_degree_sequence = w = sequence = aseq = bseq = deg_sequence
+
+ # print("starting...")
+ t(nx.maximal_independent_set, G, seed=seed)
+ t(nx.rich_club_coefficient, G, seed=seed, normalized=False)
+ t(nx.random_reference, G, seed=seed)
+ t(nx.lattice_reference, G, seed=seed)
+ t(nx.sigma, G, 1, 2, seed=seed)
+ t(nx.omega, G, 1, 2, seed=seed)
+ # print("out of smallworld.py")
+ t(nx.double_edge_swap, G, seed=seed)
+ # print("starting connected_double_edge_swap")
+ t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed)
+ # print("ending connected_double_edge_swap")
+ t(nx.random_layout, G, seed=seed)
+ t(nx.fruchterman_reingold_layout, G, seed=seed)
+ t(nx.algebraic_connectivity, G, seed=seed)
+ t(nx.fiedler_vector, G, seed=seed)
+ t(nx.spectral_ordering, G, seed=seed)
+ # print('starting average_clustering')
+ t(approx.average_clustering, G, seed=seed)
+ t(approx.simulated_annealing_tsp, H, "greedy", source=1, seed=seed)
+ t(approx.threshold_accepting_tsp, H, "greedy", source=1, seed=seed)
+ t(
+ approx.traveling_salesman_problem,
+ H,
+ method=lambda G, weight: approx.simulated_annealing_tsp(
+ G, "greedy", weight, seed=seed
+ ),
+ )
+ t(
+ approx.traveling_salesman_problem,
+ H,
+ method=lambda G, weight: approx.threshold_accepting_tsp(
+ G, "greedy", weight, seed=seed
+ ),
+ )
+ t(nx.betweenness_centrality, G, seed=seed)
+ t(nx.edge_betweenness_centrality, G, seed=seed)
+ t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed)
+ # print("kernighan")
+ t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed)
+ # nx.algorithms.community.asyn_lpa_communities(G, seed=seed)
+ t(nx.algorithms.tree.greedy_branching, G, seed=seed)
+ t(nx.algorithms.tree.Edmonds, G, seed=seed)
+ # print('done with graph argument functions')
+
+ t(nx.spectral_graph_forge, G, alpha, seed=seed)
+ t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed)
+ t(
+ nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation,
+ G,
+ k,
+ seed=seed,
+ )
+ t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed)
+
+ cs = ["d", "i", "i", "d", "d", "i"]
+ t(threshold.swap_d, cs, seed=seed)
+ t(nx.configuration_model, deg_sequence, seed=seed)
+ t(
+ nx.directed_configuration_model,
+ in_degree_sequence,
+ in_degree_sequence,
+ seed=seed,
+ )
+ t(nx.expected_degree_graph, w, seed=seed)
+ t(nx.random_degree_sequence_graph, sequence, seed=seed)
+ joint_degrees = {
+ 1: {4: 1},
+ 2: {2: 2, 3: 2, 4: 2},
+ 3: {2: 2, 4: 1},
+ 4: {1: 1, 2: 2, 3: 1},
+ }
+ t(nx.joint_degree_graph, joint_degrees, seed=seed)
+ joint_degree_sequence = [
+ (1, 0),
+ (1, 0),
+ (1, 0),
+ (2, 0),
+ (1, 0),
+ (2, 1),
+ (0, 1),
+ (0, 1),
+ ]
+ t(nx.random_clustered_graph, joint_degree_sequence, seed=seed)
+ constructor = [(3, 3, 0.5), (10, 10, 0.7)]
+ t(nx.random_shell_graph, constructor, seed=seed)
+ t(nx.random_triad, G.to_directed(), seed=seed)
+ mapping = {1: 0.4, 2: 0.3, 3: 0.3}
+ t(nx.utils.random_weighted_sample, mapping, k, seed=seed)
+ t(nx.utils.weighted_choice, mapping, seed=seed)
+ t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed)
+ t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed)
+
+ def kernel_integral(u, w, z):
+ return z - w
+
+ t(nx.random_kernel_graph, n, kernel_integral, seed=seed)
+
+ sizes = [75, 75, 300]
+ probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+ t(nx.stochastic_block_model, sizes, probs, seed=seed)
+ t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed)
+
+ # print("starting generator functions")
+ t(threshold.random_threshold_sequence, n, p, seed=seed)
+ t(nx.tournament.random_tournament, n, seed=seed)
+ t(nx.relaxed_caveman_graph, l, k, p, seed=seed)
+ t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed)
+ t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed)
+ t(nx.gn_graph, n, seed=seed)
+ t(nx.gnr_graph, n, p, seed=seed)
+ t(nx.gnc_graph, n, seed=seed)
+ t(nx.scale_free_graph, n, seed=seed)
+ t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed)
+ t(nx.random_k_out_graph, n, k, alpha, seed=seed)
+ N = 1000
+ t(nx.partial_duplication_graph, N, n, p, q, seed=seed)
+ t(nx.duplication_divergence_graph, n, p, seed=seed)
+ t(nx.random_geometric_graph, n, radius, seed=seed)
+ t(nx.soft_random_geometric_graph, n, radius, seed=seed)
+ t(nx.geographical_threshold_graph, n, theta, seed=seed)
+ t(nx.waxman_graph, n, seed=seed)
+ t(nx.navigable_small_world_graph, n, seed=seed)
+ t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed)
+ t(nx.uniform_random_intersection_graph, n, m, p, seed=seed)
+ t(nx.k_random_intersection_graph, n, m, k, seed=seed)
+
+ t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed)
+ t(nx.fast_gnp_random_graph, n, p, seed=seed)
+ t(nx.gnp_random_graph, n, p, seed=seed)
+ t(nx.dense_gnm_random_graph, n, m, seed=seed)
+ t(nx.gnm_random_graph, n, m, seed=seed)
+ t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed)
+ t(nx.watts_strogatz_graph, n, k, p, seed=seed)
+ t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed)
+ t(nx.random_regular_graph, 3, n, seed=seed)
+ t(nx.barabasi_albert_graph, n, m, seed=seed)
+ t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed)
+ t(nx.powerlaw_cluster_graph, n, m, p, seed=seed)
+ t(nx.random_lobster, n, p1, p2, seed=seed)
+ t(nx.random_powerlaw_tree, n, seed=seed, tries=5000)
+ t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000)
+ t(nx.random_tree, n, seed=seed)
+ t(nx.utils.powerlaw_sequence, n, seed=seed)
+ t(nx.utils.zipf_rv, 2.3, seed=seed)
+ cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0]
+ t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed)
+ t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed)
+ t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed)
+ LFR = nx.generators.LFR_benchmark_graph
+ t(
+ LFR,
+ 25,
+ 3,
+ 1.5,
+ 0.1,
+ average_degree=3,
+ min_community=10,
+ seed=seed,
+ max_community=20,
+ )
+ t(nx.random_internet_as_graph, n, seed=seed)
+ # print("done")
+
+
+# choose to test an integer seed, or whether a single RNG can be everywhere
+# np_rng = np.random.RandomState(14)
+# seed = np_rng
+# seed = 14
+
+
+@pytest.mark.slow
+# print("NetworkX Version:", nx.__version__)
+def test_rng_interface():
+ global progress
+
+ # try different kinds of seeds
+ for seed in [14, np.random.RandomState(14)]:
+ np.random.seed(42)
+ random.seed(42)
+ run_all_random_functions(seed)
+ progress = 0
+
+ # check that both global RNGs are unaffected
+ after_np_rv = np.random.rand()
+ # if np_rv != after_np_rv:
+ # print(np_rv, after_np_rv, "don't match np!")
+ assert np_rv == after_np_rv
+ after_py_rv = random.random()
+ # if py_rv != after_py_rv:
+ # print(py_rv, after_py_rv, "don't match py!")
+ assert py_rv == after_py_rv
+
+
+# print("\nDone testing seed:", seed)
+
+# test_rng_interface()
diff --git a/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert.py b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..44bed9438945a39bb5eb85477301f58cfcd70cf0
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert.py
@@ -0,0 +1,321 @@
+import pytest
+
+import networkx as nx
+from networkx.convert import (
+ from_dict_of_dicts,
+ from_dict_of_lists,
+ to_dict_of_dicts,
+ to_dict_of_lists,
+ to_networkx_graph,
+)
+from networkx.generators.classic import barbell_graph, cycle_graph
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class TestConvert:
+ def edgelists_equal(self, e1, e2):
+ return sorted(sorted(e) for e in e1) == sorted(sorted(e) for e in e2)
+
+ def test_simple_graphs(self):
+ for dest, source in [
+ (to_dict_of_dicts, from_dict_of_dicts),
+ (to_dict_of_lists, from_dict_of_lists),
+ ]:
+ G = barbell_graph(10, 3)
+ G.graph = {}
+ dod = dest(G)
+
+ # Dict of [dicts, lists]
+ GG = source(dod)
+ assert graphs_equal(G, GG)
+ GW = to_networkx_graph(dod)
+ assert graphs_equal(G, GW)
+ GI = nx.Graph(dod)
+ assert graphs_equal(G, GI)
+
+ # With nodelist keyword
+ P4 = nx.path_graph(4)
+ P3 = nx.path_graph(3)
+ P4.graph = {}
+ P3.graph = {}
+ dod = dest(P4, nodelist=[0, 1, 2])
+ Gdod = nx.Graph(dod)
+ assert graphs_equal(Gdod, P3)
+
+ def test_exceptions(self):
+ # NX graph
+ class G:
+ adj = None
+
+ pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+ # pygraphviz agraph
+ class G:
+ is_strict = None
+
+ pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+ # Dict of [dicts, lists]
+ G = {"a": 0}
+ pytest.raises(TypeError, to_networkx_graph, G)
+
+ # list or generator of edges
+ class G:
+ next = None
+
+ pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+ # no match
+ pytest.raises(nx.NetworkXError, to_networkx_graph, "a")
+
+ def test_digraphs(self):
+ for dest, source in [
+ (to_dict_of_dicts, from_dict_of_dicts),
+ (to_dict_of_lists, from_dict_of_lists),
+ ]:
+ G = cycle_graph(10)
+
+ # Dict of [dicts, lists]
+ dod = dest(G)
+ GG = source(dod)
+ assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+ GW = to_networkx_graph(dod)
+ assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+ GI = nx.Graph(dod)
+ assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+ G = cycle_graph(10, create_using=nx.DiGraph)
+ dod = dest(G)
+ GG = source(dod, create_using=nx.DiGraph)
+ assert sorted(G.nodes()) == sorted(GG.nodes())
+ assert sorted(G.edges()) == sorted(GG.edges())
+ GW = to_networkx_graph(dod, create_using=nx.DiGraph)
+ assert sorted(G.nodes()) == sorted(GW.nodes())
+ assert sorted(G.edges()) == sorted(GW.edges())
+ GI = nx.DiGraph(dod)
+ assert sorted(G.nodes()) == sorted(GI.nodes())
+ assert sorted(G.edges()) == sorted(GI.edges())
+
+ def test_graph(self):
+ g = nx.cycle_graph(10)
+ G = nx.Graph()
+ G.add_nodes_from(g)
+ G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
+
+ # Dict of dicts
+ dod = to_dict_of_dicts(G)
+ GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+ assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+ GW = to_networkx_graph(dod, create_using=nx.Graph)
+ assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+ GI = nx.Graph(dod)
+ assert sorted(G.nodes()) == sorted(GI.nodes())
+ assert sorted(G.edges()) == sorted(GI.edges())
+
+ # Dict of lists
+ dol = to_dict_of_lists(G)
+ GG = from_dict_of_lists(dol, create_using=nx.Graph)
+ # dict of lists throws away edge data so set it to none
+ enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
+ assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+ assert edges_equal(enone, sorted(GG.edges(data=True)))
+ GW = to_networkx_graph(dol, create_using=nx.Graph)
+ assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+ assert edges_equal(enone, sorted(GW.edges(data=True)))
+ GI = nx.Graph(dol)
+ assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+ assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+ def test_with_multiedges_self_loops(self):
+ G = cycle_graph(10)
+ XG = nx.Graph()
+ XG.add_nodes_from(G)
+ XG.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+ XGM = nx.MultiGraph()
+ XGM.add_nodes_from(G)
+ XGM.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+ XGM.add_edge(0, 1, weight=2) # multiedge
+ XGS = nx.Graph()
+ XGS.add_nodes_from(G)
+ XGS.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+ XGS.add_edge(0, 0, weight=100) # self loop
+
+ # Dict of dicts
+ # with self loops, OK
+ dod = to_dict_of_dicts(XGS)
+ GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+ assert nodes_equal(XGS.nodes(), GG.nodes())
+ assert edges_equal(XGS.edges(), GG.edges())
+ GW = to_networkx_graph(dod, create_using=nx.Graph)
+ assert nodes_equal(XGS.nodes(), GW.nodes())
+ assert edges_equal(XGS.edges(), GW.edges())
+ GI = nx.Graph(dod)
+ assert nodes_equal(XGS.nodes(), GI.nodes())
+ assert edges_equal(XGS.edges(), GI.edges())
+
+ # Dict of lists
+ # with self loops, OK
+ dol = to_dict_of_lists(XGS)
+ GG = from_dict_of_lists(dol, create_using=nx.Graph)
+ # dict of lists throws away edge data so set it to none
+ enone = [(u, v, {}) for (u, v, d) in XGS.edges(data=True)]
+ assert nodes_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
+ assert edges_equal(enone, sorted(GG.edges(data=True)))
+ GW = to_networkx_graph(dol, create_using=nx.Graph)
+ assert nodes_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
+ assert edges_equal(enone, sorted(GW.edges(data=True)))
+ GI = nx.Graph(dol)
+ assert nodes_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
+ assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+ # Dict of dicts
+ # with multiedges, OK
+ dod = to_dict_of_dicts(XGM)
+ GG = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=True)
+ assert nodes_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
+ assert edges_equal(sorted(XGM.edges()), sorted(GG.edges()))
+ GW = to_networkx_graph(dod, create_using=nx.MultiGraph, multigraph_input=True)
+ assert nodes_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
+ assert edges_equal(sorted(XGM.edges()), sorted(GW.edges()))
+ GI = nx.MultiGraph(dod)
+ assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+ assert sorted(XGM.edges()) == sorted(GI.edges())
+ GE = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=False)
+ assert nodes_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
+ assert sorted(XGM.edges()) != sorted(GE.edges())
+ GI = nx.MultiGraph(XGM)
+ assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+ assert edges_equal(sorted(XGM.edges()), sorted(GI.edges()))
+ GM = nx.MultiGraph(G)
+ assert nodes_equal(sorted(GM.nodes()), sorted(G.nodes()))
+ assert edges_equal(sorted(GM.edges()), sorted(G.edges()))
+
+ # Dict of lists
+ # with multiedges, OK, but better write as DiGraph else you'll
+ # get double edges
+ dol = to_dict_of_lists(G)
+ GG = from_dict_of_lists(dol, create_using=nx.MultiGraph)
+ assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+ GW = to_networkx_graph(dol, create_using=nx.MultiGraph)
+ assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+ GI = nx.MultiGraph(dol)
+ assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+ def test_edgelists(self):
+ P = nx.path_graph(4)
+ e = [(0, 1), (1, 2), (2, 3)]
+ G = nx.Graph(e)
+ assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+ assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+ e = [(0, 1, {}), (1, 2, {}), (2, 3, {})]
+ G = nx.Graph(e)
+ assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+ assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+ e = ((n, n + 1) for n in range(3))
+ G = nx.Graph(e)
+ assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+ assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+ assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+ def test_directed_to_undirected(self):
+ edges1 = [(0, 1), (1, 2), (2, 0)]
+ edges2 = [(0, 1), (1, 2), (0, 2)]
+ assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges1)).edges(), edges1)
+ assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges2)).edges(), edges1)
+ assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges1)).edges(), edges1)
+ assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges2)).edges(), edges1)
+
+ assert self.edgelists_equal(
+ nx.MultiGraph(nx.MultiDiGraph(edges1)).edges(), edges1
+ )
+ assert self.edgelists_equal(
+ nx.MultiGraph(nx.MultiDiGraph(edges2)).edges(), edges1
+ )
+
+ assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges1)).edges(), edges1)
+ assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges2)).edges(), edges1)
+
+ def test_attribute_dict_integrity(self):
+ # we must not replace dict-like graph data structures with dicts
+ G = nx.Graph()
+ G.add_nodes_from("abc")
+ H = to_networkx_graph(G, create_using=nx.Graph)
+ assert list(H.nodes) == list(G.nodes)
+ H = nx.DiGraph(G)
+ assert list(H.nodes) == list(G.nodes)
+
+ def test_to_edgelist(self):
+ G = nx.Graph([(1, 1)])
+ elist = nx.to_edgelist(G, nodelist=list(G))
+ assert edges_equal(G.edges(data=True), elist)
+
+ def test_custom_node_attr_dict_safekeeping(self):
+ class custom_dict(dict):
+ pass
+
+ class Custom(nx.Graph):
+ node_attr_dict_factory = custom_dict
+
+ g = nx.Graph()
+ g.add_node(1, weight=1)
+
+ h = Custom(g)
+ assert isinstance(g._node[1], dict)
+ assert isinstance(h._node[1], custom_dict)
+
+ # this raise exception
+ # h._node.update((n, dd.copy()) for n, dd in g.nodes.items())
+ # assert isinstance(h._node[1], custom_dict)
+
+
+@pytest.mark.parametrize(
+ "edgelist",
+ (
+ # Graph with no edge data
+ [(0, 1), (1, 2)],
+ # Graph with edge data
+ [(0, 1, {"weight": 1.0}), (1, 2, {"weight": 2.0})],
+ ),
+)
+def test_to_dict_of_dicts_with_edgedata_param(edgelist):
+ G = nx.Graph()
+ G.add_edges_from(edgelist)
+ # Innermost dict value == edge_data when edge_data != None.
+ # In the case when G has edge data, it is overwritten
+ expected = {0: {1: 10}, 1: {0: 10, 2: 10}, 2: {1: 10}}
+ assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_and_nodelist():
+ G = nx.path_graph(5)
+ nodelist = [2, 3, 4]
+ expected = {2: {3: 10}, 3: {2: 10, 4: 10}, 4: {3: 10}}
+ assert nx.to_dict_of_dicts(G, nodelist=nodelist, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_multigraph():
+ """Multi edge data overwritten when edge_data != None"""
+ G = nx.MultiGraph()
+ G.add_edge(0, 1, key="a")
+ G.add_edge(0, 1, key="b")
+ # Multi edge data lost when edge_data is not None
+ expected = {0: {1: 10}, 1: {0: 10}}
+ assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_networkx_graph_non_edgelist():
+ invalid_edgelist = [1, 2, 3]
+ with pytest.raises(nx.NetworkXError, match="Input is not a valid edge list"):
+ nx.to_networkx_graph(invalid_edgelist)
diff --git a/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert_pandas.py b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert_pandas.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca8d08c705f142bb24232aaf63f9b3397375409e
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_convert_pandas.py
@@ -0,0 +1,320 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+np = pytest.importorskip("numpy")
+pd = pytest.importorskip("pandas")
+
+
+class TestConvertPandas:
+ def setup_method(self):
+ self.rng = np.random.RandomState(seed=5)
+ ints = self.rng.randint(1, 11, size=(3, 2))
+ a = ["A", "B", "C"]
+ b = ["D", "A", "E"]
+ df = pd.DataFrame(ints, columns=["weight", "cost"])
+ df[0] = a # Column label 0 (int)
+ df["b"] = b # Column label 'b' (str)
+ self.df = df
+
+ mdf = pd.DataFrame([[4, 16, "A", "D"]], columns=["weight", "cost", 0, "b"])
+ self.mdf = pd.concat([df, mdf])
+
+ def test_exceptions(self):
+ G = pd.DataFrame(["a"]) # adj
+ pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+ G = pd.DataFrame(["a", 0.0]) # elist
+ pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+ df = pd.DataFrame([[1, 1], [1, 0]], dtype=int, index=[1, 2], columns=["a", "b"])
+ pytest.raises(nx.NetworkXError, nx.from_pandas_adjacency, df)
+
+ def test_from_edgelist_all_attr(self):
+ Gtrue = nx.Graph(
+ [
+ ("E", "C", {"cost": 9, "weight": 10}),
+ ("B", "A", {"cost": 1, "weight": 7}),
+ ("A", "D", {"cost": 7, "weight": 4}),
+ ]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", True)
+ assert graphs_equal(G, Gtrue)
+ # MultiGraph
+ MGtrue = nx.MultiGraph(Gtrue)
+ MGtrue.add_edge("A", "D", cost=16, weight=4)
+ MG = nx.from_pandas_edgelist(self.mdf, 0, "b", True, nx.MultiGraph())
+ assert graphs_equal(MG, MGtrue)
+
+ def test_from_edgelist_multi_attr(self):
+ Gtrue = nx.Graph(
+ [
+ ("E", "C", {"cost": 9, "weight": 10}),
+ ("B", "A", {"cost": 1, "weight": 7}),
+ ("A", "D", {"cost": 7, "weight": 4}),
+ ]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", ["weight", "cost"])
+ assert graphs_equal(G, Gtrue)
+
+ def test_from_edgelist_multi_attr_incl_target(self):
+ Gtrue = nx.Graph(
+ [
+ ("E", "C", {0: "C", "b": "E", "weight": 10}),
+ ("B", "A", {0: "B", "b": "A", "weight": 7}),
+ ("A", "D", {0: "A", "b": "D", "weight": 4}),
+ ]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", [0, "b", "weight"])
+ assert graphs_equal(G, Gtrue)
+
+ def test_from_edgelist_multidigraph_and_edge_attr(self):
+ # example from issue #2374
+ edges = [
+ ("X1", "X4", {"Co": "zA", "Mi": 0, "St": "X1"}),
+ ("X1", "X4", {"Co": "zB", "Mi": 54, "St": "X2"}),
+ ("X1", "X4", {"Co": "zB", "Mi": 49, "St": "X3"}),
+ ("X1", "X4", {"Co": "zB", "Mi": 44, "St": "X4"}),
+ ("Y1", "Y3", {"Co": "zC", "Mi": 0, "St": "Y1"}),
+ ("Y1", "Y3", {"Co": "zC", "Mi": 34, "St": "Y2"}),
+ ("Y1", "Y3", {"Co": "zC", "Mi": 29, "St": "X2"}),
+ ("Y1", "Y3", {"Co": "zC", "Mi": 24, "St": "Y3"}),
+ ("Z1", "Z3", {"Co": "zD", "Mi": 0, "St": "Z1"}),
+ ("Z1", "Z3", {"Co": "zD", "Mi": 14, "St": "X3"}),
+ ]
+ Gtrue = nx.MultiDiGraph(edges)
+ data = {
+ "O": ["X1", "X1", "X1", "X1", "Y1", "Y1", "Y1", "Y1", "Z1", "Z1"],
+ "D": ["X4", "X4", "X4", "X4", "Y3", "Y3", "Y3", "Y3", "Z3", "Z3"],
+ "St": ["X1", "X2", "X3", "X4", "Y1", "Y2", "X2", "Y3", "Z1", "X3"],
+ "Co": ["zA", "zB", "zB", "zB", "zC", "zC", "zC", "zC", "zD", "zD"],
+ "Mi": [0, 54, 49, 44, 0, 34, 29, 24, 0, 14],
+ }
+ df = pd.DataFrame.from_dict(data)
+ G1 = nx.from_pandas_edgelist(
+ df, source="O", target="D", edge_attr=True, create_using=nx.MultiDiGraph
+ )
+ G2 = nx.from_pandas_edgelist(
+ df,
+ source="O",
+ target="D",
+ edge_attr=["St", "Co", "Mi"],
+ create_using=nx.MultiDiGraph,
+ )
+ assert graphs_equal(G1, Gtrue)
+ assert graphs_equal(G2, Gtrue)
+
+ def test_from_edgelist_one_attr(self):
+ Gtrue = nx.Graph(
+ [
+ ("E", "C", {"weight": 10}),
+ ("B", "A", {"weight": 7}),
+ ("A", "D", {"weight": 4}),
+ ]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", "weight")
+ assert graphs_equal(G, Gtrue)
+
+ def test_from_edgelist_int_attr_name(self):
+ # note: this also tests that edge_attr can be `source`
+ Gtrue = nx.Graph(
+ [("E", "C", {0: "C"}), ("B", "A", {0: "B"}), ("A", "D", {0: "A"})]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", 0)
+ assert graphs_equal(G, Gtrue)
+
+ def test_from_edgelist_invalid_attr(self):
+ pytest.raises(
+ nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", "misspell"
+ )
+ pytest.raises(nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", 1)
+ # see Issue #3562
+ edgeframe = pd.DataFrame([[0, 1], [1, 2], [2, 0]], columns=["s", "t"])
+ pytest.raises(
+ nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", True
+ )
+ pytest.raises(
+ nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", "weight"
+ )
+ pytest.raises(
+ nx.NetworkXError,
+ nx.from_pandas_edgelist,
+ edgeframe,
+ "s",
+ "t",
+ ["weight", "size"],
+ )
+
+ def test_from_edgelist_no_attr(self):
+ Gtrue = nx.Graph([("E", "C", {}), ("B", "A", {}), ("A", "D", {})])
+ G = nx.from_pandas_edgelist(self.df, 0, "b")
+ assert graphs_equal(G, Gtrue)
+
+ def test_from_edgelist(self):
+ # Pandas DataFrame
+ G = nx.cycle_graph(10)
+ G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+
+ edgelist = nx.to_edgelist(G)
+ source = [s for s, t, d in edgelist]
+ target = [t for s, t, d in edgelist]
+ weight = [d["weight"] for s, t, d in edgelist]
+ edges = pd.DataFrame({"source": source, "target": target, "weight": weight})
+
+ GG = nx.from_pandas_edgelist(edges, edge_attr="weight")
+ assert nodes_equal(G.nodes(), GG.nodes())
+ assert edges_equal(G.edges(), GG.edges())
+ GW = nx.to_networkx_graph(edges, create_using=nx.Graph)
+ assert nodes_equal(G.nodes(), GW.nodes())
+ assert edges_equal(G.edges(), GW.edges())
+
+ def test_to_edgelist_default_source_or_target_col_exists(self):
+ G = nx.path_graph(10)
+ G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+ nx.set_edge_attributes(G, 0, name="source")
+ pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+ # drop source column to test an exception raised for the target column
+ for u, v, d in G.edges(data=True):
+ d.pop("source", None)
+
+ nx.set_edge_attributes(G, 0, name="target")
+ pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+ def test_to_edgelist_custom_source_or_target_col_exists(self):
+ G = nx.path_graph(10)
+ G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+ nx.set_edge_attributes(G, 0, name="source_col_name")
+ pytest.raises(
+ nx.NetworkXError, nx.to_pandas_edgelist, G, source="source_col_name"
+ )
+
+ # drop source column to test an exception raised for the target column
+ for u, v, d in G.edges(data=True):
+ d.pop("source_col_name", None)
+
+ nx.set_edge_attributes(G, 0, name="target_col_name")
+ pytest.raises(
+ nx.NetworkXError, nx.to_pandas_edgelist, G, target="target_col_name"
+ )
+
+ def test_to_edgelist_edge_key_col_exists(self):
+ G = nx.path_graph(10, create_using=nx.MultiGraph)
+ G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges()))
+ nx.set_edge_attributes(G, 0, name="edge_key_name")
+ pytest.raises(
+ nx.NetworkXError, nx.to_pandas_edgelist, G, edge_key="edge_key_name"
+ )
+
+ def test_from_adjacency(self):
+ nodelist = [1, 2]
+ dftrue = pd.DataFrame(
+ [[1, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+ )
+ G = nx.Graph([(1, 1), (1, 2)])
+ df = nx.to_pandas_adjacency(G, dtype=int)
+ pd.testing.assert_frame_equal(df, dftrue)
+
+ @pytest.mark.parametrize("graph", [nx.Graph, nx.MultiGraph])
+ def test_roundtrip(self, graph):
+ # edgelist
+ Gtrue = graph([(1, 1), (1, 2)])
+ df = nx.to_pandas_edgelist(Gtrue)
+ G = nx.from_pandas_edgelist(df, create_using=graph)
+ assert graphs_equal(Gtrue, G)
+ # adjacency
+ adj = {1: {1: {"weight": 1}, 2: {"weight": 1}}, 2: {1: {"weight": 1}}}
+ Gtrue = graph(adj)
+ df = nx.to_pandas_adjacency(Gtrue, dtype=int)
+ G = nx.from_pandas_adjacency(df, create_using=graph)
+ assert graphs_equal(Gtrue, G)
+
+ def test_from_adjacency_named(self):
+ # example from issue #3105
+ data = {
+ "A": {"A": 0, "B": 0, "C": 0},
+ "B": {"A": 1, "B": 0, "C": 0},
+ "C": {"A": 0, "B": 1, "C": 0},
+ }
+ dftrue = pd.DataFrame(data, dtype=np.intp)
+ df = dftrue[["A", "C", "B"]]
+ G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph())
+ df = nx.to_pandas_adjacency(G, dtype=np.intp)
+ pd.testing.assert_frame_equal(df, dftrue)
+
+ def test_edgekey_with_multigraph(self):
+ df = pd.DataFrame(
+ {
+ "source": {"A": "N1", "B": "N2", "C": "N1", "D": "N1"},
+ "target": {"A": "N2", "B": "N3", "C": "N1", "D": "N2"},
+ "attr1": {"A": "F1", "B": "F2", "C": "F3", "D": "F4"},
+ "attr2": {"A": 1, "B": 0, "C": 0, "D": 0},
+ "attr3": {"A": 0, "B": 1, "C": 0, "D": 1},
+ }
+ )
+ Gtrue = nx.MultiGraph(
+ [
+ ("N1", "N2", "F1", {"attr2": 1, "attr3": 0}),
+ ("N2", "N3", "F2", {"attr2": 0, "attr3": 1}),
+ ("N1", "N1", "F3", {"attr2": 0, "attr3": 0}),
+ ("N1", "N2", "F4", {"attr2": 0, "attr3": 1}),
+ ]
+ )
+ # example from issue #4065
+ G = nx.from_pandas_edgelist(
+ df,
+ source="source",
+ target="target",
+ edge_attr=["attr2", "attr3"],
+ edge_key="attr1",
+ create_using=nx.MultiGraph(),
+ )
+ assert graphs_equal(G, Gtrue)
+
+ df_roundtrip = nx.to_pandas_edgelist(G, edge_key="attr1")
+ df_roundtrip = df_roundtrip.sort_values("attr1")
+ df_roundtrip.index = ["A", "B", "C", "D"]
+ pd.testing.assert_frame_equal(
+ df, df_roundtrip[["source", "target", "attr1", "attr2", "attr3"]]
+ )
+
+ def test_edgekey_with_normal_graph_no_action(self):
+ Gtrue = nx.Graph(
+ [
+ ("E", "C", {"cost": 9, "weight": 10}),
+ ("B", "A", {"cost": 1, "weight": 7}),
+ ("A", "D", {"cost": 7, "weight": 4}),
+ ]
+ )
+ G = nx.from_pandas_edgelist(self.df, 0, "b", True, edge_key="weight")
+ assert graphs_equal(G, Gtrue)
+
+ def test_nonexisting_edgekey_raises(self):
+ with pytest.raises(nx.exception.NetworkXError):
+ nx.from_pandas_edgelist(
+ self.df,
+ source="source",
+ target="target",
+ edge_key="Not_real",
+ edge_attr=True,
+ create_using=nx.MultiGraph(),
+ )
+
+
+def test_to_pandas_adjacency_with_nodelist():
+ G = nx.complete_graph(5)
+ nodelist = [1, 4]
+ expected = pd.DataFrame(
+ [[0, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+ )
+ pd.testing.assert_frame_equal(
+ expected, nx.to_pandas_adjacency(G, nodelist, dtype=int)
+ )
+
+
+def test_to_pandas_edgelist_with_nodelist():
+ G = nx.Graph()
+ G.add_edges_from([(0, 1), (1, 2), (1, 3)], weight=2.0)
+ G.add_edge(0, 5, weight=100)
+ df = nx.to_pandas_edgelist(G, nodelist=[1, 2])
+ assert 0 not in df["source"].to_numpy()
+ assert 100 not in df["weight"].to_numpy()
diff --git a/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_exceptions.py b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf59983cb8d12a119f5744ebc8b11e7cb9075366
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/networkx/tests/test_exceptions.py
@@ -0,0 +1,40 @@
+import pytest
+
+import networkx as nx
+
+# smoke tests for exceptions
+
+
+def test_raises_networkxexception():
+ with pytest.raises(nx.NetworkXException):
+ raise nx.NetworkXException
+
+
+def test_raises_networkxerr():
+ with pytest.raises(nx.NetworkXError):
+ raise nx.NetworkXError
+
+
+def test_raises_networkx_pointless_concept():
+ with pytest.raises(nx.NetworkXPointlessConcept):
+ raise nx.NetworkXPointlessConcept
+
+
+def test_raises_networkxalgorithmerr():
+ with pytest.raises(nx.NetworkXAlgorithmError):
+ raise nx.NetworkXAlgorithmError
+
+
+def test_raises_networkx_unfeasible():
+ with pytest.raises(nx.NetworkXUnfeasible):
+ raise nx.NetworkXUnfeasible
+
+
+def test_raises_networkx_no_path():
+ with pytest.raises(nx.NetworkXNoPath):
+ raise nx.NetworkXNoPath
+
+
+def test_raises_networkx_unbounded():
+ with pytest.raises(nx.NetworkXUnbounded):
+ raise nx.NetworkXUnbounded
diff --git a/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/INSTALLER b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/LICENSE b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
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diff --git a/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/METADATA b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..fff5008e36be4ff7d4994d6f84c62e89c8b1ac8e
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/METADATA
@@ -0,0 +1,212 @@
+Metadata-Version: 2.1
+Name: peft
+Version: 0.10.0
+Summary: Parameter-Efficient Fine-Tuning (PEFT)
+Home-page: https://github.com/huggingface/peft
+Author: The HuggingFace team
+Author-email: sourab@huggingface.co
+License: Apache
+Keywords: deep learning
+Classifier: Development Status :: 5 - Production/Stable
+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 :: 3.8
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.8.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy (>=1.17)
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+Requires-Dist: torch (>=1.13.0)
+Requires-Dist: transformers
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+Requires-Dist: accelerate (>=0.21.0)
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+Requires-Dist: huggingface-hub (>=0.17.0)
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+
+
+
+
🤗 PEFT
+
+
State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods
+
+
+Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters. This significantly decreases the computational and storage costs. Recent state-of-the-art PEFT techniques achieve performance comparable to fully fine-tuned models.
+
+PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models.
+
+> [!TIP]
+> Visit the [PEFT](https://huggingface.co/PEFT) organization to read about the PEFT methods implemented in the library and to see notebooks demonstrating how to apply these methods to a variety of downstream tasks. Click the "Watch repos" button on the organization page to be notified of newly implemented methods and notebooks!
+
+Check the PEFT Adapters API Reference section for a list of supported PEFT methods, and read the [Adapters](https://huggingface.co/docs/peft/en/conceptual_guides/adapter), [Soft prompts](https://huggingface.co/docs/peft/en/conceptual_guides/prompting), and [IA3](https://huggingface.co/docs/peft/en/conceptual_guides/ia3) conceptual guides to learn more about how these methods work.
+
+## Quickstart
+
+Install PEFT from pip:
+
+```bash
+pip install peft
+```
+
+Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with `get_peft_model`. For the bigscience/mt0-large model, you're only training 0.19% of the parameters!
+
+```python
+from transformers import AutoModelForSeq2SeqLM
+from peft import get_peft_config, get_peft_model, LoraConfig, TaskType
+model_name_or_path = "bigscience/mt0-large"
+tokenizer_name_or_path = "bigscience/mt0-large"
+
+peft_config = LoraConfig(
+ task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
+)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
+model = get_peft_model(model, peft_config)
+model.print_trainable_parameters()
+"trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282"
+```
+
+To load a PEFT model for inference:
+
+```py
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+import torch
+
+model = AutoPeftModelForCausalLM.from_pretrained("ybelkada/opt-350m-lora").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+model.eval()
+inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")
+
+outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+
+"Preheat the oven to 350 degrees and place the cookie dough in the center of the oven. In a large bowl, combine the flour, baking powder, baking soda, salt, and cinnamon. In a separate bowl, combine the egg yolks, sugar, and vanilla."
+```
+
+## Why you should use PEFT
+
+There are many benefits of using PEFT but the main one is the huge savings in compute and storage, making PEFT applicable to many different use cases.
+
+### High performance on consumer hardware
+
+Consider the memory requirements for training the following models on the [ought/raft/twitter_complaints](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) dataset with an A100 80GB GPU with more than 64GB of CPU RAM.
+
+| Model | Full Finetuning | PEFT-LoRA PyTorch | PEFT-LoRA DeepSpeed with CPU Offloading |
+| --------- | ---- | ---- | ---- |
+| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU |
+| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU |
+| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU |
+
+With LoRA you can fully finetune a 12B parameter model that would've otherwise run out of memory on the 80GB GPU, and comfortably fit and train a 3B parameter model. When you look at the 3B parameter model's performance, it is comparable to a fully finetuned model at a fraction of the GPU memory.
+
+| Submission Name | Accuracy |
+| --------- | ---- |
+| Human baseline (crowdsourced) | 0.897 |
+| Flan-T5 | 0.892 |
+| lora-t0-3b | 0.863 |
+
+> [!TIP]
+> The bigscience/T0_3B model performance isn't optimized in the table above. You can squeeze even more performance out of it by playing around with the input instruction templates, LoRA hyperparameters, and other training related hyperparameters. The final checkpoint size of this model is just 19MB compared to 11GB of the full bigscience/T0_3B model. Learn more about the advantages of finetuning with PEFT in this [blog post](https://www.philschmid.de/fine-tune-flan-t5-peft).
+
+### Quantization
+
+Quantization is another method for reducing the memory requirements of a model by representing the data in a lower precision. It can be combined with PEFT methods to make it even easier to train and load LLMs for inference.
+
+* Learn how to finetune [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) with QLoRA and the [TRL](https://huggingface.co/docs/trl/index) library on a 16GB GPU in the [Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem](https://pytorch.org/blog/finetune-llms/) blog post.
+* Learn how to finetune a [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) model for multilingual automatic speech recognition with LoRA and 8-bit quantization in this [notebook](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) (see this [notebook](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) instead for an example of streaming a dataset).
+
+### Save compute and storage
+
+PEFT can help you save storage by avoiding full finetuning of models on each of downstream task or dataset. In many cases, you're only finetuning a very small fraction of a model's parameters and each checkpoint is only a few MBs in size (instead of GBs). These smaller PEFT adapters demonstrate performance comparable to a fully finetuned model. If you have many datasets, you can save a lot of storage with a PEFT model and not have to worry about catastrophic forgetting or overfitting the backbone or base model.
+
+## PEFT integrations
+
+PEFT is widely supported across the Hugging Face ecosystem because of the massive efficiency it brings to training and inference.
+
+### Diffusers
+
+The iterative diffusion process consumes a lot of memory which can make it difficult to train. PEFT can help reduce the memory requirements and reduce the storage size of the final model checkpoint. For example, consider the memory required for training a Stable Diffusion model with LoRA on an A100 80GB GPU with more than 64GB of CPU RAM. The final model checkpoint size is only 8.8MB!
+
+| Model | Full Finetuning | PEFT-LoRA | PEFT-LoRA with Gradient Checkpointing |
+| --------- | ---- | ---- | ---- |
+| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU |
+
+> [!TIP]
+> Take a look at the [examples/lora_dreambooth/train_dreambooth.py](examples/lora_dreambooth/train_dreambooth.py) training script to try training your own Stable Diffusion model with LoRA, and play around with the [smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth) Space which is running on a T4 instance. Learn more about the PEFT integration in Diffusers in this [tutorial](https://huggingface.co/docs/peft/main/en/tutorial/peft_integrations#diffusers).
+
+### Accelerate
+
+[Accelerate](https://huggingface.co/docs/accelerate/index) is a library for distributed training and inference on various training setups and hardware (GPUs, TPUs, Apple Silicon, etc.). PEFT models work with Accelerate out of the box, making it really convenient to train really large models or use them for inference on consumer hardware with limited resources.
+
+### TRL
+
+PEFT can also be applied to training LLMs with RLHF components such as the ranker and policy. Get started by reading:
+
+* [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library to learn more about the Direct Preference Optimization (DPO) method and how to apply it to a LLM.
+* [Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU](https://huggingface.co/blog/trl-peft) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library, and then try out the [gpt2-sentiment_peft.ipynb](https://github.com/huggingface/trl/blob/main/examples/notebooks/gpt2-sentiment.ipynb) notebook to optimize GPT2 to generate positive movie reviews.
+* [StackLLaMA: A hands-on guide to train LLaMA with RLHF](https://huggingface.co/blog/stackllama) with PEFT, and then try out the [stack_llama/scripts](https://github.com/huggingface/trl/tree/main/examples/research_projects/stack_llama/scripts) for supervised finetuning, reward modeling, and RL finetuning.
+
+## Model support
+
+Use this [Space](https://stevhliu-peft-methods.hf.space) or check out the [docs](https://huggingface.co/docs/peft/main/en/index) to find which models officially support a PEFT method out of the box. Even if you don't see a model listed below, you can manually configure the model config to enable PEFT for a model. Read the [New transformers architecture](https://huggingface.co/docs/peft/main/en/developer_guides/custom_models#new-transformers-architectures) guide to learn how.
+
+## Contribute
+
+If you would like to contribute to PEFT, please check out our [contribution guide](https://huggingface.co/docs/peft/developer_guides/contributing).
+
+## Citing 🤗 PEFT
+
+To use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry.
+
+```bibtex
+@Misc{peft,
+ title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},
+ author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan},
+ howpublished = {\url{https://github.com/huggingface/peft}},
+ year = {2022}
+}
+```
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diff --git a/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/WHEEL b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/top_level.txt b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc89ba063e639dfa24fd7f53340bc368adbebb7a
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/peft-0.10.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+peft
diff --git a/llmeval-env/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0 b/llmeval-env/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
new file mode 100644
index 0000000000000000000000000000000000000000..fe20f7e890dfc315e7da73607fac015cd922af72
Binary files /dev/null and b/llmeval-env/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0 differ
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/__init__.py b/llmeval-env/lib/python3.10/site-packages/tabledata/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f41c368069a1c7002ad98d98095aff52c9d1e82c
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/__init__.py
@@ -0,0 +1,24 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+from .__version__ import __author__, __copyright__, __email__, __license__, __version__
+from ._common import convert_idx_to_alphabet
+from ._constant import PatternMatch
+from ._converter import to_value_matrix
+from ._core import TableData
+from ._logger import set_log_level, set_logger
+from .error import DataError, InvalidHeaderNameError, InvalidTableNameError, NameValidationError
+
+
+__all__ = (
+ "convert_idx_to_alphabet",
+ "set_logger",
+ "to_value_matrix",
+ "PatternMatch",
+ "TableData",
+ "DataError",
+ "InvalidHeaderNameError",
+ "InvalidTableNameError",
+ "NameValidationError",
+)
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/__version__.py b/llmeval-env/lib/python3.10/site-packages/tabledata/__version__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fefee19a86f0a0eaf8f4d0e6e3c11e3af4ae6a83
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/__version__.py
@@ -0,0 +1,6 @@
+__author__ = "Tsuyoshi Hombashi"
+__copyright__ = f"Copyright 2017, {__author__}"
+__license__ = "MIT License"
+__version__ = "1.3.3"
+__maintainer__ = __author__
+__email__ = "tsuyoshi.hombashi@gmail.com"
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/_common.py b/llmeval-env/lib/python3.10/site-packages/tabledata/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..944e9474385d5ac4cace526f532564308a1fd13a
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/_common.py
@@ -0,0 +1,12 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+
+def convert_idx_to_alphabet(idx: int) -> str:
+ if idx < 26:
+ return chr(65 + idx)
+
+ div, mod = divmod(idx, 26)
+
+ return convert_idx_to_alphabet(div - 1) + convert_idx_to_alphabet(mod)
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/_constant.py b/llmeval-env/lib/python3.10/site-packages/tabledata/_constant.py
new file mode 100644
index 0000000000000000000000000000000000000000..722f1372ff8416da2a9c5733c11d8351e87c792f
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/_constant.py
@@ -0,0 +1,11 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+import enum
+
+
+@enum.unique
+class PatternMatch(enum.Enum):
+ OR = 0
+ AND = 1
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/_converter.py b/llmeval-env/lib/python3.10/site-packages/tabledata/_converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce0799f5298220aa225739c2eb4825706bf827b1
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/_converter.py
@@ -0,0 +1,35 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+from typing import Any, List, Sequence, Tuple
+
+from .error import DataError
+
+
+Row = Tuple[int, Any]
+
+
+def to_value_matrix(headers: Sequence[str], value_matrix: Sequence[Any]) -> List[Row]:
+ if not value_matrix:
+ return []
+
+ return [_to_row(headers, values, row_idx)[1] for row_idx, values in enumerate(value_matrix)]
+
+
+def _to_row(headers: Sequence[str], values: Any, row_idx: int) -> Row:
+ if headers:
+ try:
+ values = values._asdict()
+ except AttributeError:
+ pass
+
+ try:
+ return (row_idx, [values.get(header) for header in headers])
+ except (TypeError, AttributeError):
+ pass
+
+ if not isinstance(values, (tuple, list)):
+ raise DataError(f"row must be a list or tuple: actual={type(values)}")
+
+ return (row_idx, values)
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/_core.py b/llmeval-env/lib/python3.10/site-packages/tabledata/_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d16517eefdafae0ab12e555fd287242352e7968
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/_core.py
@@ -0,0 +1,510 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+import copy
+import re
+from collections import OrderedDict, namedtuple
+from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
+
+import dataproperty as dp
+import typepy
+from dataproperty import DataPropertyMatrix
+from dataproperty.typing import TypeHint
+from typepy import Nan
+
+from ._constant import PatternMatch
+from ._converter import to_value_matrix
+from ._logger import logger
+
+
+if TYPE_CHECKING:
+ import pandas
+
+
+class TableData:
+ """
+ Class to represent a table data structure.
+
+ :param table_name: Name of the table.
+ :param headers: Table header names.
+ :param rows: Data of the table.
+ """
+
+ def __init__(
+ self,
+ table_name: Optional[str],
+ headers: Sequence[str],
+ rows: Sequence,
+ dp_extractor: Optional[dp.DataPropertyExtractor] = None,
+ type_hints: Optional[Sequence[Union[str, TypeHint]]] = None,
+ max_workers: Optional[int] = None,
+ max_precision: Optional[int] = None,
+ ) -> None:
+ self.__table_name = table_name
+ self.__value_matrix: List[List[Any]] = []
+ self.__value_dp_matrix: Optional[DataPropertyMatrix] = None
+
+ if rows:
+ self.__rows = rows
+ else:
+ self.__rows = []
+
+ if dp_extractor:
+ self.__dp_extractor = copy.deepcopy(dp_extractor)
+ else:
+ self.__dp_extractor = dp.DataPropertyExtractor(max_precision=max_precision)
+
+ if type_hints:
+ self.__dp_extractor.column_type_hints = type_hints
+
+ self.__dp_extractor.strip_str_header = '"'
+
+ if max_workers:
+ self.__dp_extractor.max_workers = max_workers
+
+ if not headers:
+ self.__dp_extractor.headers = []
+ else:
+ self.__dp_extractor.headers = headers
+
+ def __repr__(self) -> str:
+ element_list = [f"table_name={self.table_name}"]
+
+ try:
+ element_list.append("headers=[{}]".format(", ".join(self.headers)))
+ except TypeError:
+ element_list.append("headers=None")
+
+ element_list.extend([f"cols={self.num_columns}", f"rows={self.num_rows}"])
+
+ return ", ".join(element_list)
+
+ def __eq__(self, other: Any) -> bool:
+ if not isinstance(other, TableData):
+ return False
+
+ return self.equals(other, cmp_by_dp=False)
+
+ def __ne__(self, other: Any) -> bool:
+ if not isinstance(other, TableData):
+ return True
+
+ return not self.equals(other, cmp_by_dp=False)
+
+ @property
+ def table_name(self) -> Optional[str]:
+ """str: Name of the table."""
+
+ return self.__table_name
+
+ @table_name.setter
+ def table_name(self, value: Optional[str]) -> None:
+ self.__table_name = value
+
+ @property
+ def headers(self) -> Sequence[str]:
+ """Sequence[str]: Table header names."""
+
+ return self.__dp_extractor.headers
+
+ @property
+ def rows(self) -> Sequence:
+ """Sequence: Original rows of tabular data."""
+
+ return self.__rows
+
+ @property
+ def value_matrix(self) -> DataPropertyMatrix:
+ """DataPropertyMatrix: Converted rows of tabular data."""
+
+ if self.__value_matrix:
+ return self.__value_matrix
+
+ self.__value_matrix = [
+ [value_dp.data for value_dp in value_dp_list] for value_dp_list in self.value_dp_matrix
+ ]
+
+ return self.__value_matrix
+
+ @property
+ def has_value_dp_matrix(self) -> bool:
+ return self.__value_dp_matrix is not None
+
+ @property
+ def max_workers(self) -> int:
+ return self.__dp_extractor.max_workers
+
+ @max_workers.setter
+ def max_workers(self, value: Optional[int]) -> None:
+ self.__dp_extractor.max_workers = value
+
+ @property
+ def num_rows(self) -> Optional[int]:
+ """Optional[int]:
+ Number of rows in the tabular data.
+ |None| if the ``rows`` is neither list nor tuple.
+ """
+
+ try:
+ return len(self.rows)
+ except TypeError:
+ return None
+
+ @property
+ def num_columns(self) -> Optional[int]:
+ if typepy.is_not_empty_sequence(self.headers):
+ return len(self.headers)
+
+ try:
+ return len(self.rows[0])
+ except TypeError:
+ return None
+ except IndexError:
+ return 0
+
+ @property
+ def value_dp_matrix(self) -> DataPropertyMatrix:
+ """DataPropertyMatrix: DataProperty for table data."""
+
+ if self.__value_dp_matrix is None:
+ self.__value_dp_matrix = self.__dp_extractor.to_dp_matrix(
+ to_value_matrix(self.headers, self.rows)
+ )
+
+ return self.__value_dp_matrix
+
+ @property
+ def header_dp_list(self) -> List[dp.DataProperty]:
+ return self.__dp_extractor.to_header_dp_list()
+
+ @property
+ def column_dp_list(self) -> List[dp.ColumnDataProperty]:
+ return self.__dp_extractor.to_column_dp_list(self.value_dp_matrix)
+
+ @property
+ def dp_extractor(self) -> dp.DataPropertyExtractor:
+ return self.__dp_extractor
+
+ def is_empty_header(self) -> bool:
+ """bool: |True| if the data :py:attr:`.headers` is empty."""
+
+ return typepy.is_empty_sequence(self.headers)
+
+ def is_empty_rows(self) -> bool:
+ """
+ :return: |True| if the tabular data has no rows.
+ :rtype: bool
+ """
+
+ return self.num_rows == 0
+
+ def is_empty(self) -> bool:
+ """
+ :return:
+ |True| if the data :py:attr:`.headers` or
+ :py:attr:`.value_matrix` is empty.
+ :rtype: bool
+ """
+
+ return any([self.is_empty_header(), self.is_empty_rows()])
+
+ def equals(self, other: "TableData", cmp_by_dp: bool = True) -> bool:
+ if cmp_by_dp:
+ return self.__equals_dp(other)
+
+ return self.__equals_raw(other)
+
+ def __equals_base(self, other: "TableData") -> bool:
+ compare_item_list = [self.table_name == other.table_name]
+
+ if self.num_rows is not None:
+ compare_item_list.append(self.num_rows == other.num_rows)
+
+ return all(compare_item_list)
+
+ def __equals_raw(self, other: "TableData") -> bool:
+ if not self.__equals_base(other):
+ return False
+
+ if self.headers != other.headers:
+ return False
+
+ for lhs_row, rhs_row in zip(self.rows, other.rows):
+ if len(lhs_row) != len(rhs_row):
+ return False
+
+ if not all(
+ [
+ lhs == rhs
+ for lhs, rhs in zip(lhs_row, rhs_row)
+ if not Nan(lhs).is_type() and not Nan(rhs).is_type()
+ ]
+ ):
+ return False
+
+ return True
+
+ def __equals_dp(self, other: "TableData") -> bool:
+ if not self.__equals_base(other):
+ return False
+
+ if self.header_dp_list != other.header_dp_list:
+ return False
+
+ if self.value_dp_matrix is None or other.value_dp_matrix is None:
+ return False
+
+ for lhs_list, rhs_list in zip(self.value_dp_matrix, other.value_dp_matrix):
+ if len(lhs_list) != len(rhs_list):
+ return False
+
+ if any([lhs != rhs for lhs, rhs in zip(lhs_list, rhs_list)]):
+ return False
+
+ return True
+
+ def in_tabledata_list(self, other: Sequence["TableData"], cmp_by_dp: bool = True) -> bool:
+ for table_data in other:
+ if self.equals(table_data, cmp_by_dp=cmp_by_dp):
+ return True
+
+ return False
+
+ def validate_rows(self) -> None:
+ """
+ :raises ValueError:
+ """
+
+ invalid_row_idx_list = []
+
+ for row_idx, row in enumerate(self.rows):
+ if isinstance(row, (list, tuple)) and len(self.headers) != len(row):
+ invalid_row_idx_list.append(row_idx)
+
+ if isinstance(row, dict):
+ if not all([header in row for header in self.headers]):
+ invalid_row_idx_list.append(row_idx)
+
+ if not invalid_row_idx_list:
+ return
+
+ for invalid_row_idx in invalid_row_idx_list:
+ logger.debug(f"invalid row (line={invalid_row_idx}): {self.rows[invalid_row_idx]}")
+
+ raise ValueError(
+ "table header length and row length are mismatch:\n"
+ + f" header(len={len(self.headers)}): {self.headers}\n"
+ + " # of miss match rows: {} ouf of {}\n".format(
+ len(invalid_row_idx_list), self.num_rows
+ )
+ )
+
+ def as_dict(self, default_key: str = "table") -> Dict[str, List["OrderedDict[str, Any]"]]:
+ """
+ Args:
+ default_key:
+ Key of a returning dictionary when the ``table_name`` is empty.
+
+ Returns:
+ dict: Table data as a |dict| instance.
+
+ Sample Code:
+ .. code:: python
+
+ from tabledata import TableData
+
+ TableData(
+ "sample",
+ ["a", "b"],
+ [[1, 2], [3.3, 4.4]]
+ ).as_dict()
+
+ Output:
+ .. code:: json
+
+ {'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}
+ """ # noqa
+
+ dict_body = []
+ for row in self.value_matrix:
+ if not row:
+ continue
+
+ values = [
+ (header, value) for header, value in zip(self.headers, row) if value is not None
+ ]
+
+ if not values:
+ continue
+
+ dict_body.append(OrderedDict(values))
+
+ table_name = self.table_name
+ if not table_name:
+ table_name = default_key
+
+ return {table_name: dict_body}
+
+ def as_tuple(self) -> Iterator[Tuple]:
+ """
+ :return: Rows of the tuple.
+ :rtype: list of |namedtuple|
+
+ :Sample Code:
+ .. code:: python
+
+ from tabledata import TableData
+
+ records = TableData(
+ "sample",
+ ["a", "b"],
+ [[1, 2], [3.3, 4.4]]
+ ).as_tuple()
+ for record in records:
+ print(record)
+
+ :Output:
+ .. code-block:: none
+
+ Row(a=1, b=2)
+ Row(a=Decimal('3.3'), b=Decimal('4.4'))
+ """
+
+ Row = namedtuple("Row", self.headers) # type: ignore
+
+ for value_dp_list in self.value_dp_matrix:
+ if typepy.is_empty_sequence(value_dp_list):
+ continue
+
+ row = Row(*(value_dp.data for value_dp in value_dp_list))
+
+ yield row
+
+ def as_dataframe(self) -> "pandas.DataFrame":
+ """
+ :return: Table data as a ``pandas.DataFrame`` instance.
+ :rtype: pandas.DataFrame
+
+ :Sample Code:
+ .. code-block:: python
+
+ from tabledata import TableData
+
+ TableData(
+ "sample",
+ ["a", "b"],
+ [[1, 2], [3.3, 4.4]]
+ ).as_dataframe()
+
+ :Output:
+ .. code-block:: none
+
+ a b
+ 0 1 2
+ 1 3.3 4.4
+
+ :Dependency Packages:
+ - `pandas `__
+ """
+
+ try:
+ from pandas import DataFrame
+ except ImportError:
+ raise RuntimeError("required 'pandas' package to execute as_dataframe method")
+
+ dataframe = DataFrame(self.value_matrix)
+ if not self.is_empty_header():
+ dataframe.columns = self.headers
+
+ return dataframe
+
+ def transpose(self) -> "TableData":
+ return TableData(
+ self.table_name,
+ self.headers,
+ [row for row in zip(*self.rows)],
+ max_workers=self.max_workers,
+ )
+
+ def filter_column(
+ self,
+ patterns: Optional[str] = None,
+ is_invert_match: bool = False,
+ is_re_match: bool = False,
+ pattern_match: PatternMatch = PatternMatch.OR,
+ ) -> "TableData":
+ logger.debug(
+ "filter_column: patterns={}, is_invert_match={}, "
+ "is_re_match={}, pattern_match={}".format(
+ patterns, is_invert_match, is_re_match, pattern_match
+ )
+ )
+
+ if not patterns:
+ return self
+
+ match_header_list = []
+ match_column_matrix = []
+
+ if pattern_match == PatternMatch.OR:
+ match_method = any
+ elif pattern_match == PatternMatch.AND:
+ match_method = all
+ else:
+ raise ValueError(f"unknown matching: {pattern_match}")
+
+ for header, column in zip(self.headers, zip(*self.rows)):
+ is_match_list = []
+ for pattern in patterns:
+ is_match = self.__is_match(header, pattern, is_re_match)
+
+ is_match_list.append(
+ any([is_match and not is_invert_match, not is_match and is_invert_match])
+ )
+
+ if match_method(is_match_list):
+ match_header_list.append(header)
+ match_column_matrix.append(column)
+
+ logger.debug(
+ "filter_column: table={}, match_header_list={}".format(
+ self.table_name, match_header_list
+ )
+ )
+
+ return TableData(
+ self.table_name,
+ match_header_list,
+ list(zip(*match_column_matrix)),
+ max_workers=self.max_workers,
+ )
+
+ @staticmethod
+ def from_dataframe(
+ dataframe: "pandas.DataFrame",
+ table_name: str = "",
+ type_hints: Optional[Sequence[TypeHint]] = None,
+ max_workers: Optional[int] = None,
+ ) -> "TableData":
+ """
+ Initialize TableData instance from a pandas.DataFrame instance.
+
+ :param pandas.DataFrame dataframe:
+ :param str table_name: Table name to create.
+ """
+
+ return TableData(
+ table_name,
+ list(dataframe.columns.values),
+ dataframe.values.tolist(),
+ type_hints=type_hints,
+ max_workers=max_workers,
+ )
+
+ @staticmethod
+ def __is_match(header: str, pattern: str, is_re_match: bool) -> bool:
+ if is_re_match:
+ return re.search(pattern, header) is not None
+
+ return header == pattern
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/error.py b/llmeval-env/lib/python3.10/site-packages/tabledata/error.py
new file mode 100644
index 0000000000000000000000000000000000000000..35084f8b1af8fa41a12f4fcaf5f0710771019f41
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/error.py
@@ -0,0 +1,27 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+
+class NameValidationError(ValueError):
+ """
+ Exception raised when a name is invalid.
+ """
+
+
+class InvalidTableNameError(NameValidationError):
+ """
+ Exception raised when a table name is invalid.
+ """
+
+
+class InvalidHeaderNameError(NameValidationError):
+ """
+ Exception raised when a table header name is invalid.
+ """
+
+
+class DataError(ValueError):
+ """
+ Exception raised when data is invalid as tabular data.
+ """
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/normalizer.py b/llmeval-env/lib/python3.10/site-packages/tabledata/normalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f5c383f51c57c49eeeb611679d3b5c8fe90ff52
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/tabledata/normalizer.py
@@ -0,0 +1,207 @@
+"""
+.. codeauthor:: Tsuyoshi Hombashi
+"""
+
+import abc
+import warnings
+from typing import List, Sequence
+
+import typepy
+from dataproperty.typing import TypeHint
+
+from ._core import TableData
+from ._logger import logger
+from .error import InvalidHeaderNameError, InvalidTableNameError
+
+
+class TableDataNormalizerInterface(metaclass=abc.ABCMeta):
+ """
+ Interface class to validate and normalize data of |TableData|.
+ """
+
+ @abc.abstractmethod
+ def validate(self) -> None: # pragma: no cover
+ pass
+
+ @abc.abstractmethod
+ def normalize(self) -> TableData: # pragma: no cover
+ pass
+
+
+class AbstractTableDataNormalizer(TableDataNormalizerInterface):
+ @property
+ def _type_hints(self) -> List[TypeHint]:
+ return self._tabledata.dp_extractor.column_type_hints
+
+ def __init__(self, tabledata: TableData) -> None:
+ self._tabledata = tabledata
+
+ def validate(self) -> None:
+ if not self._tabledata.table_name:
+ raise ValueError("table_name must not be empty")
+
+ self._validate_table_name(self._tabledata.table_name)
+ self._validate_headers()
+
+ def sanitize(self): # type: ignore
+ warnings.warn(
+ "'sanitize' method is deprecated and will be removed in the future."
+ " use 'normalize' method instead.",
+ DeprecationWarning,
+ )
+
+ return self.normalize()
+
+ def normalize(self) -> TableData:
+ """
+ :return: Sanitized table data.
+ :rtype: tabledata.TableData
+ """
+
+ logger.debug(f"normalize: {type(self).__name__}")
+
+ normalize_headers = self._normalize_headers()
+
+ return TableData(
+ self.__normalize_table_name(),
+ normalize_headers,
+ self._normalize_rows(normalize_headers),
+ dp_extractor=self._tabledata.dp_extractor,
+ type_hints=self._type_hints,
+ max_workers=self._tabledata.max_workers,
+ )
+
+ @abc.abstractmethod
+ def _preprocess_table_name(self) -> str:
+ """
+ This method is always called before table name validation.
+ You must return preprocessed table name.
+ """
+
+ @abc.abstractmethod
+ def _validate_table_name(self, table_name: str) -> None:
+ """
+ Must raise :py:class:`~.InvalidTableNameError`
+ when you consider the table name invalid.
+
+ :param str header: Table name to validate.
+ :raises tabledata.InvalidTableNameError:
+ If the table name is invalid.
+ |raises_validate_table_name|
+ """
+
+ @abc.abstractmethod
+ def _normalize_table_name(self, table_name: str) -> str:
+ """
+ Must return a valid table name.
+ The table name must be considered to be a valid name by
+ :py:meth:`~._validate_table_name` method.
+
+ This method called when :py:meth:`~._validate_table_name` method raise
+ :py:class:`~.InvalidTableNameError`.
+
+ :param str table_name: Table name to normalize.
+ :return: Sanitized table name.
+ :rtype: str
+ """
+
+ @abc.abstractmethod
+ def _preprocess_header(self, col_idx: int, header: str) -> str:
+ """
+ This method is always called before a header validation.
+ You must return preprocessed header.
+ """
+
+ @abc.abstractmethod
+ def _validate_header(self, header: str) -> None:
+ """
+ No operation.
+
+ This method called for each table header. Override this method
+ in a subclass if you want to detect invalid table header elements.
+ Raise :py:class:`~.InvalidHeaderNameError` if an invalid
+ header element found.
+
+ :param str header: Table header name.
+ :raises tabledata.InvalidHeaderNameError:
+ If the ``header`` is invalid.
+ """
+
+ @abc.abstractmethod
+ def _normalize_header(self, header: str) -> str:
+ """
+ Must return a valid header name.
+ This method called when :py:meth:`~._validate_header` method raise
+ :py:class:`~.InvalidHeaderNameError`.
+ Override this method in subclass if you want to rename invalid
+ table header element.
+
+ :param str header: Header name to normalize.
+ :return: Renamed header name.
+ :rtype: str
+ """
+
+ def _normalize_rows(self, normalize_headers: Sequence[str]) -> List:
+ return list(self._tabledata.rows)
+
+ def _validate_headers(self) -> None:
+ for header in self._tabledata.headers:
+ self._validate_header(header)
+
+ def __normalize_table_name(self) -> str:
+ preprocessed_table_name = self._preprocess_table_name()
+
+ try:
+ self._validate_table_name(preprocessed_table_name)
+ new_table_name = preprocessed_table_name
+ except InvalidTableNameError:
+ new_table_name = self._normalize_table_name(preprocessed_table_name)
+ self._validate_table_name(new_table_name)
+
+ return new_table_name
+
+ def _normalize_headers(self) -> List[str]:
+ new_header_list = []
+
+ for col_idx, header in enumerate(self._tabledata.headers):
+ header = self._preprocess_header(col_idx, header)
+
+ try:
+ self._validate_header(header)
+ new_header = header
+ except InvalidHeaderNameError:
+ new_header = self._normalize_header(header)
+ self._validate_header(new_header)
+
+ new_header_list.append(new_header)
+
+ return new_header_list
+
+
+class TableDataNormalizer(AbstractTableDataNormalizer):
+ def _preprocess_table_name(self) -> str:
+ if not self._tabledata.table_name:
+ return ""
+
+ return self._tabledata.table_name
+
+ def _validate_table_name(self, table_name: str) -> None:
+ try:
+ typepy.String(table_name).validate()
+ except TypeError as e:
+ raise InvalidTableNameError(e)
+
+ def _normalize_table_name(self, table_name: str) -> str:
+ return str(typepy.String(table_name).force_convert())
+
+ def _preprocess_header(self, col_idx: int, header: str) -> str:
+ return header
+
+ def _validate_header(self, header: str) -> None:
+ try:
+ typepy.String(header).validate()
+ except TypeError as e:
+ raise InvalidHeaderNameError(e)
+
+ def _normalize_header(self, header: str) -> str:
+ return str(typepy.String(header).force_convert())
diff --git a/llmeval-env/lib/python3.10/site-packages/tabledata/py.typed b/llmeval-env/lib/python3.10/site-packages/tabledata/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/INSTALLER b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/LICENSE b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..85cbbcbfe146a7d08e7e89f7005f3ca0df364201
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/LICENSE
@@ -0,0 +1,6687 @@
+From PyTorch:
+
+Copyright (c) 2016- Facebook, Inc (Adam Paszke)
+Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+From Caffe2:
+
+Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+
+All contributions by Kakao Brain:
+Copyright 2019-2020 Kakao Brain
+
+All contributions by Cruise LLC:
+Copyright (c) 2022 Cruise LLC.
+All rights reserved.
+
+All contributions from Caffe:
+Copyright(c) 2013, 2014, 2015, the respective contributors
+All rights reserved.
+
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+
+Caffe2 uses a copyright model similar to Caffe: each contributor holds
+copyright over their contributions to Caffe2. The project versioning records
+all such contribution and copyright details. If a contributor wants to further
+mark their specific copyright on a particular contribution, they should
+indicate their copyright solely in the commit message of the change when it is
+committed.
+
+All rights reserved.
+
+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 names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+ and IDIAP Research Institute 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 OWNER 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.
+
+
+The Pytorch repository and source distributions bundle several libraries that are
+compatibly licensed. We list these here.
+
+Name: DCGM
+License: Apache-2.0
+Files: third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM
+ For details, see the files concatenated below: third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM/LICENSE
+
+Name: FP16
+License: MIT
+Files: third_party/FP16
+ For details, see the files concatenated below: third_party/FP16/LICENSE
+
+Name: FXdiv
+License: MIT
+Files: third_party/FXdiv
+ For details, see the files concatenated below: third_party/FXdiv/LICENSE
+
+Name: NNPACK
+License: BSD-2-Clause
+Files: third_party/NNPACK
+ For details, see the files concatenated below: third_party/NNPACK/LICENSE
+
+Name: QNNPACK
+License: BSD-3-Clause
+Files: third_party/QNNPACK
+ For details, see the files concatenated below: third_party/QNNPACK/LICENSE
+
+Name: VulkanMemoryAllocator
+License: MIT
+Files: third_party/VulkanMemoryAllocator
+ For details, see the files concatenated below: third_party/VulkanMemoryAllocator/LICENSE.txt
+
+Name: XNNPACK
+License: BSD-3-Clause
+Files: third_party/XNNPACK
+ For details, see the files concatenated below: third_party/XNNPACK/LICENSE
+
+Name: benchmark
+License: Apache-2.0
+Files: third_party/benchmark,
+ third_party/onnx/third_party/benchmark,
+ third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark,
+ third_party/protobuf/third_party/benchmark
+ For details, see the files concatenated below: third_party/benchmark/LICENSE,
+ third_party/onnx/third_party/benchmark/LICENSE,
+ third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/LICENSE,
+ third_party/protobuf/third_party/benchmark/LICENSE
+
+Name: clog
+License: BSD-2-Clause
+Files: third_party/QNNPACK/deps/clog,
+ third_party/cpuinfo/deps/clog,
+ third_party/fbgemm/third_party/cpuinfo/deps/clog
+ For details, see the files concatenated below: third_party/QNNPACK/deps/clog/LICENSE,
+ third_party/cpuinfo/deps/clog/LICENSE,
+ third_party/fbgemm/third_party/cpuinfo/deps/clog/LICENSE
+
+Name: colorama
+License: BSD-3-Clause
+Files: third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM/testing/python3/libs_3rdparty/colorama
+ For details, see the files concatenated below: third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM/testing/python3/libs_3rdparty/colorama/LICENSE.txt
+
+Name: cpplint
+License: BSD-3-Clause
+Files: third_party/kineto/libkineto/third_party/dynolog/third_party/json/third_party/cpplint,
+ third_party/nlohmann/tools/cpplint
+ For details, see the files concatenated below: third_party/kineto/libkineto/third_party/dynolog/third_party/json/third_party/cpplint/LICENSE,
+ third_party/nlohmann/tools/cpplint/LICENSE
+
+Name: cpr
+License: MIT
+Files: third_party/kineto/libkineto/third_party/dynolog/third_party/cpr
+ For details, see the files concatenated below: third_party/kineto/libkineto/third_party/dynolog/third_party/cpr/LICENSE
+
+Name: cpuinfo
+License: BSD-2-Clause
+Files: third_party/cpuinfo,
+ third_party/fbgemm/third_party/cpuinfo
+ For details, see the files concatenated below: third_party/cpuinfo/LICENSE,
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+Name: cudnn_frontend
+License: MIT
+Files: third_party/cudnn_frontend
+ For details, see the files concatenated below: third_party/cudnn_frontend/LICENSE.txt
+
+Name: cutlass
+License: BSD-3-Clause
+Files: third_party/cutlass,
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+ For details, see the files concatenated below: third_party/cutlass/LICENSE.txt,
+ third_party/fbgemm/third_party/cutlass/LICENSE.txt
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+Name: dart
+License: Apache-2.0
+Files: third_party/flatbuffers/dart
+ For details, see the files concatenated below: third_party/flatbuffers/dart/LICENSE
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+Name: doctest
+License: MIT
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+ third_party/nlohmann/tests/thirdparty/doctest
+ For details, see the files concatenated below: third_party/kineto/libkineto/third_party/dynolog/third_party/json/test/thirdparty/doctest/LICENSE.txt,
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+Files: third_party/kineto/libkineto/third_party/dynolog
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+License: BSD-3-Clause
+Files: third_party/eigen
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+License: Apache-2.0
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+Name: generator
+License: Apache-2.0
+Files: third_party/fbgemm/third_party/googletest/googlemock/scripts/generator,
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+ third_party/protobuf/third_party/googletest/googlemock/scripts/generator,
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+ For details, see the files concatenated below: third_party/fbgemm/third_party/googletest/googlemock/scripts/generator/LICENSE,
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+
+Name: gloo
+License: BSD-3-Clause
+Files: third_party/gloo
+ For details, see the files concatenated below: third_party/gloo/LICENSE
+
+Name: googlemock
+License: BSD-3-Clause
+Files: third_party/fbgemm/third_party/googletest/googlemock,
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+third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM/LICENSE
+-------------------------------------------------------------------------
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+third_party/FP16/LICENSE
+------------------------
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+
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+third_party/FXdiv/LICENSE
+-------------------------
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+third_party/NNPACK/LICENSE
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+third_party/VulkanMemoryAllocator/LICENSE.txt
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+third_party/XNNPACK/LICENSE
+---------------------------
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+third_party/benchmark/LICENSE
+-----------------------------
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+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ 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.
+
+
+third_party/onnx-tensorrt/third_party/onnx/third_party/benchmark/LICENSE
+------------------------------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ 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.
+
+
+third_party/protobuf/third_party/benchmark/LICENSE
+--------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ 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.
+
+
+third_party/QNNPACK/deps/clog/LICENSE
+-------------------------------------
+Copyright (C) 2018 Marat Dukhan
+Copyright (c) 2017-2018 Facebook Inc.
+Copyright (c) 2017 Georgia Institute of Technology
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* 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.
+
+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.
+
+
+third_party/cpuinfo/deps/clog/LICENSE
+-------------------------------------
+Copyright (C) 2018 Marat Dukhan
+Copyright (c) 2017-2018 Facebook Inc.
+Copyright (c) 2017 Georgia Institute of Technology
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+* 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.
+
+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
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+
+
+third_party/fbgemm/third_party/cpuinfo/deps/clog/LICENSE
+--------------------------------------------------------
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+Copyright (c) 2017-2018 Facebook Inc.
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+third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM/testing/python3/libs_3rdparty/colorama/LICENSE.txt
+--------------------------------------------------------------------------------------------------------------------
+Copyright (c) 2010 Jonathan Hartley
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+------------------------------------------------------------------------
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+third_party/cpuinfo/LICENSE
+---------------------------
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+third_party/fbgemm/third_party/cpuinfo/LICENSE
+----------------------------------------------
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+third_party/cudnn_frontend/LICENSE.txt
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+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+ list of conditions and the following disclaimer.
+
+ * 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.
+
+ * Neither the name Facebook 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.
+
+
+third_party/flatbuffers/LICENSE
+-------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ 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.
+
+
+third_party/fmt/LICENSE
+-----------------------
+Copyright (c) 2012 - present, Victor Zverovich and {fmt} contributors
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--- Optional exception to the license ---
+
+As an exception, if, as a result of your compiling your source code, portions
+of this Software are embedded into a machine-executable object form of such
+source code, you may redistribute such embedded portions in such object form
+without including the above copyright and permission notices.
+
+
+third_party/kineto/libkineto/third_party/dynolog/third_party/fmt/LICENSE.rst
+----------------------------------------------------------------------------
+Copyright (c) 2012 - present, Victor Zverovich
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--- Optional exception to the license ---
+
+As an exception, if, as a result of your compiling your source code, portions
+of this Software are embedded into a machine-executable object form of such
+source code, you may redistribute such embedded portions in such object form
+without including the above copyright and permission notices.
+
+
+third_party/kineto/libkineto/third_party/fmt/LICENSE.rst
+--------------------------------------------------------
+Copyright (c) 2012 - present, Victor Zverovich
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--- Optional exception to the license ---
+
+As an exception, if, as a result of your compiling your source code, portions
+of this Software are embedded into a machine-executable object form of such
+source code, you may redistribute such embedded portions in such object form
+without including the above copyright and permission notices.
+
+
+third_party/foxi/LICENSE
+------------------------
+MIT License
+
+Copyright (c) 2019 Lu Fang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+third_party/gemmlowp/gemmlowp/LICENSE
+-------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ 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.
+
+
+third_party/fbgemm/third_party/googletest/googlemock/scripts/generator/LICENSE
+------------------------------------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [2007] Neal Norwitz
+ Portions Copyright [2007] Google Inc.
+
+ 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.
+
+
+third_party/googletest/googlemock/scripts/generator/LICENSE
+-----------------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [2007] Neal Norwitz
+ Portions Copyright [2007] Google Inc.
+
+ 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.
+
+
+third_party/kineto/libkineto/third_party/googletest/googlemock/scripts/generator/LICENSE
+----------------------------------------------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ (except as stated in this section) patent license to make, have made,
+ use, offer to sell, sell, import, and otherwise transfer the Work,
+ where such license applies only to those patent claims licensable
+ by such Contributor that are necessarily infringed by their
+ Contribution(s) alone or by combination of their Contribution(s)
+ with the Work to which such Contribution(s) was submitted. If You
+ institute patent litigation against any entity (including a
+ cross-claim or counterclaim in a lawsuit) alleging that the Work
+ or a Contribution incorporated within the Work constitutes direct
+ or contributory patent infringement, then any patent licenses
+ granted to You under this License for that Work shall terminate
+ as of the date such litigation is filed.
+
+ 4. Redistribution. You may reproduce and distribute copies of the
+ Work or Derivative Works thereof in any medium, with or without
+ modifications, and in Source or Object form, provided that You
+ meet the following conditions:
+
+ (a) You must give any other recipients of the Work or
+ Derivative Works a copy of this License; and
+
+ (b) You must cause any modified files to carry prominent notices
+ stating that You changed the files; and
+
+ (c) You must retain, in the Source form of any Derivative Works
+ that You distribute, all copyright, patent, trademark, and
+ attribution notices from the Source form of the Work,
+ excluding those notices that do not pertain to any part of
+ the Derivative Works; and
+
+ (d) If the Work includes a "NOTICE" text file as part of its
+ distribution, then any Derivative Works that You distribute must
+ include a readable copy of the attribution notices contained
+ within such NOTICE file, excluding those notices that do not
+ pertain to any part of the Derivative Works, in at least one
+ of the following places: within a NOTICE text file distributed
+ as part of the Derivative Works; within the Source form or
+ documentation, if provided along with the Derivative Works; or,
+ within a display generated by the Derivative Works, if and
+ wherever such third-party notices normally appear. The contents
+ of the NOTICE file are for informational purposes only and
+ do not modify the License. You may add Your own attribution
+ notices within Derivative Works that You distribute, alongside
+ or as an addendum to the NOTICE text from the Work, provided
+ that such additional attribution notices cannot be construed
+ as modifying the License.
+
+ You may add Your own copyright statement to Your modifications and
+ may provide additional or different license terms and conditions
+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
+ the conditions stated in this License.
+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ implied, including, without limitation, any warranties or conditions
+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [2007] Neal Norwitz
+ Portions Copyright [2007] Google Inc.
+
+ 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.
+
+
+third_party/protobuf/third_party/googletest/googlemock/scripts/generator/LICENSE
+--------------------------------------------------------------------------------
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
+ and distribution as defined by Sections 1 through 9 of this document.
+
+ "Licensor" shall mean the copyright owner or entity authorized by
+ the copyright owner that is granting the License.
+
+ "Legal Entity" shall mean the union of the acting entity and all
+ other entities that control, are controlled by, or are under common
+ control with that entity. For the purposes of this definition,
+ "control" means (i) the power, direct or indirect, to cause the
+ direction or management of such entity, whether by contract or
+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
+ outstanding shares, or (iii) beneficial ownership of such entity.
+
+ "You" (or "Your") shall mean an individual or Legal Entity
+ exercising permissions granted by this License.
+
+ "Source" form shall mean the preferred form for making modifications,
+ including but not limited to software source code, documentation
+ source, and configuration files.
+
+ "Object" form shall mean any form resulting from mechanical
+ transformation or translation of a Source form, including but
+ not limited to compiled object code, generated documentation,
+ and conversions to other media types.
+
+ "Work" shall mean the work of authorship, whether in Source or
+ Object form, made available under the License, as indicated by a
+ copyright notice that is included in or attached to the work
+ (an example is provided in the Appendix below).
+
+ "Derivative Works" shall mean any work, whether in Source or Object
+ form, that is based on (or derived from) the Work and for which the
+ editorial revisions, annotations, elaborations, or other modifications
+ represent, as a whole, an original work of authorship. For the purposes
+ of this License, Derivative Works shall not include works that remain
+ separable from, or merely link (or bind by name) to the interfaces of,
+ the Work and Derivative Works thereof.
+
+ "Contribution" shall mean any work of authorship, including
+ the original version of the Work and any modifications or additions
+ to that Work or Derivative Works thereof, that is intentionally
+ submitted to Licensor for inclusion in the Work by the copyright owner
+ or by an individual or Legal Entity authorized to submit on behalf of
+ the copyright owner. For the purposes of this definition, "submitted"
+ means any form of electronic, verbal, or written communication sent
+ to the Licensor or its representatives, including but not limited to
+ communication on electronic mailing lists, source code control systems,
+ and issue tracking systems that are managed by, or on behalf of, the
+ Licensor for the purpose of discussing and improving the Work, but
+ excluding communication that is conspicuously marked or otherwise
+ designated in writing by the copyright owner as "Not a Contribution."
+
+ "Contributor" shall mean Licensor and any individual or Legal Entity
+ on behalf of whom a Contribution has been received by Licensor and
+ subsequently incorporated within the Work.
+
+ 2. Grant of Copyright License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+ copyright license to reproduce, prepare Derivative Works of,
+ publicly display, publicly perform, sublicense, and distribute the
+ Work and such Derivative Works in Source or Object form.
+
+ 3. Grant of Patent License. Subject to the terms and conditions of
+ this License, each Contributor hereby grants to You a perpetual,
+ worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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+ To apply the Apache License to your work, attach the following
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+ Copyright [2007] Neal Norwitz
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+ the terms of any separate license agreement you may have executed
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+ 6. Trademarks. This License does not grant permission to use the trade
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+ agreed to in writing, Licensor provides the Work (and each
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+ appropriateness of using or redistributing the Work and assume any
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+
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+ incidental, or consequential damages of any character arising as a
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+ 9. Accepting Warranty or Additional Liability. While redistributing
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+ and charge a fee for, acceptance of support, warranty, indemnity,
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+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
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+ Copyright [2007] Neal Norwitz
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+
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+third_party/gloo/LICENSE
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+
+third_party/fbgemm/third_party/googletest/googlemock/LICENSE
+------------------------------------------------------------
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+copyright notice, this list of conditions and the following disclaimer
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+ * Neither the name of Google Inc. nor the names of its
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+
+
+third_party/kineto/libkineto/third_party/googletest/googlemock/LICENSE
+----------------------------------------------------------------------
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+Redistribution and use in source and binary forms, with or without
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+met:
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+third_party/protobuf/third_party/googletest/googlemock/LICENSE
+--------------------------------------------------------------
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+third_party/tensorpipe/third_party/googletest/googlemock/LICENSE
+----------------------------------------------------------------
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+
+
+third_party/fbgemm/third_party/googletest/LICENSE
+-------------------------------------------------
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+
+
+third_party/fbgemm/third_party/googletest/googletest/LICENSE
+------------------------------------------------------------
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+All rights reserved.
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+Redistribution and use in source and binary forms, with or without
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+met:
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+
+
+third_party/googletest/LICENSE
+------------------------------
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+met:
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+copyright notice, this list of conditions and the following disclaimer
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+third_party/kineto/libkineto/third_party/dynolog/third_party/googletest/LICENSE
+-------------------------------------------------------------------------------
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+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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+
+
+third_party/kineto/libkineto/third_party/googletest/LICENSE
+-----------------------------------------------------------
+Copyright 2008, Google Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ * Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
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+copyright notice, this list of conditions and the following disclaimer
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+third_party/ideep/mkl-dnn/tests/gtests/gtest/LICENSE
+----------------------------------------------------
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+third_party/fbgemm/third_party/hipify_torch/LICENSE.txt
+-------------------------------------------------------
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+third_party/tensorpipe/third_party/libnop/LICENSE
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+third_party/tensorpipe/third_party/libuv/LICENSE
+------------------------------------------------
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+third_party/pthreadpool/LICENSE
+-------------------------------
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+For more information on this, and how to apply and follow the GNU GPL, see
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diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/METADATA b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..3e53db84c097d0fdb0ecd40b5d3b6da5846cb056
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/METADATA
@@ -0,0 +1,523 @@
+Metadata-Version: 2.1
+Name: torch
+Version: 2.3.0
+Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
+Home-page: https://pytorch.org/
+Author: PyTorch Team
+Author-email: packages@pytorch.org
+License: BSD-3
+Download-URL: https://github.com/pytorch/pytorch/tags
+Keywords: pytorch,machine learning
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Programming Language :: C++
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Python: >=3.8.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+License-File: NOTICE
+Requires-Dist: filelock
+Requires-Dist: typing-extensions (>=4.8.0)
+Requires-Dist: sympy
+Requires-Dist: networkx
+Requires-Dist: jinja2
+Requires-Dist: fsspec
+Requires-Dist: nvidia-cuda-nvrtc-cu12 (==12.1.105) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cuda-runtime-cu12 (==12.1.105) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cuda-cupti-cu12 (==12.1.105) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cudnn-cu12 (==8.9.2.26) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cublas-cu12 (==12.1.3.1) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cufft-cu12 (==11.0.2.54) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-curand-cu12 (==10.3.2.106) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cusolver-cu12 (==11.4.5.107) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-cusparse-cu12 (==12.1.0.106) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-nccl-cu12 (==2.20.5) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: nvidia-nvtx-cu12 (==12.1.105) ; platform_system == "Linux" and platform_machine == "x86_64"
+Requires-Dist: triton (==2.3.0) ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.12"
+Requires-Dist: mkl (<=2021.4.0,>=2021.1.1) ; platform_system == "Windows"
+Provides-Extra: opt-einsum
+Requires-Dist: opt-einsum (>=3.3) ; extra == 'opt-einsum'
+Provides-Extra: optree
+Requires-Dist: optree (>=0.9.1) ; extra == 'optree'
+
+
+
+--------------------------------------------------------------------------------
+
+PyTorch is a Python package that provides two high-level features:
+- Tensor computation (like NumPy) with strong GPU acceleration
+- Deep neural networks built on a tape-based autograd system
+
+You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
+
+Our trunk health (Continuous Integration signals) can be found at [hud.pytorch.org](https://hud.pytorch.org/ci/pytorch/pytorch/main).
+
+
+
+- [More About PyTorch](#more-about-pytorch)
+ - [A GPU-Ready Tensor Library](#a-gpu-ready-tensor-library)
+ - [Dynamic Neural Networks: Tape-Based Autograd](#dynamic-neural-networks-tape-based-autograd)
+ - [Python First](#python-first)
+ - [Imperative Experiences](#imperative-experiences)
+ - [Fast and Lean](#fast-and-lean)
+ - [Extensions Without Pain](#extensions-without-pain)
+- [Installation](#installation)
+ - [Binaries](#binaries)
+ - [NVIDIA Jetson Platforms](#nvidia-jetson-platforms)
+ - [From Source](#from-source)
+ - [Prerequisites](#prerequisites)
+ - [Install Dependencies](#install-dependencies)
+ - [Get the PyTorch Source](#get-the-pytorch-source)
+ - [Install PyTorch](#install-pytorch)
+ - [Adjust Build Options (Optional)](#adjust-build-options-optional)
+ - [Docker Image](#docker-image)
+ - [Using pre-built images](#using-pre-built-images)
+ - [Building the image yourself](#building-the-image-yourself)
+ - [Building the Documentation](#building-the-documentation)
+ - [Previous Versions](#previous-versions)
+- [Getting Started](#getting-started)
+- [Resources](#resources)
+- [Communication](#communication)
+- [Releases and Contributing](#releases-and-contributing)
+- [The Team](#the-team)
+- [License](#license)
+
+
+
+## More About PyTorch
+
+[Learn the basics of PyTorch](https://pytorch.org/tutorials/beginner/basics/intro.html)
+
+At a granular level, PyTorch is a library that consists of the following components:
+
+| Component | Description |
+| ---- | --- |
+| [**torch**](https://pytorch.org/docs/stable/torch.html) | A Tensor library like NumPy, with strong GPU support |
+| [**torch.autograd**](https://pytorch.org/docs/stable/autograd.html) | A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
+| [**torch.jit**](https://pytorch.org/docs/stable/jit.html) | A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code |
+| [**torch.nn**](https://pytorch.org/docs/stable/nn.html) | A neural networks library deeply integrated with autograd designed for maximum flexibility |
+| [**torch.multiprocessing**](https://pytorch.org/docs/stable/multiprocessing.html) | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
+| [**torch.utils**](https://pytorch.org/docs/stable/data.html) | DataLoader and other utility functions for convenience |
+
+Usually, PyTorch is used either as:
+
+- A replacement for NumPy to use the power of GPUs.
+- A deep learning research platform that provides maximum flexibility and speed.
+
+Elaborating Further:
+
+### A GPU-Ready Tensor Library
+
+If you use NumPy, then you have used Tensors (a.k.a. ndarray).
+
+
+
+PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the
+computation by a huge amount.
+
+We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs
+such as slicing, indexing, mathematical operations, linear algebra, reductions.
+And they are fast!
+
+### Dynamic Neural Networks: Tape-Based Autograd
+
+PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
+
+Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world.
+One has to build a neural network and reuse the same structure again and again.
+Changing the way the network behaves means that one has to start from scratch.
+
+With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to
+change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes
+from several research papers on this topic, as well as current and past work such as
+[torch-autograd](https://github.com/twitter/torch-autograd),
+[autograd](https://github.com/HIPS/autograd),
+[Chainer](https://chainer.org), etc.
+
+While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
+You get the best of speed and flexibility for your crazy research.
+
+
+
+### Python First
+
+PyTorch is not a Python binding into a monolithic C++ framework.
+It is built to be deeply integrated into Python.
+You can use it naturally like you would use [NumPy](https://www.numpy.org/) / [SciPy](https://www.scipy.org/) / [scikit-learn](https://scikit-learn.org) etc.
+You can write your new neural network layers in Python itself, using your favorite libraries
+and use packages such as [Cython](https://cython.org/) and [Numba](http://numba.pydata.org/).
+Our goal is to not reinvent the wheel where appropriate.
+
+### Imperative Experiences
+
+PyTorch is designed to be intuitive, linear in thought, and easy to use.
+When you execute a line of code, it gets executed. There isn't an asynchronous view of the world.
+When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward.
+The stack trace points to exactly where your code was defined.
+We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.
+
+### Fast and Lean
+
+PyTorch has minimal framework overhead. We integrate acceleration libraries
+such as [Intel MKL](https://software.intel.com/mkl) and NVIDIA ([cuDNN](https://developer.nvidia.com/cudnn), [NCCL](https://developer.nvidia.com/nccl)) to maximize speed.
+At the core, its CPU and GPU Tensor and neural network backends
+are mature and have been tested for years.
+
+Hence, PyTorch is quite fast — whether you run small or large neural networks.
+
+The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives.
+We've written custom memory allocators for the GPU to make sure that
+your deep learning models are maximally memory efficient.
+This enables you to train bigger deep learning models than before.
+
+### Extensions Without Pain
+
+Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward
+and with minimal abstractions.
+
+You can write new neural network layers in Python using the torch API
+[or your favorite NumPy-based libraries such as SciPy](https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html).
+
+If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate.
+No wrapper code needs to be written. You can see [a tutorial here](https://pytorch.org/tutorials/advanced/cpp_extension.html) and [an example here](https://github.com/pytorch/extension-cpp).
+
+
+## Installation
+
+### Binaries
+Commands to install binaries via Conda or pip wheels are on our website: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
+
+
+#### NVIDIA Jetson Platforms
+
+Python wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided [here](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048) and the L4T container is published [here](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch)
+
+They require JetPack 4.2 and above, and [@dusty-nv](https://github.com/dusty-nv) and [@ptrblck](https://github.com/ptrblck) are maintaining them.
+
+
+### From Source
+
+#### Prerequisites
+If you are installing from source, you will need:
+- Python 3.8 or later (for Linux, Python 3.8.1+ is needed)
+- A compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required)
+
+We highly recommend installing an [Anaconda](https://www.anaconda.com/download) environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.
+
+If you want to compile with CUDA support, [select a supported version of CUDA from our support matrix](https://pytorch.org/get-started/locally/), then install the following:
+- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
+- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) v8.5 or above
+- [Compiler](https://gist.github.com/ax3l/9489132) compatible with CUDA
+
+Note: You could refer to the [cuDNN Support Matrix](https://docs.nvidia.com/deeplearning/cudnn/reference/support-matrix.html) for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware
+
+If you want to disable CUDA support, export the environment variable `USE_CUDA=0`.
+Other potentially useful environment variables may be found in `setup.py`.
+
+If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are [available here](https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/)
+
+If you want to compile with ROCm support, install
+- [AMD ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html) 4.0 and above installation
+- ROCm is currently supported only for Linux systems.
+
+If you want to disable ROCm support, export the environment variable `USE_ROCM=0`.
+Other potentially useful environment variables may be found in `setup.py`.
+
+#### Install Dependencies
+
+**Common**
+
+```bash
+conda install cmake ninja
+# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below
+pip install -r requirements.txt
+```
+
+**On Linux**
+
+```bash
+conda install intel::mkl-static intel::mkl-include
+# CUDA only: Add LAPACK support for the GPU if needed
+conda install -c pytorch magma-cuda110 # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo
+
+# (optional) If using torch.compile with inductor/triton, install the matching version of triton
+# Run from the pytorch directory after cloning
+make triton
+```
+
+**On MacOS**
+
+```bash
+# Add this package on intel x86 processor machines only
+conda install intel::mkl-static intel::mkl-include
+# Add these packages if torch.distributed is needed
+conda install pkg-config libuv
+```
+
+**On Windows**
+
+```bash
+conda install intel::mkl-static intel::mkl-include
+# Add these packages if torch.distributed is needed.
+# Distributed package support on Windows is a prototype feature and is subject to changes.
+conda install -c conda-forge libuv=1.39
+```
+
+#### Get the PyTorch Source
+```bash
+git clone --recursive https://github.com/pytorch/pytorch
+cd pytorch
+# if you are updating an existing checkout
+git submodule sync
+git submodule update --init --recursive
+```
+
+#### Install PyTorch
+**On Linux**
+
+If you would like to compile PyTorch with [new C++ ABI](https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html) enabled, then first run this command:
+```bash
+export _GLIBCXX_USE_CXX11_ABI=1
+```
+
+If you're compiling for AMD ROCm then first run this command:
+```bash
+# Only run this if you're compiling for ROCm
+python tools/amd_build/build_amd.py
+```
+
+Install PyTorch
+```bash
+export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
+python setup.py develop
+```
+
+> _Aside:_ If you are using [Anaconda](https://www.anaconda.com/distribution/#download-section), you may experience an error caused by the linker:
+>
+> ```plaintext
+> build/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized
+> collect2: error: ld returned 1 exit status
+> error: command 'g++' failed with exit status 1
+> ```
+>
+> This is caused by `ld` from the Conda environment shadowing the system `ld`. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.8.1+.
+
+**On macOS**
+
+```bash
+python3 setup.py develop
+```
+
+**On Windows**
+
+Choose Correct Visual Studio Version.
+
+PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise,
+Professional, or Community Editions. You can also install the build tools from
+https://visualstudio.microsoft.com/visual-cpp-build-tools/. The build tools *do not*
+come with Visual Studio Code by default.
+
+If you want to build legacy python code, please refer to [Building on legacy code and CUDA](https://github.com/pytorch/pytorch/blob/main/CONTRIBUTING.md#building-on-legacy-code-and-cuda)
+
+**CPU-only builds**
+
+In this mode PyTorch computations will run on your CPU, not your GPU
+
+```cmd
+conda activate
+python setup.py develop
+```
+
+Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking `CMAKE_INCLUDE_PATH` and `LIB`. The instruction [here](https://github.com/pytorch/pytorch/blob/main/docs/source/notes/windows.rst#building-from-source) is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.
+
+**CUDA based build**
+
+In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching
+
+[NVTX](https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm) is needed to build Pytorch with CUDA.
+NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox.
+Make sure that CUDA with Nsight Compute is installed after Visual Studio.
+
+Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If `ninja.exe` is detected in `PATH`, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.
+
If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.
+
+Additional libraries such as
+[Magma](https://developer.nvidia.com/magma), [oneDNN, a.k.a. MKLDNN or DNNL](https://github.com/oneapi-src/oneDNN), and [Sccache](https://github.com/mozilla/sccache) are often needed. Please refer to the [installation-helper](https://github.com/pytorch/pytorch/tree/main/.ci/pytorch/win-test-helpers/installation-helpers) to install them.
+
+You can refer to the [build_pytorch.bat](https://github.com/pytorch/pytorch/blob/main/.ci/pytorch/win-test-helpers/build_pytorch.bat) script for some other environment variables configurations
+
+
+```cmd
+cmd
+
+:: Set the environment variables after you have downloaded and unzipped the mkl package,
+:: else CMake would throw an error as `Could NOT find OpenMP`.
+set CMAKE_INCLUDE_PATH={Your directory}\mkl\include
+set LIB={Your directory}\mkl\lib;%LIB%
+
+:: Read the content in the previous section carefully before you proceed.
+:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.
+:: "Visual Studio 2019 Developer Command Prompt" will be run automatically.
+:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.
+set CMAKE_GENERATOR_TOOLSET_VERSION=14.27
+set DISTUTILS_USE_SDK=1
+for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%
+
+:: [Optional] If you want to override the CUDA host compiler
+set CUDAHOSTCXX=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe
+
+python setup.py develop
+
+```
+
+##### Adjust Build Options (Optional)
+
+You can adjust the configuration of cmake variables optionally (without building first), by doing
+the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done
+with such a step.
+
+On Linux
+```bash
+export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
+python setup.py build --cmake-only
+ccmake build # or cmake-gui build
+```
+
+On macOS
+```bash
+export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
+MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only
+ccmake build # or cmake-gui build
+```
+
+### Docker Image
+
+#### Using pre-built images
+
+You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+
+
+```bash
+docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest
+```
+
+Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.
+for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you
+should increase shared memory size either with `--ipc=host` or `--shm-size` command line options to `nvidia-docker run`.
+
+#### Building the image yourself
+
+**NOTE:** Must be built with a docker version > 18.06
+
+The `Dockerfile` is supplied to build images with CUDA 11.1 support and cuDNN v8.
+You can pass `PYTHON_VERSION=x.y` make variable to specify which Python version is to be used by Miniconda, or leave it
+unset to use the default.
+
+```bash
+make -f docker.Makefile
+# images are tagged as docker.io/${your_docker_username}/pytorch
+```
+
+You can also pass the `CMAKE_VARS="..."` environment variable to specify additional CMake variables to be passed to CMake during the build.
+See [setup.py](./setup.py) for the list of available variables.
+
+```bash
+CMAKE_VARS="BUILD_CAFFE2=ON BUILD_CAFFE2_OPS=ON" make -f docker.Makefile
+```
+
+### Building the Documentation
+
+To build documentation in various formats, you will need [Sphinx](http://www.sphinx-doc.org) and the
+readthedocs theme.
+
+```bash
+cd docs/
+pip install -r requirements.txt
+```
+You can then build the documentation by running `make ` from the
+`docs/` folder. Run `make` to get a list of all available output formats.
+
+If you get a katex error run `npm install katex`. If it persists, try
+`npm install -g katex`
+
+> Note: if you installed `nodejs` with a different package manager (e.g.,
+`conda`) then `npm` will probably install a version of `katex` that is not
+compatible with your version of `nodejs` and doc builds will fail.
+A combination of versions that is known to work is `node@6.13.1` and
+`katex@0.13.18`. To install the latter with `npm` you can run
+```npm install -g katex@0.13.18```
+
+### Previous Versions
+
+Installation instructions and binaries for previous PyTorch versions may be found
+on [our website](https://pytorch.org/previous-versions).
+
+
+## Getting Started
+
+Three-pointers to get you started:
+- [Tutorials: get you started with understanding and using PyTorch](https://pytorch.org/tutorials/)
+- [Examples: easy to understand PyTorch code across all domains](https://github.com/pytorch/examples)
+- [The API Reference](https://pytorch.org/docs/)
+- [Glossary](https://github.com/pytorch/pytorch/blob/main/GLOSSARY.md)
+
+## Resources
+
+* [PyTorch.org](https://pytorch.org/)
+* [PyTorch Tutorials](https://pytorch.org/tutorials/)
+* [PyTorch Examples](https://github.com/pytorch/examples)
+* [PyTorch Models](https://pytorch.org/hub/)
+* [Intro to Deep Learning with PyTorch from Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)
+* [Intro to Machine Learning with PyTorch from Udacity](https://www.udacity.com/course/intro-to-machine-learning-nanodegree--nd229)
+* [Deep Neural Networks with PyTorch from Coursera](https://www.coursera.org/learn/deep-neural-networks-with-pytorch)
+* [PyTorch Twitter](https://twitter.com/PyTorch)
+* [PyTorch Blog](https://pytorch.org/blog/)
+* [PyTorch YouTube](https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw)
+
+## Communication
+* Forums: Discuss implementations, research, etc. https://discuss.pytorch.org
+* GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.
+* Slack: The [PyTorch Slack](https://pytorch.slack.com/) hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is [PyTorch Forums](https://discuss.pytorch.org). If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1
+* Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: https://eepurl.com/cbG0rv
+* Facebook Page: Important announcements about PyTorch. https://www.facebook.com/pytorch
+* For brand guidelines, please visit our website at [pytorch.org](https://pytorch.org/)
+
+## Releases and Contributing
+
+Typically, PyTorch has three minor releases a year. Please let us know if you encounter a bug by [filing an issue](https://github.com/pytorch/pytorch/issues).
+
+We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
+
+If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us.
+Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.
+
+To learn more about making a contribution to Pytorch, please see our [Contribution page](CONTRIBUTING.md). For more information about PyTorch releases, see [Release page](RELEASE.md).
+
+## The Team
+
+PyTorch is a community-driven project with several skillful engineers and researchers contributing to it.
+
+PyTorch is currently maintained by [Soumith Chintala](http://soumith.ch), [Gregory Chanan](https://github.com/gchanan), [Dmytro Dzhulgakov](https://github.com/dzhulgakov), [Edward Yang](https://github.com/ezyang), and [Nikita Shulga](https://github.com/malfet) with major contributions coming from hundreds of talented individuals in various forms and means.
+A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Koepf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.
+
+Note: This project is unrelated to [hughperkins/pytorch](https://github.com/hughperkins/pytorch) with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.
+
+## License
+
+PyTorch has a BSD-style license, as found in the [LICENSE](LICENSE) file.
+
+
diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/NOTICE b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..6effb8b5d70709f90835f2f5d646352fd77b6943
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/NOTICE
@@ -0,0 +1,456 @@
+=======================================================================
+Software under third_party
+=======================================================================
+Software libraries under third_party are provided as github submodule
+links, and their content is not part of the Caffe2 codebase. Their
+licences can be found under the respective software repositories.
+
+=======================================================================
+Earlier BSD License
+=======================================================================
+Early development of Caffe2 in 2015 and early 2016 is licensed under the
+BSD license. The license is attached below:
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+
+All contributions by Kakao Brain:
+Copyright 2019-2020 Kakao Brain
+
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+
+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.
+
+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 OWNER 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.
+
+
+=======================================================================
+Caffe's BSD License
+=======================================================================
+Some parts of the caffe2 code is derived from the original Caffe code, which is
+created by Yangqing Jia and is now a BSD-licensed open-source project. The Caffe
+license is as follows:
+
+COPYRIGHT
+
+All contributions by the University of California:
+Copyright (c) 2014, The Regents of the University of California (Regents)
+All rights reserved.
+
+All other contributions:
+Copyright (c) 2014, the respective contributors
+All rights reserved.
+
+Caffe uses a shared copyright model: each contributor holds copyright over
+their contributions to Caffe. The project versioning records all such
+contribution and copyright details. If a contributor wants to further mark
+their specific copyright on a particular contribution, they should indicate
+their copyright solely in the commit message of the change when it is
+committed.
+
+LICENSE
+
+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
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+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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+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.
+
+CONTRIBUTION AGREEMENT
+
+By contributing to the BVLC/caffe repository through pull-request, comment,
+or otherwise, the contributor releases their content to the
+license and copyright terms herein.
+
+=======================================================================
+Caffe2's Apache License
+=======================================================================
+
+This repo contains Caffe2 code, which was previously licensed under
+Apache License Version 2.0:
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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+ The Python Imaging Library (PIL) is
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+ Copyright © 1997-2011 by Secret Labs AB
+ Copyright © 1995-2011 by Fredrik Lundh
+
+ Pillow is the friendly PIL fork. It is
+
+ Copyright © 2010-2022 by Alex Clark and contributors
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diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/RECORD b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..aa3b07244cd979c3edf030ba8517ab872d263406
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/RECORD
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diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/WHEEL b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..d77a3981e5ea20c488afb479f81f1868349f78c3
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.34.2)
+Root-Is-Purelib: false
+Tag: cp310-cp310-linux_x86_64
+
diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/entry_points.txt b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81f31e07756d03476390e468e9eb4583eb4157ec
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/entry_points.txt
@@ -0,0 +1,8 @@
+[console_scripts]
+convert-caffe2-to-onnx = caffe2.python.onnx.bin.conversion:caffe2_to_onnx
+convert-onnx-to-caffe2 = caffe2.python.onnx.bin.conversion:onnx_to_caffe2
+torchrun = torch.distributed.run:main
+
+[torchrun.logs_specs]
+default = torch.distributed.elastic.multiprocessing:DefaultLogsSpecs
+
diff --git a/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/top_level.txt b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..90d81bec1d35eb3996334268974885a5398b6c6c
--- /dev/null
+++ b/llmeval-env/lib/python3.10/site-packages/torch-2.3.0.dist-info/top_level.txt
@@ -0,0 +1,3 @@
+functorch
+torch
+torchgen