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llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/INSTALLER

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llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/LICENSE

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llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/METADATA

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+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: [email protected]
+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'
+
+<!---
+Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/huggingface/accelerate/main/docs/source/imgs/accelerate_logo.png" width="400"/>
+    <br>
+<p>
+
+<p align="center">
+    <!-- Uncomment when CircleCI is set up
+    <a href="https://circleci.com/gh/huggingface/accelerate">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
+    </a>
+    -->
+    <a href="https://github.com/huggingface/accelerate/blob/main/LICENSE">
+        <img alt="License" src="https://img.shields.io/github/license/huggingface/accelerate.svg?color=blue">
+    </a>
+    <a href="https://huggingface.co/docs/accelerate/index.html">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/accelerate/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    </a>
+    <a href="https://github.com/huggingface/accelerate/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/accelerate.svg">
+    </a>
+    <a href="https://github.com/huggingface/accelerate/blob/main/CODE_OF_CONDUCT.md">
+        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
+    </a>
+</p>
+
+<h3 align="center">
+<p>Run your *raw* PyTorch training script on any kind of device
+</h3>
+
+<h3 align="center">
+    <a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/accelerate/main/docs/source/imgs/course_banner.png"></a>
+</h3>
+
+## 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). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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}
+}
+```

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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
+

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

llmeval-env/lib/python3.10/site-packages/accelerate-0.30.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+accelerate

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"""
+    (?<!\.)  # doesn't start with a .
+    (\d+_)*\d+  # digits, possibly _ separated
+    (
+        (\.(\d+_)*\d+)?  # optional fractional part
+        e[+\-]?(\d+_)*\d+  # exponent part
+    |
+        \.(\d+_)*\d+  # required fractional part
+    )
+    """,
+    re.IGNORECASE | re.VERBOSE,
+)
+
+# internal the tokens and keep references to them
+TOKEN_ADD = intern("add")
+TOKEN_ASSIGN = intern("assign")
+TOKEN_COLON = intern("colon")
+TOKEN_COMMA = intern("comma")
+TOKEN_DIV = intern("div")
+TOKEN_DOT = intern("dot")
+TOKEN_EQ = intern("eq")
+TOKEN_FLOORDIV = intern("floordiv")
+TOKEN_GT = intern("gt")
+TOKEN_GTEQ = intern("gteq")
+TOKEN_LBRACE = intern("lbrace")
+TOKEN_LBRACKET = intern("lbracket")
+TOKEN_LPAREN = intern("lparen")
+TOKEN_LT = intern("lt")
+TOKEN_LTEQ = intern("lteq")
+TOKEN_MOD = intern("mod")
+TOKEN_MUL = intern("mul")
+TOKEN_NE = intern("ne")
+TOKEN_PIPE = intern("pipe")
+TOKEN_POW = intern("pow")
+TOKEN_RBRACE = intern("rbrace")
+TOKEN_RBRACKET = intern("rbracket")
+TOKEN_RPAREN = intern("rparen")
+TOKEN_SEMICOLON = intern("semicolon")
+TOKEN_SUB = intern("sub")
+TOKEN_TILDE = intern("tilde")
+TOKEN_WHITESPACE = intern("whitespace")
+TOKEN_FLOAT = intern("float")
+TOKEN_INTEGER = intern("integer")
+TOKEN_NAME = intern("name")
+TOKEN_STRING = intern("string")
+TOKEN_OPERATOR = intern("operator")
+TOKEN_BLOCK_BEGIN = intern("block_begin")
+TOKEN_BLOCK_END = intern("block_end")
+TOKEN_VARIABLE_BEGIN = intern("variable_begin")
+TOKEN_VARIABLE_END = intern("variable_end")
+TOKEN_RAW_BEGIN = intern("raw_begin")
+TOKEN_RAW_END = intern("raw_end")
+TOKEN_COMMENT_BEGIN = intern("comment_begin")
+TOKEN_COMMENT_END = intern("comment_end")
+TOKEN_COMMENT = intern("comment")
+TOKEN_LINESTATEMENT_BEGIN = intern("linestatement_begin")
+TOKEN_LINESTATEMENT_END = intern("linestatement_end")
+TOKEN_LINECOMMENT_BEGIN = intern("linecomment_begin")
+TOKEN_LINECOMMENT_END = intern("linecomment_end")
+TOKEN_LINECOMMENT = intern("linecomment")
+TOKEN_DATA = intern("data")
+TOKEN_INITIAL = intern("initial")
+TOKEN_EOF = intern("eof")
+
+# bind operators to token types
+operators = {
+    "+": TOKEN_ADD,
+    "-": TOKEN_SUB,
+    "/": TOKEN_DIV,
+    "//": TOKEN_FLOORDIV,
+    "*": TOKEN_MUL,
+    "%": TOKEN_MOD,
+    "**": TOKEN_POW,
+    "~": TOKEN_TILDE,
+    "[": TOKEN_LBRACKET,
+    "]": TOKEN_RBRACKET,
+    "(": TOKEN_LPAREN,
+    ")": TOKEN_RPAREN,
+    "{": TOKEN_LBRACE,
+    "}": TOKEN_RBRACE,
+    "==": TOKEN_EQ,
+    "!=": TOKEN_NE,
+    ">": 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<raw_begin>{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
+                )

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

llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/INSTALLER

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

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.

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 <[email protected]>
+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
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10256836.svg)](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=<path_to_nemo_model> \
+    --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=<path_to_nemo_model>,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=<path_to_nemo_model>,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=<number of devices> --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 <xxx>` 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="<some system prompt here>"` 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 <task1,task2,...> \
+>     --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 `<task_name>_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}
+}
+```

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

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
+

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"}

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

llmeval-env/lib/python3.10/site-packages/lm_eval-0.4.2.dist-info/top_level.txt

@@ -0,0 +1 @@
+lm_eval

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()

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)

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()