
'''simple docstring'''
import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						VideoMAEConfig()

    set_architecture_configs(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    if "finetuned" not in model_name:
        snake_case_ :    Dict     			=						False

    if "finetuned" in model_name:
        snake_case_ :    List[str]     			=						"huggingface/label-files"
        if "kinetics" in model_name:
            snake_case_ :    List[Any]     			=						4_0_0
            snake_case_ :    int     			=						"kinetics400-id2label.json"
        elif "ssv2" in model_name:
            snake_case_ :    Union[str, Any]     			=						1_7_4
            snake_case_ :    Any     			=						"something-something-v2-id2label.json"
        else:
            raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."       )
        snake_case_ :    str     			=						json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		repo_type="dataset"       ),		"r"       )       )
        snake_case_ :    List[str]     			=						{int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
        snake_case_ :    Optional[int]     			=						idalabel
        snake_case_ :    int     			=						{v: k for k, v in idalabel.items()}

    return config

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if "small" in model_name:
        snake_case_ :    Optional[int]     			=						3_8_4
        snake_case_ :    Tuple     			=						1_5_3_6
        snake_case_ :    Dict     			=						1_2
        snake_case_ :    int     			=						1_6
        snake_case_ :    Optional[int]     			=						1_2
        snake_case_ :    Union[str, Any]     			=						3
        snake_case_ :    int     			=						1_9_2
        snake_case_ :    Optional[int]     			=						7_6_8
    elif "large" in model_name:
        snake_case_ :    Any     			=						1_0_2_4
        snake_case_ :    List[str]     			=						4_0_9_6
        snake_case_ :    Tuple     			=						2_4
        snake_case_ :    Tuple     			=						1_6
        snake_case_ :    Union[str, Any]     			=						1_2
        snake_case_ :    List[str]     			=						8
        snake_case_ :    Optional[int]     			=						5_1_2
        snake_case_ :    Optional[Any]     			=						2_0_4_8
    elif "huge" in model_name:
        snake_case_ :    List[Any]     			=						1_2_8_0
        snake_case_ :    Dict     			=						5_1_2_0
        snake_case_ :    List[Any]     			=						3_2
        snake_case_ :    Optional[int]     			=						1_6
        snake_case_ :    List[str]     			=						1_2
        snake_case_ :    List[Any]     			=						8
        snake_case_ :    Optional[int]     			=						6_4_0
        snake_case_ :    Any     			=						2_5_6_0
    elif "base" not in model_name:
        raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if "encoder." in name:
        snake_case_ :    int     			=						name.replace("encoder.",		""       )
    if "cls_token" in name:
        snake_case_ :    Dict     			=						name.replace("cls_token",		"videomae.embeddings.cls_token"       )
    if "decoder_pos_embed" in name:
        snake_case_ :    Tuple     			=						name.replace("decoder_pos_embed",		"decoder.decoder_pos_embed"       )
    if "pos_embed" in name and "decoder" not in name:
        snake_case_ :    Dict     			=						name.replace("pos_embed",		"videomae.embeddings.position_embeddings"       )
    if "patch_embed.proj" in name:
        snake_case_ :    List[Any]     			=						name.replace("patch_embed.proj",		"videomae.embeddings.patch_embeddings.projection"       )
    if "patch_embed.norm" in name:
        snake_case_ :    str     			=						name.replace("patch_embed.norm",		"videomae.embeddings.norm"       )
    if "decoder.blocks" in name:
        snake_case_ :    Optional[int]     			=						name.replace("decoder.blocks",		"decoder.decoder_layers"       )
    if "blocks" in name:
        snake_case_ :    int     			=						name.replace("blocks",		"videomae.encoder.layer"       )
    if "attn.proj" in name:
        snake_case_ :    Union[str, Any]     			=						name.replace("attn.proj",		"attention.output.dense"       )
    if "attn" in name and "bias" not in name:
        snake_case_ :    Tuple     			=						name.replace("attn",		"attention.self"       )
    if "attn" in name:
        snake_case_ :    Tuple     			=						name.replace("attn",		"attention.attention"       )
    if "norm1" in name:
        snake_case_ :    int     			=						name.replace("norm1",		"layernorm_before"       )
    if "norm2" in name:
        snake_case_ :    List[Any]     			=						name.replace("norm2",		"layernorm_after"       )
    if "mlp.fc1" in name:
        snake_case_ :    Any     			=						name.replace("mlp.fc1",		"intermediate.dense"       )
    if "mlp.fc2" in name:
        snake_case_ :    int     			=						name.replace("mlp.fc2",		"output.dense"       )
    if "decoder_embed" in name:
        snake_case_ :    List[str]     			=						name.replace("decoder_embed",		"decoder.decoder_embed"       )
    if "decoder_norm" in name:
        snake_case_ :    Union[str, Any]     			=						name.replace("decoder_norm",		"decoder.decoder_norm"       )
    if "decoder_pred" in name:
        snake_case_ :    Optional[Any]     			=						name.replace("decoder_pred",		"decoder.decoder_pred"       )
    if "norm.weight" in name and "decoder" not in name and "fc" not in name:
        snake_case_ :    List[str]     			=						name.replace("norm.weight",		"videomae.layernorm.weight"       )
    if "norm.bias" in name and "decoder" not in name and "fc" not in name:
        snake_case_ :    Optional[Any]     			=						name.replace("norm.bias",		"videomae.layernorm.bias"       )
    if "head" in name and "decoder" not in name:
        snake_case_ :    Optional[int]     			=						name.replace("head",		"classifier"       )

    return name

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        snake_case_ :    Dict     			=						orig_state_dict.pop(__SCREAMING_SNAKE_CASE       )

        if key.startswith("encoder."       ):
            snake_case_ :    List[str]     			=						key.replace("encoder.",		""       )

        if "qkv" in key:
            snake_case_ :    Optional[Any]     			=						key.split("."       )
            if key.startswith("decoder.blocks"       ):
                snake_case_ :    str     			=						config.decoder_hidden_size
                snake_case_ :    str     			=						int(key_split[2]       )
                snake_case_ :    int     			=						"decoder.decoder_layers."
                if "weight" in key:
                    snake_case_ :    Any     			=						val[:dim, :]
                    snake_case_ :    Any     			=						val[dim : dim * 2, :]
                    snake_case_ :    Union[str, Any]     			=						val[-dim:, :]
            else:
                snake_case_ :    Dict     			=						config.hidden_size
                snake_case_ :    List[str]     			=						int(key_split[1]       )
                snake_case_ :    Dict     			=						"videomae.encoder.layer."
                if "weight" in key:
                    snake_case_ :    Optional[Any]     			=						val[:dim, :]
                    snake_case_ :    Dict     			=						val[dim : dim * 2, :]
                    snake_case_ :    Optional[Any]     			=						val[-dim:, :]
        else:
            snake_case_ :    Union[str, Any]     			=						val

    return orig_state_dict

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    List[str]     			=						hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video",		filename="eating_spaghetti.npy",		repo_type="dataset"       )
    snake_case_ :    Union[str, Any]     			=						np.load(__SCREAMING_SNAKE_CASE       )
    return list(__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						get_videomae_config(__SCREAMING_SNAKE_CASE       )

    if "finetuned" in model_name:
        snake_case_ :    int     			=						VideoMAEForVideoClassification(__SCREAMING_SNAKE_CASE       )
    else:
        snake_case_ :    str     			=						VideoMAEForPreTraining(__SCREAMING_SNAKE_CASE       )

    # download original checkpoint, hosted on Google Drive
    snake_case_ :    Dict     			=						"pytorch_model.bin"
    gdown.cached_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		quiet=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						torch.load(__SCREAMING_SNAKE_CASE,		map_location="cpu"       )
    if "model" in files:
        snake_case_ :    str     			=						files["model"]
    else:
        snake_case_ :    Optional[Any]     			=						files["module"]
    snake_case_ :    Any     			=						convert_state_dict(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    model.load_state_dict(__SCREAMING_SNAKE_CASE       )
    model.eval()

    # verify model on basic input
    snake_case_ :    Tuple     			=						VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],		image_std=[0.5, 0.5, 0.5]       )
    snake_case_ :    Union[str, Any]     			=						prepare_video()
    snake_case_ :    str     			=						image_processor(__SCREAMING_SNAKE_CASE,		return_tensors="pt"       )

    if "finetuned" not in model_name:
        snake_case_ :    Tuple     			=						hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos",		filename="bool_masked_pos.pt"       )
        snake_case_ :    List[str]     			=						torch.load(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						outputs.logits

    snake_case_ :    Dict     			=						[
        "videomae-small-finetuned-kinetics",
        "videomae-small-finetuned-ssv2",
        # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
        "videomae-base-short",
        "videomae-base-short-finetuned-kinetics",
        "videomae-base",
        "videomae-base-finetuned-kinetics",
        "videomae-large",
        "videomae-large-finetuned-kinetics",
        "videomae-huge-finetuned-kinetics",
        # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
        "videomae-base-short-ssv2",
        "videomae-base-short-finetuned-ssv2",
        "videomae-base-ssv2",
        "videomae-base-finetuned-ssv2",
    ]

    # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
    if model_name == "videomae-small-finetuned-kinetics":
        snake_case_ :    Tuple     			=						torch.Size([1, 4_0_0]       )
        snake_case_ :    Dict     			=						torch.tensor([-0.9291, -0.4061, -0.9307]       )
    elif model_name == "videomae-small-finetuned-ssv2":
        snake_case_ :    Union[str, Any]     			=						torch.Size([1, 1_7_4]       )
        snake_case_ :    str     			=						torch.tensor([0.2671, -0.4689, -0.8235]       )
    elif model_name == "videomae-base":
        snake_case_ :    List[str]     			=						torch.Size([1, 1_4_0_8, 1_5_3_6]       )
        snake_case_ :    Dict     			=						torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]       )
    elif model_name == "videomae-base-short":
        snake_case_ :    Any     			=						torch.Size([1, 1_4_0_8, 1_5_3_6]       )
        snake_case_ :    Any     			=						torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]       )
        # we verified the loss both for normalized and unnormalized targets for this one
        snake_case_ :    Tuple     			=						torch.tensor([0.5142]       ) if config.norm_pix_loss else torch.tensor([0.6469]       )
    elif model_name == "videomae-large":
        snake_case_ :    Union[str, Any]     			=						torch.Size([1, 1_4_0_8, 1_5_3_6]       )
        snake_case_ :    Any     			=						torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]       )
    elif model_name == "videomae-large-finetuned-kinetics":
        snake_case_ :    Tuple     			=						torch.Size([1, 4_0_0]       )
        snake_case_ :    Any     			=						torch.tensor([0.0771, 0.0011, -0.3625]       )
    elif model_name == "videomae-huge-finetuned-kinetics":
        snake_case_ :    Optional[Any]     			=						torch.Size([1, 4_0_0]       )
        snake_case_ :    str     			=						torch.tensor([0.2433, 0.1632, -0.4894]       )
    elif model_name == "videomae-base-short-finetuned-kinetics":
        snake_case_ :    Optional[int]     			=						torch.Size([1, 4_0_0]       )
        snake_case_ :    List[Any]     			=						torch.tensor([0.6588, 0.0990, -0.2493]       )
    elif model_name == "videomae-base-finetuned-kinetics":
        snake_case_ :    Optional[int]     			=						torch.Size([1, 4_0_0]       )
        snake_case_ :    Dict     			=						torch.tensor([0.3669, -0.0688, -0.2421]       )
    elif model_name == "videomae-base-short-ssv2":
        snake_case_ :    List[Any]     			=						torch.Size([1, 1_4_0_8, 1_5_3_6]       )
        snake_case_ :    Optional[Any]     			=						torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]       )
    elif model_name == "videomae-base-short-finetuned-ssv2":
        snake_case_ :    List[Any]     			=						torch.Size([1, 1_7_4]       )
        snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0537, -0.1539, -0.3266]       )
    elif model_name == "videomae-base-ssv2":
        snake_case_ :    List[str]     			=						torch.Size([1, 1_4_0_8, 1_5_3_6]       )
        snake_case_ :    Optional[Any]     			=						torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]       )
    elif model_name == "videomae-base-finetuned-ssv2":
        snake_case_ :    int     			=						torch.Size([1, 1_7_4]       )
        snake_case_ :    Union[str, Any]     			=						torch.tensor([0.1961, -0.8337, -0.6389]       )
    else:
        raise ValueError(f'Model name not supported. Should be one of {model_names}'       )

    # verify logits
    assert logits.shape == expected_shape
    if "finetuned" in model_name:
        assert torch.allclose(logits[0, :3],		__SCREAMING_SNAKE_CASE,		atol=1E-4       )
    else:
        print("Logits:",		logits[0, :3, :3]       )
        assert torch.allclose(logits[0, :3, :3],		__SCREAMING_SNAKE_CASE,		atol=1E-4       )
    print("Logits ok!"       )

    # verify loss, if applicable
    if model_name == "videomae-base-short":
        snake_case_ :    int     			=						outputs.loss
        assert torch.allclose(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		atol=1E-4       )
        print("Loss ok!"       )

    if pytorch_dump_folder_path is not None:
        print(f'Saving model and image processor to {pytorch_dump_folder_path}'       )
        image_processor.save_pretrained(__SCREAMING_SNAKE_CASE       )
        model.save_pretrained(__SCREAMING_SNAKE_CASE       )

    if push_to_hub:
        print("Pushing to the hub..."       )
        model.push_to_hub(__SCREAMING_SNAKE_CASE,		organization="nielsr"       )


if __name__ == "__main__":
     a_											=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
         type=str,
         help=(
             "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"
             " download link."
         ),
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default="/Users/nielsrogge/Documents/VideoMAE/Test",
         type=str,
         help="Path to the output PyTorch model directory.",
     )
     parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
     )

     a_											=						parser.parse_args()
     convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_											=						"src/diffusers"
a_											=						"."


# This is to make sure the diffusers module imported is the one in the repo.
a_											=						importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a_											=						spec.loader.load_module()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    return line.startswith(__SCREAMING_SNAKE_CASE       ) or len(__SCREAMING_SNAKE_CASE       ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$",		__SCREAMING_SNAKE_CASE       ) is not None

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						object_name.split("."       )
    snake_case_ :    Dict     			=						0

    # First let's find the module where our object lives.
    snake_case_ :    int     			=						parts[i]
    while i < len(__SCREAMING_SNAKE_CASE       ) and not os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       )       ):
        i += 1
        if i < len(__SCREAMING_SNAKE_CASE       ):
            snake_case_ :    List[Any]     			=						os.path.join(__SCREAMING_SNAKE_CASE,		parts[i]       )
    if i >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'       )

    with open(os.path.join(__SCREAMING_SNAKE_CASE,		f'{module}.py'       ),		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    str     			=						f.readlines()

    # Now let's find the class / func in the code!
    snake_case_ :    Any     			=						""
    snake_case_ :    Optional[Any]     			=						0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__SCREAMING_SNAKE_CASE       ) and re.search(rf'^{indent}(class|def)\s+{name}(\(|\:)',		lines[line_index]       ) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(__SCREAMING_SNAKE_CASE       ):
        raise ValueError(f' {object_name} does not match any function or class in {module}.'       )

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    snake_case_ :    Union[str, Any]     			=						line_index
    while line_index < len(__SCREAMING_SNAKE_CASE       ) and _should_continue(lines[line_index],		__SCREAMING_SNAKE_CASE       ):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]       ) <= 1:
        line_index -= 1

    snake_case_ :    Dict     			=						lines[start_index:line_index]
    return "".join(__SCREAMING_SNAKE_CASE       )


a_											=						re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_											=						re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_											=						re.compile(R"<FILL\s+[^>]*>")

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						code.split("\n"       )
    snake_case_ :    Union[str, Any]     			=						0
    while idx < len(__SCREAMING_SNAKE_CASE       ) and len(lines[idx]       ) == 0:
        idx += 1
    if idx < len(__SCREAMING_SNAKE_CASE       ):
        return re.search(r"^(\s*)\S",		lines[idx]       ).groups()[0]
    return ""

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						len(get_indent(__SCREAMING_SNAKE_CASE       )       ) > 0
    if has_indent:
        snake_case_ :    Optional[Any]     			=						f'class Bla:\n{code}'
    snake_case_ :    List[Any]     			=						black.Mode(target_versions={black.TargetVersion.PYaa},		line_length=1_1_9,		preview=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						black.format_str(__SCREAMING_SNAKE_CASE,		mode=__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Optional[Any]     			=						style_docstrings_in_code(__SCREAMING_SNAKE_CASE       )
    return result[len("class Bla:\n"       ) :] if has_indent else result

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8",		newline="\n"       ) as f:
        snake_case_ :    List[Any]     			=						f.readlines()
    snake_case_ :    str     			=						[]
    snake_case_ :    Tuple     			=						0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Union[str, Any]     			=						_re_copy_warning.search(lines[line_index]       )
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						search.groups()
        snake_case_ :    int     			=						find_code_in_diffusers(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    List[Any]     			=						get_indent(__SCREAMING_SNAKE_CASE       )

        snake_case_ :    Dict     			=						line_index + 1 if indent == theoretical_indent else line_index + 2
        snake_case_ :    Dict     			=						theoretical_indent
        snake_case_ :    Tuple     			=						start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        snake_case_ :    str     			=						True
        while line_index < len(__SCREAMING_SNAKE_CASE       ) and should_continue:
            line_index += 1
            if line_index >= len(__SCREAMING_SNAKE_CASE       ):
                break
            snake_case_ :    Dict     			=						lines[line_index]
            snake_case_ :    List[Any]     			=						_should_continue(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ) and re.search(f'^{indent}# End copy',		__SCREAMING_SNAKE_CASE       ) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]       ) <= 1:
            line_index -= 1

        snake_case_ :    Optional[int]     			=						lines[start_index:line_index]
        snake_case_ :    Optional[Any]     			=						"".join(__SCREAMING_SNAKE_CASE       )

        # Remove any nested `Copied from` comments to avoid circular copies
        snake_case_ :    int     			=						[line for line in theoretical_code.split("\n"       ) if _re_copy_warning.search(__SCREAMING_SNAKE_CASE       ) is None]
        snake_case_ :    Dict     			=						"\n".join(__SCREAMING_SNAKE_CASE       )

        # Before comparing, use the `replace_pattern` on the original code.
        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Union[str, Any]     			=						replace_pattern.replace("with",		""       ).split(","       )
            snake_case_ :    Optional[Any]     			=						[_re_replace_pattern.search(__SCREAMING_SNAKE_CASE       ) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                snake_case_			, snake_case_			, snake_case_ :    Optional[Any]     			=						pattern.groups()
                snake_case_ :    Optional[int]     			=						re.sub(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                if option.strip() == "all-casing":
                    snake_case_ :    List[Any]     			=						re.sub(obja.lower(),		obja.lower(),		__SCREAMING_SNAKE_CASE       )
                    snake_case_ :    Optional[int]     			=						re.sub(obja.upper(),		obja.upper(),		__SCREAMING_SNAKE_CASE       )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            snake_case_ :    Optional[int]     			=						blackify(lines[start_index - 1] + theoretical_code       )
            snake_case_ :    Optional[Any]     			=						theoretical_code[len(lines[start_index - 1]       ) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]       )
            if overwrite:
                snake_case_ :    List[str]     			=						lines[:start_index] + [theoretical_code] + lines[line_index:]
                snake_case_ :    List[str]     			=						start_index + 1

    if overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'       )
        with open(__SCREAMING_SNAKE_CASE,		"w",		encoding="utf-8",		newline="\n"       ) as f:
            f.writelines(__SCREAMING_SNAKE_CASE       )
    return diffs

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = False       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						glob.glob(os.path.join(__SCREAMING_SNAKE_CASE,		"**/*.py"       ),		recursive=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						[]
    for filename in all_files:
        snake_case_ :    Tuple     			=						is_copy_consistent(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__SCREAMING_SNAKE_CASE       ) > 0:
        snake_case_ :    str     			=						"\n".join(__SCREAMING_SNAKE_CASE       )
        raise Exception(
            "Found the following copy inconsistencies:\n"
            + diff
            + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  a_											=						parser.parse_args()

  check_copies(args.fix_and_overwrite)




