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

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_											=						logging.get_logger(__name__)

a_											=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_											=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

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






    for attribute in key.split("."       ):
        snake_case_ :    Optional[int]     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    if weight_type is not None:
        snake_case_ :    Union[str, Any]     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ).shape
    else:
        snake_case_ :    Any     			=						hf_pointer.shape

    assert hf_shape == value.shape, (
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'
    )

    if weight_type == "weight":
        snake_case_ :    List[Any]     			=						value
    elif weight_type == "weight_g":
        snake_case_ :    Dict     			=						value
    elif weight_type == "weight_v":
        snake_case_ :    int     			=						value
    elif weight_type == "bias":
        snake_case_ :    str     			=						value
    else:
        snake_case_ :    Tuple     			=						value

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )

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






    snake_case_ :    Union[str, Any]     			=						[]
    snake_case_ :    List[Any]     			=						fairseq_model.state_dict()

    snake_case_ :    int     			=						hf_model.feature_extractor

    # if encoder has different dim to decoder -> use proj_weight
    snake_case_ :    int     			=						None

    for name, value in fairseq_dict.items():
        snake_case_ :    Union[str, Any]     			=						False
        if "conv_layers" in name:
            load_conv_layer(
                __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		hf_model.config.feat_extract_norm == "group",		)
            snake_case_ :    str     			=						True
        elif name.split("."       )[0] == "proj":
            snake_case_ :    List[str]     			=						fairseq_model.proj
            snake_case_ :    Optional[int]     			=						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                    snake_case_ :    Optional[Any]     			=						True
                    if "*" in mapped_key:
                        snake_case_ :    int     			=						name.split(__SCREAMING_SNAKE_CASE       )[0].split("."       )[-2]
                        snake_case_ :    List[Any]     			=						mapped_key.replace("*",		__SCREAMING_SNAKE_CASE       )
                    if "weight_g" in name:
                        snake_case_ :    Optional[int]     			=						"weight_g"
                    elif "weight_v" in name:
                        snake_case_ :    Dict     			=						"weight_v"
                    elif "bias" in name:
                        snake_case_ :    List[Any]     			=						"bias"
                    elif "weight" in name:
                        snake_case_ :    Optional[int]     			=						"weight"
                    else:
                        snake_case_ :    Dict     			=						None
                    set_recursively(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                continue
        if not is_used:
            unused_weights.append(__SCREAMING_SNAKE_CASE       )

    logger.warning(f'Unused weights: {unused_weights}'       )

    return proj_weight

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






    snake_case_ :    Tuple     			=						full_name.split("conv_layers."       )[-1]
    snake_case_ :    Tuple     			=						name.split("."       )
    snake_case_ :    Any     			=						int(items[0]       )
    snake_case_ :    Tuple     			=						int(items[1]       )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            snake_case_ :    str     			=						value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
            )
            snake_case_ :    Union[str, Any]     			=						value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                " found."
            )
            snake_case_ :    Optional[Any]     			=						value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            snake_case_ :    int     			=						value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
    else:
        unused_weights.append(__SCREAMING_SNAKE_CASE       )

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






    snake_case_			, snake_case_ :    Tuple     			=						emb.weight.shape
    snake_case_ :    Optional[int]     			=						nn.Linear(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		bias=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						emb.weight.data
    return lin_layer

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






    with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8"       ) as f:
        snake_case_ :    Any     			=						f.readlines()
        snake_case_ :    List[str]     			=						[line.split(" "       )[0] for line in lines]

    snake_case_ :    Any     			=						len(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Optional[Any]     			=						{
        "<s>": 0,
        "<pad>": 1,
        "</s>": 2,
        "<unk>": 3,
    }

    vocab_dict.update(dict(zip(__SCREAMING_SNAKE_CASE,		range(4,		num_words + 4       )       )       )       )
    return vocab_dict

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						WavaVecaConfig.from_pretrained(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[str]     			=						SpeechaTextaConfig.from_pretrained(
        __SCREAMING_SNAKE_CASE,		vocab_size=__SCREAMING_SNAKE_CASE,		decoder_layers=__SCREAMING_SNAKE_CASE,		do_stable_layer_norm=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Optional[Any]     			=						WavaVecaFeatureExtractor(
        feature_size=1,		sampling_rate=1_6_0_0_0,		padding_value=0,		do_normalize=__SCREAMING_SNAKE_CASE,		return_attention_mask=__SCREAMING_SNAKE_CASE,		)

    snake_case_			, snake_case_			, snake_case_ :    List[str]     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path],		arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
    snake_case_ :    List[Any]     			=						model[0].eval()

    # set weights for wav2vec2 encoder
    snake_case_ :    Tuple     			=						WavaVecaModel(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						recursively_load_weights_wavaveca(model.encoder,		__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Any     			=						SpeechaTextaForCausalLM(__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Tuple     			=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),		strict=__SCREAMING_SNAKE_CASE       )

    # set output linear layer
    unexpected_keys.remove("embed_out"       )
    snake_case_ :    Union[str, Any]     			=						nn.Parameter(model.decoder.embed_out.detach()       )

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'       )
    logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'       )

    snake_case_ :    Union[str, Any]     			=						SpeechEncoderDecoderModel(encoder=__SCREAMING_SNAKE_CASE,		decoder=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						False

    # add projection layer
    snake_case_ :    Optional[int]     			=						nn.Parameter(projection_layer.weight       )
    snake_case_ :    Optional[int]     			=						nn.Parameter(projection_layer.bias       )

    snake_case_ :    Tuple     			=						create_vocab_dict(__SCREAMING_SNAKE_CASE       )

    with open(os.path.join(__SCREAMING_SNAKE_CASE,		"vocab.json"       ),		"w"       ) as fp:
        json.dump(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Union[str, Any]     			=						SpeechaTextaTokenizer(os.path.join(__SCREAMING_SNAKE_CASE,		"vocab.json"       )       )
    tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    int     			=						hf_wavavec.config.to_dict()
    snake_case_ :    List[Any]     			=						tokenizer.pad_token_id
    snake_case_ :    Union[str, Any]     			=						tokenizer.bos_token_id
    snake_case_ :    List[str]     			=						tokenizer.eos_token_id
    snake_case_ :    Any     			=						"speech_to_text_2"
    snake_case_ :    Any     			=						"wav2vec2"

    snake_case_ :    Optional[int]     			=						SpeechEncoderDecoderConfig.from_dict(__SCREAMING_SNAKE_CASE       )

    hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE       )
    feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  parser.add_argument(
      "--encoder_config_path",
      default="facebook/wav2vec2-large-lv60",
      type=str,
      help="Path to hf encoder wav2vec2 checkpoint config",
  )
  parser.add_argument(
      "--decoder_config_path",
      default="facebook/s2t-small-mustc-en-fr-st",
      type=str,
      help="Path to hf decoder s2t checkpoint config",
  )
  parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
  parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

  a_											=						parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.dict_path,
      encoder_config_path=args.encoder_config_path,
      decoder_config_path=args.decoder_config_path,
      vocab_size=args.vocab_size,
      num_decoder_layers=args.num_decoder_layers,
  )






'''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)




