




"""simple docstring"""



import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


SCREAMING_SNAKE_CASE							=			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						Tuple:
 return PTtoTFCommand(
     args.model_name     ,					args.local_dir     ,					args.max_error     ,					args.new_weights     ,					args.no_pr     ,					args.push     ,					args.extra_commit_description     ,					args.override_model_class     ,					)






class       UpperCAmelCase_    (		_A				):




    @staticmethod
    def 				__magic_name__				(       snake_case_		:			List[str] ) ->				int:




     '''simple docstring'''

     A__			=							parser.add_parser(
         "pt-to-tf"      ,					help=(
             "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
             " Can also be used to validate existing weights without opening PRs, with --no-pr."
         )      ,					)
     train_parser.add_argument(
         "--model-name"      ,					type=__lowercase      ,					required=__lowercase      ,					help="The model name, including owner/organization, as seen on the hub."      ,					)
     train_parser.add_argument(
         "--local-dir"      ,					type=__lowercase      ,					default=""      ,					help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"      ,					)
     train_parser.add_argument(
         "--max-error"      ,					type=__lowercase      ,					default=__lowercase      ,					help=(
             F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
         )      ,					)
     train_parser.add_argument(
         "--new-weights"      ,					action="store_true"      ,					help="Optional flag to create new TensorFlow weights, even if they already exist."      ,					)
     train_parser.add_argument(
         "--no-pr"      ,					action="store_true"      ,					help="Optional flag to NOT open a PR with converted weights." )
     train_parser.add_argument(
         "--push"      ,					action="store_true"      ,					help="Optional flag to push the weights directly to `main` (requires permissions)"      ,					)
     train_parser.add_argument(
         "--extra-commit-description"      ,					type=__lowercase      ,					default=""      ,					help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."      ,					)
     train_parser.add_argument(
         "--override-model-class"      ,					type=__lowercase      ,					default=__lowercase      ,					help="If you think you know better than the auto-detector, you can specify the model class here. "
         "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."      ,					)
     train_parser.set_defaults(func=__lowercase )




    @staticmethod
    def 				__magic_name__				(       snake_case_		:			Union[str, Any]      ,					snake_case_		:			Dict ) ->				Tuple:




     '''simple docstring'''

     A__			=							set(pt_outputs.keys() )
     A__			=							set(tf_outputs.keys() )
     if pt_out_attrs != tf_out_attrs:
      raise ValueError(
          F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
          F""" {tf_out_attrs})""" )

     # 2. For each output attribute, computes the difference
     def _find_pt_tf_differences(snake_case_		:			Union[str, Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Tuple="" ):
      # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
      # recursivelly, keeping the name of the attribute.
      if isinstance(__lowercase      ,					torch.Tensor ):
       A__			=							np.max(np.abs(pt_out.numpy() - tf_out.numpy() ) )
       A__			=							tensor_difference
      else:
       A__			=							attr_name
       for i, pt_item in enumerate(__lowercase ):
        # If it is a named attribute, we keep the name. Otherwise, just its index.
        if isinstance(__lowercase      ,					__lowercase ):
         A__			=							root_name + pt_item
         A__			=							tf_out[pt_item]
         A__			=							pt_out[pt_item]
        else:
         A__			=							root_name + F"""[{i}]"""
         A__			=							tf_out[i]
        A__			=							_find_pt_tf_differences(__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase )

      return differences

     return _find_pt_tf_differences(__lowercase      ,					__lowercase      ,					{} )




    def __init__(       self		:			Any      ,					snake_case_		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			Tuple      ,					snake_case_		:			Union[str, Any]      ,					snake_case_		:			Any      ,					snake_case_		:			List[str]      ,					snake_case_		:			Any      ,					snake_case_		:			Union[str, Any]      ,					*snake_case_		:			Tuple      ,					) ->				Union[str, Any]:




     '''simple docstring'''

     A__			=							logging.get_logger("transformers-cli/pt_to_tf" )
     A__			=							model_name
     A__			=							local_dir if local_dir else os.path.join("/tmp"      ,					__lowercase )
     A__			=							max_error
     A__			=							new_weights
     A__			=							no_pr
     A__			=							push
     A__			=							extra_commit_description
     A__			=							override_model_class




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Tuple ) ->				List[Any]:




     '''simple docstring'''

     def _get_audio_input():
      A__			=							load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,					"clean"      ,					split="validation" )
      A__			=							ds.sort("id" ).select(range(2 ) )[:2]['''audio''']
      A__			=							[x['''array'''] for x in speech_samples]
      return raw_samples

     A__			=							type(pt_model.config )
     if model_config_class in PROCESSOR_MAPPING:
      A__			=							AutoProcessor.from_pretrained(self._local_dir )
      if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
       A__			=							processor.tokenizer.eos_token
     elif model_config_class in IMAGE_PROCESSOR_MAPPING:
      A__			=							AutoImageProcessor.from_pretrained(self._local_dir )
     elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
      A__			=							AutoFeatureExtractor.from_pretrained(self._local_dir )
     elif model_config_class in TOKENIZER_MAPPING:
      A__			=							AutoTokenizer.from_pretrained(self._local_dir )
      if processor.pad_token is None:
       A__			=							processor.eos_token
     else:
      raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})""" )

     A__			=							set(inspect.signature(pt_model.forward ).parameters.keys() )
     A__			=							{}
     if "input_ids" in model_forward_signature:
      processor_inputs.update(
          {
              "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
              "padding": True,
              "truncation": True,
          } )
     if "pixel_values" in model_forward_signature:
      A__			=							load_dataset("cifar10"      ,					"plain_text"      ,					split="test" )[:2]['''img''']
      processor_inputs.update({"images": sample_images} )
     if "input_features" in model_forward_signature:
      A__			=							inspect.signature(processor.feature_extractor ).parameters
      # Pad to the largest input length by default but take feature extractor default
      # padding value if it exists e.g. "max_length" and is not False or None
      if "padding" in feature_extractor_signature:
       A__			=							feature_extractor_signature['''padding'''].default
       if default_strategy is not False and default_strategy is not None:
        A__			=							default_strategy
       else:
        A__			=							True
      else:
       A__			=							True
      processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy} )
     if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
      processor_inputs.update({"audio": _get_audio_input(), "padding": True} )
     A__			=							processor(**__lowercase      ,					return_tensors="pt" )
     A__			=							processor(**__lowercase      ,					return_tensors="tf" )

     # Extra input requirements, in addition to the input modality
     if (
         config.is_encoder_decoder
         or (hasattr(__lowercase      ,					"encoder" ) and hasattr(__lowercase      ,					"decoder" ))
         or "decoder_input_ids" in tf_dummy_inputs
     ):
      A__			=							np.asarray([[1], [1]]      ,					dtype=__lowercase ) * (pt_model.config.decoder_start_token_id or 0)
      pt_input.update({"decoder_input_ids": torch.tensor(__lowercase )} )
      tf_input.update({"decoder_input_ids": tf.convert_to_tensor(__lowercase )} )

     return pt_input, tf_input




    def 				__magic_name__				(       self		:			Any ) ->				Optional[int]:




     '''simple docstring'''

     if version.parse(huggingface_hub.__version__ ) < version.parse("0.9.0" ):
      raise ImportError(
          "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
          " installation." )
     else:
      from huggingface_hub import Repository, create_commit
      from huggingface_hub._commit_api import CommitOperationAdd

     # Fetch remote data
     A__			=							Repository(local_dir=self._local_dir      ,					clone_from=self._model_name )

     # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
     A__			=							AutoConfig.from_pretrained(self._local_dir )
     A__			=							config.architectures
     if self._override_model_class is not None:
      if self._override_model_class.startswith("TF" ):
       A__			=							[self._override_model_class[2:]]
      else:
       A__			=							[self._override_model_class]
      try:
       A__			=							getattr(import_module("transformers" )      ,					architectures[0] )
      except AttributeError:
       raise ValueError(F"""Model class {self._override_model_class} not found in transformers.""" )
      try:
       A__			=							getattr(import_module("transformers" )      ,					"TF" + architectures[0] )
      except AttributeError:
       raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers.""" )
     elif architectures is None:  # No architecture defined -- use auto classes
      A__			=							getattr(import_module("transformers" )      ,					"AutoModel" )
      A__			=							getattr(import_module("transformers" )      ,					"TFAutoModel" )
      self._logger.warning("No detected architecture, using AutoModel/TFAutoModel" )
     else:  # Architecture defined -- use it
      if len(__lowercase ) > 1:
       raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})""" )
      self._logger.warning(F"""Detected architecture: {architectures[0]}""" )
      A__			=							getattr(import_module("transformers" )      ,					architectures[0] )
      try:
       A__			=							getattr(import_module("transformers" )      ,					"TF" + architectures[0] )
      except AttributeError:
       raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.""" )

        # Check the TF dummy inputs to see what keys we need in the forward pass
     A__			=							tf_class.from_config(__lowercase )
     A__			=							tf_from_pt_model.dummy_inputs

     del tf_from_pt_model  # Try to keep only one model in memory at a time

     # Load the model and get some basic inputs
     A__			=							pt_class.from_pretrained(self._local_dir )
     pt_model.eval()

     A__			=							self.get_inputs(__lowercase      ,					__lowercase      ,					__lowercase )

     with torch.no_grad():
      A__			=							pt_model(**__lowercase      ,					output_hidden_states=__lowercase )
     del pt_model  # will no longer be used, and may have a large memory footprint

     A__			=							tf_class.from_pretrained(self._local_dir      ,					from_pt=__lowercase )
     A__			=							tf_from_pt_model(**__lowercase      ,					output_hidden_states=__lowercase      ,					training=__lowercase )

     # Confirms that cross loading PT weights into TF worked.
     A__			=							self.find_pt_tf_differences(__lowercase      ,					__lowercase )
     A__			=							{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
     A__			=							{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
     if len(__lowercase ) == 0 and architectures is not None:
      raise ValueError(
          F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
          " output was found. All outputs start with \'hidden\'" )
     A__			=							max(output_differences.values() ) if output_differences else 0.0
     A__			=							max(hidden_differences.values() )
     if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
      raise ValueError(
          "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
          + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
          + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
          + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
          + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

     # Save the weights in a TF format (if needed) and confirms that the results are still good
     A__			=							os.path.join(self._local_dir      ,					__lowercase )
     A__			=							os.path.join(self._local_dir      ,					__lowercase )
     if (not os.path.exists(__lowercase ) and not os.path.exists(__lowercase )) or self._new_weights:
      tf_from_pt_model.save_pretrained(self._local_dir )
     del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

     A__			=							tf_class.from_pretrained(self._local_dir )
     A__			=							tf_model(**__lowercase      ,					output_hidden_states=__lowercase )

     A__			=							self.find_pt_tf_differences(__lowercase      ,					__lowercase )
     A__			=							{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
     A__			=							{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
     if len(__lowercase ) == 0 and architectures is not None:
      raise ValueError(
          F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
          " output was found. All outputs start with \'hidden\'" )
     A__			=							max(output_differences.values() ) if output_differences else 0.0
     A__			=							max(hidden_differences.values() )
     if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
      raise ValueError(
          "The converted TensorFlow model has different outputs, something went wrong!\n"
          + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
          + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
          + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
          + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

     A__			=							'''Update TF weights''' if self._new_weights else '''Add TF weights'''
     if self._push:
      repo.git_add(auto_lfs_track=__lowercase )
      repo.git_commit(__lowercase )
      repo.git_push(blocking=__lowercase )  # this prints a progress bar with the upload
      self._logger.warning(F"""TF weights pushed into {self._model_name}""" )
     elif not self._no_pr:
      self._logger.warning("Uploading the weights into a new PR..." )
      A__			=							(
          '''Model converted by the [`transformers`\' `pt_to_tf`'''
          ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
          '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
          F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
          F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
          F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
          F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
      )
      if self._max_error > MAX_ERROR:
       commit_descrition += (
           F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
       )
      if self._extra_commit_description:
       commit_descrition += "\n\n" + self._extra_commit_description

      # sharded model -> adds all related files (index and .h5 shards)
      if os.path.exists(__lowercase ):
       A__			=							[
           CommitOperationAdd(path_in_repo=__lowercase      ,					path_or_fileobj=__lowercase )
       ]
       for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5" ):
        operations += [
            CommitOperationAdd(path_in_repo=os.path.basename(__lowercase )      ,					path_or_fileobj=__lowercase )
        ]
      else:
       A__			=							[CommitOperationAdd(path_in_repo=__lowercase      ,					path_or_fileobj=__lowercase )]

      A__			=							create_commit(
          repo_id=self._model_name      ,					operations=__lowercase      ,					commit_message=__lowercase      ,					commit_description=__lowercase      ,					repo_type="model"      ,					create_pr=__lowercase      ,					).pr_url
      self._logger.warning(F"""PR open in {hub_pr_url}""" )












import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case_				=      '''src/diffusers'''
snake_case_				=      '''docs/source/en'''
snake_case_				=      '''.'''


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		with open(SCREAMING_SNAKE_CASE_				,							'''r'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
				lowerCamelCase :			List[str]    		=f.readlines()
		# Find the start prompt.
		lowerCamelCase :			List[Any]    		=0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_		):
				start_index += 1
		start_index += 1

		lowerCamelCase :			Dict    		=start_index
		while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_		):
				end_index += 1
		end_index -= 1

		while len(lines[start_index]		) <= 1:
				start_index += 1
		while len(lines[end_index]		) <= 1:
				end_index -= 1
		end_index += 1
		return "".join(lines[start_index:end_index]		), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
snake_case_				=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_				=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case_				=      re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case_				=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[Any]    		=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,							SCREAMING_SNAKE_CASE_		)
		return [m.group(0		) for m in matches]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Dict    		=2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=(width - text_length) // 2
		lowerCamelCase :			List[str]    		=width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			List[str]    		=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase :			List[Any]    		={
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		lowerCamelCase :			Optional[int]    		={name: config.replace('''ConfigMixin'''				,							''''''		) for name, config in model_name_to_config.items()}

		# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=None
				if attr_name.endswith('''Tokenizer'''		):
						lowerCamelCase :			List[str]    		=slow_tokenizers
						lowerCamelCase :			List[Any]    		=attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''		):
						lowerCamelCase :			Any    		=fast_tokenizers
						lowerCamelCase :			Optional[Any]    		=attr_name[:-1_3]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			Optional[Any]    		=tf_models
						lowerCamelCase :			Any    		=_re_tf_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[str]    		=flax_models
						lowerCamelCase :			int    		=_re_flax_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[Any]    		=pt_models
						lowerCamelCase :			List[Any]    		=_re_pt_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]

				if lookup_dict is not None:
						while len(SCREAMING_SNAKE_CASE_		) > 0:
								if attr_name in model_name_to_prefix.values():
										lowerCamelCase :			Optional[Any]    		=True
										break
								# Try again after removing the last word in the name
								lowerCamelCase :			List[str]    		=''''''.join(camel_case_split(SCREAMING_SNAKE_CASE_		)[:-1]		)

    # Let's build that table!
		lowerCamelCase :			Optional[int]    		=list(model_name_to_config.keys()		)
		model_names.sort(key=str.lower		)
		lowerCamelCase :			Union[str, Any]    		=['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
		# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
		lowerCamelCase :			Tuple    		=[len(SCREAMING_SNAKE_CASE_		) + 2 for c in columns]
		lowerCamelCase :			Dict    		=max([len(SCREAMING_SNAKE_CASE_		) for name in model_names]		) + 2

		# Build the table per se
		lowerCamelCase :			List[Any]    		='''|''' + '''|'''.join([_center_text(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for c, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + '''|\n'''
		# Use ":-----:" format to center-aligned table cell texts
		table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]		) + "|\n"

		lowerCamelCase :			Optional[Any]    		={True: '''✅''', False: '''❌'''}
		for name in model_names:
				lowerCamelCase :			str    		=model_name_to_prefix[name]
				lowerCamelCase :			Dict    		=[
				    name,
				    check[slow_tokenizers[prefix]],
				    check[fast_tokenizers[prefix]],
				    check[pt_models[prefix]],
				    check[tf_models[prefix]],
				    check[flax_models[prefix]],
				]
				table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for l, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + "|\n"
		return table


def        A__						(							SCREAMING_SNAKE_CASE_=False		) ->  int:
		lowerCamelCase		, lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Tuple    		=_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							start_prompt='''<!--This table is updated automatically from the auto modules'''				,							end_prompt='''<!-- End table-->'''				,							)
		lowerCamelCase :			List[Any]    		=get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							'''w'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]		)
				else:
						raise ValueError(
						    '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''		)


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

				check_model_table(args.fix_and_overwrite)

