
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


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





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase							):
    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     _SCREAMING_SNAKE_CASE     (					_A       ):
      @staticmethod
      def A_	(	lowerCamelCase       ):
          snake_case__  = 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=__SCREAMING_SNAKE_CASE						,		required=__SCREAMING_SNAKE_CASE						,		help="The model name, including owner/organization, as seen on the hub."						,		)
          train_parser.add_argument(
              "--local-dir"						,		type=__SCREAMING_SNAKE_CASE						,		default=""						,		help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"						,		)
          train_parser.add_argument(
              "--max-error"						,		type=__SCREAMING_SNAKE_CASE						,		default=__SCREAMING_SNAKE_CASE						,		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=__SCREAMING_SNAKE_CASE						,		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=__SCREAMING_SNAKE_CASE						,		default=__SCREAMING_SNAKE_CASE						,		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=__SCREAMING_SNAKE_CASE       )
      @staticmethod
      def A_	(	lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = set(pt_outputs.keys()       )
          snake_case__  = 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(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=""       ):
              # 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(__SCREAMING_SNAKE_CASE						,		torch.Tensor       ):
                  snake_case__  = np.max(np.abs(pt_out.numpy() - tf_out.numpy()       )       )
                  snake_case__  = tensor_difference
              else:
                  snake_case__  = attr_name
                  for i, pt_item in enumerate(__SCREAMING_SNAKE_CASE       ):
                      # If it is a named attribute, we keep the name. Otherwise, just its index.
                      if isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       ):
                          snake_case__  = root_name + pt_item
                          snake_case__  = tf_out[pt_item]
                          snake_case__  = pt_out[pt_item]
                      else:
                          snake_case__  = root_name + F"""[{i}]"""
                          snake_case__  = tf_out[i]
                      snake_case__  = _find_pt_tf_differences(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )

              return differences

          return _find_pt_tf_differences(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		{}       )
      def __init__(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		*lowerCamelCase						,		):
          snake_case__  = logging.get_logger("transformers-cli/pt_to_tf"       )
          snake_case__  = model_name
          snake_case__  = local_dir if local_dir else os.path.join("/tmp"						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = max_error
          snake_case__  = new_weights
          snake_case__  = no_pr
          snake_case__  = push
          snake_case__  = extra_commit_description
          snake_case__  = override_model_class
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):

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

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

          snake_case__  = set(inspect.signature(pt_model.forward       ).parameters.keys()       )
          snake_case__  = {}
          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:
              snake_case__  = load_dataset("cifar10"						,		"plain_text"						,		split="test"       )[:2]["img"]
              processor_inputs.update({"images": sample_images}       )
          if "input_features" in model_forward_signature:
              snake_case__  = 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:
                  snake_case__  = feature_extractor_signature["padding"].default
                  if default_strategy is not False and default_strategy is not None:
                      snake_case__  = default_strategy
                  else:
                      snake_case__  = True
              else:
                  snake_case__  = 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}       )
          snake_case__  = processor(**__SCREAMING_SNAKE_CASE						,		return_tensors="pt"       )
          snake_case__  = processor(**__SCREAMING_SNAKE_CASE						,		return_tensors="tf"       )

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

          return pt_input, tf_input



      def A_	(	self       ):
          # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
          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
          snake_case__  = 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
          snake_case__  = AutoConfig.from_pretrained(self._local_dir       )
          snake_case__  = config.architectures
          if self._override_model_class is not None:
              if self._override_model_class.startswith("TF"       ):
                  snake_case__  = [self._override_model_class[2:]]
              else:
                  snake_case__  = [self._override_model_class]
              try:
                  snake_case__  = getattr(import_module("transformers"       )						,		architectures[0]       )
              except AttributeError:
                  raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""       )
              try:
                  snake_case__  = 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
              snake_case__  = getattr(import_module("transformers"       )						,		"AutoModel"       )
              snake_case__  = getattr(import_module("transformers"       )						,		"TFAutoModel"       )
              self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"       )
          else:  # Architecture defined -- use it
              if len(__SCREAMING_SNAKE_CASE       ) > 1:
                  raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""       )
              self._logger.warning(F"""Detected architecture: {architectures[0]}"""       )
              snake_case__  = getattr(import_module("transformers"       )						,		architectures[0]       )
              try:
                  snake_case__  = 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
          snake_case__  = tf_class.from_config(__SCREAMING_SNAKE_CASE       )
          snake_case__  = 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
          snake_case__  = pt_class.from_pretrained(self._local_dir       )
          pt_model.eval()

          snake_case__  ,    snake_case__  = self.get_inputs(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )

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

          snake_case__  = tf_class.from_pretrained(self._local_dir						,		from_pt=__SCREAMING_SNAKE_CASE       )
          snake_case__  = tf_from_pt_model(**__SCREAMING_SNAKE_CASE						,		output_hidden_states=__SCREAMING_SNAKE_CASE						,		training=__SCREAMING_SNAKE_CASE       )

          # Confirms that cross loading PT weights into TF worked.
          snake_case__  = self.find_pt_tf_differences(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = {k: v for k, v in crossload_differences.items() if "hidden" not in k}
          snake_case__  = {k: v for k, v in crossload_differences.items() if "hidden" in k}
          if len(__SCREAMING_SNAKE_CASE       ) == 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'"       )
          snake_case__  = max(output_differences.values()       ) if output_differences else 0.0
          snake_case__  = 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
          snake_case__  = os.path.join(self._local_dir						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = os.path.join(self._local_dir						,		__SCREAMING_SNAKE_CASE       )
          if (not os.path.exists(__SCREAMING_SNAKE_CASE       ) and not os.path.exists(__SCREAMING_SNAKE_CASE       )) 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

          snake_case__  = tf_class.from_pretrained(self._local_dir       )
          snake_case__  = tf_model(**__SCREAMING_SNAKE_CASE						,		output_hidden_states=__SCREAMING_SNAKE_CASE       )

          snake_case__  = self.find_pt_tf_differences(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE       )
          snake_case__  = {k: v for k, v in conversion_differences.items() if "hidden" not in k}
          snake_case__  = {k: v for k, v in conversion_differences.items() if "hidden" in k}
          if len(__SCREAMING_SNAKE_CASE       ) == 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'"       )
          snake_case__  = max(output_differences.values()       ) if output_differences else 0.0
          snake_case__  = 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]       )       )

          snake_case__  = "Update TF weights" if self._new_weights else "Add TF weights"
          if self._push:
              repo.git_add(auto_lfs_track=__SCREAMING_SNAKE_CASE       )
              repo.git_commit(__SCREAMING_SNAKE_CASE       )
              repo.git_push(blocking=__SCREAMING_SNAKE_CASE       )  # 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..."       )
              snake_case__  = (
                  "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(__SCREAMING_SNAKE_CASE       ):
                  snake_case__  = [
                      CommitOperationAdd(path_in_repo=__SCREAMING_SNAKE_CASE						,		path_or_fileobj=__SCREAMING_SNAKE_CASE       )
                  ]
                  for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"       ):
                      operations += [
                          CommitOperationAdd(path_in_repo=os.path.basename(__SCREAMING_SNAKE_CASE       )						,		path_or_fileobj=__SCREAMING_SNAKE_CASE       )
                      ]
              else:
                  snake_case__  = [CommitOperationAdd(path_in_repo=__SCREAMING_SNAKE_CASE						,		path_or_fileobj=__SCREAMING_SNAKE_CASE       )]

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






"""simple docstring"""




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
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					start_index
					while not lines[end_index].startswith(lowercase__      ):
										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 |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		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.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{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.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(lowercase__      ):
										A			=					None
										if attr_name.endswith("Tokenizer"      ):
															A			=					slow_tokenizers
															A			=					attr_name[:-9]
										elif attr_name.endswith("TokenizerFast"      ):
															A			=					fast_tokenizers
															A			=					attr_name[:-13]
										elif _re_tf_models.match(lowercase__      ) is not None:
															A			=					tf_models
															A			=					_re_tf_models.match(lowercase__      ).groups()[0]
										elif _re_flax_models.match(lowercase__      ) is not None:
															A			=					flax_models
															A			=					_re_flax_models.match(lowercase__      ).groups()[0]
										elif _re_pt_models.match(lowercase__      ) is not None:
															A			=					pt_models
															A			=					_re_pt_models.match(lowercase__      ).groups()[0]

										if lookup_dict is not None:
															while len(lowercase__      ) > 0:
																				if attr_name in model_name_to_prefix.values():
																									A			=					True
																									break
																				# Try again after removing the last word in the name
																				A			=					"".join(camel_case_split(lowercase__      )[:-1]      )

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["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).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

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

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    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(lowercase__			,							lowercase__      ) for l, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					return table
def    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

					if current_table != new_table:
										if overwrite:
															with open(os.path.join(lowercase__			,							"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__":
	__A     :    Any    =		argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	__A     :    str    =		parser.parse_args()

	check_model_table(args.fix_and_overwrite)





