
"""simple docstring"""




import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
    A__   :						Optional[int] 							=     True
    from torch.cuda.amp import autocast


A__   :						Union[str, Any] 							=     logging.getLogger(__name__)







@dataclass
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						, )
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Whether to log verbose messages or not.'}						, )





def 				a__			(					lowerCAmelCase						:							ModelArguments							,     lowerCAmelCase						:							TrainingArguments		):






     '''simple docstring'''
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"							,     datefmt="%m/%d/%Y %H:%M:%S"							,     handlers=[logging.StreamHandler(sys.stdout		)]							,     )
     UpperCAmelCase__				:			str							=    logging.WARNING
     if model_args.verbose_logging:
          UpperCAmelCase__				:			Optional[Any]							=    logging.DEBUG
     elif trainer_utils.is_main_process(training_args.local_rank		):
          UpperCAmelCase__				:			List[Any]							=    logging.INFO
     logger.setLevel(lowerCAmelCase		)







@dataclass
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'The name of the dataset to use (via the datasets library).'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}					)
     _A		    =				field(
         default='train'						, metadata={
             'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
         }						, )
     _A		    =				field(
         default='validation'						, metadata={
             'help': (
                 'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
             )
         }						, )
     _A		    =				field(
         default='text'						, metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}						, )
     _A		    =				field(
         default='file'						, metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}						, )
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}						, )
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}						, )
     _A		    =				field(
         default='librispeech'						, metadata={
             'help': (
                 'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
                 ' \'buckwalter\'.'
             )
         }						, )
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'The number of processes to use for the preprocessing.'}						, )







@dataclass
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				False
     _A		    =				None
     _A		    =				"|"
     _A		    =				field(default_factory=lowerCAmelCase_					)
     _A		    =				field(default_factory=lowerCAmelCase_					)
     _A		    =				None



     @classmethod
     def 			lowerCAmelCase__							(   cls			,      __UpperCamelCase	)->       Any:
          if name == "librispeech":
               return cls()
          if name == "timit":
               return cls(
                   do_lower_case=__UpperCamelCase			,      translation_table=str.maketrans({"-": " "}	)			,      )
          if name == "buckwalter":
               UpperCAmelCase__				:			str							=    {
                   "-": " ",  # sometimes used to represent pauses
                   "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
               }
               return cls(
                   vocab_file=pathlib.Path(__UpperCamelCase	).parent.joinpath("vocab/buckwalter.json"	)			,      word_delimiter_token="/"			,      translation_table=str.maketrans(__UpperCamelCase	)			,      words_to_remove={"sil"}			,      untransliterator=arabic.buckwalter.untransliterate			,      )
          raise ValueError(F"Unsupported orthography: '{name}'."	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       str:
          # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
          if len(self.translation_table	) > 0:
               UpperCAmelCase__				:			str							=    text.translate(self.translation_table	)
          if len(self.words_to_remove	) == 0:
               UpperCAmelCase__				:			str							=    " ".join(text.split()	)  # clean up whitespaces
          else:
               UpperCAmelCase__				:			Union[str, Any]							=    " ".join(w for w in text.split() if w not in self.words_to_remove	)  # and clean up whilespaces
          return text



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       WavaVecaProcessor:
          UpperCAmelCase__				:			Tuple							=    WavaVecaFeatureExtractor.from_pretrained(
              model_args.model_name_or_path			,      cache_dir=model_args.cache_dir	)
          if self.vocab_file:
               UpperCAmelCase__				:			Any							=    WavaVecaCTCTokenizer(
                   self.vocab_file			,      cache_dir=model_args.cache_dir			,      do_lower_case=self.do_lower_case			,      word_delimiter_token=self.word_delimiter_token			,      )
          else:
               UpperCAmelCase__				:			Any							=    WavaVecaCTCTokenizer.from_pretrained(
                   model_args.model_name_or_path			,      cache_dir=model_args.cache_dir			,      do_lower_case=self.do_lower_case			,      word_delimiter_token=self.word_delimiter_token			,      )
          return WavaVecaProcessor(__UpperCamelCase			,      __UpperCamelCase	)







@dataclass
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				42
     _A		    =				True
     _A		    =				None
     _A		    =				None
     _A		    =				None
     _A		    =				None



     def __call__(   self			,      __UpperCamelCase	)->       Dict[str, torch.Tensor]:
          # split inputs and labels since they have to be of different lenghts and need
          # different padding methods
          UpperCAmelCase__				:			str							=    [{"input_values": feature["input_values"]} for feature in features]
          UpperCAmelCase__				:			Tuple							=    [{"input_ids": feature["labels"]} for feature in features]

          UpperCAmelCase__				:			Optional[Any]							=    self.processor.pad(
              __UpperCamelCase			,      padding=self.padding			,      max_length=self.max_length			,      pad_to_multiple_of=self.pad_to_multiple_of			,      return_tensors="pt"			,      )
          UpperCAmelCase__				:			Tuple							=    self.processor.pad(
              labels=__UpperCamelCase			,      padding=self.padding			,      max_length=self.max_length_labels			,      pad_to_multiple_of=self.pad_to_multiple_of_labels			,      return_tensors="pt"			,      )

          # replace padding with -100 to ignore loss correctly
          UpperCAmelCase__				:			str							=    labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1	)			,      -1_00	)

          UpperCAmelCase__				:			Optional[int]							=    labels

          return batch







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       torch.Tensor:
          model.train()
          UpperCAmelCase__				:			List[Any]							=    self._prepare_inputs(__UpperCamelCase	)

          if self.use_amp:
               with autocast():
                    UpperCAmelCase__				:			Optional[Any]							=    self.compute_loss(__UpperCamelCase			,      __UpperCamelCase	)
          else:
               UpperCAmelCase__				:			str							=    self.compute_loss(__UpperCamelCase			,      __UpperCamelCase	)

          if self.args.n_gpu > 1:
               if model.module.config.ctc_loss_reduction == "mean":
                    UpperCAmelCase__				:			int							=    loss.mean()
               elif model.module.config.ctc_loss_reduction == "sum":
                    UpperCAmelCase__				:			List[str]							=    loss.sum() / (inputs["labels"] >= 0).sum()
               else:
                    raise ValueError(F"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"	)

          if self.args.gradient_accumulation_steps > 1:
               UpperCAmelCase__				:			Union[str, Any]							=    loss / self.args.gradient_accumulation_steps

          if self.use_amp:
               self.scaler.scale(__UpperCamelCase	).backward()
          elif self.use_apex:
               with amp.scale_loss(__UpperCamelCase			,      self.optimizer	) as scaled_loss:
                    scaled_loss.backward()
          elif self.deepspeed:
               self.deepspeed.backward(__UpperCamelCase	)
          else:
               loss.backward()

          return loss.detach()





def 				a__			(					):






     '''simple docstring'''
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     UpperCAmelCase__				:			List[Any]							=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Dict							=    parser.parse_args_into_dataclasses()
     configure_logger(lowerCAmelCase							,     lowerCAmelCase		)

     UpperCAmelCase__				:			Optional[Any]							=    Orthography.from_name(data_args.orthography.lower()		)
     UpperCAmelCase__				:			Optional[int]							=    orthography.create_processor(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    WavaVecaForCTC.from_pretrained(
         model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     gradient_checkpointing=training_args.gradient_checkpointing							,     vocab_size=len(processor.tokenizer		)							,     )

     UpperCAmelCase__				:			Tuple							=    datasets.load_dataset(
         data_args.dataset_name							,     data_args.dataset_config_name							,     split=data_args.train_split_name		)
     UpperCAmelCase__				:			List[Any]							=    datasets.load_dataset(
         data_args.dataset_name							,     data_args.dataset_config_name							,     split=data_args.validation_split_name		)

     UpperCAmelCase__				:			str							=    datasets.load_metric("wer"		)
     UpperCAmelCase__				:			List[Any]							=    processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
     UpperCAmelCase__				:			List[str]							=    "".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowerCAmelCase		) == 1		)
     UpperCAmelCase__				:			List[str]							=    re.compile(  # remove characters not in vocabulary
         RF"[^\s{re.escape(lowerCAmelCase		)}]"							,     flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,     )
     UpperCAmelCase__				:			List[Any]							=    []

     def prepare_example(lowerCAmelCase						:							Optional[int]		):  # TODO(elgeish) make use of multiprocessing?
          UpperCAmelCase__				,						UpperCAmelCase__				:			List[Any]							=    librosa.load(example[data_args.speech_file_column]							,     sr=lowerCAmelCase		)
          if data_args.max_duration_in_seconds is not None:
               UpperCAmelCase__				:			int							=    len(example["speech"]		) / example["sampling_rate"]
          # Normalize and clean up text; order matters!
          UpperCAmelCase__				:			Dict							=    orthography.preprocess_for_training(example[data_args.target_text_column]		)
          UpperCAmelCase__				:			Optional[Any]							=    vocabulary_text_cleaner.sub(""							,     lowerCAmelCase		)
          if updated_text != example[data_args.target_text_column]:
               text_updates.append((example[data_args.target_text_column], updated_text)		)
               UpperCAmelCase__				:			List[str]							=    updated_text
          return example

     UpperCAmelCase__				:			Tuple							=    train_dataset.map(lowerCAmelCase							,     remove_columns=[data_args.speech_file_column]		)
     UpperCAmelCase__				:			Union[str, Any]							=    val_dataset.map(lowerCAmelCase							,     remove_columns=[data_args.speech_file_column]		)

     if data_args.max_duration_in_seconds is not None:

          def filter_by_max_duration(lowerCAmelCase						:							Optional[int]		):
               return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

          UpperCAmelCase__				:			Any							=    len(lowerCAmelCase		)
          UpperCAmelCase__				:			Optional[Any]							=    len(lowerCAmelCase		)
          UpperCAmelCase__				:			List[str]							=    train_dataset.filter(lowerCAmelCase							,     remove_columns=["duration_in_seconds"]		)
          UpperCAmelCase__				:			int							=    val_dataset.filter(lowerCAmelCase							,     remove_columns=["duration_in_seconds"]		)
          if len(lowerCAmelCase		) > old_train_size:
               logger.warning(
                   F"Filtered out {len(lowerCAmelCase		) - old_train_size} train example(s) longer than"
                   F" {data_args.max_duration_in_seconds} second(s)."		)
          if len(lowerCAmelCase		) > old_val_size:
               logger.warning(
                   F"Filtered out {len(lowerCAmelCase		) - old_val_size} validation example(s) longer than"
                   F" {data_args.max_duration_in_seconds} second(s)."		)
     logger.info(F"Split sizes: {len(lowerCAmelCase		)} train and {len(lowerCAmelCase		)} validation."		)

     logger.warning(F"Updated {len(lowerCAmelCase		)} transcript(s) using '{data_args.orthography}' orthography rules."		)
     if logger.isEnabledFor(logging.DEBUG		):
          for original_text, updated_text in text_updates:
               logger.debug(F"Updated text: \"{original_text}\" -> \"{updated_text}\""		)
     UpperCAmelCase__				:			List[Any]							=    None

     def prepare_dataset(lowerCAmelCase						:							Optional[Any]		):
          # check that all files have the correct sampling rate
          assert (
              len(set(batch["sampling_rate"]		)		) == 1
          ), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

          UpperCAmelCase__				:			List[Any]							=    processor(
              audio=batch["speech"]							,     text=batch[data_args.target_text_column]							,     sampling_rate=batch["sampling_rate"][0]		)
          batch.update(lowerCAmelCase		)
          return batch

     UpperCAmelCase__				:			str							=    train_dataset.map(
         lowerCAmelCase							,     batch_size=training_args.per_device_train_batch_size							,     batched=lowerCAmelCase							,     num_proc=data_args.preprocessing_num_workers							,     )
     UpperCAmelCase__				:			List[Any]							=    val_dataset.map(
         lowerCAmelCase							,     batch_size=training_args.per_device_train_batch_size							,     batched=lowerCAmelCase							,     num_proc=data_args.preprocessing_num_workers							,     )

     UpperCAmelCase__				:			int							=    DataCollatorCTCWithPadding(processor=lowerCAmelCase							,     padding=lowerCAmelCase		)

     def compute_metrics(lowerCAmelCase						:							List[str]		):
          UpperCAmelCase__				:			Optional[int]							=    pred.predictions
          UpperCAmelCase__				:			List[Any]							=    np.argmax(lowerCAmelCase							,     axis=-1		)

          UpperCAmelCase__				:			List[Any]							=    processor.tokenizer.pad_token_id

          UpperCAmelCase__				:			Union[str, Any]							=    processor.batch_decode(lowerCAmelCase		)
          # we do not want to group tokens when computing the metrics
          UpperCAmelCase__				:			Union[str, Any]							=    processor.batch_decode(pred.label_ids							,     group_tokens=lowerCAmelCase		)
          if logger.isEnabledFor(logging.DEBUG		):
               for reference, predicted in zip(lowerCAmelCase							,     lowerCAmelCase		):
                    logger.debug(F"reference: \"{reference}\""		)
                    logger.debug(F"predicted: \"{predicted}\""		)
                    if orthography.untransliterator is not None:
                         logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(lowerCAmelCase		)}\""		)
                         logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(lowerCAmelCase		)}\""		)

          UpperCAmelCase__				:			int							=    wer_metric.compute(predictions=lowerCAmelCase							,     references=lowerCAmelCase		)

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     UpperCAmelCase__				:			List[Any]							=    CTCTrainer(
         model=lowerCAmelCase							,     data_collator=lowerCAmelCase							,     args=lowerCAmelCase							,     compute_metrics=lowerCAmelCase							,     train_dataset=lowerCAmelCase							,     eval_dataset=lowerCAmelCase							,     tokenizer=processor.feature_extractor							,     )

     trainer.train()


if __name__ == "__main__":
    main()





"""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__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase							,     "r"							,     encoding="utf-8"							,     newline="\n"		) as f:
          UpperCAmelCase__				:			List[str]							=    f.readlines()
     # Find the start prompt.
     UpperCAmelCase__				:			Union[str, Any]							=    0
     while not lines[start_index].startswith(lowerCAmelCase		):
          start_index += 1
     start_index += 1

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



def 				a__			(					lowerCAmelCase						:							List[str]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,     lowerCAmelCase		)
     return [m.group(0		) for m in matches]



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			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.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

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

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

    # Let's build that table!
     UpperCAmelCase__				:			Tuple							=    list(model_name_to_config.keys()		)
     model_names.sort(key=str.lower		)
     UpperCAmelCase__				:			Dict							=    ["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).
     UpperCAmelCase__				:			List[str]							=    [len(lowerCAmelCase		) + 2 for c in columns]
     UpperCAmelCase__				:			Union[str, Any]							=    max([len(lowerCAmelCase		) for name in model_names]		) + 2

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[Any]							=    [
              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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "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__   :						Union[str, 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)



