






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCAmelCase  :  List[str]   =			logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCAmelCase  :  str   =			{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCAmelCase  :  List[str]   =			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCAmelCase  :  int   =			sorted(arg_to_scheduler.keys())
_lowerCAmelCase  :  List[str]   =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class  lowerCAmelCase__ (						pl.LightningModule      ):
   def __init__(		self			:  Tuple       , snake_case__			:  Optional[int]       , snake_case__			:  Union[str, Any]=None       , snake_case__			:  Optional[int]="base"       , snake_case__			:  Optional[int]=None       , snake_case__			:  Any=None       , snake_case__			:  Dict=None       , **snake_case__			:  int       , ):


        '''simple docstring'''
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCamelCase_       )
        UpperCAmelCase__	:						Optional[int]       =       0
        UpperCAmelCase__	:						Optional[int]       =       Path(self.hparams.output_dir       )
        UpperCAmelCase__	:						List[str]       =       self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
             UpperCAmelCase__	:						Optional[int]       =       AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path       , **({"num_labels": num_labels} if num_labels is not None else {})       , cache_dir=lowerCamelCase_       , **lowerCamelCase_       , )
        else:
             UpperCAmelCase__	:						str       =       config

        UpperCAmelCase__	:						List[Any]       =       ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
        for p in extra_model_params:
             if getattr(self.hparams       , lowerCamelCase_       , lowerCamelCase_       ):
                  assert hasattr(self.config       , lowerCamelCase_       ), f'model config doesn\'t have a `{p}` attribute'
                  setattr(self.config       , lowerCamelCase_       , getattr(self.hparams       , lowerCamelCase_       )       )

        if tokenizer is None:
             UpperCAmelCase__	:						int       =       AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       , cache_dir=lowerCamelCase_       , )
        else:
             UpperCAmelCase__	:						Any       =       tokenizer
        UpperCAmelCase__	:						Tuple       =       MODEL_MODES[mode]
        if model is None:
             UpperCAmelCase__	:						str       =       self.model_type.from_pretrained(
                 self.hparams.model_name_or_path       , from_tf=bool(".ckpt" in self.hparams.model_name_or_path       )       , config=self.config       , cache_dir=lowerCamelCase_       , )
        else:
             UpperCAmelCase__	:						Optional[Any]       =       model
   def 			__a       (		self			:  Any       , *snake_case__			:  Dict       , **snake_case__			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.model_type.from_pretrained(*lowerCamelCase_       , **lowerCamelCase_       )
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       arg_to_scheduler[self.hparams.lr_scheduler]
        UpperCAmelCase__	:						Tuple       =       get_schedule_func(
            self.opt       , num_warmup_steps=self.hparams.warmup_steps       , num_training_steps=self.total_steps()       )
        UpperCAmelCase__	:						Optional[Any]       =       {"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler
   def 			__a       (		self			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[int]       =       self.model
        UpperCAmelCase__	:						Any       =       ["bias", "LayerNorm.weight"]
        UpperCAmelCase__	:						str       =       [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )],
                "weight_decay": 0.0,
            },
        ]
        if self.hparams.adafactor:
             UpperCAmelCase__	:						List[Any]       =       Adafactor(
                 lowerCamelCase_       , lr=self.hparams.learning_rate       , scale_parameter=lowerCamelCase_       , relative_step=lowerCamelCase_       )

        else:
             UpperCAmelCase__	:						Tuple       =       AdamW(
                 lowerCamelCase_       , lr=self.hparams.learning_rate       , eps=self.hparams.adam_epsilon       )
        UpperCAmelCase__	:						List[Any]       =       optimizer

        UpperCAmelCase__	:						str       =       self.get_lr_scheduler()

        return [optimizer], [scheduler]
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  Optional[Any]       , snake_case__			:  Union[str, Any]       ):


        '''simple docstring'''
        return self.validation_step(lowerCamelCase_       , lowerCamelCase_       )
   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  Any       ):


        '''simple docstring'''
        return self.validation_end(lowerCamelCase_       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       max(1       , self.hparams.gpus       )  # TODO: consider num_tpu_cores
        UpperCAmelCase__	:						Tuple       =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  List[str]       ):


        '''simple docstring'''

        if stage == "test":
             UpperCAmelCase__	:						Dict       =       len(self.test_dataloader().dataset       )
        else:
             UpperCAmelCase__	:						Optional[int]       =       self.get_dataloader("train"       , self.hparams.train_batch_size       , shuffle=lowerCamelCase_       )
             UpperCAmelCase__	:						int       =       len(self.train_dataloader().dataset       )
   def 			__a       (		self			:  Dict       , snake_case__			:  Any       , snake_case__			:  Dict       , snake_case__			:  Tuple = False       ):


        '''simple docstring'''
        raise NotImplementedError("You must implement this for your task"       )
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        return self.train_loader
   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        return self.get_dataloader("dev"       , self.hparams.eval_batch_size       , shuffle=lowerCamelCase_       )
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        return self.get_dataloader("test"       , self.hparams.eval_batch_size       , shuffle=lowerCamelCase_       )
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  int       ):


        '''simple docstring'''
        return os.path.join(
            self.hparams.data_dir       , "cached_{}_{}_{}".format(
                lowerCamelCase_       , list(filter(lowerCamelCase_       , self.hparams.model_name_or_path.split("/"       )       )       ).pop()       , str(self.hparams.max_seq_length       )       , )       , )
   @pl.utilities.rank_zero_only
   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.output_dir.joinpath("best_tfmr"       )
        UpperCAmelCase__	:						Optional[int]       =       self.step_count
        self.model.save_pretrained(lowerCamelCase_       )
        self.tokenizer.save_pretrained(lowerCamelCase_       )




   @staticmethod
   def 			__a       (		snake_case__			:  Optional[Any]       , snake_case__			:  Optional[int]       ):


        '''simple docstring'''
        parser.add_argument(
            "--model_name_or_path"       , default=lowerCamelCase_       , type=lowerCamelCase_       , required=lowerCamelCase_       , help="Path to pretrained model or model identifier from huggingface.co/models"       , )
        parser.add_argument(
            "--config_name"       , default=""       , type=lowerCamelCase_       , help="Pretrained config name or path if not the same as model_name"       )
        parser.add_argument(
            "--tokenizer_name"       , default=lowerCamelCase_       , type=lowerCamelCase_       , help="Pretrained tokenizer name or path if not the same as model_name"       , )
        parser.add_argument(
            "--cache_dir"       , default=""       , type=lowerCamelCase_       , help="Where do you want to store the pre-trained models downloaded from huggingface.co"       , )
        parser.add_argument(
            "--encoder_layerdrop"       , type=lowerCamelCase_       , help="Encoder layer dropout probability (Optional). Goes into model.config"       , )
        parser.add_argument(
            "--decoder_layerdrop"       , type=lowerCamelCase_       , help="Decoder layer dropout probability (Optional). Goes into model.config"       , )
        parser.add_argument(
            "--dropout"       , type=lowerCamelCase_       , help="Dropout probability (Optional). Goes into model.config"       , )
        parser.add_argument(
            "--attention_dropout"       , type=lowerCamelCase_       , help="Attention dropout probability (Optional). Goes into model.config"       , )
        parser.add_argument("--learning_rate"       , default=5e-5       , type=lowerCamelCase_       , help="The initial learning rate for Adam."       )
        parser.add_argument(
            "--lr_scheduler"       , default="linear"       , choices=lowerCamelCase_       , metavar=lowerCamelCase_       , type=lowerCamelCase_       , help="Learning rate scheduler"       , )
        parser.add_argument("--weight_decay"       , default=0.0       , type=lowerCamelCase_       , help="Weight decay if we apply some."       )
        parser.add_argument("--adam_epsilon"       , default=1e-8       , type=lowerCamelCase_       , help="Epsilon for Adam optimizer."       )
        parser.add_argument("--warmup_steps"       , default=0       , type=lowerCamelCase_       , help="Linear warmup over warmup_steps."       )
        parser.add_argument("--num_workers"       , default=4       , type=lowerCamelCase_       , help="kwarg passed to DataLoader"       )
        parser.add_argument("--num_train_epochs"       , dest="max_epochs"       , default=3       , type=lowerCamelCase_       )
        parser.add_argument("--train_batch_size"       , default=3_2       , type=lowerCamelCase_       )
        parser.add_argument("--eval_batch_size"       , default=3_2       , type=lowerCamelCase_       )
        parser.add_argument("--adafactor"       , action="store_true"       )




class  lowerCAmelCase__ (						pl.Callback      ):
   def 			__a       (		self			:  str       , snake_case__			:  Union[str, Any]       , snake_case__			:  Union[str, Any]       ):


        '''simple docstring'''

        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
             pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class  lowerCAmelCase__ (						pl.Callback      ):
   def 			__a       (		self			:  List[Any]       , snake_case__			:  Dict       , snake_case__			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[int]       =       trainer.lr_schedulers[0]["scheduler"]
        UpperCAmelCase__	:						str       =       {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
        pl_module.logger.log_metrics(lowerCamelCase_       )
   def 			__a       (		self			:  List[str]       , snake_case__			:  List[str]       , snake_case__			:  str       ):


        '''simple docstring'''
        rank_zero_info("***** Validation results *****"       )
        UpperCAmelCase__	:						str       =       trainer.callback_metrics
        # Log results
        for key in sorted(lowerCamelCase_       ):
             if key not in ["log", "progress_bar"]:
                  rank_zero_info("{} = {}\n".format(lowerCamelCase_       , str(metrics[key]       )       )       )




   def 			__a       (		self			:  Any       , snake_case__			:  List[str]       , snake_case__			:  str       ):


        '''simple docstring'''
        rank_zero_info("***** Test results *****"       )
        UpperCAmelCase__	:						Any       =       trainer.callback_metrics
        # Log and save results to file
        UpperCAmelCase__	:						Tuple       =       os.path.join(pl_module.hparams.output_dir       , "test_results.txt"       )
        with open(lowerCamelCase_       , "w"       ) as writer:
             for key in sorted(lowerCamelCase_       ):
                  if key not in ["log", "progress_bar"]:
                       rank_zero_info("{} = {}\n".format(lowerCamelCase_       , str(metrics[key]       )       )       )
                       writer.write("{} = {}\n".format(lowerCamelCase_       , str(metrics[key]       )       )       )




def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		None:






     '''simple docstring'''



     parser.add_argument(
         "--output_dir" ,				default=a_ ,				type=a_ ,				required=a_ ,				help="The output directory where the model predictions and checkpoints will be written." ,				)
     parser.add_argument(
         "--fp16" ,				action="store_true" ,				help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ,				)

     parser.add_argument(
         "--fp16_opt_level" ,				type=a_ ,				default="O2" ,				help=(
             "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
             "See details at https://nvidia.github.io/apex/amp.html"
         ) ,				)
     parser.add_argument("--n_tpu_cores" ,				dest="tpu_cores" ,				type=a_							)
     parser.add_argument("--max_grad_norm" ,				dest="gradient_clip_val" ,				default=1.0 ,				type=a_ ,				help="Max gradient norm"							)
     parser.add_argument("--do_train" ,				action="store_true" ,				help="Whether to run training."							)
     parser.add_argument("--do_predict" ,				action="store_true" ,				help="Whether to run predictions on the test set."							)
     parser.add_argument(
         "--gradient_accumulation_steps" ,				dest="accumulate_grad_batches" ,				type=a_ ,				default=1 ,				help="Number of updates steps to accumulate before performing a backward/update pass." ,				)
     parser.add_argument("--seed" ,				type=a_ ,				default=42 ,				help="random seed for initialization"							)
     parser.add_argument(
         "--data_dir" ,				default=a_ ,				type=a_ ,				required=a_ ,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,				)



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   BaseTransformer ,				snake_case					:   argparse.Namespace ,				snake_case					:   int=None ,				snake_case					:   str=True ,				snake_case					:   List[Any]=None ,				snake_case					:   List[str]=[] ,				snake_case					:   Any=None ,				snake_case					:   List[str]=None ,				**snake_case					:   int ,				)->		Optional[Any]:






     '''simple docstring'''



     pl.seed_everything(args.seed							)

     # init model
     UpperCAmelCase__	:						Dict       =       Path(model.hparams.output_dir							)
     odir.mkdir(exist_ok=a_							)

     # add custom checkpoints
     if checkpoint_callback is None:
          UpperCAmelCase__	:						Union[str, Any]       =       pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir ,				prefix="checkpoint" ,				monitor="val_loss" ,				mode="min" ,				save_top_k=1							)
     if early_stopping_callback:
          extra_callbacks.append(a_							)
     if logging_callback is None:
          UpperCAmelCase__	:						List[str]       =       LoggingCallback()

     UpperCAmelCase__	:						List[str]       =       {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          UpperCAmelCase__	:						str       =       16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          UpperCAmelCase__	:						str       =       "auto"  # "ddp"
          UpperCAmelCase__	:						Optional[Any]       =       "ddp"

     UpperCAmelCase__	:						List[Any]       =       args.accumulate_grad_batches
     UpperCAmelCase__	:						int       =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     UpperCAmelCase__	:						Any       =       "auto"

     UpperCAmelCase__	:						Union[str, Any]       =       pl.Trainer.from_argparse_args(
         a_ ,				weights_summary=a_ ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()] ,				logger=a_ ,				**a_ ,				)

     if args.do_train:
          trainer.fit(a_							)

     return trainer










"""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
_lowerCAmelCase  :  List[str]   =			"""src/diffusers"""
_lowerCAmelCase  :  Union[str, Any]   =			"""docs/source/en"""
_lowerCAmelCase  :  List[str]   =			"""."""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		str:






     '''simple docstring'''



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

     UpperCAmelCase__	:						Union[str, Any]       =       start_index
     while not lines[end_index].startswith(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 |
_lowerCAmelCase  :  str   =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase  :  List[Any]   =			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCAmelCase  :  Optional[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.
_lowerCAmelCase  :  str   =			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,				snake_case							)
     return [m.group(0							) for m in matches]


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       2 if text == "✅" or text == "❌" else len(snake_case							)
     UpperCAmelCase__	:						int       =       (width - text_length) // 2
     UpperCAmelCase__	:						Any       =       width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


def   SCREAMING_SNAKE_CASE__					(     )->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__	:						Any       =       {
         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__	:						Optional[Any]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Dict       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						List[str]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Tuple       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Any       =       collections.defaultdict(snake_case							)

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

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

    # Let's build that table!
     UpperCAmelCase__	:						int       =       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(snake_case							) + 2 for c in columns]
     UpperCAmelCase__	:						Tuple       =       max([len(snake_case							) for name in model_names]							) + 2

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

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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]=False							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       _find_text_in_file(
         filename=os.path.join(snake_case ,				"index.mdx"							) ,				start_prompt="<!--This table is updated automatically from the auto modules" ,				end_prompt="<!-- End table-->" ,				)
     UpperCAmelCase__	:						Union[str, Any]       =       get_model_table_from_auto_modules()

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

 check_model_table(args.fix_and_overwrite)


