



import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

UpperCamelCase_									=   {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

UpperCamelCase_									=   {
    '''microsoft/deberta-base''': 512,
    '''microsoft/deberta-large''': 512,
    '''microsoft/deberta-xlarge''': 512,
    '''microsoft/deberta-base-mnli''': 512,
    '''microsoft/deberta-large-mnli''': 512,
    '''microsoft/deberta-xlarge-mnli''': 512,
}

UpperCamelCase_									=   {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       int  		=					(
        list(range(ord("""!"""    )   ,		ord("""~"""    ) + 1    )    ) + list(range(ord("""¡"""    )   ,		ord("""¬"""    ) + 1    )    ) + list(range(ord("""®"""    )   ,		ord("""ÿ"""    ) + 1    )    )
    )
    UpperCAmelCase_     :       Any  		=					bs[:]
    UpperCAmelCase_     :       List[Any]  		=					0
    for b in range(2**8    ):
        if b not in bs:
            bs.append(_a    )
            cs.append(2**8 + n    )
            n += 1
    UpperCAmelCase_     :       Tuple  		=					[chr(_a    ) for n in cs]
    return dict(zip(_a   ,		_a    )    )







def       lowerCamelCase_  (    _a       :						Optional[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					set()
    UpperCAmelCase_     :       Dict  		=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)    )
        UpperCAmelCase_     :       List[str]  		=					char
    return pairs







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						VOCAB_FILES_NAMES
   A__      : str       =						PRETRAINED_VOCAB_FILES_MAP
   A__      : int       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__      : Optional[int]       =						["input_ids", "attention_mask", "token_type_ids"]


   def __init__(    self:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]="replace"  ,lowerCamelCase_:     List[str]="[CLS]"  ,lowerCamelCase_:     Any="[SEP]"  ,lowerCamelCase_:     Any="[SEP]"  ,lowerCamelCase_:     str="[CLS]"  ,lowerCamelCase_:     List[Any]="[UNK]"  ,lowerCamelCase_:     Union[str, Any]="[PAD]"  ,lowerCamelCase_:     List[str]="[MASK]"  ,lowerCamelCase_:     str=False  ,lowerCamelCase_:     Any=False  ,**lowerCamelCase_:     List[Any]  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       List[str]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else bos_token
       UpperCAmelCase_     :       Optional[int]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else eos_token
       UpperCAmelCase_     :       Tuple  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else sep_token
       UpperCAmelCase_     :       Any  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else cls_token
       UpperCAmelCase_     :       Any  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else unk_token
       UpperCAmelCase_     :       Optional[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCAmelCase_     :       List[Any]  		=					AddedToken(lowerCamelCase_  ,lstrip=lowerCamelCase_  ,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_  ,lowerCamelCase_							) else mask_token

       super().__init__(
           errors=lowerCamelCase_  ,bos_token=lowerCamelCase_  ,eos_token=lowerCamelCase_  ,unk_token=lowerCamelCase_  ,sep_token=lowerCamelCase_  ,cls_token=lowerCamelCase_  ,pad_token=lowerCamelCase_  ,mask_token=lowerCamelCase_  ,add_prefix_space=lowerCamelCase_  ,add_bos_token=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       str  		=					add_bos_token

       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as vocab_handle:
           UpperCAmelCase_     :       List[Any]  		=					json.load(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					{v: k for k, v in self.encoder.items()}
       UpperCAmelCase_     :       int  		=					errors  # how to handle errors in decoding
       UpperCAmelCase_     :       List[Any]  		=					bytes_to_unicode()
       UpperCAmelCase_     :       Tuple  		=					{v: k for k, v in self.byte_encoder.items()}
       with open(lowerCamelCase_  ,encoding="""utf-8"""							) as merges_handle:
           UpperCAmelCase_     :       List[str]  		=					merges_handle.read().split("""\n"""							)[1:-1]
       UpperCAmelCase_     :       str  		=					[tuple(merge.split()							) for merge in bpe_merges]
       UpperCAmelCase_     :       Optional[int]  		=					dict(zip(lowerCamelCase_  ,range(len(lowerCamelCase_							)							)							)							)
       UpperCAmelCase_     :       Tuple  		=					{}
       UpperCAmelCase_     :       List[str]  		=					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       UpperCAmelCase_     :       Union[str, Any]  		=					re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def       A__    (    self:     str							)	->      Optional[Any]:
       return len(self.encoder							)


   def       A__    (    self:     Tuple							)	->      Tuple:
       return dict(self.encoder  ,**self.added_tokens_encoder							)


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      int:
       if token in self.cache:
           return self.cache[token]
       UpperCAmelCase_     :       Optional[int]  		=					tuple(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					get_pairs(lowerCamelCase_							)

       if not pairs:
           return token

       while True:
           UpperCAmelCase_     :       Optional[Any]  		=					min(lowerCamelCase_  ,key=lambda lowerCamelCase_							: self.bpe_ranks.get(lowerCamelCase_  ,float("""inf"""							)							)							)
           if bigram not in self.bpe_ranks:
               break
           UpperCAmelCase_		, UpperCAmelCase_     :       Union[str, Any]  		=					bigram
           UpperCAmelCase_     :       Tuple  		=					[]
           UpperCAmelCase_     :       int  		=					0
           while i < len(lowerCamelCase_							):
               try:
                   UpperCAmelCase_     :       List[Any]  		=					word.index(lowerCamelCase_  ,lowerCamelCase_							)
               except ValueError:
                   new_word.extend(word[i:]							)
                   break
               else:
                   new_word.extend(word[i:j]							)
                   UpperCAmelCase_     :       Tuple  		=					j

               if word[i] == first and i < len(lowerCamelCase_							) - 1 and word[i + 1] == second:
                   new_word.append(first + second							)
                   i += 2
               else:
                   new_word.append(word[i]							)
                   i += 1
           UpperCAmelCase_     :       Any  		=					tuple(lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					new_word
           if len(lowerCamelCase_							) == 1:
               break
           else:
               UpperCAmelCase_     :       Tuple  		=					get_pairs(lowerCamelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					""" """.join(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					word
       return word


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCAmelCase_     :       Dict  		=					[self.cls_token_id]
       UpperCAmelCase_     :       Dict  		=					[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None  ,lowerCamelCase_:     bool = False							)	->      List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_  ,token_ids_a=lowerCamelCase_  ,already_has_special_tokens=lowerCamelCase_							)

       if token_ids_a is None:
           return [1] + ([0] * len(lowerCamelCase_							)) + [1]
       return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     List[int]  ,lowerCamelCase_:     Optional[List[int]] = None							)	->      List[int]:
       UpperCAmelCase_     :       List[Any]  		=					[self.sep_token_id]
       UpperCAmelCase_     :       Union[str, Any]  		=					[self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]


   def       A__    (    self:     int  ,lowerCamelCase_:     str							)	->      str:
       UpperCAmelCase_     :       Tuple  		=					[]
       for token in re.findall(self.pat  ,lowerCamelCase_							):
           UpperCAmelCase_     :       List[Any]  		=					"""""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_							).split(""" """							)							)
       return bpe_tokens


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     List[str]							)	->      Dict:
       return self.encoder.get(lowerCamelCase_  ,self.encoder.get(self.unk_token							)							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Tuple							)	->      Tuple:
       return self.decoder.get(lowerCamelCase_							)


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     List[str]							)	->      Dict:
       UpperCAmelCase_     :       Optional[int]  		=					"""""".join(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""  ,errors=self.errors							)
       return text


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[str] = None							)	->      Tuple[str]:
       if not os.path.isdir(lowerCamelCase_							):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
       UpperCAmelCase_     :       Dict  		=					os.path.join(
           lowerCamelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
       UpperCAmelCase_     :       List[Any]  		=					os.path.join(
           lowerCamelCase_  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

       with open(lowerCamelCase_  ,"""w"""  ,encoding="""utf-8"""							) as f:
           f.write(json.dumps(self.encoder  ,indent=2  ,sort_keys=lowerCamelCase_  ,ensure_ascii=lowerCamelCase_							) + """\n"""							)

       UpperCAmelCase_     :       Tuple  		=					0
       with open(lowerCamelCase_  ,"""w"""  ,encoding="""utf-8"""							) as writer:
           writer.write("""#version: 0.2\n"""							)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda lowerCamelCase_							: kv[1]							):
               if index != token_index:
                   logger.warning(
                       F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       """ Please check that the tokenizer is not corrupted!"""							)
                   UpperCAmelCase_     :       str  		=					token_index
               writer.write(""" """.join(lowerCamelCase_							) + """\n"""							)
               index += 1

       return vocab_file, merge_file


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     int  ,lowerCamelCase_:     str=False  ,**lowerCamelCase_:     List[Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""add_prefix_space"""  ,self.add_prefix_space							)
       if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_							) > 0 and not text[0].isspace()):
           UpperCAmelCase_     :       str  		=					""" """ + text
       return (text, kwargs)






   def       A__    (    self:     str  ,lowerCamelCase_:     "Conversation"							)	->      List[int]:
       UpperCAmelCase_     :       str  		=					[]
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(lowerCamelCase_  ,add_special_tokens=lowerCamelCase_							) + [self.eos_token_id]							)
       if len(lowerCamelCase_							) > self.model_max_length:
           UpperCAmelCase_     :       int  		=					input_ids[-self.model_max_length :]
       return input_ids







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)

