






"""simple docstring"""






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
UpperCAmelCase_   : Optional[int]   			= """src/transformers"""
UpperCAmelCase_   : Tuple   			= """docs/source/en"""
UpperCAmelCase_   : Optional[Any]   			= """."""



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		start_index
    while not lines[end_index].startswith(__a							):
        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 |
UpperCAmelCase_   : Optional[Any]   			= """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase_   : int   			= re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCAmelCase_   : Dict   			= 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.
UpperCAmelCase_   : int   			= re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_   : Optional[int]   			= direct_transformers_import(TRANSFORMERS_PATH)



def 					_A			(__a							) ->       int:
    """simple docstring"""






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



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2 if text == '''✅''' or text == '''❌''' else len(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		(width - text_length) // 2
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 					_A			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{name: config.replace('''Config'''    ,				''''''							) 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.
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(__a							):
        SCREAMING_SNAKE_CASE_      :  Any    						=		None
        if attr_name.endswith('''Tokenizer'''							):
            SCREAMING_SNAKE_CASE_      :  Dict    						=		slow_tokenizers
            SCREAMING_SNAKE_CASE_      :  Dict    						=		attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fast_tokenizers
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attr_name[:-13]
        elif _re_tf_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  int    						=		tf_models
            SCREAMING_SNAKE_CASE_      :  Dict    						=		_re_tf_models.match(__a							).groups()[0]
        elif _re_flax_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_models
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		_re_flax_models.match(__a							).groups()[0]
        elif _re_pt_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  str    						=		pt_models
            SCREAMING_SNAKE_CASE_      :  int    						=		_re_pt_models.match(__a							).groups()[0]

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

    # Let's build that table!
    SCREAMING_SNAKE_CASE_      :  Any    						=		list(model_name_to_config.keys()							)
    model_names.sort(key=str.lower							)
    SCREAMING_SNAKE_CASE_      :  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).
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[len(__a							) + 2 for c in columns]
    SCREAMING_SNAKE_CASE_      :  str    						=		max([len(__a							) for name in model_names]							) + 2

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

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{True: '''✅''', False: '''❌'''}
    for name in model_names:
        SCREAMING_SNAKE_CASE_      :  str    						=		model_name_to_prefix[name]
        SCREAMING_SNAKE_CASE_      :  int    						=		[
            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(__a    ,				__a							) for l, w in zip(__a    ,				__a							)]							) + "|\n"
    return table



def 					_A			(__a=False							) ->       List[str]:
    """simple docstring"""






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

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(__a    ,				'''index.md'''							)    ,				'''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.md` has not been updated. Run `make fix-copies` to fix this.'''							)


if __name__ == "__main__":
  UpperCAmelCase_   : Union[str, Any]   			= argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCAmelCase_   : Any   			= parser.parse_args()

  check_model_table(args.fix_and_overwrite)









def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


if __name__ == "__main__":
  print(F'''{solution() = }''')

