

import random






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					tuple:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], [], []
	for element in data:
		if element < pivot:
			less.append(lowerCamelCase__      )
		elif element > pivot:
			greater.append(lowerCamelCase__      )
		else:
			equal.append(lowerCamelCase__      )
	return less, equal, greater






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(lowerCamelCase__      ) or index < 0:
		return None

	__lowerCamelCase    :    Tuple 	=			items[random.randint(0     ,			len(lowerCamelCase__      ) - 1      )]
	__lowerCamelCase    :    str 	=			0
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			_partition(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			len(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[Any] 	=			len(lowerCamelCase__      )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(lowerCamelCase__     ,			lowerCamelCase__      )
	# must be in larger
	else:
		return quick_select(lowerCamelCase__     ,			index - (m + count)      )







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
a		="""src/transformers"""
a		="""docs/source/en"""
a		="""."""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	with open(lowerCamelCase__     ,			'r'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		__lowerCamelCase    :    Any 	=			f.readlines()
	# Find the start prompt.
	__lowerCamelCase    :    List[str] 	=			0
	while not lines[start_index].startswith(lowerCamelCase__      ):
		start_index += 1
	start_index += 1

	__lowerCamelCase    :    int 	=			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		="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a		=re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a		=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		=re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
a		=direct_transformers_import(TRANSFORMERS_PATH)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    int 	=			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,			lowerCamelCase__      )
	return [m.group(0      ) for m in matches]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    int 	=			2 if text == '✅' or text == '❌' else len(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			(width - text_length) // 2
	__lowerCamelCase    :    List[Any] 	=			width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def        SCREAMING_SNAKE_CASE__	(  )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__lowerCamelCase    :    List[str] 	=			{
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	__lowerCamelCase    :    Dict 	=			{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.
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(lowerCamelCase__      ):
		__lowerCamelCase    :    List[Any] 	=			None
		if attr_name.endswith('Tokenizer'      ):
			__lowerCamelCase    :    Dict 	=			slow_tokenizers
			__lowerCamelCase    :    List[Any] 	=			attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'      ):
			__lowerCamelCase    :    Union[str, Any] 	=			fast_tokenizers
			__lowerCamelCase    :    str 	=			attr_name[:-1_3]
		elif _re_tf_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[str] 	=			tf_models
			__lowerCamelCase    :    Optional[int] 	=			_re_tf_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_flax_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[Any] 	=			flax_models
			__lowerCamelCase    :    Optional[Any] 	=			_re_flax_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_pt_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    Optional[int] 	=			pt_models
			__lowerCamelCase    :    Any 	=			_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():
					__lowerCamelCase    :    List[Any] 	=			True
					break
				# Try again after removing the last word in the name
				__lowerCamelCase    :    str 	=			''.join(camel_case_split(lowerCamelCase__      )[:-1]      )

    # Let's build that table!
	__lowerCamelCase    :    str 	=			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    :    List[Any] 	=			[len(lowerCamelCase__      ) + 2 for c in columns]
	__lowerCamelCase    :    int 	=			max([len(lowerCamelCase__      ) for name in model_names]      ) + 2

	# Build the table per se
	__lowerCamelCase    :    Union[str, Any] 	=			'|' + '|'.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"

	__lowerCamelCase    :    List[str] 	=			{True: '✅', False: '❌'}
	for name in model_names:
		__lowerCamelCase    :    Optional[int] 	=			model_name_to_prefix[name]
		__lowerCamelCase    :    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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=False      )  ->					Any:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			_find_text_in_file(
	    filename=os.path.join(lowerCamelCase__     ,			'index.md'      )     ,			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(lowerCamelCase__     ,			'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__":
					a		=argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					a		=parser.parse_args()

					check_model_table(args.fix_and_overwrite)




