"""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
SCREAMING_SNAKE_CASE									=						'src/transformers'
SCREAMING_SNAKE_CASE									=						'docs/source/en'
SCREAMING_SNAKE_CASE									=						'.'


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






							"""simple docstring"""





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

							_lowercase :    Optional[int]							=							start_index
							while not lines[end_index].startswith(__UpperCAmelCase			):
														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 |
SCREAMING_SNAKE_CASE									=						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE									=						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_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.
SCREAMING_SNAKE_CASE									=						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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


def      __lowerCAmelCase(		__UpperCAmelCase			):






							"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






							"""simple docstring"""





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


def      __lowerCAmelCase(		):






							"""simple docstring"""





							_lowercase :    Optional[int]							=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							_lowercase :    List[Any]							=							{
							    name: config_maping_names[code]
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_maping_names
							}
							_lowercase :    str							=							{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.
							_lowercase :    int							=							collections.defaultdict(__UpperCAmelCase			)
							_lowercase :    str							=							collections.defaultdict(__UpperCAmelCase			)
							_lowercase :    Union[str, Any]							=							collections.defaultdict(__UpperCAmelCase			)
							_lowercase :    Tuple							=							collections.defaultdict(__UpperCAmelCase			)
							_lowercase :    Union[str, Any]							=							collections.defaultdict(__UpperCAmelCase			)

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

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

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

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

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


def      __lowerCAmelCase(		__UpperCAmelCase=False			):






							"""simple docstring"""





							_lowercase :    Optional[Any]							=							_find_text_in_file(
							    filename=os.path.join(__UpperCAmelCase     ,'index.md'			)     ,start_prompt='<!--This table is updated automatically from the auto modules'     ,end_prompt='<!-- End table-->'     ,)
							_lowercase :    Union[str, Any]							=							get_model_table_from_auto_modules()

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(__UpperCAmelCase     ,'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__":
						SCREAMING_SNAKE_CASE									=						argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						SCREAMING_SNAKE_CASE									=						parser.parse_args()

						check_model_table(args.fix_and_overwrite)




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if index == len(__UpperCAmelCase			):
		return True

	# Recursive Step
	for i in range(__UpperCAmelCase			):
		if valid_coloring(graph[index]     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			# Color current vertex
			_lowercase :    str							=							i
			# Validate coloring
			if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,index + 1			):
				return True
			# Backtrack
			_lowercase :    List[str]							=							-1
	return False







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[-1] * len(__UpperCAmelCase			)

	if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,0			):
		return colored_vertices

	return []



