"""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				,      _lowercase				,      _lowercase				,      _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"""







import functools


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for day in days			):
		raise ValueError('The parameter days should be a list of integers'			)

	if len(__UpperCAmelCase			) != 3 or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for cost in costs			):
		raise ValueError('The parameter costs should be a list of three integers'			)

	if len(__UpperCAmelCase			) == 0:
		return 0

	if min(__UpperCAmelCase			) <= 0:
		raise ValueError('All days elements should be greater than 0'			)

	if max(__UpperCAmelCase			) >= 366:
		raise ValueError('All days elements should be less than 366'			)

	_lowercase :    List[Any]							=							set(__UpperCAmelCase			)

	@functools.cache
	def dynamic_programming(__UpperCAmelCase			) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1			)

		return min(
		    costs[0] + dynamic_programming(index + 1			)     ,costs[1] + dynamic_programming(index + 7			)     ,costs[2] + dynamic_programming(index + 30			)     ,)

	return dynamic_programming(1			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



