



'''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
snake_case_					:      int    			=    'src/transformers'
snake_case_					:      int    			=    'docs/source/en'
snake_case_					:      List[Any]    			=    '.'






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

						_UpperCamelCase   :						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 |
snake_case_					:      List[str]    			=    'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
snake_case_					:      List[str]    			=    re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
snake_case_					:      Optional[Any]    			=    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_					:      str    			=    re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
snake_case_					:      List[Any]    			=    direct_transformers_import(TRANSFORMERS_PATH)






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'    ,    UpperCAmelCase_				)
						return [m.group(0				) for m in matches]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  2 if text == '✅' or text == '❌' else len(UpperCAmelCase_				)
						_UpperCamelCase   :						Tuple												=  (width - text_length) // 2
						_UpperCamelCase   :						Tuple												=  width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent






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

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

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

						# Build the table per se
						_UpperCamelCase   :						List[Any]												=  '|' + '|'.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"

						_UpperCamelCase   :						int												=  {True: '✅', False: '❌'}
						for name in model_names:
												_UpperCamelCase   :						Union[str, Any]												=  model_name_to_prefix[name]
												_UpperCamelCase   :						str												=  [
												    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 A__       (	UpperCAmelCase_=False				):
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						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-->'    ,    )
						_UpperCamelCase   :						str												=  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__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					snake_case_					:      Union[str, Any]    			=    parser.parse_args()

					check_model_table(args.fix_and_overwrite)







'''simple docstring'''
def A__       (	UpperCAmelCase_ = 1_0_0_0				):
						_UpperCamelCase   :						List[str]												=  3
						_UpperCamelCase   :						Any												=  0
						while a < n:
												if a % 3 == 0 or a % 5 == 0:
																		result += a
												elif a % 1_5 == 0:
																		result -= a
												a += 1
						return result


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


