




'''simple docstring'''




import argparse
import collections
import importlib.util
import os
import re


# 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__          =						"""src/diffusers"""
snake_case__          =						"""docs/source/en"""
snake_case__          =						"""."""




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

					A_ :     Optional[Any] =							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 |
snake_case__          =						"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
snake_case__          =						re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
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.
snake_case__          =						re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
snake_case__          =						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
snake_case__          =						spec.loader.load_module()




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




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




def 						snake_case__    ( )   ->   Optional[int]:
					A_ :     List[str] =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A_ :     List[Any] =							{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A_ :     List[Any] =							{name: config.replace('''ConfigMixin'''			,		''''''			) 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.
					A_ :     Optional[int] =							collections.defaultdict(lowerCamelCase__			)
					A_ :     int =							collections.defaultdict(lowerCamelCase__			)
					A_ :     Dict =							collections.defaultdict(lowerCamelCase__			)
					A_ :     int =							collections.defaultdict(lowerCamelCase__			)
					A_ :     Optional[Any] =							collections.defaultdict(lowerCamelCase__			)

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

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

					# Build the table per se
					A_ :     str =							'''|''' + '''|'''.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"

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




def 						snake_case__    ( lowerCamelCase__				:   Dict=False			)   ->   Any:
					A_ ,A_ ,A_ ,A_ :     Dict =							_find_text_in_file(
					    filename=os.path.join(lowerCamelCase__			,		'''index.mdx'''			)			,		start_prompt='''<!--This table is updated automatically from the auto modules'''			,		end_prompt='''<!-- End table-->'''			,		)
					A_ :     List[Any] =							get_model_table_from_auto_modules()

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


if __name__ == "__main__":
							snake_case__          =						argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							snake_case__          =						parser.parse_args()

							check_model_table(args.fix_and_overwrite)









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   int			)   ->   bool:
					if not isinstance(lowerCamelCase__			,		lowerCamelCase__			):
										A_ :     int =							f'Input value of [number={number}] must be an integer'
										raise TypeError(lowerCamelCase__			)
					if number < 0 or number % 2 == 1:
										return False
					A_ :     Dict =							int(number**0.5			)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



