






def        _lowerCamelCase(      lowerCAmelCase__					:		list[int]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Tuple    				= len(lowerCAmelCase__   )
				if n in (0, 1):
								return 0
				return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()







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
A										=      'src/diffusers'
A										=      'docs/source/en'
A										=      '.'



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_ :						List[str]    				= 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 diffusers module imported is the one in the repo.
A										=      importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A										=      spec.loader.load_module()



def        _lowerCamelCase(      lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







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



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[Any]     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Any    				= 2 if text == '✅' or text == '❌' else len(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						int    				= (width - text_length) // 2
				SCREAMING_SNAKE_CASE_ :						str    				= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				SCREAMING_SNAKE_CASE_ :						Union[str, 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.
				SCREAMING_SNAKE_CASE_ :						int    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						str    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= collections.defaultdict(lowerCAmelCase__   )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						List[str]    				= None
								if attr_name.endswith('Tokenizer'   ):
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= slow_tokenizers
												SCREAMING_SNAKE_CASE_ :						Dict    				= attr_name[:-9]
								elif attr_name.endswith('TokenizerFast'   ):
												SCREAMING_SNAKE_CASE_ :						Any    				= fast_tokenizers
												SCREAMING_SNAKE_CASE_ :						int    				= attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= tf_models
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= _re_tf_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= flax_models
												SCREAMING_SNAKE_CASE_ :						int    				= _re_flax_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= pt_models
												SCREAMING_SNAKE_CASE_ :						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():
																				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= True
																				break
																# Try again after removing the last word in the name
																SCREAMING_SNAKE_CASE_ :						str    				= ''.join(camel_case_split(lowerCAmelCase__   )[:-1]   )

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

				# Build the table per se
				SCREAMING_SNAKE_CASE_ :						Dict    				= '|' + '|'.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"

				SCREAMING_SNAKE_CASE_ :						int    				= {True: '✅', False: '❌'}
				for name in model_names:
								SCREAMING_SNAKE_CASE_ :						int    				= model_name_to_prefix[name]
								SCREAMING_SNAKE_CASE_ :						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        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]=False   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= _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-->'     ,     )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= 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__":
				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)