


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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   Dict	,    SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
							'''simple docstring'''





							with open(SCREAMING_SNAKE_CASE__	,    'r'	,    encoding='utf-8'	,    newline='\n'  ) as f:
														_lowerCamelCase :	Tuple						=f.readlines()
							# Find the start prompt.
							_lowerCamelCase :	Optional[int]						=0
							while not lines[start_index].startswith(SCREAMING_SNAKE_CASE__  ):
														start_index += 1
							start_index += 1

							_lowerCamelCase :	List[Any]						=start_index
							while not lines[end_index].startswith(SCREAMING_SNAKE_CASE__  ):
														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 |
lowerCamelCase		      = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCamelCase		      = re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCamelCase		      = 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.
lowerCamelCase		      = re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Dict  ):
							'''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   str  ):
							'''simple docstring'''





							_lowerCamelCase :	Union[str, Any]						=2 if text == '✅' or text == '❌' else len(SCREAMING_SNAKE_CASE__  )
							_lowerCamelCase :	Optional[int]						=(width - text_length) // 2
							_lowerCamelCase :	Optional[Any]						=width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent




def 			a_   (					):
							'''simple docstring'''





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

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

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

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

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

							_lowerCamelCase :	str						={True: '✅', False: '❌'}
							for name in model_names:
														_lowerCamelCase :	Union[str, Any]						=model_name_to_prefix[name]
														_lowerCamelCase :	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(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) for l, w in zip(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )]  ) + "|\n"
							return table




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Optional[int]=False  ):
							'''simple docstring'''





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

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

	check_model_table(args.fix_and_overwrite)






from collections.abc import Callable

lowerCamelCase		      = Callable[[float], float]  # type alias for a real -> real function




def 			a_   (					SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   int	,    ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=float(SCREAMING_SNAKE_CASE__  )
       while True:
              try:
                     _lowerCamelCase :	int						=prev_guess - function(SCREAMING_SNAKE_CASE__  ) / derivative(SCREAMING_SNAKE_CASE__  )
              except ZeroDivisionError:
                     raise ZeroDivisionError('Could not find root'  ) from None
              if abs(prev_guess - next_guess  ) < 10**-5:
                     return next_guess
              _lowerCamelCase :	Union[str, Any]						=next_guess




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return (x**3) - (2 * x) - 5




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


