






"""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
_UpperCamelCase   :						Tuple     						= """src/diffusers"""
_UpperCamelCase   :						Tuple     						= """docs/source/en"""
_UpperCamelCase   :						Optional[Any]     						= """."""




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

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


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




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




def 				a (     )  ->		Tuple:
 __A	:       Union[str, Any]       =	diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 __A	:       Optional[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	:       Any       =	collections.defaultdict(UpperCamelCase_		)
 __A	:       Tuple       =	collections.defaultdict(UpperCamelCase_		)
 __A	:       List[str]       =	collections.defaultdict(UpperCamelCase_		)
 __A	:       Dict       =	collections.defaultdict(UpperCamelCase_		)
 __A	:       Dict       =	collections.defaultdict(UpperCamelCase_		)

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

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

 # Build the table per se
 __A	:       Optional[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"

 __A	:       Tuple       =	{True: '✅', False: '❌'}
 for name in model_names:
  __A	:       Any       =	model_name_to_prefix[name]
  __A	:       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 				a (     UpperCamelCase_							:				Tuple=False		)  ->		Dict:
 __A    ,					__A    ,					__A    ,					__A	:       Union[str, Any]       =	_find_text_in_file(
     filename=os.path.join(UpperCamelCase_     ,	'index.mdx'		)     ,	start_prompt='<!--This table is updated automatically from the auto modules'     ,	end_prompt='<!-- End table-->'     ,	)
 __A	:       Optional[int]       =	get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(UpperCamelCase_     ,	'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__":
   _UpperCamelCase   :						Union[str, Any]     						= argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   _UpperCamelCase   :						List[Any]     						= parser.parse_args()

   check_model_table(args.fix_and_overwrite)













"""simple docstring"""


def 				a (     UpperCamelCase_							:				list     ,	UpperCamelCase_							:				int		)  ->		int:
 for index, item in enumerate(UpperCamelCase_		):
  if item == target:
   return index
 return -1




def 				a (     UpperCamelCase_							:				list     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				int		)  ->		int:
 if not (0 <= high < len(UpperCamelCase_		) and 0 <= low < len(UpperCamelCase_		)):
  raise Exception('Invalid upper or lower bound!'		)
 if high < low:
  return -1
 if sequence[low] == target:
  return low
 if sequence[high] == target:
  return high
 return rec_linear_search(UpperCamelCase_     ,	low + 1     ,	high - 1     ,	UpperCamelCase_		)


if __name__ == "__main__":
   _UpperCamelCase   :						Any     						= input("""Enter numbers separated by comma:\n""").strip()
   _UpperCamelCase   :						Optional[Any]     						= [int(item.strip()) for item in user_input.split(""",""")]

   _UpperCamelCase   :						List[str]     						= int(input("""Enter a single number to be found in the list:\n""").strip())
   _UpperCamelCase   :						List[Any]     						= linear_search(sequence, target)
   if result != -1:
      print(f'''linear_search({sequence}, {target}) = {result}''')
   else:
      print(f'''{target} was not found in {sequence}''')





