
'''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
a__						:				List[str]           =    "src/diffusers"
a__						:				Any           =    "docs/source/en"
a__						:				int           =    "."





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Any:







      """simple docstring"""
      with open(lowercase__ ,					'r' ,					encoding='utf-8' ,					newline='\n'		) as f:
            __A    		=     f.readlines()
      # Find the start prompt.
      __A    		=     0
      while not lines[start_index].startswith(lowercase__		):
            start_index += 1
      start_index += 1

      __A    		=     start_index
      while not lines[end_index].startswith(lowercase__		):
            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__						:				List[str]           =    "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
a__						:				Any           =    re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
a__						:				List[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.
a__						:				List[str]           =    re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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





def     snake_case							(						UpperCAmelCase		)->     Any:







      """simple docstring"""
      __A    		=     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,					lowercase__		)
      return [m.group(0		) for m in matches]





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Tuple:







      """simple docstring"""
      __A    		=     2 if text == "✅" or text == "❌" else len(lowercase__		)
      __A    		=     (width - text_length) // 2
      __A    		=     width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent





def     snake_case							(						)->     Dict:







      """simple docstring"""
      __A    		=     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      __A    		=     {
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      __A    		=     {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    		=     collections.defaultdict(lowercase__		)
      __A    		=     collections.defaultdict(lowercase__		)
      __A    		=     collections.defaultdict(lowercase__		)
      __A    		=     collections.defaultdict(lowercase__		)
      __A    		=     collections.defaultdict(lowercase__		)

      # Let's lookup through all diffusers object (once).
      for attr_name in dir(lowercase__		):
            __A    		=     None
            if attr_name.endswith('Tokenizer'		):
                  __A    		=     slow_tokenizers
                  __A    		=     attr_name[:-9]
            elif attr_name.endswith('TokenizerFast'		):
                  __A    		=     fast_tokenizers
                  __A    		=     attr_name[:-1_3]
            elif _re_tf_models.match(lowercase__		) is not None:
                  __A    		=     tf_models
                  __A    		=     _re_tf_models.match(lowercase__		).groups()[0]
            elif _re_flax_models.match(lowercase__		) is not None:
                  __A    		=     flax_models
                  __A    		=     _re_flax_models.match(lowercase__		).groups()[0]
            elif _re_pt_models.match(lowercase__		) is not None:
                  __A    		=     pt_models
                  __A    		=     _re_pt_models.match(lowercase__		).groups()[0]

            if lookup_dict is not None:
                  while len(lowercase__		) > 0:
                        if attr_name in model_name_to_prefix.values():
                              __A    		=     True
                              break
                        # Try again after removing the last word in the name
                        __A    		=     "".join(camel_case_split(lowercase__		)[:-1]		)

    # Let's build that table!
      __A    		=     list(model_name_to_config.keys()		)
      model_names.sort(key=str.lower		)
      __A    		=     ["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    		=     [len(lowercase__		) + 2 for c in columns]
      __A    		=     max([len(lowercase__		) for name in model_names]		) + 2

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

      __A    		=     {True: "✅", False: "❌"}
      for name in model_names:
            __A    		=     model_name_to_prefix[name]
            __A    		=     [
                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(lowercase__ ,					lowercase__		) for l, w in zip(lowercase__ ,					lowercase__		)]		) + "|\n"
      return table





def     snake_case							(						UpperCAmelCase=False		)->     Union[str, Any]:







      """simple docstring"""
      __A    		=     _find_text_in_file(
          filename=os.path.join(lowercase__ ,					'index.mdx'		) ,					start_prompt='<!--This table is updated automatically from the auto modules' ,					end_prompt='<!-- End table-->' ,					)
      __A    		=     get_model_table_from_auto_modules()

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

 check_model_table(args.fix_and_overwrite)








'''simple docstring'''





import itertools
from pathlib import Path





def     snake_case							(						UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     [tuple(UpperCAmelCase		) for login in logins]

      __A    		=     {char for login in split_logins for char in login}

      for permutation in itertools.permutations(UpperCAmelCase		):
            __A    		=     True
            for login in logins:
                  if not (
                      permutation.index(login[0]		)
                      < permutation.index(login[1]		)
                      < permutation.index(login[2]		)
                  ):
                        __A    		=     False
                        break

            if satisfied:
                  return int(''.join(UpperCAmelCase		)		)

      raise Exception('Unable to find the secret passcode'		)





def     snake_case							(						UpperCAmelCase = "keylog.txt"		)->     int:







      """simple docstring"""
      __A    		=     Path(UpperCAmelCase		).parent.joinpath(UpperCAmelCase		).read_text().splitlines()

      return find_secret_passcode(UpperCAmelCase		)


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






