
'''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_      =  "src/diffusers"
a_      =  "docs/source/en"
a_      =  "."

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    with open(__SCREAMING_SNAKE_CASE,						"r",						encoding="utf-8",						newline="\n"				) as f:
        snake_case_       :			Any   	=  f.readlines()
    # Find the start prompt.
    snake_case_       :			Tuple   	=  0
    while not lines[start_index].startswith(__SCREAMING_SNAKE_CASE				):
        start_index += 1
    start_index += 1

    snake_case_       :			Optional[int]   	=  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 |
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   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			str   	=  re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)",						__SCREAMING_SNAKE_CASE				)
    return [m.group(0				) for m in matches]

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Any   	=  2 if text == "✅" or text == "❌" else len(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Optional[Any]   	=  (width - text_length) // 2
    snake_case_       :			Optional[Any]   	=  width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent

def   UpperCamelCase_   (		):
    """simple docstring"""






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

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

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

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

    snake_case_       :			Dict   	=  {True: "✅", False: "❌"}
    for name in model_names:
        snake_case_       :			List[Any]   	=  model_name_to_prefix[name]
        snake_case_       :			Tuple   	=  [
            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   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE=False				):
    """simple docstring"""






    snake_case_	,       snake_case_	,       snake_case_	,       snake_case_       :			Optional[int]   	=  _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-->",						)
    snake_case_       :			Optional[Any]   	=  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__":
  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)








UpperCAmelCase			=    10**8






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  bool:




      """simple docstring"""

      if n % 10 == 0:
            return False
      snake_case_       =      str(SCREAMING_SNAKE_CASE					)
      return s == s[::-1]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      set()
      snake_case_       =      1
      snake_case_       =      5
      while sum_squares < LIMIT:
            snake_case_       =      first_square + 1
            while sum_squares < LIMIT:
                  if is_palindrome(SCREAMING_SNAKE_CASE					):
                        answer.add(SCREAMING_SNAKE_CASE					)
                  last_square += 1
                  sum_squares += last_square**2
            first_square += 1
            snake_case_       =      first_square**2 + (first_square + 1) ** 2

      return sum(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   print(solution())