



'''simple docstring'''





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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/transformers'''
_lowerCAmelCase										=     '''docs/source/en'''
_lowerCAmelCase										=     '''.'''
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





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

    lowerCAmelCase__			:  str											= 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 |
_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 transformers module imported is the one in the repo.
_lowerCAmelCase										=     direct_transformers_import(TRANSFORMERS_PATH)
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""							,   UpperCamelCase      )
    return [m.group(0      ) for m in matches]
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= 2 if text == """✅""" or text == """❌""" else len(UpperCamelCase      )
    lowerCAmelCase__			:  Union[str, Any]											= (width - text_length) // 2
    lowerCAmelCase__			:  Union[str, Any]											= width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Tuple											= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    lowerCAmelCase__			:  List[Any]											= {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    lowerCAmelCase__			:  Tuple											= {name: config.replace("""Config"""							,   """"""      ) 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__			:  Optional[Any]											= collections.defaultdict(UpperCamelCase      )
    lowerCAmelCase__			:  Any											= collections.defaultdict(UpperCamelCase      )
    lowerCAmelCase__			:  Union[str, Any]											= collections.defaultdict(UpperCamelCase      )
    lowerCAmelCase__			:  List[str]											= collections.defaultdict(UpperCamelCase      )
    lowerCAmelCase__			:  Optional[Any]											= collections.defaultdict(UpperCamelCase      )

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

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

    # Build the table per se
    lowerCAmelCase__			:  Union[str, 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"

    lowerCAmelCase__			:  Union[str, Any]											= {True: """✅""", False: """❌"""}
    for name in model_names:
        lowerCAmelCase__			:  List[str]											= model_name_to_prefix[name]
        lowerCAmelCase__			:  Optional[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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase=False      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[Any]											= _find_text_in_file(
        filename=os.path.join(UpperCamelCase							,   """index.md"""      )							,   start_prompt="""<!--This table is updated automatically from the auto modules"""							,   end_prompt="""<!-- End table-->"""							,   )
    lowerCAmelCase__			:  str											= get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(UpperCamelCase							,   """index.md"""      )							,   """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.md` 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)







'''simple docstring'''





import os
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= len(grid[0]      )
    lowerCAmelCase__			:  int											= len(UpperCamelCase      )

    lowerCAmelCase__			:  Optional[int]											= 0
    lowerCAmelCase__			:  Tuple											= 0
    lowerCAmelCase__			:  Optional[Any]											= 0

    # Check vertically, horizontally, diagonally at the same time (only works
    # for nxn grid)
    for i in range(UpperCamelCase      ):
        for j in range(n_rows - 3      ):
            lowerCAmelCase__			:  str											= grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
            lowerCAmelCase__			:  Optional[int]											= grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

            # Left-to-right diagonal (\) product
            if i < n_columns - 3:
                lowerCAmelCase__			:  Optional[int]											= (
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )

            # Right-to-left diagonal(/) product
            if i > 2:
                lowerCAmelCase__			:  Tuple											= (
                    grid[i][j]
                    * grid[i - 1][j + 1]
                    * grid[i - 2][j + 2]
                    * grid[i - 3][j + 3]
                )

            lowerCAmelCase__			:  Dict											= max(
                UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
            if max_product > largest:
                lowerCAmelCase__			:  Any											= max_product

    return largest
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= []
    with open(os.path.dirname(UpperCamelCase      ) + """/grid.txt"""      ) as file:
        for line in file:
            grid.append(line.strip("""\n"""      ).split(""" """      )      )

    lowerCAmelCase__			:  Dict											= [[int(UpperCamelCase      ) for i in grid[j]] for j in range(len(UpperCamelCase      )      )]

    return largest_product(UpperCamelCase      )


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


