





'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache









'''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
_SCREAMING_SNAKE_CASE	 =							"src/transformers"
_SCREAMING_SNAKE_CASE	 =							"docs/source/en"
_SCREAMING_SNAKE_CASE	 =							"."


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





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

    _lowerCAmelCase				=      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 |
_SCREAMING_SNAKE_CASE	 =							"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE	 =							re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_SCREAMING_SNAKE_CASE	 =							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.
_SCREAMING_SNAKE_CASE	 =							re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE	 =							direct_transformers_import(TRANSFORMERS_PATH)


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      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_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      2 if text == "✅" or text == "❌" else len(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      (width - text_length) // 2
    _lowerCAmelCase				=      width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    _lowerCAmelCase				=      {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    _lowerCAmelCase				=      {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				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )

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

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

    # Build the table per se
    _lowerCAmelCase				=      "|" + "|".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				=      {True: "✅", False: "❌"}
    for name in model_names:
        _lowerCAmelCase				=      model_name_to_prefix[name]
        _lowerCAmelCase				=      [
            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_  :					Union[str, Any]=False    ):



    '''simple docstring'''





    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      _find_text_in_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_		,       "index.md"    )		,       start_prompt="<!--This table is updated automatically from the auto modules"		,       end_prompt="<!-- End table-->"		,       )
    _lowerCAmelCase				=      get_model_table_from_auto_modules()

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

       check_model_table(args.fix_and_overwrite)


