
import argparse
import os
import re


_A   =			"src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_A   =			re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
_A   =			re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")



def 						lowercase_      (       A__			,		A__ = False		)							->							Any:
    """simple docstring"""

    with open(A__			,		"r"			,		encoding="utf-8"		) as f:
        snake_case								= f.read()

    snake_case								= content.split("\n"		)
    snake_case								= []
    snake_case								= 0
    while line_idx < len(A__		):
        if _re_intro_mapping.search(lines[line_idx]		) is not None:
            snake_case								= len(re.search(r"^(\s*)\S"			,		lines[line_idx]		).groups()[0]		) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(" " * indent + "("		):
                new_lines.append(lines[line_idx]		)
                line_idx += 1

            snake_case								= []
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    snake_case								= line_idx
                    while not lines[line_idx].startswith(" " * indent + ")"		):
                        line_idx += 1
                    blocks.append("\n".join(lines[start_idx : line_idx + 1]		)		)
                else:
                    blocks.append(lines[line_idx]		)
                line_idx += 1

            # Sort blocks by their identifiers
            snake_case								= sorted(A__			,		key=lambda A__		: _re_identifier.search(A__		).groups()[0]		)
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx]		)
            line_idx += 1

    if overwrite:
        with open(A__			,		"w"			,		encoding="utf-8"		) as f:
            f.write("\n".join(A__		)		)
    elif "\n".join(A__		) != content:
        return True



def 						lowercase_      (       A__ = False		)							->							Any:
    """simple docstring"""

    snake_case								= [os.path.join(A__			,		A__		) for f in os.listdir(A__		) if f.endswith(".py"		)]
    snake_case								= [sort_auto_mapping(A__			,		overwrite=A__		) for fname in fnames]

    if not overwrite and any(A__		):
        snake_case								= [f for f, d in zip(A__			,		A__		) if d]
        raise ValueError(
            F'The following files have auto mappings that need sorting: {", ".join(A__		)}. Run `make style` to fix'
            " this."		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
   _A   =			parser.parse_args()

   sort_all_auto_mappings(not args.check_only)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							str									=		"realm"





     def __init__(self				:   Optional[int]    ,   _A				:   Optional[Any]=3_0_5_2_2    ,   _A				:   Tuple=7_6_8    ,   _A				:   List[str]=1_2_8    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Dict=1_2    ,   _A				:   Tuple=8    ,   _A				:   Dict=3_0_7_2    ,   _A				:   Union[str, Any]="gelu_new"    ,   _A				:   Any=0.1    ,   _A				:   int=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   List[str]=2    ,   _A				:   Any=0.02    ,   _A				:   int=1E-12    ,   _A				:   Tuple=2_5_6    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Any=1E-3    ,   _A				:   int=5    ,   _A				:   int=3_2_0    ,   _A				:   Dict=1_3_3_5_3_7_1_8    ,   _A				:   Any=5_0_0_0    ,   _A				:   Union[str, Any]=1    ,   _A				:   Dict=0    ,   _A				:   int=2    ,   **_A				:   Union[str, Any]    ,   )						->						Optional[Any]:
         super().__init__(pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   **_A       )

         # Common config
         snake_case								= vocab_size
         snake_case								= max_position_embeddings
         snake_case								= hidden_size
         snake_case								= retriever_proj_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= num_candidates
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= initializer_range
         snake_case								= type_vocab_size
         snake_case								= layer_norm_eps

         # Reader config
         snake_case								= span_hidden_size
         snake_case								= max_span_width
         snake_case								= reader_layer_norm_eps
         snake_case								= reader_beam_size
         snake_case								= reader_seq_len

         # Retrieval config
         snake_case								= num_block_records
         snake_case								= searcher_beam_size

