






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


a						:			Optional[Any]     	=					logging.get_logger(__name__)

a						:			Optional[int]     	=					{
    "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        a       (					lowercase__  ):



      """simple docstring"""


      a    :					List[str]							=      'realm'
      def __init__( self   :     List[str]						, __lowercase   :     Any=30522						, __lowercase   :     Any=768						, __lowercase   :     str=128						, __lowercase   :     Optional[Any]=12						, __lowercase   :     Any=12						, __lowercase   :     int=8						, __lowercase   :     Any=3072						, __lowercase   :     Any="gelu_new"						, __lowercase   :     List[Any]=0.1						, __lowercase   :     Any=0.1						, __lowercase   :     Optional[Any]=512						, __lowercase   :     List[str]=2						, __lowercase   :     List[Any]=0.02						, __lowercase   :     int=1e-1_2						, __lowercase   :     int=256						, __lowercase   :     Tuple=10						, __lowercase   :     str=1e-3						, __lowercase   :     Any=5						, __lowercase   :     List[str]=320						, __lowercase   :     Optional[int]=13353718						, __lowercase   :     int=5000						, __lowercase   :     Optional[int]=1						, __lowercase   :     Optional[Any]=0						, __lowercase   :     int=2						, **__lowercase   :     Tuple						, )				-> List[Any]:
        super().__init__(pad_token_id=__lowercase						, bos_token_id=__lowercase						, eos_token_id=__lowercase						, **__lowercase   )

        # Common config
        __UpperCAmelCase				:      Tuple	 =    vocab_size
        __UpperCAmelCase				:      List[Any]	 =    max_position_embeddings
        __UpperCAmelCase				:      Dict	 =    hidden_size
        __UpperCAmelCase				:      Optional[Any]	 =    retriever_proj_size
        __UpperCAmelCase				:      Optional[int]	 =    num_hidden_layers
        __UpperCAmelCase				:      int	 =    num_attention_heads
        __UpperCAmelCase				:      List[Any]	 =    num_candidates
        __UpperCAmelCase				:      Optional[Any]	 =    intermediate_size
        __UpperCAmelCase				:      Union[str, Any]	 =    hidden_act
        __UpperCAmelCase				:      Tuple	 =    hidden_dropout_prob
        __UpperCAmelCase				:      Union[str, Any]	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      int	 =    initializer_range
        __UpperCAmelCase				:      Optional[Any]	 =    type_vocab_size
        __UpperCAmelCase				:      Optional[Any]	 =    layer_norm_eps

        # Reader config
        __UpperCAmelCase				:      int	 =    span_hidden_size
        __UpperCAmelCase				:      str	 =    max_span_width
        __UpperCAmelCase				:      Tuple	 =    reader_layer_norm_eps
        __UpperCAmelCase				:      List[str]	 =    reader_beam_size
        __UpperCAmelCase				:      str	 =    reader_seq_len

        # Retrieval config
        __UpperCAmelCase				:      Optional[int]	 =    num_block_records
        __UpperCAmelCase				:      Tuple	 =    searcher_beam_size




'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




