

"""simple docstring"""




from __future__ import annotations

import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   np.shape(snake_case__     )
			if rows != columns:
						SCREAMING_SNAKE_CASE__										=   (
						    """'table' has to be of square shaped array but got a """
						    f"""{rows}x{columns} array:\n{table}"""
						)
						raise ValueError(snake_case__     )

			SCREAMING_SNAKE_CASE__										=   np.zeros((rows, columns)     )
			SCREAMING_SNAKE_CASE__										=   np.zeros((rows, columns)     )
			for i in range(snake_case__     ):
						for j in range(snake_case__     ):
									SCREAMING_SNAKE_CASE__										=   sum(lower[i][k] * upper[k][j] for k in range(snake_case__     )     )
									if upper[j][j] == 0:
												raise ArithmeticError("""No LU decomposition exists"""     )
									SCREAMING_SNAKE_CASE__										=   (table[i][j] - total) / upper[j][j]
						SCREAMING_SNAKE_CASE__										=   1
						for j in range(snake_case__		,					snake_case__     ):
									SCREAMING_SNAKE_CASE__										=   sum(lower[i][k] * upper[k][j] for k in range(snake_case__     )     )
									SCREAMING_SNAKE_CASE__										=   table[i][j] - total
			return lower, upper


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""




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


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      Optional[int]           =			{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[int]       		=				'dpr'

			def __init__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    List[str]=3_0_5_2_2     ,					__UpperCAmelCase					:    int=7_6_8     ,					__UpperCAmelCase					:    Optional[Any]=1_2     ,					__UpperCAmelCase					:    Dict=1_2     ,					__UpperCAmelCase					:    Any=3_0_7_2     ,					__UpperCAmelCase					:    int="gelu"     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Optional[Any]=0.1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    Union[str, Any]=2     ,					__UpperCAmelCase					:    Any=0.02     ,					__UpperCAmelCase					:    Dict=1e-12     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Union[str, Any]="absolute"     ,					__UpperCAmelCase					:    int = 0     ,					**__UpperCAmelCase					:    Dict     ,					) ->     Tuple:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   type_vocab_size
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   projection_dim
						SCREAMING_SNAKE_CASE__										=   position_embedding_type


