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


snake_case :		Any							      =  logging.get_logger(__name__)

snake_case :		Dict							      =  {
    "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       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'dpr'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=0						,   _a="absolute"						,   _a = 0						,   **_a						,   ):
				super().__init__(pad_token_id=_a						,   **_a		)

				__magic_name__		:		Optional[int]	   =     vocab_size
				__magic_name__		:		List[str]	   =     hidden_size
				__magic_name__		:		Optional[Any]	   =     num_hidden_layers
				__magic_name__		:		Dict	   =     num_attention_heads
				__magic_name__		:		Optional[Any]	   =     hidden_act
				__magic_name__		:		Union[str, Any]	   =     intermediate_size
				__magic_name__		:		Optional[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Union[str, Any]	   =     attention_probs_dropout_prob
				__magic_name__		:		List[str]	   =     max_position_embeddings
				__magic_name__		:		int	   =     type_vocab_size
				__magic_name__		:		Tuple	   =     initializer_range
				__magic_name__		:		Optional[int]	   =     layer_norm_eps
				__magic_name__		:		Tuple	   =     projection_dim
				__magic_name__		:		Dict	   =     position_embedding_type


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


	if a < 0:
		raise ValueError("math domain error"   )

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()
