



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


SCREAMING_SNAKE_CASE_:int 			=		logging.get_logger(__name__)

# TODO: upload to AWS
SCREAMING_SNAKE_CASE_:Tuple 			=		{
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__	(						SCREAMING_SNAKE_CASE_       ):




    '''simple docstring'''






    __lowerCamelCase     :					Optional[Any]   = 'retribert'






    def __init__(			self,						lowerCamelCase__=3_0522,						lowerCamelCase__=768,						lowerCamelCase__=8,						lowerCamelCase__=12,						lowerCamelCase__=3072,						lowerCamelCase__="gelu",						lowerCamelCase__=0.1,						lowerCamelCase__=0.1,						lowerCamelCase__=512,						lowerCamelCase__=2,						lowerCamelCase__=0.02,						lowerCamelCase__=1e-12,						lowerCamelCase__=True,						lowerCamelCase__=128,						lowerCamelCase__=0,						**lowerCamelCase__,						):
        super().__init__(pad_token_id=lowerCamelCase__,						**lowerCamelCase__						)

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







def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data