






'''simple docstring'''




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


__lowerCamelCase   :					Any   							=					logging.get_logger(__name__)

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







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''retribert'''

							def __init__(		self			:Any       ,							lowerCAmelCase__			:List[Any]=30_522       ,							lowerCAmelCase__			:Any=768       ,							lowerCAmelCase__			:Tuple=8       ,							lowerCAmelCase__			:str=12       ,							lowerCAmelCase__			:Any=3_072       ,							lowerCAmelCase__			:Union[str, Any]="gelu"       ,							lowerCAmelCase__			:List[str]=0.1       ,							lowerCAmelCase__			:Any=0.1       ,							lowerCAmelCase__			:Any=512       ,							lowerCAmelCase__			:int=2       ,							lowerCAmelCase__			:Tuple=0.0_2       ,							lowerCAmelCase__			:List[Any]=1E-1_2       ,							lowerCAmelCase__			:Any=True       ,							lowerCAmelCase__			:Dict=128       ,							lowerCAmelCase__			:Optional[int]=0       ,							**lowerCAmelCase__			:List[str]       ,							)				->  Dict:


										'''simple docstring'''




										super().__init__(pad_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)

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












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return math.sqrt(sum(pow(a - b     ,2						) for a, b in zip(__magic_name__     ,__magic_name__						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

			try:
						if dataset.shape[1] != value_array.shape[1]:
									snake_case_				:					Dict   =       (
									    "Wrong input data's shape... "
									    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
									)
									raise ValueError(__magic_name__						)
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape"						)

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return np.dot(__magic_name__     ,__magic_name__						) / (norm(__magic_name__						) * norm(__magic_name__						))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




