


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def        UpperCamelCase_(				_A   :np.ndarray				,     _A   :np.ndarray  )->		float:
			return math.sqrt(sum(pow(a - b				,     2  ) for a, b in zip(_A				,     _A  )  )  )
def        UpperCamelCase_(				_A   :np.ndarray				,     _A   :np.ndarray  )->		list[list[list[float] | float]]:

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

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

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

			UpperCamelCase__					=       []

			for value in value_array:
						UpperCamelCase__					=       euclidean(_A				,     dataset[0]  )
						UpperCamelCase__					=       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									UpperCamelCase__					=       euclidean(_A				,     _A  )

									if dist > temp_dist:
												UpperCamelCase__					=       temp_dist
												UpperCamelCase__					=       dataset_value.tolist()

						answer.append([vector, dist]  )

			return answer
def        UpperCamelCase_(				_A   :np.ndarray				,     _A   :np.ndarray  )->		float:
			return np.dot(_A				,     _A  ) / (norm(_A  ) * norm(_A  ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_token_id=snake_case      ,						**snake_case     )

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       use_cache
						UpperCamelCase__					=       classifier_dropout







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__					=       {0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__					=       {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]     )


