






'''simple docstring'''

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def 				snake_case       (  a_				:			np.ndarray     , a_				:			np.ndarray						)							->					float:





      """simple docstring"""

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



def 				snake_case       (  a_				:			np.ndarray     , a_				:			np.ndarray						)							->					list[list[list[float] | float]]:





      """simple docstring"""

      if dataset.ndim != value_array.ndim:
            UpperCamelCase_					: Optional[Any]            =				(
                """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_					: Any            =				(
                      """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_					: Dict            =				(
                """Input data have different datatype... """
                f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
            )
            raise TypeError(a_						)

      UpperCamelCase_					: List[Any]            =				[]

      for value in value_array:
            UpperCamelCase_					: Dict            =				euclidean(a_     , dataset[0]						)
            UpperCamelCase_					: List[Any]            =				dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  UpperCamelCase_					: Optional[int]            =				euclidean(a_     , a_						)

                  if dist > temp_dist:
                        UpperCamelCase_					: Dict            =				temp_dist
                        UpperCamelCase_					: Optional[int]            =				dataset_value.tolist()

            answer.append([vector, dist]						)

      return answer



def 				snake_case       (  a_				:			np.ndarray     , a_				:			np.ndarray						)							->					float:





      """simple docstring"""

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()














'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






