






"""simple docstring"""



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


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






     '''simple docstring'''



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


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






     '''simple docstring'''



     if dataset.ndim != value_array.ndim:
          UpperCAmelCase__						:    Optional[int]							=							(
              'Wrong input data\'s dimensions... '
              f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
          )
          raise ValueError(lowerCamelCase_				)

     try:
          if dataset.shape[1] != value_array.shape[1]:
               UpperCAmelCase__						:    List[Any]							=							(
                   'Wrong input data\'s shape... '
                   f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
               )
               raise ValueError(lowerCamelCase_				)
     except IndexError:
          if dataset.ndim != value_array.ndim:
               raise TypeError("Wrong shape"				)

     if dataset.dtype != value_array.dtype:
          UpperCAmelCase__						:    Tuple							=							(
              'Input data have different datatype... '
              f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
          )
          raise TypeError(lowerCamelCase_				)

     UpperCAmelCase__						:    Optional[Any]							=							[]

     for value in value_array:
          UpperCAmelCase__						:    str							=							euclidean(lowerCamelCase_			,					dataset[0]				)
          UpperCAmelCase__						:    List[Any]							=							dataset[0].tolist()

          for dataset_value in dataset[1:]:
               UpperCAmelCase__						:    Optional[Any]							=							euclidean(lowerCamelCase_			,					lowerCamelCase_				)

               if dist > temp_dist:
                    UpperCAmelCase__						:    Optional[int]							=							temp_dist
                    UpperCAmelCase__						:    Union[str, Any]							=							dataset_value.tolist()

          answer.append([vector, dist]				)

     return answer


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






     '''simple docstring'''



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


if __name__ == "__main__":
 import doctest

 doctest.testmod()








import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



