


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return math.sqrt(sum(pow(a - b ,			2       ) for a, b in zip(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):

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

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

  if dataset.dtype != value_array.dtype:
    A_      :				Union[str, Any]		     =					(
        '''Input data have different datatype... '''
        f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
    )
    raise TypeError(SCREAMING_SNAKE_CASE       )

  A_      :				Optional[Any]		     =					[]

  for value in value_array:
    A_      :				Tuple		     =					euclidean(SCREAMING_SNAKE_CASE ,			dataset[0]       )
    A_      :				Union[str, Any]		     =					dataset[0].tolist()

    for dataset_value in dataset[1:]:
      A_      :				List[Any]		     =					euclidean(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

      if dist > temp_dist:
        A_      :				Dict		     =					temp_dist
        A_      :				int		     =					dataset_value.tolist()

    answer.append([vector, dist]       )

  return answer


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return np.dot(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) / (norm(SCREAMING_SNAKE_CASE       ) * norm(SCREAMING_SNAKE_CASE       ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




