



"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def     UpperCamelCase_   (   lowerCAmelCase__				:    np.ndarray   ,       lowerCAmelCase__				:    np.ndarray						)						->       Optional[int]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    np.ndarray   ,       lowerCAmelCase__				:    np.ndarray						)						->       Union[str, Any]:

  """simple docstring"""




  if dataset.ndim != value_array.ndim:
    lowerCAmelCase_       :    str								=			(
        '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]:
      lowerCAmelCase_       :    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:
    lowerCAmelCase_       :    Optional[int]								=			(
        'Input data have different datatype... '
        f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
    )
    raise TypeError(lowerCamelCase__						)

  lowerCAmelCase_       :    Dict								=			[]

  for value in value_array:
    lowerCAmelCase_       :    List[Any]								=			euclidean(lowerCamelCase__   ,       dataset[0]						)
    lowerCAmelCase_       :    Tuple								=			dataset[0].tolist()

    for dataset_value in dataset[1:]:
      lowerCAmelCase_       :    Optional[Any]								=			euclidean(lowerCamelCase__   ,       lowerCamelCase__						)

      if dist > temp_dist:
        lowerCAmelCase_       :    Any								=			temp_dist
        lowerCAmelCase_       :    int								=			dataset_value.tolist()

    answer.append([vector, dist]						)

  return answer

def     UpperCamelCase_   (   lowerCAmelCase__				:    np.ndarray   ,       lowerCAmelCase__				:    np.ndarray						)						->       Union[str, Any]:

  """simple docstring"""




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


if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowercase__		:    Optional[int]				   =     {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    Dict				   =     [
         """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TrOCRForCausalLM""",
         """TrOCRPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
     from .processing_trocr import TrOCRProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
     import sys

     lowercase__		:    int				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





