




'''simple docstring'''


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					np.ndarray ,    UpperCamelCase__    :					np.ndarray				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					np.ndarray ,    UpperCamelCase__    :					np.ndarray				):




     """simple docstring"""


     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(UpperCamelCase__				)

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

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

     a_     :							List[Any]     						=  []

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

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

               if dist > temp_dist:
                    a_     :							Any     						=  temp_dist
                    a_     :							List[str]     						=  dataset_value.tolist()

          answer.append([vector, dist]				)

     return answer



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					np.ndarray ,    UpperCamelCase__    :					np.ndarray				):




     """simple docstring"""


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


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
