






'''simple docstring'''







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					list[list[list[float] | float]]:
     """simple docstring"""




     if dataset.ndim != value_array.ndim:
          __a       =   (
              """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       =   (
                   """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       =   (
              """Input data have different datatype... """
              F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
          )
          raise TypeError(__SCREAMING_SNAKE_CASE		)

     __a       =   []

     for value in value_array:
          __a       =   euclidean(__SCREAMING_SNAKE_CASE				,  dataset[0]		)
          __a       =   dataset[0].tolist()

          for dataset_value in dataset[1:]:
               __a       =   euclidean(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

               if dist > temp_dist:
                    __a       =   temp_dist
                    __a       =   dataset_value.tolist()

          answer.append([vector, dist]		)

     return answer




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					float:
     """simple docstring"""




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


if __name__ == "__main__":
 import doctest

 doctest.testmod()










'''simple docstring'''







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_      =			{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure)






