


"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_	)->				list[list[list[float] | float]]:
  '''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(A_	)

  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(A_	)
  except IndexError:
    if dataset.ndim != value_array.ndim:
      raise TypeError("Wrong shape"	)

  if dataset.dtype != value_array.dtype:
    a						:		List[str]											=      (
        "Input data have different datatype... "
        F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
    )
    raise TypeError(A_	)

  a						:		Optional[Any]											=      []

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

    for dataset_value in dataset[1:]:
      a						:		int											=      euclidean(A_  ,       A_	)

      if dist > temp_dist:
        a						:		List[Any]											=      temp_dist
        a						:		Tuple											=      dataset_value.tolist()

    answer.append([vector, dist]	)

  return answer


def 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







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


if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






