




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



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



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):

  if dataset.ndim != value_array.ndim:
    lowercase			:Optional[int]							=  (
        "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]:
      lowercase			: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:
    lowercase			:int							=  (
        "Input data have different datatype... "
        F"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
    )
    raise TypeError(lowerCamelCase      )

  lowercase			:str							=  []

  for value in value_array:
    lowercase			:Optional[int]							=  euclidean(lowerCamelCase,	dataset[0]      )
    lowercase			:Union[str, Any]							=  dataset[0].tolist()

    for dataset_value in dataset[1:]:
      lowercase			:List[str]							=  euclidean(lowerCamelCase,	lowerCamelCase      )

      if dist > temp_dist:
        lowercase			:str							=  temp_dist
        lowercase			:Optional[int]							=  dataset_value.tolist()

    answer.append([vector, dist]      )

  return answer



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  return np.dot(lowerCamelCase,	lowerCamelCase      ) / (norm(lowerCamelCase      ) * norm(lowerCamelCase      ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Any	=   [
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   [
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



