



'''simple docstring'''





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,	__A):



     """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(_lowerCAmelCase)

     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(_lowerCAmelCase)
     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(_lowerCAmelCase)

     _a      =  []

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

          for dataset_value in dataset[1:]:
               _a      =  euclidean(_lowerCAmelCase  ,	_lowerCAmelCase)

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

          answer.append([vector, dist])

     return answer




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



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


if __name__ == "__main__":
  import doctest

  doctest.testmod()








'''simple docstring'''





__a          =		frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])

__a          =		frozenset([])

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "negative_prompt"])

__a          =		frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "mask_image", "negative_prompt"])

__a          =		frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image", "mask_image"])

__a          =		frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["example_image", "image", "mask_image"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])
__a          =		frozenset(["input_tokens"])

__a          =		frozenset(["input_tokens"])
