



'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    float:
							return math.sqrt(sum(pow(a - b			,       2	) for a, b in zip(_lowercase			,       _lowercase	)	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[list[float] | float]]:

							if dataset.ndim != value_array.ndim:
														UpperCAmelCase  :	str							  =	(
														    """Wrong input data's dimensions... """
														    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
														)
														raise ValueError(_lowercase	)

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

							if dataset.dtype != value_array.dtype:
														UpperCAmelCase  :	Any							  =	(
														    """Input data have different datatype... """
														    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
														)
														raise TypeError(_lowercase	)

							UpperCAmelCase  :	Any							  =	[]

							for value in value_array:
														UpperCAmelCase  :	int							  =	euclidean(_lowercase			,       dataset[0]	)
														UpperCAmelCase  :	Any							  =	dataset[0].tolist()

														for dataset_value in dataset[1:]:
																					UpperCAmelCase  :	List[Any]							  =	euclidean(_lowercase			,       _lowercase	)

																					if dist > temp_dist:
																												UpperCAmelCase  :	str							  =	temp_dist
																												UpperCAmelCase  :	Any							  =	dataset_value.tolist()

														answer.append([vector, dist]	)

							return answer



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    float:
							return np.dot(_lowercase			,       _lowercase	) / (norm(_lowercase	) * norm(_lowercase	))


if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

