






'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@property
							def 							_A	(		self			:Any				)				->  Tuple:


										'''simple docstring'''




										torch.manual_seed(0				)
										snake_case_				:					str   =       UNetaDModel(
										    block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=("DownBlock2D", "AttnDownBlock2D")       ,							up_block_types=("AttnUpBlock2D", "UpBlock2D")       ,							)
										return model






							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       self.dummy_uncond_unet
										snake_case_				:					Any   =       PNDMScheduler()

										snake_case_				:					int   =       PNDMPipeline(unet=A_       ,							scheduler=A_				)
										pndm.to(A_				)
										pndm.set_progress_bar_config(disable=A_				)

										snake_case_				:					Tuple   =       torch.manual_seed(0				)
										snake_case_				:					Any   =       pndm(generator=A_       ,							num_inference_steps=20       ,							output_type="numpy"				).images

										snake_case_				:					Any   =       torch.manual_seed(0				)
										snake_case_				:					List[Any]   =       pndm(generator=A_       ,							num_inference_steps=20       ,							output_type="numpy"       ,							return_dict=A_				)[0]

										snake_case_				:					Any   =       image[0, -3:, -3:, -1]
										snake_case_				:					Optional[int]   =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_				:					Tuple   =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







@slow
@require_torch
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:List[Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       "google/ddpm-cifar10-32"

										snake_case_				:					Tuple   =       UNetaDModel.from_pretrained(A_				)
										snake_case_				:					str   =       PNDMScheduler()

										snake_case_				:					Union[str, Any]   =       PNDMPipeline(unet=A_       ,							scheduler=A_				)
										pndm.to(A_				)
										pndm.set_progress_bar_config(disable=A_				)
										snake_case_				:					Tuple   =       torch.manual_seed(0				)
										snake_case_				:					Tuple   =       pndm(generator=A_       ,							output_type="numpy"				).images

										snake_case_				:					Optional[int]   =       image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case_				:					int   =       np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

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

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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


if __name__ == "__main__":
		import doctest

		doctest.testmod()




