


'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 							__UpperCamelCase    (		unittest.TestCase ):




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	Union[str, Any]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.dummy_uncond_unet
						__a      :	Dict    =  KarrasVeScheduler()

						__a      :	List[str]    =  KarrasVePipeline(unet=__a						,    scheduler=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	int    =  torch.manual_seed(0							)
						__a      :	Any    =  pipe(num_inference_steps=2						,    generator=__a						,    output_type='numpy'							).images

						__a      :	Dict    =  torch.manual_seed(0							)
						__a      :	Optional[Any]    =  pipe(num_inference_steps=2						,    generator=__a						,    output_type='numpy'						,    return_dict=__a							)[0]

						__a      :	Dict    =  image[0, -3:, -3:, -1]
						__a      :	List[Any]    =  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__a      :	Optional[int]    =  np.array([0.0, 1.0, 0.0, 0.0, 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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  'google/ncsnpp-celebahq-256'
						__a      :	str    =  UNetaDModel.from_pretrained(__a							)
						__a      :	Dict    =  KarrasVeScheduler()

						__a      :	Optional[Any]    =  KarrasVePipeline(unet=__a						,    scheduler=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	Tuple    =  torch.manual_seed(0							)
						__a      :	str    =  pipe(num_inference_steps=20						,    generator=__a						,    output_type='numpy'							).images

						__a      :	int    =  image[0, -3:, -3:, -1]
						assert image.shape == (1, 256, 256, 3)
						__a      :	List[str]    =  np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       int       ):
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										raise ValueError('iterations must be defined as integers'       )
					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'       )
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'       )

					__a      :	Dict    =  ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(_SCREAMING_SNAKE_CASE       )

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()



