


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              torch.manual_seed(0    )
              UpperCamelCase     :						Dict 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		'''cpu'''
              UpperCamelCase     :						Optional[int] 						=		self.dummy_uncond_unet
              UpperCamelCase     :						str 						=		DDPMScheduler()

              UpperCamelCase     :						str 						=		DDPMPipeline(unet=__SCREAMING_SNAKE_CASE			,       scheduler=__SCREAMING_SNAKE_CASE    )
              ddpm.to(__SCREAMING_SNAKE_CASE    )
              ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[Any] 						=		torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(0    )
              UpperCamelCase     :						Dict 						=		ddpm(generator=__SCREAMING_SNAKE_CASE			,       num_inference_steps=2			,       output_type='''numpy'''    ).images

              UpperCamelCase     :						Optional[Any] 						=		torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(0    )
              UpperCamelCase     :						str 						=		ddpm(generator=__SCREAMING_SNAKE_CASE			,       num_inference_steps=2			,       output_type='''numpy'''			,       return_dict=__SCREAMING_SNAKE_CASE    )[0]

              UpperCamelCase     :						str 						=		image[0, -3:, -3:, -1]
              UpperCamelCase     :						Union[str, Any] 						=		image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase     :						List[str] 						=		np.array(
                  [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]    )

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.dummy_uncond_unet
              UpperCamelCase     :						str 						=		DDPMScheduler(prediction_type='''sample'''    )

              UpperCamelCase     :						Optional[Any] 						=		DDPMPipeline(unet=__SCREAMING_SNAKE_CASE			,       scheduler=__SCREAMING_SNAKE_CASE    )
              ddpm.to(__SCREAMING_SNAKE_CASE    )
              ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Dict 						=		torch.manual_seed(0    )
              UpperCamelCase     :						Any 						=		ddpm(generator=__SCREAMING_SNAKE_CASE			,       num_inference_steps=2			,       output_type='''numpy'''    ).images

              UpperCamelCase     :						Optional[int] 						=		torch.manual_seed(0    )
              UpperCamelCase     :						Union[str, Any] 						=		ddpm(generator=__SCREAMING_SNAKE_CASE			,       num_inference_steps=2			,       output_type='''numpy'''    )[0]

              UpperCamelCase     :						int 						=		image[0, -3:, -3:, -1]
              UpperCamelCase     :						Any 						=		image_eps[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase     :						Union[str, Any] 						=		1e-2 if torch_device != '''mps''' else 3e-2
              assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance




@slow
@require_torch_gpu
class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		'''google/ddpm-cifar10-32'''

              UpperCamelCase     :						str 						=		UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Optional[int] 						=		DDPMPipeline(unet=__SCREAMING_SNAKE_CASE			,       scheduler=__SCREAMING_SNAKE_CASE    )
              ddpm.to(__SCREAMING_SNAKE_CASE    )
              ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		torch.manual_seed(0    )
              UpperCamelCase     :						List[Any] 						=		ddpm(generator=__SCREAMING_SNAKE_CASE			,       output_type='''numpy'''    ).images

              UpperCamelCase     :						Tuple 						=		image[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase     :						Tuple 						=		np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]    )
              assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2





def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		len(SCREAMING_SNAKE_CASE_							)
       for i in range(length - 1							):
              UpperCamelCase     :						Optional[int] 						=		False
              for j in range(length - 1 - i							):
                     if collection[j] > collection[j + 1]:
                            UpperCamelCase     :						Optional[int] 						=		True
                            UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
  import doctest
  import time

  doctest.testmod()

  __UpperCAmelCase			:			Any			      =					input("Enter numbers separated by a comma:").strip()
  __UpperCAmelCase			:			List[str]			      =					[int(item) for item in user_input.split(",")]
  __UpperCAmelCase			:			List[Any]			      =					time.process_time()
  print(*bubble_sort(unsorted), sep=",")
  print(f'''Processing time: {(time.process_time() - start)%1E9 + 7}''')

