




'''simple docstring'''



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       lowerCAmelCase				(	unittest.TestCase):


       @property
       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            torch.manual_seed(0					)
            __snake_case         =					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  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					'''cpu'''
            __snake_case         =					self.dummy_uncond_unet
            __snake_case         =					DDPMScheduler()

            __snake_case         =					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE      ,					scheduler=__SCREAMING_SNAKE_CASE					)
            ddpm.to(__SCREAMING_SNAKE_CASE					)
            ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(0					)
            __snake_case         =					ddpm(generator=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=2      ,					output_type='''numpy'''					).images

            __snake_case         =					torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(0					)
            __snake_case         =					ddpm(generator=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=2      ,					output_type='''numpy'''      ,					return_dict=__SCREAMING_SNAKE_CASE					)[0]

            __snake_case         =					image[0, -3:, -3:, -1]
            __snake_case         =					image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case         =					np.array(
                [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-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  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					self.dummy_uncond_unet
            __snake_case         =					DDPMScheduler(prediction_type='''sample'''					)

            __snake_case         =					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE      ,					scheduler=__SCREAMING_SNAKE_CASE					)
            ddpm.to(__SCREAMING_SNAKE_CASE					)
            ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					torch.manual_seed(0					)
            __snake_case         =					ddpm(generator=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=2      ,					output_type='''numpy'''					).images

            __snake_case         =					torch.manual_seed(0					)
            __snake_case         =					ddpm(generator=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=2      ,					output_type='''numpy'''					)[0]

            __snake_case         =					image[0, -3:, -3:, -1]
            __snake_case         =					image_eps[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case         =					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       lowerCAmelCase				(	unittest.TestCase):


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					'''google/ddpm-cifar10-32'''

            __snake_case         =					UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					DDPMPipeline(unet=__SCREAMING_SNAKE_CASE      ,					scheduler=__SCREAMING_SNAKE_CASE					)
            ddpm.to(__SCREAMING_SNAKE_CASE					)
            ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

            __snake_case         =					torch.manual_seed(0					)
            __snake_case         =					ddpm(generator=__SCREAMING_SNAKE_CASE      ,					output_type='''numpy'''					).images

            __snake_case         =					image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case         =					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












'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					data
            __snake_case         =					None




       def __str__(				self					) ->					str:



            '''simple docstring'''




            return F'''{self.data}'''





class       lowerCAmelCase				:


       def __init__(				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None


       def __iter__(				self					) ->					Iterator[Any]:



            '''simple docstring'''




            __snake_case         =					self.front
            while node:
                 yield node.data
                 __snake_case         =					node.next


       def __len__(				self					) ->					int:



            '''simple docstring'''




            return len(tuple(iter(self					)					)					)


       def __str__(				self					) ->					str:



            '''simple docstring'''




            return " <- ".join(str(__SCREAMING_SNAKE_CASE					) for item in self					)


       def  lowerCAmelCase (				self					) ->					bool:



            '''simple docstring'''




            return len(self					) == 0


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					Node(__SCREAMING_SNAKE_CASE					)
            if self.is_empty():
                 __snake_case         =					__snake_case         =					node
            else:
                 assert isinstance(self.rear      ,					__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					node
                 __snake_case         =					node


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            if self.is_empty():
                 raise IndexError('''dequeue from empty queue'''					)
            assert isinstance(self.front      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.front
            __snake_case         =					self.front.next
            if self.front is None:
                 __snake_case         =					None
            return node.data




       def  lowerCAmelCase (				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					__snake_case         =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()






