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 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           torch.manual_seed(0    )
           _lowerCamelCase           =					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    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					'''cpu'''
           _lowerCamelCase           =					self.dummy_uncond_unet
           _lowerCamelCase           =					DDPMScheduler()

           _lowerCamelCase           =					DDPMPipeline(unet=A_					,					scheduler=A_    )
           ddpm.to(A_    )
           ddpm.set_progress_bar_config(disable=A_    )

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

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

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

           assert image.shape == (1, 32, 32, 3)
           _lowerCamelCase           =					np.array(
               [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]    )

           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 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					self.dummy_uncond_unet
           _lowerCamelCase           =					DDPMScheduler(prediction_type='''sample'''    )

           _lowerCamelCase           =					DDPMPipeline(unet=A_					,					scheduler=A_    )
           ddpm.to(A_    )
           ddpm.set_progress_bar_config(disable=A_    )

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

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

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

           assert image.shape == (1, 32, 32, 3)
           _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					'''google/ddpm-cifar10-32'''

           _lowerCamelCase           =					UNetaDModel.from_pretrained(A_    )
           _lowerCamelCase           =					DDPMScheduler.from_pretrained(A_    )

           _lowerCamelCase           =					DDPMPipeline(unet=A_					,					scheduler=A_    )
           ddpm.to(A_    )
           ddpm.set_progress_bar_config(disable=A_    )

           _lowerCamelCase           =					torch.manual_seed(0    )
           _lowerCamelCase           =					ddpm(generator=A_					,					output_type='''numpy'''    ).images

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

           assert image.shape == (1, 32, 32, 3)
           _lowerCamelCase           =					np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]    )
           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
import torch

from accelerate import Accelerator, DistributedType



class 			UpperCamelCase					:

     '''simple docstring'''

     def __enter__(       self    )			->					int:






           """simple docstring"""
           if self.enabled:
                 _lowerCamelCase           =					self.model.no_sync()
                 self.model_sync_obj.__enter__()

           return self



     def __exit__(       self					,					A_					,					A_					,					A_    )			->					Union[str, Any]:






           """simple docstring"""
           if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(A_					,					A_					,					A_    )



     def __init__(       self					,					A_					,					A_					,					A_					,					A_ = True    )			->					List[str]:






           """simple docstring"""
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                 raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
           _lowerCamelCase           =					enabled and accelerator.distributed_type != DistributedType.NO
           _lowerCamelCase           =					0
           if self.enabled:
                 _lowerCamelCase           =					accelerator
                 _lowerCamelCase           =					model
                 _lowerCamelCase           =					local_sgd_steps



     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           self.num_steps += 1
           if not self.enabled:
                 return

           if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()





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






           """simple docstring"""
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                 for param in self.model.parameters():
                       _lowerCamelCase           =					self.accelerator.reduce(param.data					,					reduction='''mean'''    )