



'''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 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      'cpu'
					SCREAMING_SNAKE_CASE_						:int										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:str										=      DDPMScheduler()

					SCREAMING_SNAKE_CASE_						:Tuple										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:str										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'      ,       return_dict=SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Any										=      np.array(
					    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 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 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler(prediction_type='sample'	)

					SCREAMING_SNAKE_CASE_						:Any										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'google/ddpm-cifar10-32'

					SCREAMING_SNAKE_CASE_						:Any										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]	)
					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



