

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class        _SCREAMING_SNAKE_CASE	(   _UpperCAmelCase):
						_UpperCamelCase:List[str]     =						(DPMSolverSDEScheduler,)
						_UpperCamelCase:Dict     =						10







						def     _snake_case							(       self			,		**_SCREAMING_SNAKE_CASE				)->				int:
													lowerCamelCase_				     ={
													    '''num_train_timesteps''': 1100,
													    '''beta_start''': 0.0_0_0_1,
													    '''beta_end''': 0.0_2,
													    '''beta_schedule''': '''linear''',
													    '''noise_sampler_seed''': 0,
													}

													config.update(**A_				)
													return config







						def     _snake_case							(       self				)->				List[str]:





													for timesteps in [10, 50, 100, 1000]:
																				self.check_over_configs(num_train_timesteps=A_				)







						def     _snake_case							(       self				)->				List[Any]:





													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,		[0.0_0_0_2, 0.0_0_2, 0.0_2]				):
																				self.check_over_configs(beta_start=A_			,		beta_end=A_				)







						def     _snake_case							(       self				)->				Optional[int]:





													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=A_				)







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





													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=A_				)







						def     _snake_case							(       self				)->				List[Any]:
													lowerCamelCase_				     =self.scheduler_classes[0]
													lowerCamelCase_				     =self.get_scheduler_config()
													lowerCamelCase_				     =scheduler_class(**A_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													lowerCamelCase_				     =self.dummy_model()
													lowerCamelCase_				     =self.dummy_sample_deter * scheduler.init_noise_sigma
													lowerCamelCase_				     =sample.to(A_				)

													for i, t in enumerate(scheduler.timesteps				):
																				lowerCamelCase_				     =scheduler.scale_model_input(A_			,		A_				)

																				lowerCamelCase_				     =model(A_			,		A_				)

																				lowerCamelCase_				     =scheduler.step(A_			,		A_			,		A_				)
																				lowerCamelCase_				     =output.prev_sample

													lowerCamelCase_				     =torch.sum(torch.abs(A_				)				)
													lowerCamelCase_				     =torch.mean(torch.abs(A_				)				)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5				) < 1E-2
																				assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7				) < 1E-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6				) < 1E-2
																				assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2				) < 1E-3
													else:
																				assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1E-2
																				assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6				) < 1E-3







						def     _snake_case							(       self				)->				Tuple:
													lowerCamelCase_				     =self.scheduler_classes[0]
													lowerCamelCase_				     =self.get_scheduler_config(prediction_type="""v_prediction"""				)
													lowerCamelCase_				     =scheduler_class(**A_				)

													scheduler.set_timesteps(self.num_inference_steps				)

													lowerCamelCase_				     =self.dummy_model()
													lowerCamelCase_				     =self.dummy_sample_deter * scheduler.init_noise_sigma
													lowerCamelCase_				     =sample.to(A_				)

													for i, t in enumerate(scheduler.timesteps				):
																				lowerCamelCase_				     =scheduler.scale_model_input(A_			,		A_				)

																				lowerCamelCase_				     =model(A_			,		A_				)

																				lowerCamelCase_				     =scheduler.step(A_			,		A_			,		A_				)
																				lowerCamelCase_				     =output.prev_sample

													lowerCamelCase_				     =torch.sum(torch.abs(A_				)				)
													lowerCamelCase_				     =torch.mean(torch.abs(A_				)				)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3				) < 1E-2
																				assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4				) < 1E-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3				) < 1E-2
																				assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7				) < 1E-3
													else:
																				assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5				) < 1E-2
																				assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1				) < 1E-3







						def     _snake_case							(       self				)->				Optional[Any]:
													lowerCamelCase_				     =self.scheduler_classes[0]
													lowerCamelCase_				     =self.get_scheduler_config()
													lowerCamelCase_				     =scheduler_class(**A_				)

													scheduler.set_timesteps(self.num_inference_steps			,		device=A_				)

													lowerCamelCase_				     =self.dummy_model()
													lowerCamelCase_				     =self.dummy_sample_deter.to(A_				) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																				lowerCamelCase_				     =scheduler.scale_model_input(A_			,		A_				)

																				lowerCamelCase_				     =model(A_			,		A_				)

																				lowerCamelCase_				     =scheduler.step(A_			,		A_			,		A_				)
																				lowerCamelCase_				     =output.prev_sample

													lowerCamelCase_				     =torch.sum(torch.abs(A_				)				)
													lowerCamelCase_				     =torch.mean(torch.abs(A_				)				)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8				) < 1E-2
																				assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5				) < 1E-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2				) < 1E-2
																				assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1				) < 1E-3
													else:
																				assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2				) < 1E-2
																				assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6				) < 1E-3







						def     _snake_case							(       self				)->				Optional[int]:
													lowerCamelCase_				     =self.scheduler_classes[0]
													lowerCamelCase_				     =self.get_scheduler_config()
													lowerCamelCase_				     =scheduler_class(**A_			,		use_karras_sigmas=A_				)

													scheduler.set_timesteps(self.num_inference_steps			,		device=A_				)

													lowerCamelCase_				     =self.dummy_model()
													lowerCamelCase_				     =self.dummy_sample_deter.to(A_				) * scheduler.init_noise_sigma
													lowerCamelCase_				     =sample.to(A_				)

													for t in scheduler.timesteps:
																				lowerCamelCase_				     =scheduler.scale_model_input(A_			,		A_				)

																				lowerCamelCase_				     =model(A_			,		A_				)

																				lowerCamelCase_				     =scheduler.step(A_			,		A_			,		A_				)
																				lowerCamelCase_				     =output.prev_sample

													lowerCamelCase_				     =torch.sum(torch.abs(A_				)				)
													lowerCamelCase_				     =torch.mean(torch.abs(A_				)				)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8				) < 1E-2
																				assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5				) < 1E-2
																				assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2
													else:
																				assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2				) < 1E-2
																				assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1				) < 1E-2



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[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					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 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   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


