






"""simple docstring"""



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							(DDPMParallelScheduler,)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    **snake_case      :						str							):
													'''simple docstring'''





													A__						:  List[str]		  =  {
													    """num_train_timesteps""": 1000,
													    """beta_start""": 0.0001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """variance_type""": """fixed_small""",
													    """clip_sample""": True,
													}

													config.update(**snake_case							)
													return config


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													for timesteps in [1, 5, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=snake_case							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]			,    [0.002, 0.02, 0.2, 2]							):
																			self.check_over_configs(beta_start=snake_case			,    beta_end=snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													for variance in ["fixed_small", "fixed_large", "other"]:
																			self.check_over_configs(variance_type=snake_case							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case							)


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													self.check_over_configs(thresholding=snake_case							)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case			,    prediction_type=snake_case			,    sample_max_value=snake_case			,    )


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													for t in [0, 500, 999]:
																			self.check_over_forward(time_step=snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  List[str]		  =  self.scheduler_classes[0]
													A__						:  int		  =  self.get_scheduler_config()
													A__						:  Tuple		  =  scheduler_class(**snake_case							)

													assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.00979							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1e-5


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Dict		  =  self.scheduler_classes[0]
													A__						:  List[Any]		  =  self.get_scheduler_config()
													A__						:  Optional[int]		  =  scheduler_class(**snake_case							)

													A__						:  Tuple		  =  len(snake_case							)

													A__						:  Optional[Any]		  =  self.dummy_model()
													A__						:  Tuple		  =  self.dummy_sample_deter
													A__						:  Optional[int]		  =  self.dummy_sample_deter + 0.1
													A__						:  int		  =  self.dummy_sample_deter - 0.1

													A__						:  List[Any]		  =  samplea.shape[0]
													A__						:  Tuple		  =  torch.stack([samplea, samplea, samplea]			,    dim=0							)
													A__						:  Dict		  =  torch.arange(snake_case							)[0:3, None].repeat(1			,    snake_case							)

													A__						:  int		  =  model(samples.flatten(0			,    1							)			,    timesteps.flatten(0			,    1							)							)
													A__						:  List[Any]		  =  scheduler.batch_step_no_noise(snake_case			,    timesteps.flatten(0			,    1							)			,    samples.flatten(0			,    1							)							)

													A__						:  List[str]		  =  torch.sum(torch.abs(snake_case							)							)
													A__						:  Optional[int]		  =  torch.mean(torch.abs(snake_case							)							)

													assert abs(result_sum.item() - 1153.1833							) < 1e-2
													assert abs(result_mean.item() - 0.5005							) < 1e-3


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  self.scheduler_classes[0]
													A__						:  Dict		  =  self.get_scheduler_config()
													A__						:  Any		  =  scheduler_class(**snake_case							)

													A__						:  Optional[Any]		  =  len(snake_case							)

													A__						:  Dict		  =  self.dummy_model()
													A__						:  Optional[int]		  =  self.dummy_sample_deter
													A__						:  str		  =  torch.manual_seed(0							)

													for t in reversed(range(snake_case							)							):
																			# 1. predict noise residual
																			A__						:  Dict		  =  model(snake_case			,    snake_case							)

																			# 2. predict previous mean of sample x_t-1
																			A__						:  Any		  =  scheduler.step(snake_case			,    snake_case			,    snake_case			,    generator=snake_case							).prev_sample

																			A__						:  str		  =  pred_prev_sample

													A__						:  Tuple		  =  torch.sum(torch.abs(snake_case							)							)
													A__						:  List[Any]		  =  torch.mean(torch.abs(snake_case							)							)

													assert abs(result_sum.item() - 258.9606							) < 1e-2
													assert abs(result_mean.item() - 0.3372							) < 1e-3


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  str		  =  self.scheduler_classes[0]
													A__						:  str		  =  self.get_scheduler_config(prediction_type="""v_prediction"""							)
													A__						:  List[Any]		  =  scheduler_class(**snake_case							)

													A__						:  List[str]		  =  len(snake_case							)

													A__						:  List[str]		  =  self.dummy_model()
													A__						:  Union[str, Any]		  =  self.dummy_sample_deter
													A__						:  Optional[Any]		  =  torch.manual_seed(0							)

													for t in reversed(range(snake_case							)							):
																			# 1. predict noise residual
																			A__						:  Union[str, Any]		  =  model(snake_case			,    snake_case							)

																			# 2. predict previous mean of sample x_t-1
																			A__						:  List[Any]		  =  scheduler.step(snake_case			,    snake_case			,    snake_case			,    generator=snake_case							).prev_sample

																			A__						:  List[str]		  =  pred_prev_sample

													A__						:  Dict		  =  torch.sum(torch.abs(snake_case							)							)
													A__						:  int		  =  torch.mean(torch.abs(snake_case							)							)

													assert abs(result_sum.item() - 202.0296							) < 1e-2
													assert abs(result_mean.item() - 0.2631							) < 1e-3


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Tuple		  =  self.scheduler_classes[0]
													A__						:  Union[str, Any]		  =  self.get_scheduler_config()
													A__						:  Dict		  =  scheduler_class(**snake_case							)

													A__						:  Optional[int]		  =  [100, 87, 50, 1, 0]

													scheduler.set_timesteps(timesteps=snake_case							)

													A__						:  List[Any]		  =  scheduler.timesteps

													for i, timestep in enumerate(snake_case							):
																			if i == len(snake_case							) - 1:
																									A__						:  List[str]		  =  -1
																			else:
																									A__						:  int		  =  timesteps[i + 1]

																			A__						:  List[str]		  =  scheduler.previous_timestep(snake_case							)
																			A__						:  Optional[int]		  =  prev_t.item()

																			self.assertEqual(snake_case			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  Dict		  =  self.scheduler_classes[0]
													A__						:  Union[str, Any]		  =  self.get_scheduler_config()
													A__						:  str		  =  scheduler_class(**snake_case							)

													A__						:  Tuple		  =  [100, 87, 50, 51, 0]

													with self.assertRaises(snake_case			,    msg="""`custom_timesteps` must be in descending order."""							):
																			scheduler.set_timesteps(timesteps=snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  self.scheduler_classes[0]
													A__						:  Union[str, Any]		  =  self.get_scheduler_config()
													A__						:  List[Any]		  =  scheduler_class(**snake_case							)

													A__						:  str		  =  [100, 87, 50, 1, 0]
													A__						:  Optional[int]		  =  len(snake_case							)

													with self.assertRaises(snake_case			,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""							):
																			scheduler.set_timesteps(num_inference_steps=snake_case			,    timesteps=snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  self.scheduler_classes[0]
													A__						:  int		  =  self.get_scheduler_config()
													A__						:  List[str]		  =  scheduler_class(**snake_case							)

													A__						:  Optional[Any]		  =  [scheduler.config.num_train_timesteps]

													with self.assertRaises(
													    snake_case			,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,    ):
																			scheduler.set_timesteps(timesteps=snake_case							)














"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[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      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  np.array([0.0, 1.0, 0.0, 0.0, 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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

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

													assert image.shape == (1, 256, 256, 3)

													A__						:  Dict		  =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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






