


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[Any]      = (DDIMParallelScheduler,)
     lowerCamelCase			:			Union[str, Any]      = (('''eta''', 0.0), ('''num_inference_steps''', 50))







     def 							lowercase__       (						self :						Optional[int]   ,   **_lowercase :						Any      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		{
               '''num_train_timesteps''': 10_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**_lowercase      )
           return config







     def 							lowercase__       (						self :						Optional[Any]   ,   **_lowercase :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_scheduler_config(**_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		10, 0.0

           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_sample_deter

           scheduler.set_timesteps(_lowercase      )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model(_lowercase   ,   _lowercase      )
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		scheduler.step(_lowercase   ,   _lowercase   ,   _lowercase   ,   _lowercase      ).prev_sample

           return sample







     def 							lowercase__       (						self :						List[str]      ):
           for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.get_scheduler_config(steps_offset=1      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		scheduler_class(**_lowercase      )
           scheduler.set_timesteps(5      )
           assert torch.equal(scheduler.timesteps   ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           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=_lowercase   ,   beta_end=_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_lowercase      )







     def 							lowercase__       (						self :						List[Any]      ):
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_lowercase      )







     def 							lowercase__       (						self :						List[str]      ):
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_lowercase      )







     def 							lowercase__       (						self :						Optional[int]      ):
           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=_lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=_lowercase      )







     def 							lowercase__       (						self :						str      ):
           self.check_over_configs(thresholding=_lowercase      )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_lowercase   ,   prediction_type=_lowercase   ,   sample_max_value=_lowercase   ,   )







     def 							lowercase__       (						self :						List[Any]      ):
           for t in [1, 10, 49]:
                 self.check_over_forward(time_step=_lowercase      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           for t, num_inference_steps in zip([1, 10, 50]   ,   [10, 50, 5_00]      ):
                 self.check_over_forward(time_step=_lowercase   ,   num_inference_steps=_lowercase      )







     def 							lowercase__       (						self :						Any      ):
           for t, eta in zip([1, 10, 49]   ,   [0.0, 0.5, 1.0]      ):
                 self.check_over_forward(time_step=_lowercase   ,   eta=_lowercase      )







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		scheduler_class(**_lowercase      )

           assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_20   ,   4_00      ) - 0.14771      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_80   ,   9_60      ) - 0.32460      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0      ) - 0.0      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_87   ,   4_86      ) - 0.00979      )      ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_99   ,   9_98      ) - 0.02      )      ) < 1E-5







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__       :  int						     =		scheduler_class(**_lowercase      )

           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		10, 0.0
           scheduler.set_timesteps(_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.dummy_model()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.dummy_sample_deter + 0.1
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.dummy_sample_deter - 0.1

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		samplea.shape[0]
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.stack([samplea, samplea, samplea]   ,   dim=0      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.arange(_lowercase      )[0:3, None].repeat(1   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model(samples.flatten(0   ,   1      )   ,   timesteps.flatten(0   ,   1      )      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		scheduler.batch_step_no_noise(_lowercase   ,   timesteps.flatten(0   ,   1      )   ,   samples.flatten(0   ,   1      )   ,   _lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 1147.7904      ) < 1E-2
           assert abs(result_mean.item() - 0.4982      ) < 1E-3







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.full_loop()

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 172.0067      ) < 1E-2
           assert abs(result_mean.item() - 0.223967      ) < 1E-3







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		self.full_loop(prediction_type='''v_prediction'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 52.5302      ) < 1E-2
           assert abs(result_mean.item() - 0.0684      ) < 1E-3







     def 							lowercase__       (						self :						Optional[Any]      ):
           # We specify different beta, so that the first alpha is 0.99
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.full_loop(set_alpha_to_one=_lowercase   ,   beta_start=0.01      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  int						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 149.8295      ) < 1E-2
           assert abs(result_mean.item() - 0.1951      ) < 1E-3







     def 							lowercase__       (						self :						Tuple      ):
           # We specify different beta, so that the first alpha is 0.99
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.full_loop(set_alpha_to_one=_lowercase   ,   beta_start=0.01      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		torch.sum(torch.abs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		torch.mean(torch.abs(_lowercase      )      )

           assert abs(result_sum.item() - 149.0784      ) < 1E-2
           assert abs(result_mean.item() - 0.1941      ) < 1E-3









import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, 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 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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