

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class SCREAMING_SNAKE_CASE  (		UpperCamelCase_							):






			"""simple docstring"""




			__A						  =  (DEISMultistepScheduler,)
			__A						  =  (("""num_inference_steps""", 2_5),)


			def 			__lowerCAmelCase (    self ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {
				    'num_train_timesteps': 10_00,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'solver_order': 2,
				}

				config.update(**__a							)
				return config


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0 ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= dict(self.forward_default_kwargs							)
				snake_case_      		= kwargs.pop('num_inference_steps' ,				__a							)
				snake_case_      		= self.dummy_sample
				snake_case_      		= 0.1 * sample
				snake_case_      		= [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					snake_case_      		= self.get_scheduler_config(**__a							)
					snake_case_      		= scheduler_class(**__a							)
					scheduler.set_timesteps(__a							)
					# copy over dummy past residuals
					snake_case_      		= dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(__a							)
						snake_case_      		= scheduler_class.from_pretrained(__a							)
						new_scheduler.set_timesteps(__a							)
						# copy over dummy past residuals
						snake_case_      		= dummy_past_residuals[: new_scheduler.config.solver_order]

					snake_case_      		= sample, sample
					for t in range(__a ,				time_step + scheduler.config.solver_order + 1							):
						snake_case_      		= scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample
						snake_case_      		= new_scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample

						assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=0 ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= dict(self.forward_default_kwargs							)
				snake_case_      		= kwargs.pop('num_inference_steps' ,				__a							)
				snake_case_      		= self.dummy_sample
				snake_case_      		= 0.1 * sample
				snake_case_      		= [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					snake_case_      		= self.get_scheduler_config()
					snake_case_      		= scheduler_class(**__a							)
					scheduler.set_timesteps(__a							)

					# copy over dummy past residuals (must be after setting timesteps)
					snake_case_      		= dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(__a							)
						snake_case_      		= scheduler_class.from_pretrained(__a							)
						# copy over dummy past residuals
						new_scheduler.set_timesteps(__a							)

						# copy over dummy past residual (must be after setting timesteps)
						snake_case_      		= dummy_past_residuals[: new_scheduler.config.solver_order]

					snake_case_      		= scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample
					snake_case_      		= new_scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample

					assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				if scheduler is None:
					snake_case_      		= self.scheduler_classes[0]
					snake_case_      		= self.get_scheduler_config(**__a							)
					snake_case_      		= scheduler_class(**__a							)

				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config(**__a							)
				snake_case_      		= scheduler_class(**__a							)

				snake_case_      		= 10
				snake_case_      		= self.dummy_model()
				snake_case_      		= self.dummy_sample_deter
				scheduler.set_timesteps(__a							)

				for i, t in enumerate(scheduler.timesteps							):
					snake_case_      		= model(__a ,				__a							)
					snake_case_      		= scheduler.step(__a ,				__a ,				__a							).prev_sample

				return sample


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= dict(self.forward_default_kwargs							)

				snake_case_      		= kwargs.pop('num_inference_steps' ,				__a							)

				for scheduler_class in self.scheduler_classes:
					snake_case_      		= self.get_scheduler_config()
					snake_case_      		= scheduler_class(**__a							)

					snake_case_      		= self.dummy_sample
					snake_case_      		= 0.1 * sample

					if num_inference_steps is not None and hasattr(__a ,				'set_timesteps'							):
						scheduler.set_timesteps(__a							)
					elif num_inference_steps is not None and not hasattr(__a ,				'set_timesteps'							):
						snake_case_      		= num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					snake_case_      		= [residual + 0.2, residual + 0.15, residual + 0.10]
					snake_case_      		= dummy_past_residuals[: scheduler.config.solver_order]

					snake_case_      		= scheduler.timesteps[5]
					snake_case_      		= scheduler.timesteps[6]

					snake_case_      		= scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample
					snake_case_      		= scheduler.step(__a ,				__a ,				__a ,				**__a							).prev_sample

					self.assertEqual(output_a.shape ,				sample.shape							)
					self.assertEqual(output_a.shape ,				output_a.shape							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= DEISMultistepScheduler(**self.get_scheduler_config()							)
				snake_case_      		= self.full_loop(scheduler=__a							)
				snake_case_      		= torch.mean(torch.abs(__a							)							)

				assert abs(result_mean.item() - 0.2_3916							) < 1E-3

				snake_case_      		= DPMSolverSinglestepScheduler.from_config(scheduler.config							)
				snake_case_      		= DPMSolverMultistepScheduler.from_config(scheduler.config							)
				snake_case_      		= UniPCMultistepScheduler.from_config(scheduler.config							)
				snake_case_      		= DEISMultistepScheduler.from_config(scheduler.config							)

				snake_case_      		= self.full_loop(scheduler=__a							)
				snake_case_      		= torch.mean(torch.abs(__a							)							)

				assert abs(result_mean.item() - 0.2_3916							) < 1E-3


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for timesteps in [25, 50, 1_00, 9_99, 10_00]:
					self.check_over_configs(num_train_timesteps=__a							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.check_over_configs(thresholding=__a							)
				for order in [1, 2, 3]:
					for solver_type in ["logrho"]:
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    thresholding=__a ,				prediction_type=__a ,				sample_max_value=__a ,				algorithm_type='deis' ,				solver_order=__a ,				solver_type=__a ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for algorithm_type in ["deis"]:
					for solver_type in ["logrho"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=__a ,				solver_type=__a ,				prediction_type=__a ,				algorithm_type=__a ,				)
								snake_case_      		= self.full_loop(
								    solver_order=__a ,				solver_type=__a ,				prediction_type=__a ,				algorithm_type=__a ,				)
								assert not torch.isnan(__a							).any(), "Samples have nan numbers"


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.check_over_configs(lower_order_final=__a							)
				self.check_over_configs(lower_order_final=__a							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
					self.check_over_forward(num_inference_steps=__a ,				time_step=0							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop()
				snake_case_      		= torch.mean(torch.abs(__a							)							)

				assert abs(result_mean.item() - 0.2_3916							) < 1E-3


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.full_loop(prediction_type='v_prediction'							)
				snake_case_      		= torch.mean(torch.abs(__a							)							)

				assert abs(result_mean.item() - 0.091							) < 1E-3







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.scheduler_classes[0]
				snake_case_      		= self.get_scheduler_config(thresholding=__a ,				dynamic_thresholding_ratio=0							)
				snake_case_      		= scheduler_class(**__a							)

				snake_case_      		= 10
				snake_case_      		= self.dummy_model()
				snake_case_      		= self.dummy_sample_deter.half()
				scheduler.set_timesteps(__a							)

				for i, t in enumerate(scheduler.timesteps							):
					snake_case_      		= model(__a ,				__a							)
					snake_case_      		= scheduler.step(__a ,				__a ,				__a							).prev_sample

				assert sample.dtype == torch.floataa



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							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

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

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= 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							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

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

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= 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
