
"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(DPMSolverMultistepScheduler,)
	lowercase_        =	(('num_inference_steps', 2_5),)




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			Optional[Any]		=    {
			    'num_train_timesteps': 1000,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			    'solver_order': 2,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			    'sample_max_value': 1.0,
			    'algorithm_type': 'dpmsolver++',
			    'solver_type': 'midpoint',
			    'lower_order_final': False,
			    'lambda_min_clipped': -float('inf'					),
			    'variance_type': None,
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			Dict		=    dict(self.forward_default_kwargs					)
			lowerCamelCase     :			str		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample
			lowerCamelCase     :			Optional[int]		=    0.1 * sample
			lowerCamelCase     :			str		=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			List[str]		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			str		=    scheduler_class(**UpperCAmelCase_					)
					scheduler.set_timesteps(UpperCAmelCase_					)
					# copy over dummy past residuals
					lowerCamelCase     :			Optional[Any]		=    dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(UpperCAmelCase_					)
							lowerCamelCase     :			Optional[Any]		=    scheduler_class.from_pretrained(UpperCAmelCase_					)
							new_scheduler.set_timesteps(UpperCAmelCase_					)
							# copy over dummy past residuals
							lowerCamelCase     :			Dict		=    dummy_past_residuals[: new_scheduler.config.solver_order]

					lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    sample, sample
					for t in range(UpperCAmelCase_		, time_step + scheduler.config.solver_order + 1					):
							lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
							lowerCamelCase     :			Tuple		=    new_scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			pass




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0		, **UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			str		=    dict(self.forward_default_kwargs					)
			lowerCamelCase     :			Optional[int]		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    self.dummy_sample
			lowerCamelCase     :			Dict		=    0.1 * sample
			lowerCamelCase     :			Any		=    [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			int		=    self.get_scheduler_config()
					lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)
					scheduler.set_timesteps(UpperCAmelCase_					)

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

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

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

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
					lowerCamelCase     :			List[str]		=    new_scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=None		, **UpperCAmelCase_					)		->							Optional[Any]:
			if scheduler is None:
					lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
					lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			List[str]		=    scheduler_class(**UpperCAmelCase_					)

			lowerCamelCase     :			str		=    10
			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Any		=    self.dummy_sample_deter
			scheduler.set_timesteps(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    model(UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Any		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					).prev_sample

			return sample




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			List[str]		=    dict(self.forward_default_kwargs					)

			lowerCamelCase     :			Tuple		=    kwargs.pop('num_inference_steps'		, UpperCAmelCase_					)

			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			Optional[int]		=    self.get_scheduler_config()
					lowerCamelCase     :			Tuple		=    scheduler_class(**UpperCAmelCase_					)

					lowerCamelCase     :			Optional[int]		=    self.dummy_sample
					lowerCamelCase     :			str		=    0.1 * sample

					if num_inference_steps is not None and hasattr(UpperCAmelCase_		, 'set_timesteps'					):
							scheduler.set_timesteps(UpperCAmelCase_					)
					elif num_inference_steps is not None and not hasattr(UpperCAmelCase_		, 'set_timesteps'					):
							lowerCamelCase     :			Dict		=    num_inference_steps

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

					lowerCamelCase     :			Optional[int]		=    scheduler.timesteps[5]
					lowerCamelCase     :			str		=    scheduler.timesteps[6]

					lowerCamelCase     :			Union[str, Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample
					lowerCamelCase     :			Optional[Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, **UpperCAmelCase_					).prev_sample

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




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			self.check_over_configs(thresholding=UpperCAmelCase_					)
			for order in [1, 2, 3]:
					for solver_type in ["midpoint", "heun"]:
							for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, sample_max_value=UpperCAmelCase_		, algorithm_type='dpmsolver++'		, solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, )




	def 			_UpperCamelCase    ( self					)		->							Dict:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
															continue
											else:
													self.check_over_configs(
													    solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, algorithm_type=UpperCAmelCase_		, )
											lowerCamelCase     :			Any		=    self.full_loop(
											    solver_order=UpperCAmelCase_		, solver_type=UpperCAmelCase_		, prediction_type=UpperCAmelCase_		, algorithm_type=UpperCAmelCase_		, )
											assert not torch.isnan(UpperCAmelCase_					).any(), "Samples have nan numbers"




	def 			_UpperCamelCase    ( self					)		->							Dict:
			self.check_over_configs(lower_order_final=UpperCAmelCase_					)
			self.check_over_configs(lower_order_final=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			self.check_over_configs(lambda_min_clipped=-float('inf'					)					)
			self.check_over_configs(lambda_min_clipped=-5.1					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			self.check_over_configs(variance_type=UpperCAmelCase_					)
			self.check_over_configs(variance_type='learned_range'					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
					self.check_over_forward(num_inference_steps=UpperCAmelCase_		, time_step=0					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Optional[Any]		=    self.full_loop()
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Tuple		=    self.full_loop(thresholding=UpperCAmelCase_		, dynamic_thresholding_ratio=0.87		, sample_max_value=0.5					)
			lowerCamelCase     :			Dict		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 1.1364					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[Any]		=    self.full_loop(prediction_type='v_prediction'					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.2251					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    self.full_loop(prediction_type='v_prediction'		, use_karras_sigmas=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.2096					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							str:
			# make sure that iterating over schedulers with same config names gives same results
			# for defaults
			lowerCamelCase     :			Optional[int]		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
			lowerCamelCase     :			str		=    self.full_loop(scheduler=UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3

			lowerCamelCase     :			List[str]		=    DPMSolverSinglestepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			str		=    UniPCMultistepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			List[Any]		=    DEISMultistepScheduler.from_config(scheduler.config					)
			lowerCamelCase     :			Dict		=    DPMSolverMultistepScheduler.from_config(scheduler.config					)

			lowerCamelCase     :			Dict		=    self.full_loop(scheduler=UpperCAmelCase_					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_mean.item() - 0.3301					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Optional[Any]		=    self.get_scheduler_config(thresholding=UpperCAmelCase_		, dynamic_thresholding_ratio=0					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			Optional[int]		=    self.dummy_model()
			lowerCamelCase     :			List[str]		=    self.dummy_sample_deter.half()
			scheduler.set_timesteps(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Optional[int]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					).prev_sample

			assert sample.dtype == torch.floataa




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Dict:
			for scheduler_class in self.scheduler_classes:
					lowerCamelCase     :			Dict		=    self.get_scheduler_config(**UpperCAmelCase_					)
					lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

					scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
					assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps









'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     List[str]          =RePaintPipeline
			__A   :     List[str]          =IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			__A   :     List[Any]          =PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			__A   :     str          =IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     Union[str, Any]          =False






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, 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")       ,)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler()
				UpperCAmelCase_			:	List[Any]      =   {"unet": unet, "scheduler": scheduler}
				return components






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case=0			):
				if str(_snake_case			).startswith("mps"			):
					UpperCAmelCase_			:	Optional[Any]      =   torch.manual_seed(_snake_case			)
				else:
					UpperCAmelCase_			:	Union[str, Any]      =   torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
				UpperCAmelCase_			:	List[str]      =   np.random.RandomState(_snake_case			).standard_normal((1, 3, 32, 32)			)
				UpperCAmelCase_			:	str      =   torch.from_numpy(_snake_case			).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   (image > 0).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   {
				    "image": image,
				    "mask_image": mask,
				    "generator": generator,
				    "num_inference_steps": 5,
				    "eta": 0.0,
				    "jump_length": 2,
				    "jump_n_sample": 2,
				    "output_type": "numpy",
				}
				return inputs






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_			:	List[Any]      =   self.get_dummy_components()
				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(**_snake_case			)
				UpperCAmelCase_			:	Dict      =   sd_pipe.to(_snake_case			)
				sd_pipe.set_progress_bar_config(disable=_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   sd_pipe(**_snake_case			).images
				UpperCAmelCase_			:	Dict      =   image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_			:	List[Any]      =   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_local()






			@unittest.skip("non-deterministic pipeline"			)
			def 						UpperCamelCase__				(   self			):
				return super().test_inference_batch_single_identical()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_optional_components()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class  _snake_case						(unittest.TestCase):






			def 						UpperCamelCase__				(   self			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256.png"			)
				UpperCAmelCase_			:	Any      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"			)
				UpperCAmelCase_			:	str      =   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256_result.npy"			)

				UpperCAmelCase_			:	Optional[Any]      =   "google/ddpm-ema-celebahq-256"
				UpperCAmelCase_			:	Optional[int]      =   UNetaDModel.from_pretrained(_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler.from_pretrained(_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(unet=_snake_case       ,scheduler=_snake_case			).to(_snake_case			)
				repaint.set_progress_bar_config(disable=_snake_case			)
				repaint.enable_attention_slicing()

				UpperCAmelCase_			:	Any      =   torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   repaint(
				    _snake_case       ,_snake_case       ,num_inference_steps=2_50       ,eta=0.0       ,jump_length=10       ,jump_n_sample=10       ,generator=_snake_case       ,output_type="np"       ,)
				UpperCAmelCase_			:	Union[str, Any]      =   output.images[0]

				assert image.shape == (2_56, 2_56, 3)
				assert np.abs(expected_image - image			).mean() < 1E-2




