import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		a__						( A__ ):
					A     	=							(DPMSolverMultistepScheduler,)
					A     	=							(('num_inference_steps', 25),)

					def     __UpperCamelCase       (   self				:	Union[str, Any],**_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		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(**_A   )
							return config

					def     __UpperCamelCase       (   self				:	Any,_A				:	Tuple=0,**_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   dict(self.forward_default_kwargs   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   kwargs.pop("num_inference_steps",_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.dummy_sample
							SCREAMING_SNAKE_CASE_  :		str    				=   0.1 * sample
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		str    				=   sample, sample
									for t in range(_A,time_step + scheduler.config.solver_order + 1   ):
											SCREAMING_SNAKE_CASE_  :		List[str]    				=   scheduler.step(_A,_A,_A,**_A   ).prev_sample
											SCREAMING_SNAKE_CASE_  :		Any    				=   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     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							pass

					def     __UpperCamelCase       (   self				:	Dict,_A				:	List[str]=0,**_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   dict(self.forward_default_kwargs   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   kwargs.pop("num_inference_steps",_A   )
							SCREAMING_SNAKE_CASE_  :		int    				=   self.dummy_sample
							SCREAMING_SNAKE_CASE_  :		str    				=   0.1 * sample
							SCREAMING_SNAKE_CASE_  :		Dict    				=   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_  :		str    				=   scheduler_class(**_A   )
									scheduler.set_timesteps(_A   )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(_A   )
											SCREAMING_SNAKE_CASE_  :		int    				=   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)
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE_  :		Dict    				=   scheduler.step(_A,_A,_A,**_A   ).prev_sample
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   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     __UpperCamelCase       (   self				:	Tuple,_A				:	List[Any]=None,**_A				:	int   ):







							"""simple docstring"""






							if scheduler is None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.get_scheduler_config(**_A   )
									SCREAMING_SNAKE_CASE_  :		str    				=   scheduler_class(**_A   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   10
							SCREAMING_SNAKE_CASE_  :		int    				=   self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.dummy_sample_deter
							scheduler.set_timesteps(_A   )

							for i, t in enumerate(scheduler.timesteps   ):
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   model(_A,_A   )
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   scheduler.step(_A,_A,_A   ).prev_sample

							return sample

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   dict(self.forward_default_kwargs   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   kwargs.pop("num_inference_steps",_A   )

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_  :		Dict    				=   scheduler_class(**_A   )

									SCREAMING_SNAKE_CASE_  :		int    				=   self.dummy_sample
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   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"   ):
											SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   num_inference_steps

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

									SCREAMING_SNAKE_CASE_  :		Any    				=   scheduler.timesteps[5]
									SCREAMING_SNAKE_CASE_  :		Dict    				=   scheduler.timesteps[6]

									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   scheduler.step(_A,_A,_A,**_A   ).prev_sample
									SCREAMING_SNAKE_CASE_  :		int    				=   scheduler.step(_A,_A,_A,**_A   ).prev_sample

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

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=_A   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							self.check_over_configs(thresholding=_A   )
							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=_A,prediction_type=_A,sample_max_value=_A,algorithm_type="dpmsolver++",solver_order=_A,solver_type=_A,)

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							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=_A,solver_type=_A,prediction_type=_A,algorithm_type=_A,)
															SCREAMING_SNAKE_CASE_  :		Dict    				=   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     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							self.check_over_configs(lower_order_final=_A   )
							self.check_over_configs(lower_order_final=_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
							self.check_over_configs(lambda_min_clipped=-5.1   )

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							self.check_over_configs(variance_type=_A   )
							self.check_over_configs(variance_type="learned_range"   )

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=_A,time_step=0   )

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.full_loop()
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.mean(torch.abs(_A   )   )

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

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.full_loop(thresholding=_A,dynamic_thresholding_ratio=0.87,sample_max_value=0.5   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.mean(torch.abs(_A   )   )

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

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.full_loop(prediction_type="v_prediction"   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.mean(torch.abs(_A   )   )

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

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.full_loop(prediction_type="v_prediction",use_karras_sigmas=_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   torch.mean(torch.abs(_A   )   )

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

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.full_loop(scheduler=_A   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.mean(torch.abs(_A   )   )

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

							SCREAMING_SNAKE_CASE_  :		Dict    				=   DPMSolverSinglestepScheduler.from_config(scheduler.config   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   UniPCMultistepScheduler.from_config(scheduler.config   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   DEISMultistepScheduler.from_config(scheduler.config   )
							SCREAMING_SNAKE_CASE_  :		int    				=   DPMSolverMultistepScheduler.from_config(scheduler.config   )

							SCREAMING_SNAKE_CASE_  :		int    				=   self.full_loop(scheduler=_A   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.mean(torch.abs(_A   )   )

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

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.get_scheduler_config(thresholding=_A,dynamic_thresholding_ratio=0   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   scheduler_class(**_A   )

							SCREAMING_SNAKE_CASE_  :		int    				=   10
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.dummy_sample_deter.half()
							scheduler.set_timesteps(_A   )

							for i, t in enumerate(scheduler.timesteps   ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model(_A,_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   scheduler.step(_A,_A,_A   ).prev_sample

							assert sample.dtype == torch.floataa






					def     __UpperCamelCase       (   self				:	Tuple,**_A				:	List[str]   ):







							"""simple docstring"""






							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE_  :		int    				=   self.get_scheduler_config(**_A   )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   scheduler_class(**_A   )

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

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase :     List[Any]          =  False






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

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

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