

"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       (DEISMultistepScheduler,)
					lowercase__				 =       (("num_inference_steps", 25),)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,**lowercase_			:				List[str]		):
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''solver_order''': 2,
						}

						config.update(**lowercase_		)
						return config







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Optional[int]=0   ,**lowercase_			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dummy_sample
						lowerCAmelCase__	:						str  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

							lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	sample, sample
							for t in range(lowercase_   ,time_step + scheduler.config.solver_order + 1		):
								lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
								lowerCAmelCase__	:						int  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

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







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str=0   ,**lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	dict(self.forward_default_kwargs		)
						lowerCAmelCase__	:						Union[str, Any]  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	self.dummy_sample
						lowerCAmelCase__	:						Any  				=	0.1 * sample
						lowerCAmelCase__	:						Tuple  				=	[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						str  				=	scheduler_class(**lowercase_		)
							scheduler.set_timesteps(lowercase_		)

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

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

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

							lowerCAmelCase__	:						Tuple  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						Any  				=	new_scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

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







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str=None   ,**lowercase_			:				str		):
						if scheduler is None:
							lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
							lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(**lowercase_		)
							lowerCAmelCase__	:						Tuple  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Optional[int]  				=	self.get_scheduler_config(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	1_0
						lowerCAmelCase__	:						Dict  				=	self.dummy_model()
						lowerCAmelCase__	:						int  				=	self.dummy_sample_deter
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						Optional[int]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						int  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						return sample







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	dict(self.forward_default_kwargs		)

						lowerCAmelCase__	:						Dict  				=	kwargs.pop('''num_inference_steps'''   ,lowercase_		)

						for scheduler_class in self.scheduler_classes:
							lowerCAmelCase__	:						str  				=	self.get_scheduler_config()
							lowerCAmelCase__	:						Optional[int]  				=	scheduler_class(**lowercase_		)

							lowerCAmelCase__	:						int  				=	self.dummy_sample
							lowerCAmelCase__	:						int  				=	0.1 * sample

							if num_inference_steps is not None and hasattr(lowercase_   ,'''set_timesteps'''		):
								scheduler.set_timesteps(lowercase_		)
							elif num_inference_steps is not None and not hasattr(lowercase_   ,'''set_timesteps'''		):
								lowerCAmelCase__	:						Tuple  				=	num_inference_steps

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

							lowerCAmelCase__	:						str  				=	scheduler.timesteps[5]
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.timesteps[6]

							lowerCAmelCase__	:						Dict  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample
							lowerCAmelCase__	:						List[str]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

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







					def        __lowerCAmelCase (       self			:				int		):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						lowerCAmelCase__	:						str  				=	DEISMultistepScheduler(**self.get_scheduler_config()		)
						lowerCAmelCase__	:						str  				=	self.full_loop(scheduler=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

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

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

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

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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):
						self.check_over_configs(thresholding=lowercase_		)
						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=lowercase_   ,prediction_type=lowercase_   ,sample_max_value=lowercase_   ,algorithm_type='''deis'''   ,solver_order=lowercase_   ,solver_type=lowercase_   ,)







					def        __lowerCAmelCase (       self			:				List[str]		):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						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=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										lowerCAmelCase__	:						str  				=	self.full_loop(
										    solver_order=lowercase_   ,solver_type=lowercase_   ,prediction_type=lowercase_   ,algorithm_type=lowercase_   ,)
										assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"







					def        __lowerCAmelCase (       self			:				List[Any]		):
						self.check_over_configs(lower_order_final=lowercase_		)
						self.check_over_configs(lower_order_final=lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=lowercase_   ,time_step=0		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.full_loop()
						lowerCAmelCase__	:						Any  				=	torch.mean(torch.abs(lowercase_		)		)

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







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	self.full_loop(prediction_type='''v_prediction'''		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.mean(torch.abs(lowercase_		)		)

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







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_scheduler_config(thresholding=lowercase_   ,dynamic_thresholding_ratio=0		)
						lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	1_0
						lowerCAmelCase__	:						Optional[Any]  				=	self.dummy_model()
						lowerCAmelCase__	:						str  				=	self.dummy_sample_deter.half()
						scheduler.set_timesteps(lowercase_		)

						for i, t in enumerate(scheduler.timesteps		):
							lowerCAmelCase__	:						List[Any]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						Optional[Any]  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						assert sample.dtype == torch.floataa












"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


