


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       (UniPCMultistepScheduler,)
     __lowercase				:							Dict	   =       (('num_inference_steps', 25),)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:int   ):
       snake_case__						= {
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
           '''solver_type''': '''bh2''',
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Dict=0     ,		**_a:Tuple   ):
       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__				,				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 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str=0     ,		**_a:List[str]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]=None     ,		**_a:List[str]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       snake_case__						= UniPCMultistepScheduler(**self.get_scheduler_config()   )
       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

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

       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for timesteps in [25, 50, 1_00, 9_99, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.check_over_configs(thresholding=_a   )
       for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
           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     ,		solver_order=_a     ,		solver_type=_a     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for solver_type in ["bh1", "bh2"]:
         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     ,		)
             snake_case__						= self.full_loop(
                 solver_order=_a     ,		solver_type=_a     ,		prediction_type=_a     ,		)
             assert not torch.isnan(_a   ).any(), "Samples have nan numbers"

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       self.check_over_configs(lower_order_final=_a   )
       self.check_over_configs(lower_order_final=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.full_loop()
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.full_loop(prediction_type='''v_prediction'''   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.1014   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       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







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		**_a:List[str]   ):
       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )

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









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).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
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





