


"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )









import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





