code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import PNDMScheduler
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      UNetaDModel      ,      _lowerCAmelCase						:      PNDMScheduler      ):
  			super().__init__()
  
  			A							  =      PNDMScheduler.from_config(scheduler.config      )
  
  			self.register_modules(unet=_lowerCAmelCase      ,      scheduler=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@torch.no_grad()
  		def __call__(self						:      List[Any]      ,      _lowerCAmelCase						:      int = 1      ,      _lowerCAmelCase						:      int = 50      ,      _lowerCAmelCase						:      Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      _lowerCAmelCase						:      Optional[str] = "pil"      ,      _lowerCAmelCase						:      bool = True      ,      **_lowerCAmelCase						:      Optional[Any]      ,      ):
  			A							  =      randn_tensor(
  			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,      generator=_lowerCAmelCase      ,      device=self.device      ,      )
  
  			self.scheduler.set_timesteps(_lowerCAmelCase      )
  			for t in self.progress_bar(self.scheduler.timesteps      ):
  				A							  =      self.unet(_lowerCAmelCase      ,      _lowerCAmelCase      ).sample
  
  				A							  =      self.scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ).prev_sample
  
  			A							  =      (image / 2 + 0.5).clamp(0      ,      1      )
  			A							  =      image.cpu().permute(0      ,      2      ,      3      ,      1      ).numpy()
  			if output_type == "pil":
  				A							  =      self.numpy_to_pil(_lowerCAmelCase      )
  
  			if not return_dict:
  				return (image,)
  
  			return ImagePipelineOutput(images=_lowerCAmelCase      )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
  		__lowerCAmelCase 				=    10
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
  			A							  =      {
  			    """num_train_timesteps""": 1100,
  			    """beta_start""": 0.0_001,
  			    """beta_end""": 0.02,
  			    """beta_schedule""": """linear""",
  			}
  
  			config.update(**_lowerCAmelCase      )
  			return config
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			for timesteps in [10, 50, 100, 1000]:
  				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]      ):
  				self.check_over_configs(beta_start=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			for schedule in ["linear", "scaled_linear"]:
  				self.check_over_configs(beta_schedule=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      )
  
  			A							  =      torch.manual_seed(0      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  			A							  =      sample.to(_lowerCAmelCase      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
  			assert abs(result_mean.item() - 18.0_331      ) < 5e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			for prediction_type in ["epsilon", "v_prediction"]:
  				self.check_over_configs(prediction_type=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  			A							  =      sample.to(_lowerCAmelCase      )
  
  			A							  =      torch.manual_seed(0      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 328.9_970      ) < 1e-2
  			assert abs(result_mean.item() - 0.4_284      ) < 1e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
  			A							  =      torch.manual_seed(0      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma
  
  			for t in scheduler.timesteps:
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
  			assert abs(result_mean.item() - 18.0_331      ) < 1e-3
  
  