




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					(DDIMScheduler,)
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					(("eta", 0.0), ("num_inference_steps", 50))






   def  _a						(	self   :       Dict						,					**UpperCamelCase_   :       List[Any]     )->			int:
     _lowercase: Dict						     =    {
         'num_train_timesteps': 10_00,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'clip_sample': True,
     }

     config.update(**UpperCamelCase_     )
     return config






   def  _a						(	self   :       Optional[Any]						,					**UpperCamelCase_   :       List[Any]     )->			Tuple:
     _lowercase: Union[str, Any]						     =    self.scheduler_classes[0]
     _lowercase: Dict						     =    self.get_scheduler_config(**UpperCamelCase_     )
     _lowercase: List[Any]						     =    scheduler_class(**UpperCamelCase_     )

     _lowercase     ,					_lowercase: Tuple						     =    10, 0.0

     _lowercase: str						     =    self.dummy_model()
     _lowercase: Optional[int]						     =    self.dummy_sample_deter

     scheduler.set_timesteps(UpperCamelCase_     )

     for t in scheduler.timesteps:
       _lowercase: List[Any]						     =    model(UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: str						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     return sample






   def  _a						(	self   :       Tuple     )->			Optional[int]:
     for timesteps in [1_00, 5_00, 10_00]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			int:
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=UpperCamelCase_     )

     _lowercase: Optional[int]						     =    self.scheduler_classes[0]
     _lowercase: str						     =    self.get_scheduler_config(steps_offset=1     )
     _lowercase: Tuple						     =    scheduler_class(**UpperCamelCase_     )
     scheduler.set_timesteps(5     )
     assert torch.equal(scheduler.timesteps						,					torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]     )     )






   def  _a						(	self   :       Any     )->			Tuple:
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,					[0.002, 0.02, 0.2, 2]     ):
       self.check_over_configs(beta_start=UpperCamelCase_						,					beta_end=UpperCamelCase_     )






   def  _a						(	self   :       List[Any]     )->			Union[str, Any]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_     )






   def  _a						(	self   :       List[Any]     )->			List[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			Union[str, Any]:
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=UpperCamelCase_     )






   def  _a						(	self   :       str     )->			Optional[int]:
     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			List[str]:
     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_     )






   def  _a						(	self   :       int     )->			Optional[int]:
     self.check_over_configs(thresholding=UpperCamelCase_     )
     for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=UpperCamelCase_						,					prediction_type=UpperCamelCase_						,					sample_max_value=UpperCamelCase_						,					)






   def  _a						(	self   :       Tuple     )->			Optional[Any]:
     for t in [1, 10, 49]:
       self.check_over_forward(time_step=UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			str:
     for t, num_inference_steps in zip([1, 10, 50]						,					[10, 50, 5_00]     ):
       self.check_over_forward(time_step=UpperCamelCase_						,					num_inference_steps=UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			str:
     for t, eta in zip([1, 10, 49]						,					[0.0, 0.5, 1.0]     ):
       self.check_over_forward(time_step=UpperCamelCase_						,					eta=UpperCamelCase_     )






   def  _a						(	self   :       List[Any]     )->			List[str]:
     _lowercase: List[Any]						     =    self.scheduler_classes[0]
     _lowercase: Optional[int]						     =    self.get_scheduler_config()
     _lowercase: List[str]						     =    scheduler_class(**UpperCamelCase_     )

     assert torch.sum(torch.abs(scheduler._get_variance(0						,					0     ) - 0.0     )     ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_20						,					4_00     ) - 0.14_771     )     ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80						,					9_60     ) - 0.32_460     )     ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0						,					0     ) - 0.0     )     ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87						,					4_86     ) - 0.00_979     )     ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99						,					9_98     ) - 0.02     )     ) < 1E-5






   def  _a						(	self   :       Optional[int]     )->			Dict:
     _lowercase: Union[str, Any]						     =    self.full_loop()

     _lowercase: List[str]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: List[Any]						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 172.0_067     ) < 1E-2
     assert abs(result_mean.item() - 0.223_967     ) < 1E-3






   def  _a						(	self   :       Dict     )->			Tuple:
     _lowercase: Any						     =    self.full_loop(prediction_type='v_prediction'     )

     _lowercase: Optional[int]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: str						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 52.5_302     ) < 1E-2
     assert abs(result_mean.item() - 0.0_684     ) < 1E-3






   def  _a						(	self   :       int     )->			Tuple:
     # We specify different beta, so that the first alpha is 0.99
     _lowercase: List[str]						     =    self.full_loop(set_alpha_to_one=UpperCamelCase_						,					beta_start=0.01     )
     _lowercase: int						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Any						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 149.8_295     ) < 1E-2
     assert abs(result_mean.item() - 0.1_951     ) < 1E-3






   def  _a						(	self   :       Optional[Any]     )->			Union[str, Any]:
     # We specify different beta, so that the first alpha is 0.99
     _lowercase: int						     =    self.full_loop(set_alpha_to_one=UpperCamelCase_						,					beta_start=0.01     )
     _lowercase: Any						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Any						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 149.0_784     ) < 1E-2
     assert abs(result_mean.item() - 0.1_941     ) < 1E-3






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

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

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).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: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

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

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

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