
"""simple docstring"""




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__   :					Tuple							=   logging.get_logger(__name__)  # pylint: disable=invalid-name




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  warnings.warn(
      '''The preprocess method is deprecated and will be removed in a future version. Please'''
      ''' use VaeImageProcessor.preprocess instead''' ,				_UpperCamelCase ,				)
  if isinstance(_UpperCamelCase ,				torch.Tensor						):
    return image
  elif isinstance(_UpperCamelCase ,				PIL.Image.Image						):
    _lowercase:			List[str]       =     [image]

  if isinstance(image[0] ,				PIL.Image.Image						):
    _lowercase ,				_lowercase:			Union[str, Any]       =     image[0].size
    _lowercase ,				_lowercase:			List[str]       =     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

    _lowercase:			str       =     [np.array(i.resize((w, h) ,				resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
    _lowercase:			Optional[Any]       =     np.concatenate(_UpperCamelCase ,				axis=0						)
    _lowercase:			Any       =     np.array(_UpperCamelCase						).astype(np.floataa						) / 255.0
    _lowercase:			Tuple       =     image.transpose(0 ,				3 ,				1 ,				2						)
    _lowercase:			Optional[Any]       =     2.0 * image - 1.0
    _lowercase:			Tuple       =     torch.from_numpy(_UpperCamelCase						)
  elif isinstance(image[0] ,				torch.Tensor						):
    _lowercase:			Optional[Any]       =     torch.cat(_UpperCamelCase ,				dim=0						)
  return image




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if isinstance(_UpperCamelCase ,				torch.Tensor						):
    return mask
  elif isinstance(_UpperCamelCase ,				PIL.Image.Image						):
    _lowercase:			int       =     [mask]

  if isinstance(mask[0] ,				PIL.Image.Image						):
    _lowercase ,				_lowercase:			List[str]       =     mask[0].size
    _lowercase ,				_lowercase:			str       =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    _lowercase:			Any       =     [np.array(m.convert('''L'''						).resize((w, h) ,				resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
    _lowercase:			List[str]       =     np.concatenate(_UpperCamelCase ,				axis=0						)
    _lowercase:			List[Any]       =     mask.astype(np.floataa						) / 255.0
    _lowercase:			Union[str, Any]       =     0
    _lowercase:			Optional[int]       =     1
    _lowercase:			List[Any]       =     torch.from_numpy(_UpperCamelCase						)
  elif isinstance(mask[0] ,				torch.Tensor						):
    _lowercase:			str       =     torch.cat(_UpperCamelCase ,				dim=0						)
  return mask







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		42
       UpperCamelCase_   =		42
       def __init__(     self ,				A_ ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         super().__init__()
         self.register_modules(unet=A_ ,				scheduler=A_       )



       @torch.no_grad()
       def __call__(     self ,				A_ ,				A_ ,				A_ = 250 ,				A_ = 0.0 ,				A_ = 10 ,				A_ = 10 ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				)     ->	Union[ImagePipelineOutput, Tuple]:





         """simple docstring"""




         _lowercase:			Dict       =     image

         _lowercase:			str       =     _preprocess_image(A_       )
         _lowercase:			int       =     original_image.to(device=self.device ,				dtype=self.unet.dtype       )
         _lowercase:			List[Any]       =     _preprocess_mask(A_       )
         _lowercase:			Dict       =     mask_image.to(device=self.device ,				dtype=self.unet.dtype       )

         _lowercase:			Dict       =     original_image.shape[0]

         # sample gaussian noise to begin the loop
         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.'''       )

         _lowercase:			List[Any]       =     original_image.shape
         _lowercase:			List[str]       =     randn_tensor(A_ ,				generator=A_ ,				device=self.device ,				dtype=self.unet.dtype       )

         # set step values
         self.scheduler.set_timesteps(A_ ,				A_ ,				A_ ,				self.device       )
         _lowercase:			Tuple       =     eta

         _lowercase:			str       =     self.scheduler.timesteps[0] + 1
         _lowercase:			List[str]       =     generator[0] if isinstance(A_ ,				A_       ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
           if t < t_last:
             # predict the noise residual
             _lowercase:			str       =     self.unet(A_ ,				A_       ).sample
             # compute previous image: x_t -> x_t-1
             _lowercase:			Union[str, Any]       =     self.scheduler.step(A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       ).prev_sample

           else:
             # compute the reverse: x_t-1 -> x_t
             _lowercase:			Tuple       =     self.scheduler.undo_step(A_ ,				A_ ,				A_       )
           _lowercase:			Tuple       =     t

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=A_       )


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
