


"""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


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







def     __lowerCAmelCase		(		lowercase    :    Union[List, PIL.Image.Image, torch.Tensor]					)      ->		int:


    """simple docstring"""





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

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

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







def     __lowerCAmelCase		(		lowercase    :    Union[List, PIL.Image.Image, torch.Tensor]					)      ->		Dict:


    """simple docstring"""





    if isinstance(lowercase		,				torch.Tensor					):
        return mask
    elif isinstance(lowercase		,				PIL.Image.Image					):
        snake_case       : Optional[int]    =       [mask]

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







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			UNetaDModel
   __UpperCAmelCase					:			RePaintScheduler







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case       : Optional[Any]    =       image

       snake_case       : int    =       _preprocess_image(UpperCamelCase__		)
       snake_case       : List[Any]    =       original_image.to(device=self.device		,      dtype=self.unet.dtype		)
       snake_case       : Tuple    =       _preprocess_mask(UpperCamelCase__		)
       snake_case       : str    =       mask_image.to(device=self.device		,      dtype=self.unet.dtype		)

       snake_case       : int    =       original_image.shape[0]

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

       snake_case       : Dict    =       original_image.shape
       snake_case       : Tuple    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=self.unet.dtype		)

       # set step values
       self.scheduler.set_timesteps(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      self.device		)
       snake_case       : str    =       eta

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

           else:
               # compute the reverse: x_t-1 -> x_t
               snake_case       : Optional[int]    =       self.scheduler.undo_step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       t

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)









"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (KDPMaDiscreteScheduler,)
   __UpperCAmelCase					:			List[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           "num_train_timesteps": 1100,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,      [0.0002, 0.002, 0.02]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : str    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : List[str]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : Optional[Any]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : int    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : int    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       output.prev_sample

       snake_case       : Optional[Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
           assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 4.693_4286_5017_0972e-07		) < 1e-2
           assert abs(result_mean.item() - 0.0002		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Optional[Any]    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : Union[str, Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : List[str]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : List[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : int    =       output.prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Union[str, Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

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

       snake_case       : Optional[Any]    =       self.dummy_model()
       snake_case       : Optional[int]    =       self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

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

           snake_case       : Any    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Dict    =       output.prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if str(UpperCamelCase__		).startswith("cpu"		):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3





