





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


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




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
       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  ):
              UpperCamelCase_          =  [image]

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

              UpperCamelCase_          =  [np.array(i.resize((w, h)					,		resample=PIL_INTERPOLATION["lanczos"]  )  )[None, :] for i in image]
              UpperCamelCase_          =  np.concatenate(UpperCamelCase_					,		axis=0  )
              UpperCamelCase_          =  np.array(UpperCamelCase_  ).astype(np.floataa  ) / 2_55.0
              UpperCamelCase_          =  image.transpose(0					,		3					,		1					,		2  )
              UpperCamelCase_          =  2.0 * image - 1.0
              UpperCamelCase_          =  torch.from_numpy(UpperCamelCase_  )
       elif isinstance(image[0]					,		torch.Tensor  ):
              UpperCamelCase_          =  torch.cat(UpperCamelCase_					,		dim=0  )
       return image




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Any:
       if isinstance(UpperCamelCase_					,		torch.Tensor  ):
              return mask
       elif isinstance(UpperCamelCase_					,		PIL.Image.Image  ):
              UpperCamelCase_          =  [mask]

       if isinstance(mask[0]					,		PIL.Image.Image  ):
              UpperCamelCase_          =  mask[0].size
              UpperCamelCase_          =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
              UpperCamelCase_          =  [np.array(m.convert("L"  ).resize((w, h)					,		resample=PIL_INTERPOLATION["nearest"]  )  )[None, :] for m in mask]
              UpperCamelCase_          =  np.concatenate(UpperCamelCase_					,		axis=0  )
              UpperCamelCase_          =  mask.astype(np.floataa  ) / 2_55.0
              UpperCamelCase_          =  0
              UpperCamelCase_          =  1
              UpperCamelCase_          =  torch.from_numpy(UpperCamelCase_  )
       elif isinstance(mask[0]					,		torch.Tensor  ):
              UpperCamelCase_          =  torch.cat(UpperCamelCase_					,		dim=0  )
       return mask







class 							_UpperCamelCase					(	snake_case__     ):
       _UpperCamelCase				:					UNetaDModel
       _UpperCamelCase				:					RePaintScheduler

       def __init__(		self: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: int		)       -> List[Any]:




              """simple docstring"""


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



       @torch.no_grad()
       def __call__(		self: Any					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: int = 250					,   _SCREAMING_SNAKE_CASE: Tuple = 0.0					,   _SCREAMING_SNAKE_CASE: Optional[int] = 10					,   _SCREAMING_SNAKE_CASE: Tuple = 10					,   _SCREAMING_SNAKE_CASE: List[Any] = None					,   _SCREAMING_SNAKE_CASE: int = "pil"					,   _SCREAMING_SNAKE_CASE: int = True					,   )       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  image

              UpperCamelCase_          =  _preprocess_image(_A		)
              UpperCamelCase_          =  original_image.to(device=self.device					,   dtype=self.unet.dtype		)
              UpperCamelCase_          =  _preprocess_mask(_A		)
              UpperCamelCase_          =  mask_image.to(device=self.device					,   dtype=self.unet.dtype		)

              UpperCamelCase_          =  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.'''		)

              UpperCamelCase_          =  original_image.shape
              UpperCamelCase_          =  randn_tensor(_A					,   generator=_A					,   device=self.device					,   dtype=self.unet.dtype		)

              # set step values
              self.scheduler.set_timesteps(_A					,   _A					,   _A					,   self.device		)
              UpperCamelCase_          =  eta

              UpperCamelCase_          =  self.scheduler.timesteps[0] + 1
              UpperCamelCase_          =  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
                            UpperCamelCase_          =  self.unet(_A					,   _A		).sample
                            # compute previous image: x_t -> x_t-1
                            UpperCamelCase_          =  self.scheduler.step(_A					,   _A					,   _A					,   _A					,   _A					,   _A		).prev_sample

                     else:
                            # compute the reverse: x_t-1 -> x_t
                            UpperCamelCase_          =  self.scheduler.undo_step(_A					,   _A					,   _A		)
                     UpperCamelCase_          =  t

              UpperCamelCase_          =  (image / 2 + 0.5).clamp(0					,   1		)
              UpperCamelCase_          =  image.cpu().permute(0					,   2					,   3					,   1		).numpy()
              if output_type == "pil":
                     UpperCamelCase_          =  self.numpy_to_pil(_A		)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_A		)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: pyspark.sql.DataFrame					,   _SCREAMING_SNAKE_CASE: Optional[NamedSplit] = None					,   _SCREAMING_SNAKE_CASE: Optional[Features] = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = "arrow"					,   **_SCREAMING_SNAKE_CASE: Union[str, Any]					,   )       -> Any:




              """simple docstring"""


              super().__init__(
                  split=_SCREAMING_SNAKE_CASE					,   features=_SCREAMING_SNAKE_CASE					,   cache_dir=_SCREAMING_SNAKE_CASE					,   keep_in_memory=_SCREAMING_SNAKE_CASE					,   streaming=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  load_from_cache_file
              UpperCamelCase_          =  file_format
              UpperCamelCase_          =  Spark(
                  df=_SCREAMING_SNAKE_CASE					,   features=_SCREAMING_SNAKE_CASE					,   cache_dir=_SCREAMING_SNAKE_CASE					,   working_dir=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )



       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              if self.streaming:
                     return self.builder.as_streaming_dataset(split=self.split		)
              UpperCamelCase_          =  None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
              self.builder.download_and_prepare(
                  download_mode=_SCREAMING_SNAKE_CASE					,   file_format=self._file_format					,   )
              return self.builder.as_dataset(split=self.split		)

