



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 						UpperCamelCase      (  snake_case__			:       Union[List, PIL.Image.Image, torch.Tensor]      )      ->	int:
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead'			,	snake_case__			,	)
   if isinstance(snake_case__			,	torch.Tensor      ):
      return image
   elif isinstance(snake_case__			,	PIL.Image.Image      ):
      UpperCamelCase       :    List[Any]		      =      [image]

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

      UpperCamelCase       :    Dict		      =      [np.array(i.resize((w, h)			,	resample=PIL_INTERPOLATION['lanczos']      )      )[None, :] for i in image]
      UpperCamelCase       :    Tuple		      =      np.concatenate(snake_case__			,	axis=0      )
      UpperCamelCase       :    int		      =      np.array(snake_case__      ).astype(np.floataa      ) / 255.0
      UpperCamelCase       :    Optional[int]		      =      image.transpose(0			,	3			,	1			,	2      )
      UpperCamelCase       :    Dict		      =      2.0 * image - 1.0
      UpperCamelCase       :    Tuple		      =      torch.from_numpy(snake_case__      )
   elif isinstance(image[0]			,	torch.Tensor      ):
      UpperCamelCase       :    Any		      =      torch.cat(snake_case__			,	dim=0      )
   return image
def 						UpperCamelCase      (  snake_case__			:       Union[List, PIL.Image.Image, torch.Tensor]      )      ->	Optional[int]:
   if isinstance(snake_case__			,	torch.Tensor      ):
      return mask
   elif isinstance(snake_case__			,	PIL.Image.Image      ):
      UpperCamelCase       :    List[str]		      =      [mask]

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



class 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   UNetaDModel
       UpperCAmelCase__							:   RePaintScheduler
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[Any]:
          super().__init__()
          self.register_modules(unet=SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_			)






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

          UpperCamelCase       :    Optional[Any]		      =      _preprocess_image(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      original_image.to(device=self.device,     dtype=self.unet.dtype			)
          UpperCamelCase       :    Tuple		      =      _preprocess_mask(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[str]		      =      mask_image.to(device=self.device,     dtype=self.unet.dtype			)

          UpperCamelCase       :    Optional[Any]		      =      original_image.shape[0]

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

          UpperCamelCase       :    str		      =      original_image.shape
          UpperCamelCase       :    Dict		      =      randn_tensor(SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     device=self.device,     dtype=self.unet.dtype			)

          # set step values
          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.device			)
          UpperCamelCase       :    List[str]		      =      eta

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

             else:
                # compute the reverse: x_t-1 -> x_t
                UpperCamelCase       :    Union[str, Any]		      =      self.scheduler.undo_step(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Tuple		      =      t

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

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)





"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

