



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]     )     ->					Dict:
   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   :  Any						     =     [image]

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

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

   if isinstance(mask[0]	,					PIL.Image.Image     ):
      UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     mask[0].size
      UpperCamelCase     ,					UpperCamelCase   :  Any						     =     (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   :  List[str]						     =     np.concatenate(snake_case__	,					axis=0     )
      UpperCamelCase   :  Tuple						     =     mask.astype(np.floataa     ) / 255.0
      UpperCamelCase   :  Union[str, Any]						     =     0
      UpperCamelCase   :  Optional[int]						     =     1
      UpperCamelCase   :  List[str]						     =     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_	)					->		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   :  Dict						     =     image

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

          UpperCamelCase   :  Dict						     =     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   :  Dict						     =     original_image.shape
          UpperCamelCase   :  Tuple						     =     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   :  Optional[int]						     =     eta

          UpperCamelCase   :  Tuple						     =     self.scheduler.timesteps[0] + 1
          UpperCamelCase   :  str						     =     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   :  str						     =     self.unet(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	).sample
                # compute previous image: x_t -> x_t-1
                UpperCamelCase   :  List[str]						     =     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   :  List[str]						     =     self.scheduler.undo_step(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  str						     =     t

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

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase	       =	abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   from transformers.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(snake_case__     )


def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   from transformers.testing_utils import pytest_terminal_summary_main

   UpperCamelCase   :  Optional[int]						     =     terminalreporter.config.getoption('--make-reports'     )
   if make_reports:
      pytest_terminal_summary_main(snake_case__	,					id=snake_case__     )



