
"""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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)  # pylint: disable=invalid-name
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


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

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

														__snake_case     			=       [np.array(i.resize((w, h)							,    resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
														__snake_case     			=       np.concatenate(SCREAMING_SNAKE_CASE							,    axis=0 )
														__snake_case     			=       np.array(SCREAMING_SNAKE_CASE ).astype(np.floataa ) / 255.0
														__snake_case     			=       image.transpose(0							,    3							,    1							,    2 )
														__snake_case     			=       2.0 * image - 1.0
														__snake_case     			=       torch.from_numpy(SCREAMING_SNAKE_CASE )
							elif isinstance(image[0]							,    torch.Tensor ):
														__snake_case     			=       torch.cat(SCREAMING_SNAKE_CASE							,    dim=0 )
							return image
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							if isinstance(SCREAMING_SNAKE_CASE							,    torch.Tensor ):
														return mask
							elif isinstance(SCREAMING_SNAKE_CASE							,    PIL.Image.Image ):
														__snake_case     			=       [mask]

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







class 							__magic_name__     (		snake_case__   ):

					'''simple docstring'''





					_SCREAMING_SNAKE_CASE       :					UNetaDModel
					_SCREAMING_SNAKE_CASE       :					RePaintScheduler





					def __init__(				self   :							Optional[int]							,						snake_case_   :							List[str]							,						snake_case_   :							Union[str, Any]    ):
												super().__init__()
												self.register_modules(unet=_A							,						scheduler=_A    )





					@torch.no_grad()
					def __call__(				self   :							List[str]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[Any] = 250							,						snake_case_   :							str = 0.0							,						snake_case_   :							List[str] = 10							,						snake_case_   :							List[str] = 10							,						snake_case_   :							Any = None							,						snake_case_   :							Union[str, Any] = "pil"							,						snake_case_   :							Tuple = True							,						):
												__snake_case     			=       image

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

												__snake_case     			=       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.'''    )

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

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

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

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

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

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=_A    )


"""simple docstring"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
