


"""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							:    Union[str, Any]		=    logging.get_logger(__name__)  # pylint: disable=invalid-name







def     _a				(      _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							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_       ,						snake_case_           =		image[0].size
														snake_case_       ,						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     _a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							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_       ,						snake_case_           =		mask[0].size
														snake_case_       ,						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      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		UNetaDModel
							__lowercase:		RePaintScheduler



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



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



							@torch.no_grad()
							def __call__(				self							:      Dict		,       UpperCAmelCase_							:      Union[torch.Tensor, PIL.Image.Image]		,       UpperCAmelCase_							:      Union[torch.Tensor, PIL.Image.Image]		,       UpperCAmelCase_							:      int = 250		,       UpperCAmelCase_							:      float = 0.0		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,       UpperCAmelCase_							:      Optional[str] = "pil"		,       UpperCAmelCase_							:      bool = True		,       )					->Union[ImagePipelineOutput, Tuple]:



														"""simple docstring"""



														snake_case_           =		image

														snake_case_           =		_preprocess_image(UpperCAmelCase_       )
														snake_case_           =		original_image.to(device=self.device		,       dtype=self.unet.dtype       )
														snake_case_           =		_preprocess_mask(UpperCAmelCase_       )
														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(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_           =		original_image.shape
														snake_case_           =		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_           =		eta

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

																					else:
																												# compute the reverse: x_t-1 -> x_t
																												snake_case_           =		self.scheduler.undo_step(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
																					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(UpperCAmelCase_       )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCAmelCase_       )







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



