






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





def 	lowerCAmelCase_		(lowercase__						:       Union[List, PIL.Image.Image, torch.Tensor]       )		->    int:


				'''simple docstring'''




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

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

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





def 	lowerCAmelCase_		(lowercase__						:       Union[List, PIL.Image.Image, torch.Tensor]       )		->    Tuple:


				'''simple docstring'''




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

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




class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:UNetaDModel
							UpperCamelCase_		:RePaintScheduler




							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   str	):
											super().__init__()
											self.register_modules(unet=SCREAMING_SNAKE_CASE_ ,							scheduler=SCREAMING_SNAKE_CASE_	)




							@torch.no_grad()
							def __call__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[torch.Tensor, PIL.Image.Image] ,							SCREAMING_SNAKE_CASE_     :   Union[torch.Tensor, PIL.Image.Image] ,							SCREAMING_SNAKE_CASE_     :   int = 250 ,							SCREAMING_SNAKE_CASE_     :   float = 0.0 ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "pil" ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						image

											lowerCAmelCase__      			=						_preprocess_image(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						original_image.to(device=self.device ,							dtype=self.unet.dtype	)
											lowerCAmelCase__      			=						_preprocess_mask(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						mask_image.to(device=self.device ,							dtype=self.unet.dtype	)

											lowerCAmelCase__      			=						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.'	)

											lowerCAmelCase__      			=						original_image.shape
											lowerCAmelCase__      			=						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	)
											lowerCAmelCase__      			=						eta

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

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

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)













from pathlib import Path

import fire





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       int       )		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						Path(lowercase__       )
				lowerCAmelCase__      			=						Path(lowercase__       )
				dest_dir.mkdir(exist_ok=lowercase__       )
				for path in src_dir.iterdir():
								lowerCAmelCase__      			=						[x.rstrip() for x in list(path.open().readlines()       )][:n]
								lowerCAmelCase__      			=						dest_dir.joinpath(path.name       )
								print(lowercase__       )
								dest_path.open('''w'''       ).write('''\n'''.join(lowercase__       )       )


if __name__ == "__main__":
			fire.Fire(minify)





