
"""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


lowerCamelCase__           =	logging.get_logger(__name__)  # pylint: disable=invalid-name
def 					__lowerCAmelCase       (_UpperCamelCase	):
	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead' ,						a_ ,						)
	if isinstance(a_ ,						torch.Tensor	):
		return image
	elif isinstance(a_ ,						PIL.Image.Image	):
		__lowerCAmelCase    :		Union[str, Any]		    =   [image]

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

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






	if isinstance(a_ ,						torch.Tensor	):
		return mask
	elif isinstance(a_ ,						PIL.Image.Image	):
		__lowerCAmelCase    :		Optional[int]		    =   [mask]

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




class 				A__ (			_UpperCAmelCase):
				A_		: List[str]            =			4_2
				A_		: Optional[int]            =			4_2

				def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					super().__init__()
					self.register_modules(unet=lowerCamelCase_ ,	scheduler=lowerCamelCase_				)







				@torch.no_grad()
				def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 2_50 ,	_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 ,	):
					__lowerCAmelCase    :		Optional[int]		    =   image

					__lowerCAmelCase    :		Any		    =   _preprocess_image(lowerCamelCase_				)
					__lowerCAmelCase    :		Dict		    =   original_image.to(device=self.device ,	dtype=self.unet.dtype				)
					__lowerCAmelCase    :		Optional[Any]		    =   _preprocess_mask(lowerCamelCase_				)
					__lowerCAmelCase    :		int		    =   mask_image.to(device=self.device ,	dtype=self.unet.dtype				)

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

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

					__lowerCAmelCase    :		Tuple		    =   original_image.shape
					__lowerCAmelCase    :		List[Any]		    =   randn_tensor(lowerCamelCase_ ,	generator=lowerCamelCase_ ,	device=self.device ,	dtype=self.unet.dtype				)

					# set step values
					self.scheduler.set_timesteps(lowerCamelCase_ ,	lowerCamelCase_ ,	lowerCamelCase_ ,	self.device				)
					__lowerCAmelCase    :		Optional[Any]		    =   eta

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

						else:
							# compute the reverse: x_t-1 -> x_t
							__lowerCAmelCase    :		Union[str, Any]		    =   self.scheduler.undo_step(lowerCamelCase_ ,	lowerCamelCase_ ,	lowerCamelCase_				)
						__lowerCAmelCase    :		Optional[int]		    =   t

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

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=lowerCamelCase_				)

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()