




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def lowercase						(     ) ->       Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	HfArgumentParser(SCREAMING_SNAKE_CASE   )
			try:
						SCREAMING_SNAKE_CASE_			=	parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						SCREAMING_SNAKE_CASE_			=	'Arg --no_{0} is no longer used, please use --no-{0} instead.'
						SCREAMING_SNAKE_CASE_			=	' '.join(str(SCREAMING_SNAKE_CASE   ).split(' '   )[:-1]   )
						SCREAMING_SNAKE_CASE_			=	''
						SCREAMING_SNAKE_CASE_			=	eval(str(SCREAMING_SNAKE_CASE   ).split(' '   )[-1]   )
						SCREAMING_SNAKE_CASE_			=	[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]   )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE   )
						if len(SCREAMING_SNAKE_CASE   ) > 0:
									SCREAMING_SNAKE_CASE_			=	full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE   )
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE_			=	PyTorchBenchmark(args=SCREAMING_SNAKE_CASE   )
			benchmark.run()


if __name__ == "__main__":
				main()






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[images]
				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)

				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[..., None]

				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(images.transpose(0			,    3			,    1			,    2			)			)
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
				return images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				return 2.0 * images - 1.0





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				return (images / 2 + 0.5).clamp(0			,    1			)





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:




				"""simple docstring"""



				if height is None:
							SCREAMING_SNAKE_CASE_			=	image.height
				if width is None:
							SCREAMING_SNAKE_CASE_			=	image.width

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(
				    x - x % self.config.vae_scale_factor for x in (width, height)
				)  # resize to integer multiple of vae_scale_factor
				SCREAMING_SNAKE_CASE_			=	image.resize((width, height)			,    resample=PIL_INTERPOLATION[self.config.resample]			)
				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	[image]
				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
							raise ValueError(
							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)

				if isinstance(image[0]			,    PIL.Image.Image			):
							if self.config.do_convert_rgb:
										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
							if self.config.do_resize:
										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt

				elif isinstance(image[0]			,    np.ndarray			):
							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

				elif isinstance(image[0]			,    torch.Tensor			):
							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
										return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
										raise ValueError(
										    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)

        # expected range [0,1], normalize to [-1,1]
				SCREAMING_SNAKE_CASE_			=	self.config.do_normalize
				if image.min() < 0:
							warnings.warn(
							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
							    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'			,    SCREAMING_SNAKE_CASE			,    )
							SCREAMING_SNAKE_CASE_			=	False

				if do_normalize:
							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)

				return image





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if output_type == "latent":
							return image

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				if output_type == "pt":
							return image

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							return image

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)




class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   CONFIG_NAME





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:




				"""simple docstring"""



				super().__init__()





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
				else:
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]

				return pil_images





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""simple docstring"""



				return image[:, :, 1] * 2**8 + image[:, :, 2]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:




				"""simple docstring"""



				if images.ndim == 3:
							SCREAMING_SNAKE_CASE_			=	images[None, ...]
				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
				if images.shape[-1] == 6:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
							SCREAMING_SNAKE_CASE_			=	[
							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
							]
				elif images.shape[-1] == 4:
							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
				else:
							raise Exception('Not supported'			)

				return pil_images





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
							raise ValueError(
							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
				if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_			=	(
							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	'np'

				if do_denormalize is None:
							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]

				SCREAMING_SNAKE_CASE_			=	torch.stack(
				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)

				if output_type == "np":
							if image.shape[-1] == 6:
										SCREAMING_SNAKE_CASE_			=	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,    axis=0			)
							else:
										SCREAMING_SNAKE_CASE_			=	image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

				if output_type == "pil":
							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
				else:
							raise Exception(f'This type {output_type} is not supported'			)
