


'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_								=    logging.get_logger(__name__)




class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''







							def __init__(		self  :				Dict		,				*SCREAMING_SNAKE_CASE_  :				str		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
													warnings.warn(
													    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
													    ' use ViTImageProcessor instead.'		,				SCREAMING_SNAKE_CASE_		,				)
													super().__init__(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
						_a     =			min(img.size				)

						if s < target_image_size:
												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)

						_a     =			target_image_size / s
						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
						return img


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
						_a     =			2.0 * x - 1.0
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
						_a     =			x.detach().cpu()
						if process:
												_a     =			post_process_tensor(_UpperCAmelCase				)
						_a     =			x.numpy()
						if process:
												_a     =			(255 * x).astype(np.uinta				)
						_a     =			Image.fromarray(_UpperCAmelCase				)
						if not x.mode == mode:
												_a     =			x.convert(_UpperCAmelCase				)
						return x


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
						_a     =			(x + 1.0) / 2.0
						_a     =			x.permute(1							,     2							,     0				)
						return x






def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
						_a     =			post_process_tensor(x.squeeze()				)
						return x.permute(2							,     0							,     1				).unsqueeze(0				)


