

"""simple docstring"""






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 		A_	(   __UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				Optional[int]=2_56				):
		lowercase										=							min(img.size				)

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

		lowercase										=							target_image_size / s
		lowercase										=							(round(r * img.size[1]				), round(r * img.size[0]				))
		lowercase										=							TF.resize(__UpperCamelCase       ,	__UpperCamelCase       ,	interpolation=PIL.Image.LANCZOS				)
		lowercase										=							TF.center_crop(__UpperCamelCase       ,	output_size=2 * [target_image_size]				)
		lowercase										=							torch.unsqueeze(T.ToTensor()(__UpperCamelCase				)       ,	0				)
		return img
def 		A_	(   __UpperCamelCase     :				int				):
		lowercase										=							2.0 * x - 1.0
		return x
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Tuple=True       ,	__UpperCamelCase     :				List[str]="RGB"				):
		lowercase										=							x.detach().cpu()
		if process:
				lowercase										=							post_process_tensor(__UpperCamelCase				)
		lowercase										=							x.numpy()
		if process:
				lowercase										=							(2_55 * x).astype(np.uinta				)
		lowercase										=							Image.fromarray(__UpperCamelCase				)
		if not x.mode == mode:
				lowercase										=							x.convert(__UpperCamelCase				)
		return x
def 		A_	(   __UpperCamelCase     :				Union[str, Any]				):
		lowercase										=							torch.clamp(__UpperCamelCase       ,	-1.0       ,	1.0				)
		lowercase										=							(x + 1.0) / 2.0
		lowercase										=							x.permute(1       ,	2       ,	0				)
		return x



def 		A_	(   __UpperCamelCase     :				Any				):
		lowercase										=							post_process_tensor(x.squeeze()				)
		return x.permute(2       ,	0       ,	1				).unsqueeze(0				)


"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__lowerCAmelCase				      =   logging.get_logger(__name__)
class 	_lowerCAmelCase    (      __snake_case       ):

							def __init__( self       :    Dict ,		*a       :    Any ,		**a       :    List[str]    )   ->    None:

									"""simple docstring"""
									warnings.warn(
									    '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use ConvNextImageProcessor instead.''' ,		a ,		)
									super().__init__(*a ,		**a    )