


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






def 	lowerCamelCase_       (			_a      ,  _a=256			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[int]       	=   min(img.size			)

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

		lowerCAmelCase__  :     int       	=   target_image_size / s
		lowerCAmelCase__  :     Union[str, Any]       	=   (round(r * img.size[1]			), round(r * img.size[0]			))
		lowerCAmelCase__  :     Optional[int]       	=   TF.resize(_a      ,  _a      ,  interpolation=PIL.Image.LANCZOS			)
		lowerCAmelCase__  :     List[str]       	=   TF.center_crop(_a      ,  output_size=2 * [target_image_size]			)
		lowerCAmelCase__  :     Dict       	=   torch.unsqueeze(T.ToTensor()(_a			)      ,  0			)
		return img






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Dict       	=   2.0 * x - 1.0
		return x






def 	lowerCamelCase_       (			_a      ,  _a=True      ,  _a="RGB"			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   x.detach().cpu()
		if process:
				lowerCAmelCase__  :     Tuple       	=   post_process_tensor(_a			)
		lowerCAmelCase__  :     int       	=   x.numpy()
		if process:
				lowerCAmelCase__  :     Dict       	=   (255 * x).astype(np.uinta			)
		lowerCAmelCase__  :     Union[str, Any]       	=   Image.fromarray(_a			)
		if not x.mode == mode:
				lowerCAmelCase__  :     Any       	=   x.convert(_a			)
		return x






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     str       	=   torch.clamp(_a      ,  -1.0      ,  1.0			)
		lowerCAmelCase__  :     Any       	=   (x + 1.0) / 2.0
		lowerCAmelCase__  :     List[str]       	=   x.permute(1      ,  2      ,  0			)
		return x






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   post_process_tensor(x.squeeze()			)
		return x.permute(2      ,  0      ,  1			).unsqueeze(0			)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




