


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

def 		snake_case_   (				lowercase__     :    List[str]       ,     lowercase__     :    List[str]=2_56     ):






				'''simple docstring'''






				_lowerCAmelCase  						=min(img.size     )

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

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

def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=2.0 * x - 1.0
				return x

def 		snake_case_   (				lowercase__     :    List[Any]       ,     lowercase__     :    Optional[Any]=True       ,     lowercase__     :    Optional[int]="RGB"     ):






				'''simple docstring'''






				_lowerCAmelCase  						=x.detach().cpu()
				if process:
								_lowerCAmelCase  						=post_process_tensor(lowercase__     )
				_lowerCAmelCase  						=x.numpy()
				if process:
								_lowerCAmelCase  						=(2_55 * x).astype(np.uinta     )
				_lowerCAmelCase  						=Image.fromarray(lowercase__     )
				if not x.mode == mode:
								_lowerCAmelCase  						=x.convert(lowercase__     )
				return x

def 		snake_case_   (				lowercase__     :    Dict     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.clamp(lowercase__       ,     -1.0       ,     1.0     )
				_lowerCAmelCase  						=(x + 1.0) / 2.0
				_lowerCAmelCase  						=x.permute(1       ,     2       ,     0     )
				return x





def 		snake_case_   (				lowercase__     :    Union[str, Any]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=post_process_tensor(x.squeeze()     )
				return x.permute(2       ,     0       ,     1     ).unsqueeze(0     )





__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}

