





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      (	snake_case_		:						List[str] ,     snake_case_		:						List[Any]=256						):
     __magic_name__				=       min(img.size						)

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

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




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[Any]						):
     __magic_name__				=       2.0 * x - 1.0
     return x




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Tuple ,     snake_case_		:						Optional[Any]=True ,     snake_case_		:						Any="RGB"						):
     __magic_name__				=       x.detach().cpu()
     if process:
          __magic_name__				=       post_process_tensor(snake_case_						)
     __magic_name__				=       x.numpy()
     if process:
          __magic_name__				=       (255 * x).astype(np.uinta						)
     __magic_name__				=       Image.fromarray(snake_case_						)
     if not x.mode == mode:
          __magic_name__				=       x.convert(snake_case_						)
     return x




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[Any]						):
     __magic_name__				=       torch.clamp(snake_case_ ,     -1.0 ,     1.0						)
     __magic_name__				=       (x + 1.0) / 2.0
     __magic_name__				=       x.permute(1 ,     2 ,     0						)
     return x




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Dict						):
     __magic_name__				=       post_process_tensor(x.squeeze()						)
     return x.permute(2 ,     0 ,     1						).unsqueeze(0						)




'''simple docstring'''






import warnings

from .generation import GenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'     ,		__A     ,		)



