


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  ["image_processor", "tokenizer"]
							_A  				=  "AutoImageProcessor"
							_A  				=  "AutoTokenizer"







							def __init__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				List[Any]=None		,				SCREAMING_SNAKE_CASE_  :				str=None		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
													_a     =			None
													if "feature_extractor" in kwargs:
																			warnings.warn(
																			    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																			    ' instead.'		,				SCREAMING_SNAKE_CASE_		,				)
																			_a     =			kwargs.pop('feature_extractor'		)

													_a     =			image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																			raise ValueError('You need to specify an `image_processor`.'		)
													if tokenizer is None:
																			raise ValueError('You need to specify a `tokenizer`.'		)

													super().__init__(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
													_a     =			self.image_processor
													_a     =			False







							def __call__(		self  :				Any		,				*SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
													# For backward compatibility
													if self._in_target_context_manager:
																			return self.current_processor(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)

													_a     =			kwargs.pop('images'		,				SCREAMING_SNAKE_CASE_		)
													_a     =			kwargs.pop('text'		,				SCREAMING_SNAKE_CASE_		)
													if len(SCREAMING_SNAKE_CASE_		) > 0:
																			_a     =			args[0]
																			_a     =			args[1:]

													if images is None and text is None:
																			raise ValueError('You need to specify either an `images` or `text` input to process.'		)

													if images is not None:
																			_a     =			self.image_processor(SCREAMING_SNAKE_CASE_		,				*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
													if text is not None:
																			_a     =			self.tokenizer(SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)

													if text is None:
																			return inputs
													elif images is None:
																			return encodings
													else:
																			_a     =			encodings['input_ids']
																			return inputs







							def 		_UpperCAmelCase	(		self  :				str		,				*SCREAMING_SNAKE_CASE_  :				List[str]		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
													return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				*SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				**SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)







							@contextmanager
							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
													warnings.warn(
													    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
													    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
													    'your images inputs, or in a separate call.'		)
													_a     =			True
													_a     =			self.tokenizer
													yield
													_a     =			self.image_processor
													_a     =			False







							@property
							def 		_UpperCAmelCase	(		self  :				Any		):
													warnings.warn(
													    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
													return self.image_processor_class







							@property
							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
													warnings.warn(
													    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
													return self.image_processor






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				)


