




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 							lowerCamelCase		( _UpperCAmelCase				):
   lowercase       :  Tuple      =				['image_processor', 'tokenizer']
   lowercase       :  Any      =				'AutoImageProcessor'
   lowercase       :  Optional[int]      =				'AutoTokenizer'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Optional[Any]          =							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_    ,      )
           UpperCamelCase				:   Optional[int]          =							kwargs.pop("""feature_extractor""" )

       UpperCamelCase				:   Union[str, Any]          =							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_ )
       UpperCamelCase				:   Tuple          =							self.image_processor
       UpperCamelCase				:   List[Any]          =							False







   def __call__(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   List[str]          =							kwargs.pop("""images"""    ,      SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   List[str]          =							kwargs.pop("""text"""    ,      SCREAMING_SNAKE_CASE_ )
       if len(SCREAMING_SNAKE_CASE_ ) > 0:
           UpperCamelCase				:   Dict          =							args[0]
           UpperCamelCase				:   Tuple          =							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:
           UpperCamelCase				:   List[str]          =							self.image_processor(SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
       if text is not None:
           UpperCamelCase				:   Dict          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       if text is None:
           return inputs
       elif images is None:
           return encodings
       else:
           UpperCamelCase				:   Tuple          =							encodings["""input_ids"""]
           return inputs







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   @contextmanager
   def       a_							(							self ):
       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.""" )
       UpperCamelCase				:   int          =							True
       UpperCamelCase				:   Union[str, Any]          =							self.tokenizer
       yield
       UpperCamelCase				:   Dict          =							self.image_processor
       UpperCamelCase				:   Any          =							False







   @property
   def       a_							(							self ):
       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       a_							(							self ):
       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,      SCREAMING_SNAKE_CASE_    ,      )
       return self.image_processor










"""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_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

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

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







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




