


'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""pixel_values"""]






  def __init__(							self :       Tuple	,_a :       bool = True	,_a :       int = 32	,_a :       List[Any]=PILImageResampling.BILINEAR	,_a :       bool = True	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize
        A_						:  List[Any]          =				do_rescale
        A_						:  int          =				size_divisor
        A_						:  Optional[Any]          =				resample
        super().__init__(**_a )






  def        _a							(							self :       Optional[int]	,_a :       np.ndarray	,_a :       int	,_a :       Any	,_a :       Optional[ChannelDimension] = None	,**_a :       List[str] ):

        '''simple docstring'''




        A_						:  List[Any]          =				get_image_size(_a )
        # Rounds the height and width down to the closest multiple of size_divisor
        A_						:  str          =				height // size_divisor * size_divisor
        A_						:  Optional[Any]          =				width // size_divisor * size_divisor
        A_						:  List[str]          =				resize(_a	,(new_h, new_w)	,resample=_a	,data_format=_a	,**_a )
        return image






  def        _a							(							self :       List[Any]	,_a :       np.ndarray	,_a :       float	,_a :       Optional[ChannelDimension] = None	,**_a :       int ):

        '''simple docstring'''




        return rescale(image=_a	,scale=_a	,data_format=_a	,**_a )






  def        _a							(							self :       Union[str, Any]	,_a :       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]	,_a :       Optional[bool] = None	,_a :       Optional[int] = None	,_a :       Optional[Any]=None	,_a :       Optional[bool] = None	,_a :       Optional[Union[TensorType, str]] = None	,_a :       ChannelDimension = ChannelDimension.FIRST	,**_a :       List[str]	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				do_resize if do_resize is not None else self.do_resize
        A_						:  Optional[Any]          =				do_rescale if do_rescale is not None else self.do_rescale
        A_						:  Optional[Any]          =				size_divisor if size_divisor is not None else self.size_divisor
        A_						:  Union[str, Any]          =				resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError("""size_divisor is required for resizing""" )

        A_						:  Optional[Any]          =				make_list_of_images(_a )

        if not valid_images(_a ):
              raise ValueError("""Invalid image(s)""" )

        # All transformations expect numpy arrays.
        A_						:  Optional[Any]          =				[to_numpy_array(_a ) for img in images]

        if do_resize:
              A_						:  Any          =				[self.resize(_a	,size_divisor=_a	,resample=_a ) for image in images]

        if do_rescale:
              A_						:  Dict          =				[self.rescale(_a	,scale=1 / 255 ) for image in images]

        A_						:  Optional[Any]          =				[to_channel_dimension_format(_a	,_a ) for image in images]

        A_						:  Tuple          =				{"""pixel_values""": images}
        return BatchFeature(data=_a	,tensor_type=_a )




'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=None	,_a :       List[str]=None	,**_a :       Any ):

        '''simple docstring'''




        A_						:  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."""	,_a	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[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__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  int          =				self.tokenizer(
            text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_token_type_ids=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
        # add pixel_values + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




        return self.tokenizer.batch_decode(*_a	,**_a )






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




        return self.tokenizer.decode(*_a	,**_a )






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,_a	,)
        return self.image_processor_class






  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,_a	,)
        return self.image_processor
