




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	str     =["image_processor", "tokenizer"]
     _snake_case:	str     ="AutoImageProcessor"
     _snake_case:	Tuple     ="AutoTokenizer"




     def __init__(    self  :   Optional[int]	,  A__  :   Optional[Any]=None	,  A__  :   Tuple=None	,  **A__  :   List[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				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__	,  )
                 lowerCamelCase__        =				kwargs.pop('feature_extractor'			)

           lowerCamelCase__        =				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__			)
           lowerCamelCase__        =				self.image_processor
           lowerCamelCase__        =				False




     def __call__(    self  :   List[str]	,  *A__  :   List[Any]	,  **A__  :   Dict			)				->       List[str]:
           # For backward compatibility
           if self._in_target_context_manager:
                 return self.current_processor(*A__	,  **A__			)

           lowerCamelCase__        =				kwargs.pop('images'	,  A__			)
           lowerCamelCase__        =				kwargs.pop('text'	,  A__			)
           if len(A__			) > 0:
                 lowerCamelCase__        =				args[0]
                 lowerCamelCase__        =				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:
                 lowerCamelCase__        =				self.image_processor(A__	,  *A__	,  **A__			)
           if text is not None:
                 lowerCamelCase__        =				self.tokenizer(A__	,  **A__			)

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




     def  __snake_case   (    self  :   Union[str, Any]	,  *A__  :   List[str]	,  **A__  :   List[Any]			)				->       Union[str, Any]:
           return self.tokenizer.batch_decode(*A__	,  **A__			)




     def  __snake_case   (    self  :   str	,  *A__  :   List[Any]	,  **A__  :   Dict			)				->       Any:
           return self.tokenizer.decode(*A__	,  **A__			)




     @contextmanager
     def  __snake_case   (    self  :   Tuple			)				->       str:
           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.'			)
           lowerCamelCase__        =				True
           lowerCamelCase__        =				self.tokenizer
           yield
           lowerCamelCase__        =				self.image_processor
           lowerCamelCase__        =				False




     @property
     def  __snake_case   (    self  :   str			)				->       int:
           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  __snake_case   (    self  :   Union[str, Any]			)				->       Optional[int]:
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'	,  A__	,  )
           return self.image_processor





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["pixel_values"]




     def __init__(    self  :   Optional[Any]	,  A__  :   bool = True	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = PILImageResampling.BILINEAR	,  A__  :   bool = True	,  A__  :   bool = False	,  A__  :   bool = True	,  A__  :   bool = True	,  A__  :   Union[int, float] = 1 / 2_5_5	,  A__  :   bool = True	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  **A__  :   List[str]	,  )				->       None:
           super().__init__(**A__			)

           lowerCamelCase__        =				size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
           if isinstance(A__	,  (tuple, list)			):
                 # The previous feature extractor size parameter was in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)

           lowerCamelCase__        =				do_resize
           lowerCamelCase__        =				size
           lowerCamelCase__        =				resample
           lowerCamelCase__        =				do_thumbnail
           lowerCamelCase__        =				do_align_long_axis
           lowerCamelCase__        =				do_pad
           lowerCamelCase__        =				do_rescale
           lowerCamelCase__        =				rescale_factor
           lowerCamelCase__        =				do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase__        =				image_std if image_std is not None else IMAGENET_STANDARD_STD




     def  __snake_case   (    self  :   str	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   Optional[Union[str, ChannelDimension]] = None			)				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           if (output_width < output_height and input_width > input_height) or (
               output_width > output_height and input_width < input_height
           ):
                 lowerCamelCase__        =				np.rotaa(A__	,  3			)

           if data_format is not None:
                 lowerCamelCase__        =				to_channel_dimension_format(A__	,  A__			)

           return image




     def  __snake_case   (    self  :   Tuple	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   bool = False	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)

           lowerCamelCase__        =				output_width - input_width
           lowerCamelCase__        =				output_height - input_height

           if random_padding:
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_height + 1			)
                 lowerCamelCase__        =				np.random.randint(low=0	,  high=delta_width + 1			)
           else:
                 lowerCamelCase__        =				delta_height // 2
                 lowerCamelCase__        =				delta_width // 2

           lowerCamelCase__        =				delta_height - pad_top
           lowerCamelCase__        =				delta_width - pad_left

           lowerCamelCase__        =				((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(A__	,  A__	,  data_format=A__			)




     def  __snake_case   (    self  :   int	,  *A__  :   Tuple	,  **A__  :   Dict			)				->       List[Any]:
           logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
           return self.pad_image(*A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

           # We always resize to the smallest of either the input or output size.
           lowerCamelCase__        =				min(A__	,  A__			)
           lowerCamelCase__        =				min(A__	,  A__			)

           if height == input_height and width == input_width:
                 return image

           if input_height > input_width:
                 lowerCamelCase__        =				int(input_width * height / input_height			)
           elif input_width > input_height:
                 lowerCamelCase__        =				int(input_height * width / input_width			)

           return resize(
               A__	,  size=(height, width)	,  resample=A__	,  reducing_gap=2.0	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   List[str]	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				min(size['height']	,  size['width']			)
           lowerCamelCase__        =				get_resize_output_image_size(A__	,  size=A__	,  default_to_square=A__			)
           lowerCamelCase__        =				resize(A__	,  size=A__	,  resample=A__	,  data_format=A__	,  **A__			)
           return resized_image




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   np.ndarray	,  A__  :   Union[int, float]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Optional[int]	,  )				->       Tuple:
           return rescale(A__	,  scale=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Union[float, List[float]]	,  A__  :   Union[float, List[float]]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Union[str, Any]	,  )				->       np.ndarray:
           return normalize(A__	,  mean=A__	,  std=A__	,  data_format=A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   ImageInput	,  A__  :   bool = None	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = False	,  A__  :   bool = None	,  A__  :   float = None	,  A__  :   bool = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[str, TensorType]] = None	,  A__  :   Optional[ChannelDimension] = ChannelDimension.FIRST	,  **A__  :   Any	,  )				->       PIL.Image.Image:
           lowerCamelCase__        =				do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__        =				size if size is not None else self.size
           if isinstance(A__	,  (tuple, list)			):
                 # Previous feature extractor had size in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				resample if resample is not None else self.resample
           lowerCamelCase__        =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           lowerCamelCase__        =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           lowerCamelCase__        =				do_pad if do_pad is not None else self.do_pad
           lowerCamelCase__        =				do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__        =				rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__        =				do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__        =				image_std if image_std is not None else self.image_std

           lowerCamelCase__        =				make_list_of_images(A__			)

           if not valid_images(A__			):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'			)

           if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'			)

           if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

           if do_pad and size is None:
                 raise ValueError('Size must be specified if do_pad is True.'			)

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

           # All transformations expect numpy arrays.
           lowerCamelCase__        =				[to_numpy_array(A__			) for image in images]

           if do_align_long_axis:
                 lowerCamelCase__        =				[self.align_long_axis(A__	,  size=A__			) for image in images]

           if do_resize:
                 lowerCamelCase__        =				[self.resize(image=A__	,  size=A__	,  resample=A__			) for image in images]

           if do_thumbnail:
                 lowerCamelCase__        =				[self.thumbnail(image=A__	,  size=A__			) for image in images]

           if do_pad:
                 lowerCamelCase__        =				[self.pad_image(image=A__	,  size=A__	,  random_padding=A__			) for image in images]

           if do_rescale:
                 lowerCamelCase__        =				[self.rescale(image=A__	,  scale=A__			) for image in images]

           if do_normalize:
                 lowerCamelCase__        =				[self.normalize(image=A__	,  mean=A__	,  std=A__			) for image in images]

           lowerCamelCase__        =				[to_channel_dimension_format(A__	,  A__			) for image in images]

           lowerCamelCase__        =				{'pixel_values': images}
           return BatchFeature(data=A__	,  tensor_type=A__			)