



'''simple docstring'''





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


A			:   Any			=      logging.get_logger(__name__)


if is_vision_available():
      import PIL





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	['pixel_values']





    def __init__(    self    :    Union[str, Any],    _snake_case    :    bool = True,    _snake_case    :    Dict[str, int] = None,    _snake_case    :    PILImageResampling = PILImageResampling.BILINEAR,    _snake_case    :    bool = True,    _snake_case    :    bool = False,    _snake_case    :    bool = True,    _snake_case    :    bool = True,    _snake_case    :    Union[int, float] = 1 / 255,    _snake_case    :    bool = True,    _snake_case    :    Optional[Union[float, List[float]]] = None,    _snake_case    :    Optional[Union[float, List[float]]] = None,    **_snake_case    :    Any,    ):



       '''simple docstring'''







       super().__init__(**_snake_case     )

       snake_case   :							str						  =size if size is not None else {'''height''': 2_560, '''width''': 1_920}
       if isinstance(_snake_case,    (tuple, list)     ):
          # The previous feature extractor size parameter was in (width, height) format
          snake_case   :							Any						  =size[::-1]
       snake_case   :							Optional[int]						  =get_size_dict(_snake_case     )

       snake_case   :							List[Any]						  =do_resize
       snake_case   :							Any						  =size
       snake_case   :							Any						  =resample
       snake_case   :							List[str]						  =do_thumbnail
       snake_case   :							int						  =do_align_long_axis
       snake_case   :							Tuple						  =do_pad
       snake_case   :							Optional[Any]						  =do_rescale
       snake_case   :							Dict						  =rescale_factor
       snake_case   :							List[Any]						  =do_normalize
       snake_case   :							Tuple						  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case   :							Union[str, Any]						  =image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 					__snake_case       (    self    :    str,    _snake_case    :    np.ndarray,    _snake_case    :    Dict[str, int],    _snake_case    :    Optional[Union[str, ChannelDimension]] = None     ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							Optional[int]						  =get_image_size(_snake_case     )
       snake_case  ,    snake_case   :							Optional[int]						  =size['''height'''], size['''width''']

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
          snake_case   :							Tuple						  =np.rotaa(_snake_case,    3     )

       if data_format is not None:
          snake_case   :							str						  =to_channel_dimension_format(_snake_case,    _snake_case     )

       return image





    def 					__snake_case       (    self    :    int,    _snake_case    :    np.ndarray,    _snake_case    :    Dict[str, int],    _snake_case    :    bool = False,    _snake_case    :    Optional[Union[str, ChannelDimension]] = None,    ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							Optional[Any]						  =size['''height'''], size['''width''']
       snake_case  ,    snake_case   :							List[str]						  =get_image_size(_snake_case     )

       snake_case   :							Optional[int]						  =output_width - input_width
       snake_case   :							Dict						  =output_height - input_height

       if random_padding:
          snake_case   :							Optional[int]						  =np.random.randint(low=0,    high=delta_height + 1     )
          snake_case   :							Dict						  =np.random.randint(low=0,    high=delta_width + 1     )
       else:
          snake_case   :							str						  =delta_height // 2
          snake_case   :							Any						  =delta_width // 2

       snake_case   :							Any						  =delta_height - pad_top
       snake_case   :							int						  =delta_width - pad_left

       snake_case   :							Tuple						  =((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(_snake_case,    _snake_case,    data_format=_snake_case     )





    def 					__snake_case       (    self    :    Any,    *_snake_case    :    Optional[int],    **_snake_case    :    Optional[int]     ):



       '''simple docstring'''







       logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''     )
       return self.pad_image(*_snake_case,    **_snake_case     )





    def 					__snake_case       (    self    :    Tuple,    _snake_case    :    np.ndarray,    _snake_case    :    Dict[str, int],    _snake_case    :    PILImageResampling = PILImageResampling.BICUBIC,    _snake_case    :    Optional[Union[str, ChannelDimension]] = None,    **_snake_case    :    Union[str, Any],    ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							List[Any]						  =get_image_size(_snake_case     )
       snake_case  ,    snake_case   :							Union[str, Any]						  =size['''height'''], size['''width''']

       # We always resize to the smallest of either the input or output size.
       snake_case   :							List[str]						  =min(_snake_case,    _snake_case     )
       snake_case   :							Optional[Any]						  =min(_snake_case,    _snake_case     )

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

       if input_height > input_width:
          snake_case   :							Optional[int]						  =int(input_width * height / input_height     )
       elif input_width > input_height:
          snake_case   :							Union[str, Any]						  =int(input_height * width / input_width     )

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





    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    np.ndarray,    _snake_case    :    Dict[str, int],    _snake_case    :    PILImageResampling = PILImageResampling.BICUBIC,    _snake_case    :    Optional[Union[str, ChannelDimension]] = None,    **_snake_case    :    Dict,    ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  =get_size_dict(_snake_case     )
       snake_case   :							Any						  =min(size['''height'''],    size['''width''']     )
       snake_case   :							List[str]						  =get_resize_output_image_size(_snake_case,    size=_snake_case,    default_to_square=_snake_case     )
       snake_case   :							List[Any]						  =resize(_snake_case,    size=_snake_case,    resample=_snake_case,    data_format=_snake_case,    **_snake_case     )
       return resized_image





    def 					__snake_case       (    self    :    int,    _snake_case    :    np.ndarray,    _snake_case    :    Union[int, float],    _snake_case    :    Optional[Union[str, ChannelDimension]] = None,    **_snake_case    :    List[str],    ):



       '''simple docstring'''







       return rescale(_snake_case,    scale=_snake_case,    data_format=_snake_case,    **_snake_case     )





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



       '''simple docstring'''







       return normalize(_snake_case,    mean=_snake_case,    std=_snake_case,    data_format=_snake_case,    **_snake_case     )






    def 					__snake_case       (    self    :    List[str],    _snake_case    :    ImageInput,    _snake_case    :    bool = None,    _snake_case    :    Dict[str, int] = None,    _snake_case    :    PILImageResampling = None,    _snake_case    :    bool = None,    _snake_case    :    bool = None,    _snake_case    :    bool = None,    _snake_case    :    bool = False,    _snake_case    :    bool = None,    _snake_case    :    float = None,    _snake_case    :    bool = None,    _snake_case    :    Optional[Union[float, List[float]]] = None,    _snake_case    :    Optional[Union[float, List[float]]] = None,    _snake_case    :    Optional[Union[str, TensorType]] = None,    _snake_case    :    Optional[ChannelDimension] = ChannelDimension.FIRST,    **_snake_case    :    List[Any],    ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  =do_resize if do_resize is not None else self.do_resize
       snake_case   :							Tuple						  =size if size is not None else self.size
       if isinstance(_snake_case,    (tuple, list)     ):
          # Previous feature extractor had size in (width, height) format
          snake_case   :							Dict						  =size[::-1]
       snake_case   :							int						  =get_size_dict(_snake_case     )
       snake_case   :							Dict						  =resample if resample is not None else self.resample
       snake_case   :							List[Any]						  =do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       snake_case   :							Optional[int]						  =do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       snake_case   :							Optional[int]						  =do_pad if do_pad is not None else self.do_pad
       snake_case   :							str						  =do_rescale if do_rescale is not None else self.do_rescale
       snake_case   :							str						  =rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case   :							Tuple						  =do_normalize if do_normalize is not None else self.do_normalize
       snake_case   :							Optional[Any]						  =image_mean if image_mean is not None else self.image_mean
       snake_case   :							str						  =image_std if image_std is not None else self.image_std

       snake_case   :							str						  =make_list_of_images(_snake_case     )

       if not valid_images(_snake_case     ):
          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.
       snake_case   :							List[str]						  =[to_numpy_array(_snake_case     ) for image in images]

       if do_align_long_axis:
          snake_case   :							Any						  =[self.align_long_axis(_snake_case,    size=_snake_case     ) for image in images]

       if do_resize:
          snake_case   :							List[str]						  =[self.resize(image=_snake_case,    size=_snake_case,    resample=_snake_case     ) for image in images]

       if do_thumbnail:
          snake_case   :							List[str]						  =[self.thumbnail(image=_snake_case,    size=_snake_case     ) for image in images]

       if do_pad:
          snake_case   :							Tuple						  =[self.pad_image(image=_snake_case,    size=_snake_case,    random_padding=_snake_case     ) for image in images]

       if do_rescale:
          snake_case   :							Union[str, Any]						  =[self.rescale(image=_snake_case,    scale=_snake_case     ) for image in images]

       if do_normalize:
          snake_case   :							Dict						  =[self.normalize(image=_snake_case,    mean=_snake_case,    std=_snake_case     ) for image in images]

       snake_case   :							Tuple						  =[to_channel_dimension_format(_snake_case,    _snake_case     ) for image in images]

       snake_case   :							List[Any]						  ={'''pixel_values''': images}
       return BatchFeature(data=_snake_case,    tensor_type=_snake_case     )










'''simple docstring'''





def 		_a   (  lowerCamelCase_							):
   return "".join(chr(ord(lowerCamelCase_							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





