'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




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







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""shortest_edge""": 256}
    lowercase_     =       get_size_dict(lowercase_   ,				default_to_square=lowercase_      )
    lowercase_     =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       do_center_crop
    lowercase_     =       crop_size
    lowercase_     =       do_rescale
    lowercase_     =       rescale_factor
    lowercase_     =       do_normalize
    lowercase_     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    lowercase_     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




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







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_   ,				default_to_square=lowercase_      )
    if "shortest_edge" not in size:
      raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
    lowercase_     =       get_resize_output_image_size(lowercase_   ,				size=size["""shortest_edge"""]   ,				default_to_square=lowercase_      )
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       Any   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    return center_crop(lowercase_   ,				size=(size["""height"""], size["""width"""])   ,				data_format=lowercase_   ,				**lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       float   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


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




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







    '''simple docstring'''


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





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







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_   ,				default_to_square=lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       do_center_crop if do_center_crop is not None else self.do_center_crop
    lowercase_     =       crop_size if crop_size is not None else self.crop_size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       do_rescale if do_rescale is not None else self.do_rescale
    lowercase_     =       rescale_factor if rescale_factor is not None else self.rescale_factor
    lowercase_     =       do_normalize if do_normalize is not None else self.do_normalize
    lowercase_     =       image_mean if image_mean is not None else self.image_mean
    lowercase_     =       image_std if image_std is not None else self.image_std

    lowercase_     =       make_list_of_images(lowercase_      )

    if not valid_images(lowercase_      ):
      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_center_crop and crop_size is None:
      raise ValueError("""Crop size must be specified if do_center_crop is True."""      )

    if do_rescale and rescale_factor is None:
      raise ValueError("""Rescale factor must be specified if do_rescale 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.
    lowercase_     =       [to_numpy_array(lowercase_      ) for image in images]

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

    if do_center_crop:
      lowercase_     =       [self.center_crop(image=lowercase_   ,				size=lowercase_      ) for image in images]

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

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

    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

    lowercase_     =       {"""pixel_values""": images}
    return BatchFeature(data=lowercase_   ,				tensor_type=lowercase_      )


'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if number < 0:
    raise ValueError("""number must not be negative"""						)
  return number & (number - 1) == 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

