





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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Any   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = None,		__magic_name__ = PILImageResampling.BICUBIC,		__magic_name__ = True,		__magic_name__ = 1 / 255,		__magic_name__ = True,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       super().__init__(**__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      size if size is not None else {'''height''': 384, '''width''': 384}
       UpperCamelCase__							:				List[str]          =      get_size_dict(__magic_name__,		default_to_square=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      do_resize
       UpperCamelCase__							:				List[str]          =      size
       UpperCamelCase__							:				Any          =      resample
       UpperCamelCase__							:				Any          =      do_rescale
       UpperCamelCase__							:				Tuple          =      rescale_factor
       UpperCamelCase__							:				Dict          =      do_normalize
       UpperCamelCase__							:				Optional[Any]          =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCamelCase__							:				str          =      image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCamelCase__							:				str          =      do_convert_rgb




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = PILImageResampling.BICUBIC,		__magic_name__ = None,		**__magic_name__,		) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      get_size_dict(__magic_name__,		default_to_square=__magic_name__							)
       if "height" not in size or "width" not in size:
           raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"							)
       UpperCamelCase__							:				Any          =      (size['''height'''], size['''width'''])
       return resize(__magic_name__,		size=__magic_name__,		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       return rescale(__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__,		) ->      np.ndarray:






       """simple docstring"""
       return normalize(__magic_name__,		mean=__magic_name__,		std=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      PIL.Image.Image:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				List[str]          =      resample if resample is not None else self.resample
       UpperCamelCase__							:				Any          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Dict          =      rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__							:				List[Any]          =      do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__							:				Any          =      image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__							:				Any          =      image_std if image_std is not None else self.image_std
       UpperCamelCase__							:				int          =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCamelCase__							:				Any          =      size if size is not None else self.size
       UpperCamelCase__							:				int          =      get_size_dict(__magic_name__,		default_to_square=__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           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 or resample is None:
           raise ValueError('''Size and resample 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_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.'''							)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           UpperCamelCase__							:				List[str]          =      [convert_to_rgb(__magic_name__							) for image in images]

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Union[str, Any]          =      [to_numpy_array(__magic_name__							) for image in images]

       if do_resize:
           UpperCamelCase__							:				Optional[Any]          =      [self.resize(image=__magic_name__,		size=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[str]          =      [self.rescale(image=__magic_name__,		scale=__magic_name__							) for image in images]

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

       UpperCamelCase__							:				Optional[int]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      BatchFeature(data={'''pixel_values''': images},		tensor_type=__magic_name__							)

       return encoded_outputs








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''      )

    UpperCamelCase__							:				Optional[Any]          =      0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()

