





"""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, 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, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase_			=							logging.get_logger(__name__)



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  ["""pixel_values"""]






       def __init__(   self    ,  a__ = True    ,  a__ = None    ,  a__ = PIL.Image.BICUBIC    ,  a__ = True    ,  a__ = None    ,  a__ = 1 / 2_55    ,  a__ = True    ,  a__ = True    ,  a__ = None    ,  a__ = None    ,  **a__    ,  ):
              super().__init__(**a__     )
              _UpperCAmelCase     =     size if size is not None else {'height': 2_56, 'width': 2_56}
              _UpperCAmelCase     =     get_size_dict(a__     )
              _UpperCAmelCase     =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
              _UpperCAmelCase     =     get_size_dict(a__    ,  param_name='crop_size'     )

              _UpperCAmelCase     =     do_resize
              _UpperCAmelCase     =     size
              _UpperCAmelCase     =     resample
              _UpperCAmelCase     =     do_center_crop
              _UpperCAmelCase     =     crop_size
              _UpperCAmelCase     =     do_rescale
              _UpperCAmelCase     =     rescale_factor
              _UpperCAmelCase     =     do_normalize
              _UpperCAmelCase     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              _UpperCAmelCase     =     image_std if image_std is not None else IMAGENET_STANDARD_STD






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = PIL.Image.BICUBIC    ,  a__ = None    ,  **a__    ,  ):
              _UpperCAmelCase     =     get_size_dict(a__     )
              if "height" not in size or "width" not in size:
                     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
              return resize(
                  a__    ,  size=(size['height'], size['width'])    ,  resample=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = None    ,  **a__    ,  ):
              _UpperCAmelCase     =     get_size_dict(a__     )
              if "height" not in size or "width" not in size:
                     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
              return center_crop(a__    ,  size=(size['height'], size['width'])    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = None    ,  **a__    ,  ):
              return rescale(a__    ,  scale=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__ = None    ,  **a__    ,  ):
              return normalize(a__    ,  mean=a__    ,  std=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = None    ,  a__=None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = ChannelDimension.FIRST    ,  **a__    ,  ):
              _UpperCAmelCase     =     do_resize if do_resize is not None else self.do_resize
              _UpperCAmelCase     =     resample if resample is not None else self.resample
              _UpperCAmelCase     =     do_center_crop if do_center_crop is not None else self.do_center_crop
              _UpperCAmelCase     =     do_rescale if do_rescale is not None else self.do_rescale
              _UpperCAmelCase     =     rescale_factor if rescale_factor is not None else self.rescale_factor
              _UpperCAmelCase     =     do_normalize if do_normalize is not None else self.do_normalize
              _UpperCAmelCase     =     image_mean if image_mean is not None else self.image_mean
              _UpperCAmelCase     =     image_std if image_std is not None else self.image_std

              _UpperCAmelCase     =     size if size is not None else self.size
              _UpperCAmelCase     =     get_size_dict(a__     )
              _UpperCAmelCase     =     crop_size if crop_size is not None else self.crop_size
              _UpperCAmelCase     =     get_size_dict(a__    ,  param_name='crop_size'     )

              _UpperCAmelCase     =     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 or resample is None:
                     raise ValueError('Size and resample 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.
              _UpperCAmelCase     =     [to_numpy_array(a__     ) for image in images]

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

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

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

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

              _UpperCAmelCase     =     [to_channel_dimension_format(a__    ,  a__     ) for image in images]

              _UpperCAmelCase     =     {'pixel_values': images}
              return BatchFeature(data=a__    ,  tensor_type=a__     )













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number | (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number & ~(1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return number ^ (1 << position)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       bool:






       """simple docstring"""




       return ((number >> position) & 1) == 1

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return int((number & (1 << position)) != 0							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






