





"""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 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


lowerCamelCase_								=		logging.get_logger(__name__)






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  ['''pixel_values''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 1 / 2_55		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = True		,**SCREAMING_SNAKE_CASE__		,)			->      None:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  size if size is not None else {'''height''': 3_84, '''width''': 3_84}
             __SCREAMING_SNAKE_CASE   :Dict						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__							)

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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__							)
             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()}'''							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  (size['''height'''], size['''width'''])
             return resize(SCREAMING_SNAKE_CASE__		,size=SCREAMING_SNAKE_CASE__		,resample=SCREAMING_SNAKE_CASE__		,data_format=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






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




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      np.ndarray:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  do_resize if do_resize is not None else self.do_resize
             __SCREAMING_SNAKE_CASE   :List[Any]						=  resample if resample is not None else self.resample
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  do_rescale if do_rescale is not None else self.do_rescale
             __SCREAMING_SNAKE_CASE   :str						=  rescale_factor if rescale_factor is not None else self.rescale_factor
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  do_normalize if do_normalize is not None else self.do_normalize
             __SCREAMING_SNAKE_CASE   :int						=  image_mean if image_mean is not None else self.image_mean
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  image_std if image_std is not None else self.image_std
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             __SCREAMING_SNAKE_CASE   :int						=  size if size is not None else self.size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  get_size_dict(SCREAMING_SNAKE_CASE__		,default_to_square=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  make_list_of_images(SCREAMING_SNAKE_CASE__							)

             if not valid_images(SCREAMING_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 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:
                   __SCREAMING_SNAKE_CASE   :Dict						=  [convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

             # All transformations expect numpy arrays.
             __SCREAMING_SNAKE_CASE   :int						=  [to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

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

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

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

             __SCREAMING_SNAKE_CASE   :List[Any]						=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BatchFeature(data={'''pixel_values''': images}		,tensor_type=SCREAMING_SNAKE_CASE__							)

             return encoded_outputs






"""simple docstring"""
def      __lowerCamelCase   (							a_     :		int							)			->      bool:
      if not isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :int						=  f'''Input value of [number={number}] must be an integer'''
            raise TypeError(a_							)
      if number < 0:
            return False
      __SCREAMING_SNAKE_CASE   :Any						=  number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()