


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


__snake_case				=				logging.get_logger(__name__)


if is_vision_available():
   import PIL


class 			__lowerCamelCase     (    a_ ):

     '''simple docstring'''
     A_				:		Any			=						['pixel_values']


     def __init__(					self			, __UpperCAmelCase = True			, __UpperCAmelCase = None			, __UpperCAmelCase = PILImageResampling.BICUBIC			, __UpperCAmelCase = True			, __UpperCAmelCase = None			, __UpperCAmelCase = True			, __UpperCAmelCase = 1 / 255			, __UpperCAmelCase = True			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = True			, **__UpperCAmelCase			, )				->							int:
          super().__init__(**lowercase_							)
          _a					= size if size is not None else {'''shortest_edge''': 224}
          _a					= get_size_dict(lowercase_			, default_to_square=lowercase_							)
          _a					= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          _a					= get_size_dict(lowercase_			, default_to_square=lowercase_			, param_name='''crop_size'''							)

          _a					= do_resize
          _a					= size
          _a					= resample
          _a					= do_center_crop
          _a					= crop_size
          _a					= do_rescale
          _a					= rescale_factor
          _a					= do_normalize
          _a					= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          _a					= image_std if image_std is not None else OPENAI_CLIP_STD
          _a					= do_convert_rgb


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = PILImageResampling.BICUBIC			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							Dict:
          _a					= 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()}'							)
          _a					= 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 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							str:
          _a					= get_size_dict(lowercase_							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'							)
          return center_crop(lowercase_			, size=(size['''height'''], size['''width'''])			, data_format=lowercase_			, **lowercase_							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							List[Any]:
          return rescale(lowercase_			, scale=lowercase_			, data_format=lowercase_			, **lowercase_							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							Union[str, Any]:
          return normalize(lowercase_			, mean=lowercase_			, std=lowercase_			, data_format=lowercase_			, **lowercase_							)





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = ChannelDimension.FIRST			, **__UpperCAmelCase			, )				->							Union[str, Any]:
          _a					= do_resize if do_resize is not None else self.do_resize
          _a					= size if size is not None else self.size
          _a					= get_size_dict(lowercase_			, param_name='''size'''			, default_to_square=lowercase_							)
          _a					= resample if resample is not None else self.resample
          _a					= do_center_crop if do_center_crop is not None else self.do_center_crop
          _a					= crop_size if crop_size is not None else self.crop_size
          _a					= get_size_dict(lowercase_			, param_name='''crop_size'''			, default_to_square=lowercase_							)
          _a					= do_rescale if do_rescale is not None else self.do_rescale
          _a					= rescale_factor if rescale_factor is not None else self.rescale_factor
          _a					= do_normalize if do_normalize is not None else self.do_normalize
          _a					= image_mean if image_mean is not None else self.image_mean
          _a					= image_std if image_std is not None else self.image_std
          _a					= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          _a					= 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.'''							)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
               _a					= [convert_to_rgb(lowercase_							) for image in images]

          # All transformations expect numpy arrays.
          _a					= [to_numpy_array(lowercase_							) for image in images]

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

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

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

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

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

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


"""simple docstring"""





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	return numa ^ numa < 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




