

'''simple docstring'''







from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):
    lowercase						=		["pixel_values"]



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





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )
           A_						=      size if size is not None else {"""shortest_edge""": 224}
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
           A_						=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           A_						=      get_size_dict(UpperCamelCase__		,				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 IMAGENET_DEFAULT_MEAN
           A_						=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



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





           '''simple docstring'''
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                  A_						=      int((256 / 224) * size["""shortest_edge"""]       )
                  A_						=      get_resize_output_image_size(UpperCamelCase__		,				size=UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
                  A_						=      {"""height""": output_size[0], """width""": output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                  raise ValueError(
                      f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''       )
           return resize(
               UpperCamelCase__		,				size=(size_dict["""height"""], size_dict["""width"""])		,				resample=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



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





           '''simple docstring'''
           A_						=      get_size_dict(UpperCamelCase__       )
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
           return center_crop(UpperCamelCase__		,				size=(size["""height"""], size["""width"""])		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



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





           '''simple docstring'''
           return rescale(UpperCamelCase__		,				scale=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )



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





           '''simple docstring'''
           return normalize(UpperCamelCase__		,				mean=UpperCamelCase__		,				std=UpperCamelCase__		,				data_format=UpperCamelCase__		,				**UpperCamelCase__       )







    def 					snake_case_			(					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__ = ChannelDimension.FIRST		,				**UpperCamelCase__		,				)						->      BatchFeature:





           '''simple docstring'''
           A_						=      do_resize if do_resize is not None else self.do_resize
           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_						=      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_						=      size if size is not None else self.size
           A_						=      get_size_dict(UpperCamelCase__		,				default_to_square=UpperCamelCase__       )
           A_						=      crop_size if crop_size is not None else self.crop_size
           A_						=      get_size_dict(UpperCamelCase__		,				param_name="""crop_size"""       )

           A_						=      make_list_of_images(UpperCamelCase__       )

           if not valid_images(UpperCamelCase__       ):
                  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.
           A_						=      [to_numpy_array(UpperCamelCase__       ) for image in images]

           if do_resize:
                  A_						=      [self.resize(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ) for image in images]

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

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

           if do_normalize:
                  A_						=      [self.normalize(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ) for image in images]

           A_						=      [to_channel_dimension_format(UpperCamelCase__		,				UpperCamelCase__       ) for image in images]

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



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
