"""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


__UpperCamelCase									=			logging.get_logger(__name__)


if is_vision_available():
    import PIL




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ["pixel_values"]







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

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







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = PILImageResampling.BICUBIC,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							default_to_square=lowerCAmelCase__)
            if "shortest_edge" not in size:
                 raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
            snake_case_					       =					get_resize_output_image_size(lowerCAmelCase__,							size=size['shortest_edge'],							default_to_square=lowerCAmelCase__)
            return resize(lowerCAmelCase__,							size=lowerCAmelCase__,							resample=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            snake_case_					       =					get_size_dict(lowerCAmelCase__)
            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(lowerCAmelCase__,							size=(size['height'], size['width']),							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      Optional[int]:
            return rescale(lowerCAmelCase__,							scale=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      np.ndarray:
            return normalize(lowerCAmelCase__,							mean=lowerCAmelCase__,							std=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = ChannelDimension.FIRST,							**lowerCAmelCase__,							)      ->      PIL.Image.Image:
            snake_case_					       =					do_resize if do_resize is not None else self.do_resize
            snake_case_					       =					size if size is not None else self.size
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							param_name='size',							default_to_square=lowerCAmelCase__)
            snake_case_					       =					resample if resample is not None else self.resample
            snake_case_					       =					do_center_crop if do_center_crop is not None else self.do_center_crop
            snake_case_					       =					crop_size if crop_size is not None else self.crop_size
            snake_case_					       =					get_size_dict(lowerCAmelCase__,							param_name='crop_size',							default_to_square=lowerCAmelCase__)
            snake_case_					       =					do_rescale if do_rescale is not None else self.do_rescale
            snake_case_					       =					rescale_factor if rescale_factor is not None else self.rescale_factor
            snake_case_					       =					do_normalize if do_normalize is not None else self.do_normalize
            snake_case_					       =					image_mean if image_mean is not None else self.image_mean
            snake_case_					       =					image_std if image_std is not None else self.image_std
            snake_case_					       =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            snake_case_					       =					make_list_of_images(lowerCAmelCase__)

            if not valid_images(lowerCAmelCase__):
                 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:
                 snake_case_					       =					[convert_to_rgb(lowerCAmelCase__) for image in images]

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

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

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

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

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

            snake_case_					       =					[to_channel_dimension_format(lowerCAmelCase__,							lowerCAmelCase__) for image in images]

            snake_case_					       =					{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__,							tensor_type=lowerCAmelCase__)



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 10**9				):
		__SCREAMING_SNAKE_CASE            = 1
		__SCREAMING_SNAKE_CASE            = 2

		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		while perimeter <= max_perimeter:
				perimeters_sum += perimeter

				prev_value += 2 * value
				value += prev_value

				__SCREAMING_SNAKE_CASE            = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
				i += 1

		return perimeters_sum


if __name__ == "__main__":
							print(F"""{solution() = }""")




