





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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    :					Optional[Any] =      logging.get_logger(__name__)


class   A			(__a    ):
			'''simple docstring'''




			__lowerCamelCase	:       Dict							=    ['''pixel_values''']






			def __init__(			self   :    Any							,	__lowerCAmelCase   :    List[str] = True							,	__lowerCAmelCase   :    Optional[int] = None							,	__lowerCAmelCase   :    Optional[Any] = PILImageResampling.BICUBIC							,	__lowerCAmelCase   :    Dict = True							,	__lowerCAmelCase   :    Union[str, Any] = True							,	__lowerCAmelCase   :    List[Any] = 1 / 2_55							,	__lowerCAmelCase   :    Tuple = None							,	__lowerCAmelCase   :    str = True							,	__lowerCAmelCase   :    Union[str, Any] = None							,	__lowerCAmelCase   :    List[str] = None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Optional[int]:







					"""simple docstring"""






					super().__init__(**lowerCAmelCase__   )
					A__        =   size if size is not None else {"""height""": 2_24, """width""": 2_24}
					A__        =   get_size_dict(lowerCAmelCase__   )
					A__        =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					A__        =   get_size_dict(lowerCAmelCase__							,	default_to_square=lowerCAmelCase__							,	param_name="""crop_size"""   )

					A__        =   do_resize
					A__        =   do_rescale
					A__        =   do_normalize
					A__        =   do_center_crop
					A__        =   crop_size
					A__        =   size
					A__        =   resample
					A__        =   rescale_factor
					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    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Optional[int] = PILImageResampling.BILINEAR							,	__lowerCAmelCase   :    List[str] = None							,	**__lowerCAmelCase   :    int							,	)	->	List[Any]:







					"""simple docstring"""






					A__        =   get_size_dict(lowerCAmelCase__   )

					if "shortest_edge" in size:
							A__        =   get_resize_output_image_size(lowerCAmelCase__							,	size=size["""shortest_edge"""]							,	default_to_square=lowerCAmelCase__   )
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
					elif "height" in size and "width" in size:
							A__        =   (size["""height"""], size["""width"""])
					else:
							raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
					return resize(lowerCAmelCase__							,	size=lowerCAmelCase__							,	resample=lowerCAmelCase__							,	data_format=lowerCAmelCase__							,	**lowerCAmelCase__   )






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    str = None							,	**__lowerCAmelCase   :    Tuple							,	)	->	int:







					"""simple docstring"""






					A__        =   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   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Union[str, Any] = None							,	**__lowerCAmelCase   :    Tuple   )	->	int:







					"""simple docstring"""






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






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Dict = None							,	**__lowerCAmelCase   :    Any							,	)	->	int:







					"""simple docstring"""






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







			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Tuple = None							,	__lowerCAmelCase   :    int = None							,	__lowerCAmelCase   :    str = None							,	__lowerCAmelCase   :    Union[str, Any] = None							,	__lowerCAmelCase   :    int = None							,	__lowerCAmelCase   :    List[Any] = None							,	__lowerCAmelCase   :    int = None							,	__lowerCAmelCase   :    Optional[Any] = None							,	__lowerCAmelCase   :    Any = None							,	__lowerCAmelCase   :    List[str] = None							,	__lowerCAmelCase   :    Tuple = None							,	__lowerCAmelCase   :    Tuple = ChannelDimension.FIRST							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	List[str]:







					"""simple docstring"""






					A__        =   do_resize if do_resize is not None else self.do_resize
					A__        =   do_rescale if do_rescale is not None else self.do_rescale
					A__        =   do_normalize if do_normalize is not None else self.do_normalize
					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(lowerCAmelCase__							,	param_name="""crop_size"""							,	default_to_square=lowerCAmelCase__   )
					A__        =   resample if resample is not None else self.resample
					A__        =   rescale_factor if rescale_factor is not None else self.rescale_factor
					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(lowerCAmelCase__   )

					if not is_batched(lowerCAmelCase__   ):
							A__        =   [images]

					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."""   )

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

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

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

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

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

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

					A__        =   {"""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() = }""")




