





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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


UpperCamelCase__      =logging.get_logger(__name__)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    ['pixel_values']




       def __init__(      self    ,						__lowerCamelCase = True    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = PILImageResampling.BILINEAR    ,						__lowerCamelCase = True    ,						__lowerCamelCase = 1 / 2_5_5    ,						__lowerCamelCase = True    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     None:
            super().__init__(**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							size if size is not None else {"shortest_edge": 3_8_4}
            _SCREAMING_SNAKE_CASE       :	Dict						    =							get_size_dict(__lowerCamelCase    ,						default_to_square=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	int						    =							do_resize
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							size
            # Default value set here for backwards compatibility where the value in config is None
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							resample
            _SCREAMING_SNAKE_CASE       :	Dict						    =							do_rescale
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							rescale_factor
            _SCREAMING_SNAKE_CASE       :	Dict						    =							do_normalize
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							image_std if image_std is not None else IMAGENET_STANDARD_STD




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase = PILImageResampling.BICUBIC    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     np.ndarray:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							get_size_dict(__lowerCamelCase    ,						default_to_square=__lowerCamelCase							)
            if "shortest_edge" not in size:
                 raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
            _SCREAMING_SNAKE_CASE       :	int						    =							size["shortest_edge"]

            if shortest_edge < 3_8_4:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 _SCREAMING_SNAKE_CASE       :	Any						    =							int(shortest_edge / crop_pct							)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							get_resize_output_image_size(__lowerCamelCase    ,						size=__lowerCamelCase    ,						default_to_square=__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							resize(image=__lowerCamelCase    ,						size=__lowerCamelCase    ,						resample=__lowerCamelCase    ,						data_format=__lowerCamelCase    ,						**__lowerCamelCase							)
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=__lowerCamelCase    ,						size=(shortest_edge, shortest_edge)    ,						data_format=__lowerCamelCase    ,						**__lowerCamelCase							)
            else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     __lowerCamelCase    ,						size=(shortest_edge, shortest_edge)    ,						resample=__lowerCamelCase    ,						data_format=__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     Optional[Any]:
            return rescale(__lowerCamelCase    ,						scale=__lowerCamelCase    ,						data_format=__lowerCamelCase    ,						**__lowerCamelCase							)




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







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__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:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							do_resize if do_resize is not None else self.do_resize
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							crop_pct if crop_pct is not None else self.crop_pct
            _SCREAMING_SNAKE_CASE       :	Union[str, 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       :	List[Any]						    =							rescale_factor if rescale_factor is not None else self.rescale_factor
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							do_normalize if do_normalize is not None else self.do_normalize
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							image_mean if image_mean is not None else self.image_mean
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							image_std if image_std is not None else self.image_std

            _SCREAMING_SNAKE_CASE       :	Any						    =							size if size is not None else self.size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							get_size_dict(__lowerCamelCase    ,						default_to_square=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							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 or resample is None:
                 raise ValueError("Size and resample must be specified if do_resize is True."							)

            if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
                 raise ValueError("crop_pct must be specified if size < 384."							)

            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.
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[to_numpy_array(__lowerCamelCase							) for image in images]

            if do_resize:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[self.resize(image=__lowerCamelCase    ,						size=__lowerCamelCase    ,						crop_pct=__lowerCamelCase    ,						resample=__lowerCamelCase							) for image in images]

            if do_rescale:
                 _SCREAMING_SNAKE_CASE       :	str						    =							[self.rescale(image=__lowerCamelCase    ,						scale=__lowerCamelCase							) for image in images]

            if do_normalize:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[self.normalize(image=__lowerCamelCase    ,						mean=__lowerCamelCase    ,						std=__lowerCamelCase							) for image in images]

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							[to_channel_dimension_format(__lowerCamelCase    ,						__lowerCamelCase							) for image in images]

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							{"pixel_values": images}
            return BatchFeature(data=__lowerCamelCase    ,						tensor_type=__lowerCamelCase							)






def     lowerCamelCase__			(__lowerCamelCase = 10**9	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							1
     _SCREAMING_SNAKE_CASE       :	Any						    =							2

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							0
     while perimeter <= max_perimeter:
          perimeters_sum += perimeter

          prev_value += 2 * value
          value += prev_value

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							2 * value + 2 if i % 2 == 0 else 2 * value - 2
          i += 1

     return perimeters_sum


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