


"""simple docstring"""







def _A						(						lowercase = 10**9		):







    """simple docstring"""
    a					=1
    a					=2

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

        prev_value += 2 * value
        value += prev_value

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

    return perimeters_sum


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



"""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


lowerCamelCase_       :       List[Any]      						=  logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  ["pixel_values"]


 def __init__(		self			,   __A = True			,   __A = None			,   __A = PILImageResampling.BICUBIC			,   __A = True			,   __A = None			,   __A = True			,   __A = 1 / 255			,   __A = True			,   __A = None			,   __A = None			,   __A = True			,   **__A			,   ) ->					None:
     super().__init__(**__A						)
     a					=size if size is not None else {'''shortest_edge''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A						)
     a					=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A			,   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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = PILImageResampling.BICUBIC			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A			,   default_to_square=__A						)
     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(__A			,   size=size['''shortest_edge''']			,   default_to_square=__A						)
     return resize(__A			,   size=__A			,   resample=__A			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A						)
     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(__A			,   size=(size['''height'''], size['''width'''])			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					Any:
     return rescale(__A			,   scale=__A			,   data_format=__A			,   **__A						)


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





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = ChannelDimension.FIRST			,   **__A			,   ) ->					PIL.Image.Image:
     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(__A			,   param_name='''size'''			,   default_to_square=__A						)
     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(__A			,   param_name='''crop_size'''			,   default_to_square=__A						)
     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(__A						)

     if not valid_images(__A						):
         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(__A						) for image in images]

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

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

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

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

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

     a					=[to_channel_dimension_format(__A			,   __A						) for image in images]

     a					={'''pixel_values''': images}
     return BatchFeature(data=__A			,   tensor_type=__A						)