



'''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


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






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							List[str]					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Union[int, float] = 1 / 255					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,lowerCamelCase__   :							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,**lowerCamelCase__   :							Optional[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  size if size is not None else {'shortest_edge': 224}
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Tuple												=  get_size_dict(lowerCamelCase__					,param_name='crop_size'					)

							_UpperCamelCase   :						List[Any]												=  do_resize
							_UpperCamelCase   :						Optional[Any]												=  size
							_UpperCamelCase   :						Optional[int]												=  resample
							_UpperCamelCase   :						List[str]												=  do_center_crop
							_UpperCamelCase   :						Optional[int]												=  crop_size
							_UpperCamelCase   :						List[str]												=  do_rescale
							_UpperCamelCase   :						Any												=  rescale_factor
							_UpperCamelCase   :						Any												=  do_normalize
							_UpperCamelCase   :						int												=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase   :						Union[str, Any]												=  image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Union[str, Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					)
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_UpperCamelCase   :						Union[str, Any]												=  int((256 / 224) * size['shortest_edge']					)
													_UpperCamelCase   :						Tuple												=  get_resize_output_image_size(lowerCamelCase__					,size=lowerCamelCase__					,default_to_square=lowerCamelCase__					)
													_UpperCamelCase   :						int												=  {'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(
							    lowerCamelCase__					,size=(size_dict['height'], size_dict['width'])					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  get_size_dict(lowerCamelCase__					)
							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(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[int, float]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Union[str, Any]					,):







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





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							int					,):







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





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[Dict[str, int]] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[Dict[str, int]] = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[float] = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[Union[float, Iterable[float]]] = None					,lowerCamelCase__   :							Optional[Union[float, Iterable[float]]] = None					,lowerCamelCase__   :							Optional[TensorType] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[str]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Tuple												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						int												=  do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase   :						int												=  do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase   :						Optional[Any]												=  rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase   :						List[Any]												=  do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase   :						int												=  image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase   :						Tuple												=  image_std if image_std is not None else self.image_std

							_UpperCamelCase   :						Dict												=  size if size is not None else self.size
							_UpperCamelCase   :						Any												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase   :						Any												=  get_size_dict(lowerCamelCase__					,param_name='crop_size'					)

							_UpperCamelCase   :						Optional[int]												=  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.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						List[str]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if do_resize:
													_UpperCamelCase   :						Union[str, Any]												=  [self.resize(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					) for image in images]

							if do_center_crop:
													_UpperCamelCase   :						Optional[Any]												=  [self.center_crop(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							if do_rescale:
													_UpperCamelCase   :						str												=  [self.rescale(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							if do_normalize:
													_UpperCamelCase   :						List[Any]												=  [self.normalize(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Optional[int]												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Tuple												=  {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__					)







'''simple docstring'''
from math import ceil






def A__       (	UpperCAmelCase_ = 1_0_0_1				):
						_UpperCamelCase   :						int												=  1

						for i in range(1    ,    int(ceil(n / 2.0				)				)				):
												_UpperCamelCase   :						Dict												=  2 * i + 1
												_UpperCamelCase   :						Tuple												=  2 * i
												_UpperCamelCase   :						Any												=  total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															snake_case_					:      Tuple    			=    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')


