
from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


A     :	Dict    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    ['''pixel_values''']




				def __init__(     self		,     _snake_case = True		,     _snake_case = None		,     _snake_case = PILImageResampling.BILINEAR		,     _snake_case = True		,     _snake_case = None		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = None		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    size if size is not None else {'''shortest_edge''': 256}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								__a										=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__a										=    get_size_dict(_snake_case		,     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 IMAGENET_STANDARD_MEAN
								__a										=    image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								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(_snake_case		,     size=size['''shortest_edge''']		,     default_to_square=_snake_case				)
								return resize(_snake_case		,     size=_snake_case		,     resample=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case				)
								if "height" not in size or "width" not in size:
												raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
								return center_crop(_snake_case		,     size=(size['''height'''], size['''width'''])		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case				)    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = ChannelDimension.FIRST		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								__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(_snake_case		,     default_to_square=_snake_case				)
								__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(_snake_case		,     param_name='''crop_size'''				)
								__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										=    make_list_of_images(_snake_case				)

								if not valid_images(_snake_case				):
												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.
								__a										=    [to_numpy_array(_snake_case				) for image in images]

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

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

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

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

								__a										=    [to_channel_dimension_format(_snake_case		,     _snake_case				) for image in images]

								__a										=    {'''pixel_values''': images}
								return BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
												if len(_snake_case				) != len(_snake_case				):
																raise ValueError(
																    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

												if is_torch_tensor(_snake_case				):
																__a										=    target_sizes.numpy()

												__a										=    []

												for idx in range(len(_snake_case				)				):
																__a										=    torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0				)		,     size=target_sizes[idx]		,     mode='''bilinear'''		,     align_corners=_snake_case				)
																__a										=    resized_logits[0].argmax(dim=0				)
																semantic_segmentation.append(_snake_case				)
								else:
												__a										=    logits.argmax(dim=1				)
												__a										=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

								return semantic_segmentation

from math import ceil


def     __lowerCAmelCase			(			a__ = 1001  )       ->  int:
				__a										=    1

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

				return total


if __name__ == "__main__":
					import sys

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