





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__lowerCamelCase     :							Tuple  							=				logging.get_logger(__name__)

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple[int, int]:
						def constraint_to_multiple_of(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=0    ,   lowerCamelCase_=None    ):
												UpperCAmelCase         =     round(val / multiple    ) * multiple

												if max_val is not None and x > max_val:
																		UpperCAmelCase         =     math.floor(val / multiple    ) * multiple

												if x < min_val:
																		UpperCAmelCase         =     math.ceil(val / multiple    ) * multiple

												return x

						UpperCAmelCase         =     (output_size, output_size) if isinstance(lowerCamelCase_    ,   lowerCamelCase_    ) else output_size

						UpperCAmelCase       ,		UpperCAmelCase         =     get_image_size(lowerCamelCase_    )
						UpperCAmelCase       ,		UpperCAmelCase         =     output_size

						# determine new height and width
						UpperCAmelCase         =     output_height / input_height
						UpperCAmelCase         =     output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width    ) < abs(1 - scale_height    ):
																		# fit width
																		UpperCAmelCase         =     scale_width
												else:
																		# fit height
																		UpperCAmelCase         =     scale_height

						UpperCAmelCase         =     constraint_to_multiple_of(scale_height * input_height    ,   multiple=lowerCamelCase_    )
						UpperCAmelCase         =     constraint_to_multiple_of(scale_width * input_width    ,   multiple=lowerCamelCase_    )

						return (new_height, new_width)





class  __magic_name__   (							A__						):
		lowercase      :       Optional[int]          =['''pixel_values''']






		def __init__(						self      :     int				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BILINEAR				,		UpperCamelCase__      :     bool = False				,		UpperCamelCase__      :     int = 1				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Union[int, float] = 1 / 2_55				,		UpperCamelCase__      :     bool = True				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		**UpperCamelCase__      :     Dict				,		)				->					None:
								'''simple docstring'''







								super().__init__(**UpperCamelCase__      )
								UpperCAmelCase         =     size if size is not None else {"height": 3_84, "width": 3_84}
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__      )
								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								UpperCAmelCase         =     keep_aspect_ratio
								UpperCAmelCase         =     ensure_multiple_of
								UpperCAmelCase         =     resample
								UpperCAmelCase         =     do_rescale
								UpperCAmelCase         =     rescale_factor
								UpperCAmelCase         =     do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase         =     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Dict[str, int]				,		UpperCamelCase__      :     bool = False				,		UpperCamelCase__      :     int = 1				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BICUBIC				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Tuple				,		)				->					np.ndarray:
								'''simple docstring'''







								UpperCAmelCase         =     get_size_dict(UpperCamelCase__      )
								if "height" not in size or "width" not in size:
														raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'      )
								UpperCAmelCase         =     get_resize_output_image_size(
								    UpperCamelCase__				,		output_size=(size["height"], size["width"])				,		keep_aspect_ratio=UpperCamelCase__				,		multiple=UpperCamelCase__				,		)
								return resize(UpperCamelCase__				,		size=UpperCamelCase__				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[int, float]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     str				,		)				->					Dict:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[int]				,		)				->					np.ndarray:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     ImageInput				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     int = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     int = None				,		UpperCamelCase__      :     PILImageResampling = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     ChannelDimension = ChannelDimension.FIRST				,		**UpperCamelCase__      :     Tuple				,		)				->					PIL.Image.Image:
								'''simple docstring'''







								UpperCAmelCase         =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase         =     size if size is not None else self.size
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__      )
								UpperCAmelCase         =     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								UpperCAmelCase         =     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								UpperCAmelCase         =     resample if resample is not None else self.resample
								UpperCAmelCase         =     do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase         =     rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase         =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase         =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase         =     image_std if image_std is not None else self.image_std

								UpperCAmelCase         =     make_list_of_images(UpperCamelCase__      )

								if not valid_images(UpperCamelCase__      ):
														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_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         =     [to_numpy_array(UpperCamelCase__      ) for image in images]

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

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

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

								UpperCAmelCase         =     [to_channel_dimension_format(UpperCamelCase__				,		UpperCamelCase__      ) for image in images]

								UpperCAmelCase         =     {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase__				,		tensor_type=UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[Tuple] = None      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     outputs.logits

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

														if is_torch_tensor(UpperCamelCase__      ):
																				UpperCAmelCase         =     target_sizes.numpy()

														UpperCAmelCase         =     []

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

								return semantic_segmentation








def    lowerCamelCase_(lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     len(lowerCamelCase_    )
						for i in range(lowerCamelCase_    ):
												for j in range(i + 1    ,   lowerCamelCase_    ):
																		if numbers[j] < numbers[i]:
																								UpperCAmelCase       ,		UpperCAmelCase         =     numbers[j], numbers[i]
						return numbers


if __name__ == "__main__":
		__lowerCamelCase     :							int  							=				input("Enter numbers separated by a comma:\n").strip()
		__lowerCamelCase     :							Dict  							=				[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))



