





'''simple docstring'''


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


lowerCAmelCase_   :   Tuple       =					logging.get_logger(__name__)






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			str								=						["pixel_values"]







	def __init__(				self			:   Dict , lowerCamelCase			:   bool = True , lowerCamelCase			:   Optional[Dict[str, int]] = None , lowerCamelCase			:   PILImageResampling = PILImageResampling.BILINEAR , lowerCamelCase			:   bool = True , lowerCamelCase			:   Dict[str, int] = None , lowerCamelCase			:   bool = True , lowerCamelCase			:   Union[int, float] = 1 / 2_5_5 , lowerCamelCase			:   bool = True , lowerCamelCase			:   Optional[Union[float, List[float]]] = None , lowerCamelCase			:   Optional[Union[float, List[float]]] = None , **lowerCamelCase			:   List[str] , ):




				'''simple docstring'''
				super().__init__(**lowerCamelCase		)
				a__     = size if size is not None else {"shortest_edge": 2_5_6}
				a__     = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase		)
				a__     = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
				a__     = get_size_dict(lowerCamelCase , 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 						__a   (				self			:   Any , lowerCamelCase			:   np.ndarray , lowerCamelCase			:   Dict[str, int] , lowerCamelCase			:   PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase			:   Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase			:   Optional[int] , ):




				'''simple docstring'''
				a__     = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase		)
				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(lowerCamelCase , size=size["shortest_edge"] , default_to_square=lowerCamelCase		)
				return resize(lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase		)







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




				'''simple docstring'''
				a__     = get_size_dict(lowerCamelCase		)
				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(lowerCamelCase , size=(size["height"], size["width"]) , data_format=lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   np.ndarray , lowerCamelCase			:   float , lowerCamelCase			:   Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase			:   Dict		):




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







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




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







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




				'''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(lowerCamelCase , default_to_square=lowerCamelCase		)
				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(lowerCamelCase , 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(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.
				a__     = [to_numpy_array(lowerCamelCase		) for image in images]

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

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

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

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

				a__     = [to_channel_dimension_format(lowerCamelCase , lowerCamelCase		) for image in images]

				a__     = {"pixel_values": images}
				return BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any] , lowerCamelCase			:   Any , lowerCamelCase			:   List[Tuple] = None		):




				'''simple docstring'''
				a__     = outputs.logits

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

							if is_torch_tensor(lowerCamelCase		):
										a__     = target_sizes.numpy()

							a__     = []

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

				return semantic_segmentation










'''simple docstring'''


import math







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int = 0					,  __lowerCamelCase  :     int = 0     )   ->    list:
			a__     = end or len(__lowerCamelCase     )
			for i in range(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = i
						a__     = array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									a__     = array[temp_index - 1]
									temp_index -= 1
						a__     = temp_index_value
			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    None:  # Max Heap
			a__     = index
			a__     = 2 * index + 1  # Left Node
			a__     = 2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						a__     = left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						a__     = right_index

			if largest != index:
						a__   ,				a__     = array[largest], array[index]
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			a__     = len(__lowerCamelCase     )

			for i in range(n // 2					,  -1					,  -1     ):
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

			for i in range(n - 1					,  0					,  -1     ):
						a__   ,				a__     = array[0], array[i]
						heapify(__lowerCamelCase					,  0					,  __lowerCamelCase     )

			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			a__     = low
			a__     = high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						a__   ,				a__     = array[j], array[i]
						i += 1







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			if len(__lowerCamelCase     ) == 0:
						return array
			a__     = 2 * math.ceil(math.loga(len(__lowerCamelCase     )     )     )
			a__     = 16
			return intro_sort(__lowerCamelCase					,  0					,  len(__lowerCamelCase     )					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    list:
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase     )
						max_depth -= 1
						a__     = median_of_a(__lowerCamelCase					,  __lowerCamelCase					,  start + ((end - start) // 2) + 1					,  end - 1     )
						a__     = partition(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						intro_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						a__     = p
			return insertion_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase_   :   Optional[int]       =					input("Enter numbers separated by a comma : ").strip()
		lowerCAmelCase_   :   List[str]       =					[float(item) for item in user_input.split(",")]
		print(sort(unsorted))



