


"""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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["pixel_values"]


 def __init__(		self ,    __A = True ,    __A = None ,    __A = PILImageResampling.BICUBIC ,    __A = True ,    __A = True ,    __A = 1 / 255 ,    __A = None ,    __A = True ,    __A = None ,    __A = None ,    **__A ,    )					->     None:
     super().__init__(**__A						)
     a 		=size if size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__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 		=do_rescale
     a 		=do_normalize
     a 		=do_center_crop
     a 		=crop_size
     a 		=size
     a 		=resample
     a 		=rescale_factor
     a 		=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     a 		=image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = PILImageResampling.BILINEAR ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=get_size_dict(__A						)

     if "shortest_edge" in size:
         a 		=get_resize_output_image_size(__A ,    size=size['''shortest_edge'''] ,    default_to_square=__A						)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
         a 		=(size['''height'''], size['''width'''])
     else:
         raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''						)
     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						)					->     np.ndarray:
     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 = ChannelDimension.FIRST ,    **__A ,    )					->     BatchFeature:
     a 		=do_resize if do_resize is not None else self.do_resize
     a 		=do_rescale if do_rescale is not None else self.do_rescale
     a 		=do_normalize if do_normalize is not None else self.do_normalize
     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 		=resample if resample is not None else self.resample
     a 		=rescale_factor if rescale_factor is not None else self.rescale_factor
     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 		=size if size is not None else self.size
     a 		=get_size_dict(__A						)

     if not is_batched(__A						):
         a 		=[images]

     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.'''						)

     # 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						)

import math


def 						UpperCAmelCase			(	a_		,   a_ = 0		,   a_ = 0						)		->	list:







				"""simple docstring"""



				__A   			= end or len(a_						)
				for i in range(a_		,   a_						):
								__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 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	None:  # Max Heap







				"""simple docstring"""



				__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(a_		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				__A   			= len(a_						)

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

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

				return array


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



				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 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



				__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 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) == 0:
								return array
				__A   			= 2 * math.ceil(math.loga(len(a_						)						)						)
				__A   			= 1_6
				return intro_sort(a_		,   0		,   len(a_						)		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	list:







				"""simple docstring"""



				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(a_						)
								max_depth -= 1
								__A   			= median_of_a(a_		,   a_		,   start + ((end - start) // 2) + 1		,   end - 1						)
								__A   			= partition(a_		,   a_		,   a_		,   a_						)
								intro_sort(a_		,   a_		,   a_		,   a_		,   a_						)
								__A   			= p
				return insertion_sort(a_		,   a_		,   a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE							:List[Any]							 =				input('Enter numbers separated by a comma : ').strip()
					SCREAMING_SNAKE_CASE							:str							 =				[float(item) for item in user_input.split(',')]
					print(sort(unsorted))
