






"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A__       :				Tuple 					=					logging.get_logger(__name__)


if is_vision_available():
						import PIL







class    lowercase__			(							snake_case__   ):
			_UpperCAmelCase		:Any   			= ["pixel_values"]


			def __init__(      self     :  str   ,       snake_case__     :  List[str] = True   ,       snake_case__     :  Any = None   ,       snake_case__     :  Optional[Any] = PILImageResampling.BICUBIC   ,       snake_case__     :  str = True   ,       snake_case__     :  Any = None   ,       snake_case__     :  Optional[int] = True   ,       snake_case__     :  Any = 1 / 255   ,       snake_case__     :  int = True   ,       snake_case__     :  int = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  List[Any] = True   ,       **snake_case__     :  Union[str, Any]   ,       ):
										super().__init__(**lowerCamelCase_					)
										lowerCamelCase_       :							Tuple									=size if size is not None else {"shortest_edge": 224}
										lowerCamelCase_       :							Dict									=get_size_dict(lowerCamelCase_   ,       default_to_square=lowerCamelCase_					)
										lowerCamelCase_       :							List[str]									=crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase_       :							Optional[int]									=get_size_dict(lowerCamelCase_   ,       default_to_square=lowerCamelCase_   ,       param_name="crop_size"					)

										lowerCamelCase_       :							Any									=do_resize
										lowerCamelCase_       :							List[Any]									=size
										lowerCamelCase_       :							int									=resample
										lowerCamelCase_       :							List[str]									=do_center_crop
										lowerCamelCase_       :							Dict									=crop_size
										lowerCamelCase_       :							Any									=do_rescale
										lowerCamelCase_       :							Tuple									=rescale_factor
										lowerCamelCase_       :							List[str]									=do_normalize
										lowerCamelCase_       :							Dict									=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase_       :							Optional[int]									=image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase_       :							List[str]									=do_convert_rgb


			def 	UpperCAmelCase__						(      self     :  Dict   ,       snake_case__     :  List[str]   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  Optional[int] = PILImageResampling.BICUBIC   ,       snake_case__     :  str = None   ,       **snake_case__     :  Union[str, Any]   ,       ):
										lowerCamelCase_       :							Tuple									=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()}"""					)
										lowerCamelCase_       :							int									=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 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Optional[int] = None   ,       **snake_case__     :  Union[str, Any]   ,       ):
										lowerCamelCase_       :							List[str]									=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, width). Got {size.keys()}"""					)
										return center_crop(lowerCamelCase_   ,       size=(size["height"], size["width"])   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Dict = None   ,       **snake_case__     :  Union[str, Any]   ,       ):
										return rescale(lowerCamelCase_   ,       scale=lowerCamelCase_   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_					)


			def 	UpperCAmelCase__						(      self     :  Any   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  str   ,       snake_case__     :  Optional[Any] = None   ,       **snake_case__     :  str   ,       ):
										return normalize(lowerCamelCase_   ,       mean=lowerCamelCase_   ,       std=lowerCamelCase_   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_					)


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Dict   ,       snake_case__     :  int = None   ,       snake_case__     :  List[Any] = None   ,       snake_case__     :  str = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Any = None   ,       snake_case__     :  Any = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  List[Any] = None   ,       snake_case__     :  Any = None   ,       snake_case__     :  List[str] = ChannelDimension.FIRST   ,       **snake_case__     :  Union[str, Any]   ,       ):
										lowerCamelCase_       :							Tuple									=do_resize if do_resize is not None else self.do_resize
										lowerCamelCase_       :							List[str]									=size if size is not None else self.size
										lowerCamelCase_       :							Dict									=get_size_dict(lowerCamelCase_   ,       param_name="size"   ,       default_to_square=lowerCamelCase_					)
										lowerCamelCase_       :							Optional[int]									=resample if resample is not None else self.resample
										lowerCamelCase_       :							List[str]									=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase_       :							int									=crop_size if crop_size is not None else self.crop_size
										lowerCamelCase_       :							Optional[Any]									=get_size_dict(lowerCamelCase_   ,       param_name="crop_size"   ,       default_to_square=lowerCamelCase_					)
										lowerCamelCase_       :							Tuple									=do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase_       :							Union[str, Any]									=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase_       :							Any									=do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase_       :							List[str]									=image_mean if image_mean is not None else self.image_mean
										lowerCamelCase_       :							Any									=image_std if image_std is not None else self.image_std
										lowerCamelCase_       :							str									=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase_       :							Union[str, Any]									=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."					)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																	lowerCamelCase_       :							Optional[Any]									=[convert_to_rgb(lowerCamelCase_					) for image in images]

										# All transformations expect numpy arrays.
										lowerCamelCase_       :							Tuple									=[to_numpy_array(lowerCamelCase_					) for image in images]

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

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

										if do_rescale:
																	lowerCamelCase_       :							List[str]									=[self.rescale(image=lowerCamelCase_   ,       scale=lowerCamelCase_					) for image in images]

										if do_normalize:
																	lowerCamelCase_       :							Optional[int]									=[self.normalize(image=lowerCamelCase_   ,       mean=lowerCamelCase_   ,       std=lowerCamelCase_					) for image in images]

										lowerCamelCase_       :							List[Any]									=[to_channel_dimension_format(lowerCamelCase_   ,       lowerCamelCase_					) for image in images]

										lowerCamelCase_       :							Optional[Any]									={"pixel_values": images}
										return BatchFeature(data=lowerCamelCase_   ,       tensor_type=lowerCamelCase_					)








import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
