

'''simple docstring'''

from __future__ import annotations

from random import randrange





def 				A_					(	snake_case	):
  if len(snake_case	) < 2:
    return collection
  SCREAMING_SNAKE_CASE:Optional[int]   =   randrange(len(snake_case	)	)  # Use random element as pivot
  SCREAMING_SNAKE_CASE:str   =   collection[pivot_index]
  SCREAMING_SNAKE_CASE:list[int]   =   []  # All elements greater than pivot
  SCREAMING_SNAKE_CASE:list[int]   =   []  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(snake_case	)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(snake_case	)

  return [*quick_sort(snake_case	), pivot, *quick_sort(snake_case	)]


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









'''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_      =							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case	(    _a  ):
   _A    :       List[str]      				=   ['''pixel_values''']
   def __init__(	self     : int       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Union[int, float] = 1 / 255       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,**SCREAMING_SNAKE_CASE__     : int       ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple   =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:Any   =   get_size_dict(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple   =   do_resize
     SCREAMING_SNAKE_CASE:Any   =   size
     SCREAMING_SNAKE_CASE:Any   =   resample
     SCREAMING_SNAKE_CASE:Tuple   =   do_center_crop
     SCREAMING_SNAKE_CASE:List[str]   =   crop_size
     SCREAMING_SNAKE_CASE:Dict   =   do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]   =   rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   do_normalize
     SCREAMING_SNAKE_CASE:List[str]   =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Dict   =   image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Any   =   do_convert_rgb
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : str       ,):
     SCREAMING_SNAKE_CASE:List[str]   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   get_resize_output_image_size(
         SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[Any]       ,):
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__							)
     return center_crop(SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Dict       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[int, float]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[int]       ,):
     return rescale(SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     return normalize(SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)







   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : ImageInput       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : int = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : float = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : bool = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, TensorType]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[ChannelDimension] = ChannelDimension.FIRST       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     SCREAMING_SNAKE_CASE:List[Any]   =   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:int   =   size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Dict   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int   =   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Optional[int]   =   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Any   =   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:Dict   =   get_size_dict(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:Any   =   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:Tuple   =   do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Tuple   =   image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Dict   =   image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE:Any   =   make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_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."							)

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

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

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

     if do_center_crop:
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   [self.center_crop(image=SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.rescale(image=SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:List[Any]   =   [self.normalize(image=SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int   =   {"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,tensor_type=SCREAMING_SNAKE_CASE__							)






