



"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


__A      :							int   =      logging.get_logger(__name__)
class 	_a       (    lowercase_):
			"""simple docstring"""
			UpperCamelCase__						=		["""pixel_values"""]



			def __init__(						self   :			Dict ,      __UpperCamelCase   :			Union[str, Any] = True ,      __UpperCamelCase   :			List[Any] = None ,      __UpperCamelCase   :			int = None ,      __UpperCamelCase   :			Optional[int] = PILImageResampling.BILINEAR ,      __UpperCamelCase   :			List[str] = True ,      __UpperCamelCase   :			List[str] = 1 / 2_5_5 ,      __UpperCamelCase   :			Optional[Any] = True ,      __UpperCamelCase   :			int = None ,      __UpperCamelCase   :			Any = None ,      **__UpperCamelCase   :			str ,      )->None:
							super().__init__(**__UpperCamelCase      )
							_UpperCAmelCase							=			size if size is not None else {'shortest_edge': 3_8_4}
							_UpperCAmelCase							=			get_size_dict(__UpperCamelCase ,      default_to_square=__UpperCamelCase      )

							_UpperCAmelCase							=			do_resize
							_UpperCAmelCase							=			size
							# Default value set here for backwards compatibility where the value in config is None
							_UpperCAmelCase							=			crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
							_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        lowercase__  (						self   :			str ,      __UpperCamelCase   :			List[str] ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			Union[str, Any] = PILImageResampling.BICUBIC ,      __UpperCamelCase   :			Tuple = None ,      **__UpperCamelCase   :			Dict ,      )->np.ndarray:
							_UpperCAmelCase							=			get_size_dict(__UpperCamelCase ,      default_to_square=__UpperCamelCase      )
							if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
							_UpperCAmelCase							=			size['shortest_edge']

							if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											_UpperCAmelCase							=			int(shortest_edge / crop_pct      )
											_UpperCAmelCase							=			get_resize_output_image_size(__UpperCamelCase ,      size=__UpperCamelCase ,      default_to_square=__UpperCamelCase      )
											_UpperCAmelCase							=			resize(image=__UpperCamelCase ,      size=__UpperCamelCase ,      resample=__UpperCamelCase ,      data_format=__UpperCamelCase ,      **__UpperCamelCase      )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=__UpperCamelCase ,      size=(shortest_edge, shortest_edge) ,      data_format=__UpperCamelCase ,      **__UpperCamelCase      )
							else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    __UpperCamelCase ,      size=(shortest_edge, shortest_edge) ,      resample=__UpperCamelCase ,      data_format=__UpperCamelCase ,      **__UpperCamelCase      )



			def        lowercase__  (						self   :			List[str] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			List[Any] ,      __UpperCamelCase   :			Any = None ,      **__UpperCamelCase   :			int ,      )->int:
							return rescale(__UpperCamelCase ,      scale=__UpperCamelCase ,      data_format=__UpperCamelCase ,      **__UpperCamelCase      )



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Union[str, Any] = None ,      **__UpperCamelCase   :			Dict ,      )->np.ndarray:
							return normalize(__UpperCamelCase ,      mean=__UpperCamelCase ,      std=__UpperCamelCase ,      data_format=__UpperCamelCase ,      **__UpperCamelCase      )



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			Any ,      __UpperCamelCase   :			Dict = None ,      __UpperCamelCase   :			Tuple = None ,      __UpperCamelCase   :			List[str] = None ,      __UpperCamelCase   :			List[Any] = None ,      __UpperCamelCase   :			Dict = None ,      __UpperCamelCase   :			List[Any] = None ,      __UpperCamelCase   :			Tuple = None ,      __UpperCamelCase   :			Union[str, Any] = None ,      __UpperCamelCase   :			Dict = None ,      __UpperCamelCase   :			Any = None ,      __UpperCamelCase   :			List[str] = ChannelDimension.FIRST ,      **__UpperCamelCase   :			Tuple ,      )->PIL.Image.Image:
							_UpperCAmelCase							=			do_resize if do_resize is not None else self.do_resize
							_UpperCAmelCase							=			crop_pct if crop_pct is not None else self.crop_pct
							_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							=			size if size is not None else self.size
							_UpperCAmelCase							=			get_size_dict(__UpperCamelCase ,      default_to_square=__UpperCamelCase      )

							_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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''      )

							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 ,      crop_pct=__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      )




"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     if len(__SCREAMING_SNAKE_CASE      ) < 2:
          return collection

     def circle_sort_util(__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ) -> bool:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	high

          while left < right:
               if collection[left] > collection[right]:
                    lowercase_							,  lowercase_	:				Optional[Any]													=	(
                        collection[right],
                        collection[left],
                    )
                    lowercase_	:				Tuple													=	True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               lowercase_							,  lowercase_	:				Dict													=	(
                   collection[right + 1],
                   collection[left],
               )

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


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

