
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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


_snake_case      = logging.get_logger(__name__)
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=["pixel_values"]

 def __init__(						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			int = 0.9					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BICUBIC					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			Union[int, float] = 1 / 2_55					, SCREAMING_SNAKE_CASE__			:			bool = True					, 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__			:			Any					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'shortest_edge': 2_24}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, param_name='crop_size'   )

      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					crop_pct
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_center_crop
      UpperCamelCase						    =					crop_size
      UpperCamelCase						    =					do_rescale
      UpperCamelCase						    =					rescale_factor
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCamelCase						    =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			Optional[float] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BICUBIC					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      if "shortest_edge" not in size and ("height" not in size or "width" not in size):
           raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'   )
      if crop_pct is not None:
           if "shortest_edge" in size:
                UpperCamelCase						    =					int(size['shortest_edge'] / crop_pct   )
           elif "height" in size and "width" in size:
                if size["height"] == size["width"]:
                     UpperCamelCase						    =					int(size['height'] / crop_pct   )
                else:
                     UpperCamelCase						    =					(int(size['height'] / crop_pct   ), int(size['width'] / crop_pct   ))
           else:
                raise ValueError('Invalid size for resize: {}'.format(SCREAMING_SNAKE_CASE__   )   )

           UpperCamelCase						    =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__					, size=SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      else:
           if "shortest_edge" in size:
                UpperCamelCase						    =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__					, size=size['shortest_edge']					, default_to_square=SCREAMING_SNAKE_CASE__   )
           elif "height" in size and "width" in size:
                UpperCamelCase						    =					(size['height'], size['width'])
           else:
                raise ValueError('Invalid size for resize: {}'.format(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      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[Any]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      if "height" not in size or "width" not in size:
           raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'   )
      return center_crop(SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

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






      """simple docstring"""



      return rescale(SCREAMING_SNAKE_CASE__					, scale=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[Any]					, 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__			:			int					, ):






      """simple docstring"""



      return normalize(SCREAMING_SNAKE_CASE__					, mean=SCREAMING_SNAKE_CASE__					, std=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			int = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, 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__			:			Optional[Union[str, TensorType]] = None					, SCREAMING_SNAKE_CASE__			:			ChannelDimension = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			int					, ):






      """simple docstring"""



      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_center_crop if do_center_crop is not None else self.do_center_crop
      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(SCREAMING_SNAKE_CASE__					, default_to_square=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					crop_size if crop_size is not None else self.crop_size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__					, param_name='crop_size'   )

      UpperCamelCase						    =					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 or resample is None:
           raise ValueError('Size and resample must be specified if do_resize is True.'   )

      if do_center_crop and crop_pct is None:
           raise ValueError('Crop_pct 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.
      UpperCamelCase						    =					[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

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

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

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

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

      UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					{'pixel_values': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )




def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


