
"""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


_a														=		logging.get_logger(__name__)






class    _UpperCAmelCase(   lowerCamelCase     ):
  lowercase__      =				['pixel_values']



  def __init__( self    ,		__a = True    ,		__a = None    ,		__a = PILImageResampling.BICUBIC    ,		__a = True    ,		__a = True    ,		__a = 1 / 2_55    ,		__a = None    ,		__a = True    ,		__a = None    ,		__a = None    ,		**__a    ,		)	->	None:






       '''simple docstring'''
       super().__init__(**__a)
       _UpperCamelCase										=  size if size is not None else {'''height''': 2_24, '''width''': 2_24}
       _UpperCamelCase										=  get_size_dict(__a)
       _UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       _UpperCamelCase										=  get_size_dict(__a    ,		default_to_square=__a    ,		param_name='''crop_size''')

       _UpperCamelCase										=  do_resize
       _UpperCamelCase										=  do_rescale
       _UpperCamelCase										=  do_normalize
       _UpperCamelCase										=  do_center_crop
       _UpperCamelCase										=  crop_size
       _UpperCamelCase										=  size
       _UpperCamelCase										=  resample
       _UpperCamelCase										=  rescale_factor
       _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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = PILImageResampling.BILINEAR    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






       '''simple docstring'''
       _UpperCamelCase										=  get_size_dict(__a)

       if "shortest_edge" in size:
            _UpperCamelCase										=  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:
            _UpperCamelCase										=  (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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






       '''simple docstring'''
       _UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a)	->	np.ndarray:






       '''simple docstring'''
       return rescale(__a    ,		scale=__a    ,		data_format=__a    ,		**__a)



  def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






       '''simple docstring'''
       return normalize(__a    ,		mean=__a    ,		std=__a    ,		data_format=__a    ,		**__a)




  def 						UpperCAmelCase							( 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:






       '''simple docstring'''
       _UpperCamelCase										=  do_resize if do_resize is not None else self.do_resize
       _UpperCamelCase										=  do_rescale if do_rescale is not None else self.do_rescale
       _UpperCamelCase										=  do_normalize if do_normalize is not None else self.do_normalize
       _UpperCamelCase										=  do_center_crop if do_center_crop is not None else self.do_center_crop
       _UpperCamelCase										=  crop_size if crop_size is not None else self.crop_size
       _UpperCamelCase										=  get_size_dict(__a    ,		param_name='''crop_size'''    ,		default_to_square=__a)
       _UpperCamelCase										=  resample if resample is not None else self.resample
       _UpperCamelCase										=  rescale_factor if rescale_factor is not None else self.rescale_factor
       _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(__a)

       if not is_batched(__a):
            _UpperCamelCase										=  [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.
       _UpperCamelCase										=  [to_numpy_array(__a) for image in images]

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

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

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

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

       _UpperCamelCase										=  [to_channel_dimension_format(__a    ,		__a) for image in images]

       _UpperCamelCase										=  {'''pixel_values''': images}
       return BatchFeature(data=__a    ,		tensor_type=__a)








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






