





"""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, 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


lowerCAmelCase_      =				logging.get_logger(__name__)



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	["""pixel_values"""]






       def __init__( self     ,	a__ = True     ,	a__ = None     ,	a__ = PIL.Image.NEAREST     ,	a__ = False     ,	a__ = None     ,	a__ = 1 / 2_55     ,	a__ = False     ,	a__ = True     ,	a__ = True     ,	a__ = None     ,	a__ = None     ,	a__ = True     ,	**a__     ,	):
              super().__init__(**a__  )
              _UpperCAmelCase			     =  size if size is not None else {'height': 3_46, 'width': 3_46}
              _UpperCAmelCase			     =  get_size_dict(a__  )
              _UpperCAmelCase			     =  crop_size if crop_size is not None else {'height': 2_89, 'width': 2_89}
              _UpperCAmelCase			     =  get_size_dict(a__     ,	param_name='crop_size'  )

              _UpperCAmelCase			     =  do_resize
              _UpperCAmelCase			     =  size
              _UpperCAmelCase			     =  resample
              _UpperCAmelCase			     =  do_center_crop
              _UpperCAmelCase			     =  crop_size
              _UpperCAmelCase			     =  do_rescale
              _UpperCAmelCase			     =  rescale_factor
              _UpperCAmelCase			     =  rescale_offset
              _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
              _UpperCAmelCase			     =  include_top






       def 				__A			( self     ,	a__     ,	a__     ,	a__ = PIL.Image.NEAREST     ,	a__ = None     ,	**a__     ,	):
              _UpperCAmelCase			     =  get_size_dict(a__  )
              if "height" not in size or "width" not in size:
                     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""  )
              return resize(
                  a__     ,	size=(size['height'], size['width'])     ,	resample=a__     ,	data_format=a__     ,	**a__  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__ = None     ,	**a__     ,	):
              _UpperCAmelCase			     =  get_size_dict(a__  )
              if "height" not in size or "width" not in size:
                     raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""  )
              return center_crop(a__     ,	size=(size['height'], size['width'])     ,	data_format=a__     ,	**a__  )






       def 				__A			( self     ,	a__     ,	a__     ,	a__ = True     ,	a__ = None     ,	**a__     ,	):
              if offset:
                     _UpperCAmelCase			     =  (image - 127.5) * scale
                     if data_format is not None:
                            _UpperCAmelCase			     =  to_channel_dimension_format(a__     ,	a__  )
                     _UpperCAmelCase			     =  rescaled_image.astype(np.floataa  )
              else:
                     _UpperCAmelCase			     =  rescale(a__     ,	scale=a__     ,	data_format=a__     ,	**a__  )
              return rescaled_image






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__ = None     ,	**a__     ,	):
              return normalize(a__     ,	mean=a__     ,	std=a__     ,	data_format=a__     ,	**a__  )






       def 				__A			( 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__ = None     ,	a__ = None     ,	a__ = ChannelDimension.FIRST     ,	**a__     ,	):
              _UpperCAmelCase			     =  do_resize if do_resize is not None else self.do_resize
              _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			     =  rescale_offset if rescale_offset is not None else self.rescale_offset
              _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			     =  include_top if include_top is not None else self.include_top

              _UpperCAmelCase			     =  size if size is not None else self.size
              _UpperCAmelCase			     =  get_size_dict(a__  )
              _UpperCAmelCase			     =  crop_size if crop_size is not None else self.crop_size
              _UpperCAmelCase			     =  get_size_dict(a__     ,	param_name='crop_size'  )

              _UpperCAmelCase			     =  make_list_of_images(a__  )

              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 or resample is None:
                     raise ValueError('Size and resample 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.'  )

              # 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__     ,	offset=a__  ) for image in images]

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

              if include_top:
                     _UpperCAmelCase			     =  [self.normalize(image=a__     ,	mean=[0, 0, 0]     ,	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"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  [701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(SCREAMING_SNAKE_CASE,len(SCREAMING_SNAKE_CASE				)				):
                     _UpperCAmelCase			     =  collection[i]
                     _UpperCAmelCase			     =  i
                     while j >= gap and collection[j - gap] > insert_value:
                            _UpperCAmelCase			     =  collection[j - gap]
                            j -= gap
                     if j != i:
                            _UpperCAmelCase			     =  insert_value
       return collection


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    lowerCAmelCase_      =				input('''Enter numbers separated by a comma:\n''').strip()
    lowerCAmelCase_      =				[int(item) for item in user_input.split(''',''')]
    print(shell_sort(unsorted))






