



'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


lowerCAmelCase:					List[str]						       =						logging.get_logger(__name__)
def       lowerCamelCase__       (						_A				):
  if isinstance(A__ ,			(list, tuple)				) and isinstance(videos[0] ,			(list, tuple)				) and is_valid_image(videos[0][0]				):
    return videos

  elif isinstance(A__ ,			(list, tuple)				) and is_valid_image(videos[0]				):
    return [videos]

  elif is_valid_image(A__				):
    return [[videos]]

  raise ValueError(f"""Could not make batched video from {videos}"""				)

class 							a__(					_a	):
    lowercase__ =    ["""pixel_values"""]







    def __init__(      self :  Optional[Any]						,			__snake_case :  bool = True						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  PILImageResampling = PILImageResampling.BILINEAR						,			__snake_case :  bool = True						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  bool = True						,			__snake_case :  Union[int, float] = 1 / 2_55						,			__snake_case :  bool = True						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			**__snake_case :  List[Any]						,			):
      super().__init__(**UpperCamelCase__	)
      a						:					int        =     size if size is not None else {'shortest_edge': 2_24}
      a						:					str        =     get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__	)
      a						:					Dict        =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      a						:					int        =     get_size_dict(UpperCamelCase__						,			param_name='crop_size'	)

      a						:					Dict        =     do_resize
      a						:					List[Any]        =     size
      a						:					Dict        =     do_center_crop
      a						:					Tuple        =     crop_size
      a						:					Dict        =     resample
      a						:					Optional[Any]        =     do_rescale
      a						:					str        =     rescale_factor
      a						:					Union[str, Any]        =     do_normalize
      a						:					Any        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a						:					Union[str, Any]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD







    def 			lowercase_       (      self :  Union[str, Any]						,			__snake_case :  np.ndarray						,			__snake_case :  Dict[str, int]						,			__snake_case :  PILImageResampling = PILImageResampling.BILINEAR						,			__snake_case :  Optional[Union[str, ChannelDimension]] = None						,			**__snake_case :  Optional[int]						,			):
      a						:					List[str]        =     get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__	)
      if "shortest_edge" in size:
        a						:					str        =     get_resize_output_image_size(UpperCamelCase__						,			size['shortest_edge']						,			default_to_square=UpperCamelCase__	)
      elif "height" in size and "width" in size:
        a						:					List[Any]        =     (size['height'], size['width'])
      else:
        raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""	)
      return resize(UpperCamelCase__						,			size=UpperCamelCase__						,			resample=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__	)







    def 			lowercase_       (      self :  Dict						,			__snake_case :  np.ndarray						,			__snake_case :  Dict[str, int]						,			__snake_case :  Optional[Union[str, ChannelDimension]] = None						,			**__snake_case :  Optional[Any]						,			):
      a						:					List[str]        =     get_size_dict(UpperCamelCase__	)
      if "height" not in size or "width" not in size:
        raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""	)
      return center_crop(UpperCamelCase__						,			size=(size['height'], size['width'])						,			data_format=UpperCamelCase__						,			**UpperCamelCase__	)







    def 			lowercase_       (      self :  List[str]						,			__snake_case :  np.ndarray						,			__snake_case :  Union[int, float]						,			__snake_case :  Optional[Union[str, ChannelDimension]] = None						,			**__snake_case :  str						,			):
      return rescale(UpperCamelCase__						,			scale=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__	)







    def 			lowercase_       (      self :  Any						,			__snake_case :  np.ndarray						,			__snake_case :  Union[float, List[float]]						,			__snake_case :  Union[float, List[float]]						,			__snake_case :  Optional[Union[str, ChannelDimension]] = None						,			**__snake_case :  Dict						,			):
      return normalize(UpperCamelCase__						,			mean=UpperCamelCase__						,			std=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__	)







    def 			lowercase_       (      self :  Optional[Any]						,			__snake_case :  ImageInput						,			__snake_case :  bool = None						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  PILImageResampling = None						,			__snake_case :  bool = None						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  bool = None						,			__snake_case :  float = None						,			__snake_case :  bool = None						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			__snake_case :  Optional[ChannelDimension] = ChannelDimension.FIRST						,			):







      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.
      a						:					Tuple        =     to_numpy_array(UpperCamelCase__	)

      if do_resize:
        a						:					Dict        =     self.resize(image=UpperCamelCase__						,			size=UpperCamelCase__						,			resample=UpperCamelCase__	)

      if do_center_crop:
        a						:					List[str]        =     self.center_crop(UpperCamelCase__						,			size=UpperCamelCase__	)

      if do_rescale:
        a						:					List[str]        =     self.rescale(image=UpperCamelCase__						,			scale=UpperCamelCase__	)

      if do_normalize:
        a						:					Tuple        =     self.normalize(image=UpperCamelCase__						,			mean=UpperCamelCase__						,			std=UpperCamelCase__	)

      a						:					Optional[int]        =     to_channel_dimension_format(UpperCamelCase__						,			UpperCamelCase__	)
      return image







    def 			lowercase_       (      self :  List[str]						,			__snake_case :  ImageInput						,			__snake_case :  bool = None						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  PILImageResampling = None						,			__snake_case :  bool = None						,			__snake_case :  Dict[str, int] = None						,			__snake_case :  bool = None						,			__snake_case :  float = None						,			__snake_case :  bool = None						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			__snake_case :  Optional[Union[float, List[float]]] = None						,			__snake_case :  Optional[Union[str, TensorType]] = None						,			__snake_case :  ChannelDimension = ChannelDimension.FIRST						,			**__snake_case :  Union[str, Any]						,			):
      a						:					Dict        =     do_resize if do_resize is not None else self.do_resize
      a						:					str        =     resample if resample is not None else self.resample
      a						:					Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
      a						:					Optional[Any]        =     do_rescale if do_rescale is not None else self.do_rescale
      a						:					Dict        =     rescale_factor if rescale_factor is not None else self.rescale_factor
      a						:					Optional[Any]        =     do_normalize if do_normalize is not None else self.do_normalize
      a						:					List[str]        =     image_mean if image_mean is not None else self.image_mean
      a						:					Dict        =     image_std if image_std is not None else self.image_std

      a						:					Optional[Any]        =     size if size is not None else self.size
      a						:					Union[str, Any]        =     get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__	)
      a						:					Optional[int]        =     crop_size if crop_size is not None else self.crop_size
      a						:					Optional[Any]        =     get_size_dict(UpperCamelCase__						,			param_name='crop_size'	)

      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.'	)

      a						:					Optional[Any]        =     make_batched(UpperCamelCase__	)

      a						:					Any        =     [
          [
              self._preprocess_image(
                  image=UpperCamelCase__						,			do_resize=UpperCamelCase__						,			size=UpperCamelCase__						,			resample=UpperCamelCase__						,			do_center_crop=UpperCamelCase__						,			crop_size=UpperCamelCase__						,			do_rescale=UpperCamelCase__						,			rescale_factor=UpperCamelCase__						,			do_normalize=UpperCamelCase__						,			image_mean=UpperCamelCase__						,			image_std=UpperCamelCase__						,			data_format=UpperCamelCase__						,			)
              for img in video
          ]
          for video in videos
      ]

      a						:					Any        =     {'pixel_values': videos}
      return BatchFeature(data=UpperCamelCase__						,			tensor_type=UpperCamelCase__	)




'''simple docstring'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


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

