





'''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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase   )    ->       List[List[ImageInput]]:

   '''simple docstring'''







   if isinstance(__UpperCAmelCase				,						(list, tuple)   ) and isinstance(videos[0]				,						(list, tuple)   ) and is_valid_image(videos[0][0]   ):
      return videos

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

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

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







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						['pixel_values']





   def __init__(       self					:      Dict					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Union[int, float] = 1 / 255					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,lowerCamelCase					:      Optional[Union[float, List[float]]] = None					,**lowerCamelCase					:      Union[str, Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""shortest_edge""": 224}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,default_to_square=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							do_center_crop
      __SCREAMING_SNAKE_CASE   				=							crop_size
      __SCREAMING_SNAKE_CASE   				=							resample
      __SCREAMING_SNAKE_CASE   				=							do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __SCREAMING_SNAKE_CASE   				=							image_std if image_std is not None else IMAGENET_STANDARD_STD





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,default_to_square=lowerCamelCase     )
      if "shortest_edge" in size:
         __SCREAMING_SNAKE_CASE   				=							get_resize_output_image_size(lowerCamelCase					,size["""shortest_edge"""]					,default_to_square=lowerCamelCase     )
      elif "height" in size and "width" in size:
         __SCREAMING_SNAKE_CASE   				=							(size["""height"""], size["""width"""])
      else:
         raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""     )
      return resize(lowerCamelCase					,size=lowerCamelCase					,resample=lowerCamelCase					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      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(lowerCamelCase					,size=(size["""height"""], size["""width"""])					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Union[int, float]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

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





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

      '''simple docstring'''

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





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

      '''simple docstring'''

      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.
      __SCREAMING_SNAKE_CASE   				=							to_numpy_array(lowerCamelCase     )

      if do_resize:
         __SCREAMING_SNAKE_CASE   				=							self.resize(image=lowerCamelCase					,size=lowerCamelCase					,resample=lowerCamelCase     )

      if do_center_crop:
         __SCREAMING_SNAKE_CASE   				=							self.center_crop(lowerCamelCase					,size=lowerCamelCase     )

      if do_rescale:
         __SCREAMING_SNAKE_CASE   				=							self.rescale(image=lowerCamelCase					,scale=lowerCamelCase     )

      if do_normalize:
         __SCREAMING_SNAKE_CASE   				=							self.normalize(image=lowerCamelCase					,mean=lowerCamelCase					,std=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							to_channel_dimension_format(lowerCamelCase					,lowerCamelCase     )
      return image





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE   				=							resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE   				=							do_center_crop if do_center_crop is not None else self.do_center_crop
      __SCREAMING_SNAKE_CASE   				=							do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor if rescale_factor is not None else self.rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE   				=							image_mean if image_mean is not None else self.image_mean
      __SCREAMING_SNAKE_CASE   				=							image_std if image_std is not None else self.image_std

      __SCREAMING_SNAKE_CASE   				=							size if size is not None else self.size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,default_to_square=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,param_name="""crop_size"""     )

      if not valid_images(lowerCamelCase     ):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray."""     )

      __SCREAMING_SNAKE_CASE   				=							make_batched(lowerCamelCase     )

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

      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": videos}
      return BatchFeature(data=lowerCamelCase					,tensor_type=lowerCamelCase     )









'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


