
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   UpperCAmelCase		(						__a ):
   A__					:		List[str]						     =     ["pixel_values"]



   def __init__(self		:					List[Any]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = PIL.Image.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          super().__init__(**a_ )
          snake_case       :   int							=      size if size is not None else {"""height""": 2_56, """width""": 2_56}
          snake_case       :   Optional[int]							=      get_size_dict(a_ )
          snake_case       :   Union[str, Any]							=      crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
          snake_case       :   str							=      get_size_dict(a_    ,						param_name="crop_size" )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PIL.Image.BICUBIC    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[Any]    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					int    ,						)		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[int, float]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Union[str, Any]    ,						)		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[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__		:					int    ,						)		->	Union[str, Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					ImageInput    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					Union[str, Any]=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__		:					Optional[Any]    ,						)		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   Optional[Any]							=      resample if resample is not None else self.resample
          snake_case       :   List[Any]							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   List[Any]							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   Union[str, Any]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   List[Any]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   Optional[Any]							=      image_std if image_std is not None else self.image_std

          snake_case       :   Union[str, Any]							=      size if size is not None else self.size
          snake_case       :   Dict							=      get_size_dict(a_ )
          snake_case       :   Dict							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   List[Any]							=      get_size_dict(a_    ,						param_name="crop_size" )

          snake_case       :   List[str]							=      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.
          snake_case       :   Union[str, Any]							=      [to_numpy_array(a_ ) for image in images]

          if do_resize:
                 snake_case       :   Optional[int]							=      [self.resize(image=a_    ,						size=a_    ,						resample=a_ ) for image in images]

          if do_center_crop:
                 snake_case       :   Any							=      [self.center_crop(image=a_    ,						size=a_ ) for image in images]

          if do_rescale:
                 snake_case       :   Optional[Any]							=      [self.rescale(image=a_    ,						scale=a_ ) for image in images]

          if do_normalize:
                 snake_case       :   List[Any]							=      [self.normalize(image=a_    ,						mean=a_    ,						std=a_ ) for image in images]

          snake_case       :   int							=      [to_channel_dimension_format(a_    ,						a_ ) for image in images]

          snake_case       :   int							=      {"""pixel_values""": images}
          return BatchFeature(data=a_    ,						tensor_type=a_ )











"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

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

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


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


