"""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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase											=  logging.get_logger(__name__)




class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						List[str]													=		['pixel_values']





						def __init__(       self		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = PILImageResampling.BILINEAR		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = True		,__UpperCAmelCase = 1 / 2_55		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				None:
													super().__init__(**UpperCamelCase_     )
													A__	     =							size if size is not None else {'shortest_edge': 2_56}
													A__	     =							get_size_dict(UpperCamelCase_		,default_to_square=UpperCamelCase_     )
													A__	     =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__	     =							get_size_dict(UpperCamelCase_     )
													A__	     =							do_resize
													A__	     =							size
													A__	     =							resample
													A__	     =							do_center_crop
													A__	     =							crop_size
													A__	     =							do_rescale
													A__	     =							rescale_factor
													A__	     =							do_normalize
													A__	     =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__	     =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = PILImageResampling.BICUBIC		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													A__	     =							get_size_dict(UpperCamelCase_		,default_to_square=UpperCamelCase_     )
													if "shortest_edge" not in size:
																				raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
													A__	     =							get_resize_output_image_size(UpperCamelCase_		,size=size['shortest_edge']		,default_to_square=UpperCamelCase_     )
													return resize(UpperCamelCase_		,size=UpperCamelCase_		,resample=UpperCamelCase_		,data_format=UpperCamelCase_		,**UpperCamelCase_     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													A__	     =							get_size_dict(UpperCamelCase_     )
													return center_crop(UpperCamelCase_		,size=(size['height'], size['width'])		,data_format=UpperCamelCase_		,**UpperCamelCase_     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase     )					->				np.ndarray:
													return rescale(UpperCamelCase_		,scale=UpperCamelCase_		,data_format=UpperCamelCase_		,**UpperCamelCase_     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													return normalize(UpperCamelCase_		,mean=UpperCamelCase_		,std=UpperCamelCase_		,data_format=UpperCamelCase_		,**UpperCamelCase_     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = ChannelDimension.FIRST		,**__UpperCAmelCase		,)					->				Dict:
													A__	     =							do_resize if do_resize is not None else self.do_resize
													A__	     =							size if size is not None else self.size
													A__	     =							get_size_dict(UpperCamelCase_		,default_to_square=UpperCamelCase_     )
													A__	     =							resample if resample is not None else self.resample
													A__	     =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__	     =							crop_size if crop_size is not None else self.crop_size
													A__	     =							get_size_dict(UpperCamelCase_     )
													A__	     =							do_rescale if do_rescale is not None else self.do_rescale
													A__	     =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__	     =							do_normalize if do_normalize is not None else self.do_normalize
													A__	     =							image_mean if image_mean is not None else self.image_mean
													A__	     =							image_std if image_std is not None else self.image_std

													A__	     =							make_list_of_images(UpperCamelCase_     )

													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.'     )

													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.'     )

													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__	     =							[to_numpy_array(UpperCamelCase_     ) for image in images]

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

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

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

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

													A__	     =							[to_channel_dimension_format(UpperCamelCase_		,UpperCamelCase_     ) for image in images]

													A__	     =							{'pixel_values': images}
													return BatchFeature(data=UpperCamelCase_		,tensor_type=UpperCamelCase_     )






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							[]

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

														generate(k - 1       ,    UpperCamelCase__				)

														for i in range(k - 1				):
																					if k % 2 == 0:  # k is even
																												A__     ,  A__	     =							arr[k - 1], arr[i]
																					else:  # k is odd
																												A__     ,  A__	     =							arr[k - 1], arr[0]
																					generate(k - 1       ,    UpperCamelCase__				)

							generate(len(UpperCamelCase__				)       ,    UpperCamelCase__				)
							return res


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





