


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE							:      str						=      logging.get_logger(__name__)


if is_vision_available():
				import PIL





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      ["""pixel_values"""]



							def __init__(			self							: Tuple						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: Dict[str, int] = None						,    UpperCAmelCase_							: PILImageResampling = PILImageResampling.BICUBIC						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: Dict[str, int] = None						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: Union[int, float] = 1 / 255						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: Optional[Union[float, List[float]]] = None						,    UpperCAmelCase_							: Optional[Union[float, List[float]]] = None						,    UpperCAmelCase_							: bool = True						,    **UpperCAmelCase_							: Tuple						,    )   ->None:



														"""simple docstring"""



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

														snake_case_          =						do_resize
														snake_case_          =						size
														snake_case_          =						resample
														snake_case_          =						do_center_crop
														snake_case_          =						crop_size
														snake_case_          =						do_rescale
														snake_case_          =						rescale_factor
														snake_case_          =						do_normalize
														snake_case_          =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														snake_case_          =						image_std if image_std is not None else OPENAI_CLIP_STD
														snake_case_          =						do_convert_rgb



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: Dict[str, int]						,    UpperCAmelCase_							: PILImageResampling = PILImageResampling.BICUBIC						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None						,    **UpperCAmelCase_							: Union[str, Any]						,    )   ->np.ndarray:



														"""simple docstring"""



														snake_case_          =						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()}"""    )
														snake_case_          =						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 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: Dict[str, int]						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None						,    **UpperCAmelCase_							: Optional[int]						,    )   ->np.ndarray:



														"""simple docstring"""



														snake_case_          =						get_size_dict(UpperCAmelCase_    )
														if "height" not in size or "width" not in size:
																					raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
														return center_crop(UpperCAmelCase_						,    size=(size["""height"""], size["""width"""])						,    data_format=UpperCAmelCase_						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: Union[int, float]						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None						,    **UpperCAmelCase_							: Optional[int]						,    )   ->Union[str, Any]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: np.ndarray						,    UpperCAmelCase_							: Union[float, List[float]]						,    UpperCAmelCase_							: Union[float, List[float]]						,    UpperCAmelCase_							: Optional[Union[str, ChannelDimension]] = None						,    **UpperCAmelCase_							: str						,    )   ->np.ndarray:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: ImageInput						,    UpperCAmelCase_							: bool = None						,    UpperCAmelCase_							: Dict[str, int] = None						,    UpperCAmelCase_							: PILImageResampling = None						,    UpperCAmelCase_							: bool = None						,    UpperCAmelCase_							: int = None						,    UpperCAmelCase_							: bool = None						,    UpperCAmelCase_							: float = None						,    UpperCAmelCase_							: bool = None						,    UpperCAmelCase_							: Optional[Union[float, List[float]]] = None						,    UpperCAmelCase_							: Optional[Union[float, List[float]]] = None						,    UpperCAmelCase_							: bool = None						,    UpperCAmelCase_							: Optional[Union[str, TensorType]] = None						,    UpperCAmelCase_							: Optional[ChannelDimension] = ChannelDimension.FIRST						,    **UpperCAmelCase_							: Union[str, Any]						,    )   ->PIL.Image.Image:



														"""simple docstring"""



														snake_case_          =						do_resize if do_resize is not None else self.do_resize
														snake_case_          =						size if size is not None else self.size
														snake_case_          =						get_size_dict(UpperCAmelCase_						,    param_name="""size"""						,    default_to_square=UpperCAmelCase_    )
														snake_case_          =						resample if resample is not None else self.resample
														snake_case_          =						do_center_crop if do_center_crop is not None else self.do_center_crop
														snake_case_          =						crop_size if crop_size is not None else self.crop_size
														snake_case_          =						get_size_dict(UpperCAmelCase_						,    param_name="""crop_size"""						,    default_to_square=UpperCAmelCase_    )
														snake_case_          =						do_rescale if do_rescale is not None else self.do_rescale
														snake_case_          =						rescale_factor if rescale_factor is not None else self.rescale_factor
														snake_case_          =						do_normalize if do_normalize is not None else self.do_normalize
														snake_case_          =						image_mean if image_mean is not None else self.image_mean
														snake_case_          =						image_std if image_std is not None else self.image_std
														snake_case_          =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														snake_case_          =						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."""    )

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					snake_case_          =						[convert_to_rgb(UpperCAmelCase_    ) for image in images]

														# All transformations expect numpy arrays.
														snake_case_          =						[to_numpy_array(UpperCAmelCase_    ) for image in images]

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

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

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

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

														snake_case_          =						[to_channel_dimension_format(UpperCAmelCase_						,    UpperCAmelCase_    ) for image in images]

														snake_case_          =						{"""pixel_values""": images}
														return BatchFeature(data=UpperCAmelCase_						,    tensor_type=UpperCAmelCase_    )







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list:

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

							snake_case_          =						[]

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

														generate(k - 1			,	_SCREAMING_SNAKE_CASE			)

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

							generate(len(_SCREAMING_SNAKE_CASE			)			,	_SCREAMING_SNAKE_CASE			)
							return res


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



