


'''simple docstring'''

def 							__magic_name__(						lowerCamelCase):

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

						__lowerCAmelCase							       =				[]

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

												generate(k - 1,				lowerCamelCase)

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

						generate(len(lowerCamelCase),				lowerCamelCase)
						return res


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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

