


"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE							:      List[str]						=      {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      [
				    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLMForMultipleChoice',
				    'XLMForQuestionAnswering',
				    'XLMForQuestionAnsweringSimple',
				    'XLMForSequenceClassification',
				    'XLMForTokenClassification',
				    'XLMModel',
				    'XLMPreTrainedModel',
				    'XLMWithLMHeadModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      [
				    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLMForMultipleChoice',
				    'TFXLMForQuestionAnsweringSimple',
				    'TFXLMForSequenceClassification',
				    'TFXLMForTokenClassification',
				    'TFXLMMainLayer',
				    'TFXLMModel',
				    'TFXLMPreTrainedModel',
				    'TFXLMWithLMHeadModel',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:      Dict						=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""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))



