
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['FunnelTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FunnelBaseModel',
						    'FunnelForMaskedLM',
						    'FunnelForMultipleChoice',
						    'FunnelForPreTraining',
						    'FunnelForQuestionAnswering',
						    'FunnelForSequenceClassification',
						    'FunnelForTokenClassification',
						    'FunnelModel',
						    'FunnelPreTrainedModel',
						    'load_tf_weights_in_funnel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFFunnelBaseModel',
						    'TFFunnelForMaskedLM',
						    'TFFunnelForMultipleChoice',
						    'TFFunnelForPreTraining',
						    'TFFunnelForQuestionAnswering',
						    'TFFunnelForSequenceClassification',
						    'TFFunnelForTokenClassification',
						    'TFFunnelModel',
						    'TFFunnelPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
						from .tokenization_funnel import FunnelTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_funnel_fast import FunnelTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_funnel import (
												    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FunnelBaseModel,
												    FunnelForMaskedLM,
												    FunnelForMultipleChoice,
												    FunnelForPreTraining,
												    FunnelForQuestionAnswering,
												    FunnelForSequenceClassification,
												    FunnelForTokenClassification,
												    FunnelModel,
												    FunnelPreTrainedModel,
												    load_tf_weights_in_funnel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_funnel import (
												    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFFunnelBaseModel,
												    TFFunnelForMaskedLM,
												    TFFunnelForMultipleChoice,
												    TFFunnelForPreTraining,
												    TFFunnelForQuestionAnswering,
												    TFFunnelForSequenceClassification,
												    TFFunnelForTokenClassification,
												    TFFunnelModel,
												    TFFunnelPreTrainedModel,
												)

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






def 				a						(							snake_case__:					list					):


				'''simple docstring'''

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

				lowercase_       =					[]

				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
																lowercase_  ,	lowercase_       =					arr[k - 1], arr[i]
												else:  # k is odd
																lowercase_  ,	lowercase_       =					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))




