

from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







def 					lowerCamelCase_     (							UpperCAmelCase_							:     list	)						->				list:




	'''simple docstring'''




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

	_UpperCamelCase   :		List[Any]			=						[]

	def generate(UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     list	):
		_UpperCamelCase   :		Optional[int]			=						[0] * n
		res.append(tuple(UpperCAmelCase_	)	)

		_UpperCamelCase   :		List[Any]			=						0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[0]
				else:
					_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						arr[i], arr[c[i]]
				res.append(tuple(UpperCAmelCase_	)	)
				c[i] += 1
				_UpperCamelCase   :		Tuple			=						0
			else:
				_UpperCamelCase   :		Tuple			=						0
				i += 1

	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))




