





"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCamelCase__						= {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "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:
  lowerCamelCase__						= [
      "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

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







"""simple docstring"""


def        lowercase__     (       lowercase_      )       ->		list:




      """simple docstring"""

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

      _UpperCamelCase   :						Optional[Any]    =							[]

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

            generate(k - 1		,lowercase_      )

            for i in range(k - 1      ):
                  if k % 2 == 0:  # k is even
                        _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							arr[k - 1], arr[i]
                  else:  # k is odd
                        _UpperCamelCase,	_UpperCamelCase   :						int    =							arr[k - 1], arr[0]
                  generate(k - 1		,lowercase_      )

      generate(len(lowercase_      )		,lowercase_      )
      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))
