





"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=						{
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  __A											=						_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))
