






"""simple docstring"""







from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_UpperCamelCase					      =  Lock()

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      global process_lock

      # we perform n swaps since after n swaps we know we are sorted
      # we *could* stop early if we are sorted already, but it takes as long to
      # find out we are sorted as it does to sort the list with this algorithm
      for i in range(0				,	10							):
            if (i + position) % 2 == 0 and r_send is not None:
                  # send your value to your right neighbor
                  process_lock.acquire()
                  r_send[1].send(_snake_case							)
                  process_lock.release()

                  # receive your right neighbor's value
                  process_lock.acquire()
                  UpperCAmelCase          =	rr_cv[0].recv()
                  process_lock.release()

                  # take the lower value since you are on the left
                  UpperCAmelCase          =	min(_snake_case				,	_snake_case							)
            elif (i + position) % 2 != 0 and l_send is not None:
                  # send your value to your left neighbor
                  process_lock.acquire()
                  l_send[1].send(_snake_case							)
                  process_lock.release()

                  # receive your left neighbor's value
                  process_lock.acquire()
                  UpperCAmelCase          =	lr_cv[0].recv()
                  process_lock.release()

                  # take the higher value since you are on the right
                  UpperCAmelCase          =	max(_snake_case				,	_snake_case							)
    # after all swaps are performed, send the values back to main
      result_pipe[1].send(_snake_case							)

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]
      # initialize the list of pipes where the values will be retrieved
      for _ in arr:
            result_pipe.append(Pipe()							)
      # creates the processes
      # the first and last process only have one neighbor so they are made outside
      # of the loop
      UpperCAmelCase          =	Pipe()
      UpperCAmelCase          =	Pipe()
      process_array_.append(
          Process(
              target=_snake_case				,	args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])				,	)							)
      UpperCAmelCase          =	temp_rs
      UpperCAmelCase          =	temp_rr

      for i in range(1				,	len(_snake_case							) - 1							):
            UpperCAmelCase          =	Pipe()
            UpperCAmelCase          =	Pipe()
            process_array_.append(
                Process(
                    target=_snake_case				,	args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])				,	)							)
            UpperCAmelCase          =	temp_rs
            UpperCAmelCase          =	temp_rr

      process_array_.append(
          Process(
              target=_snake_case				,	args=(
                  len(_snake_case							) - 1,
                  arr[len(_snake_case							) - 1],
                  temp_ls,
                  None,
                  temp_lr,
                  None,
                  result_pipe[len(_snake_case							) - 1],
              )				,	)							)

      # start the processes
      for p in process_array_:
            p.start()

      # wait for the processes to end and write their values to the list
      for p in range(0				,	len(_snake_case							)							):
            UpperCAmelCase          =	result_pipe[p][0].recv()
            process_array_[p].join()
      return arr

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	list(range(10				,	0				,	-1							)							)
      print("""Initial List"""							)
      print(*_snake_case							)
      UpperCAmelCase          =	odd_even_transposition(_snake_case							)
      print("""Sorted List\n"""							)
      print(*_snake_case							)


if __name__ == "__main__":
  main()











"""simple docstring"""







from typing import TYPE_CHECKING

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


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

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

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

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



