






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__snake_case      :      Union[str, Any]             =       Lock()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Union[str, Any]						)					->				str:

      """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(__lowerCAmelCase						)
                  process_lock.release()

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

                  # take the lower value since you are on the left
                  lowerCAmelCase__        =  min(__lowerCAmelCase      ,    __lowerCAmelCase						)
            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(__lowerCAmelCase						)
                  process_lock.release()

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

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				Union[str, Any]:

      """simple docstring"""



      lowerCAmelCase__        =  []
      lowerCAmelCase__        =  []
      # 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
      lowerCAmelCase__        =  Pipe()
      lowerCAmelCase__        =  Pipe()
      process_array_.append(
          Process(
              target=__lowerCAmelCase      ,    args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])      ,    )						)
      lowerCAmelCase__        =  temp_rs
      lowerCAmelCase__        =  temp_rr

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

      process_array_.append(
          Process(
              target=__lowerCAmelCase      ,    args=(
                  len(__lowerCAmelCase						) - 1,
                  arr[len(__lowerCAmelCase						) - 1],
                  temp_ls,
                  None,
                  temp_lr,
                  None,
                  result_pipe[len(__lowerCAmelCase						) - 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(__lowerCAmelCase						)						):
            lowerCAmelCase__        =  result_pipe[p][0].recv()
            process_array_[p].join()
      return arr







def 		_UpperCamelCase				(      )					->				List[Any]:

      """simple docstring"""



      lowerCAmelCase__        =  list(range(10      ,    0      ,    -1						)						)
      print('Initial List'						)
      print(*__lowerCAmelCase						)
      lowerCAmelCase__        =  odd_even_transposition(__lowerCAmelCase						)
      print('Sorted List\n'						)
      print(*__lowerCAmelCase						)


if __name__ == "__main__":
     main()













from typing import TYPE_CHECKING

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


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

					__snake_case      :      Dict             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





