


'''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      :Optional[Any]			    =       Lock()




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     int      , _lowercase :     Tuple      , _lowercase :     Dict      , _lowercase :     Union[str, Any]      , _lowercase :     Dict      , _lowercase :     Tuple  ):





     '''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(_lowercase  )
               process_lock.release()

               # receive your right neighbor's value
               process_lock.acquire()
               __UpperCAmelCase     :			Union[str, Any]  							=      rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               __UpperCAmelCase     :			Union[str, Any]  							=      min(_lowercase      , _lowercase  )
          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(_lowercase  )
               process_lock.release()

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

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




def      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      []
     __UpperCAmelCase     :			Dict  							=      []
     # 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     :			int  							=      Pipe()
     __UpperCAmelCase     :			List[Any]  							=      Pipe()
     process_array_.append(
         Process(
             target=_lowercase      , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])      , )  )
     __UpperCAmelCase     :			Union[str, Any]  							=      temp_rs
     __UpperCAmelCase     :			int  							=      temp_rr

     for i in range(1      , len(_lowercase  ) - 1  ):
          __UpperCAmelCase     :			Dict  							=      Pipe()
          __UpperCAmelCase     :			str  							=      Pipe()
          process_array_.append(
              Process(
                  target=_lowercase      , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])      , )  )
          __UpperCAmelCase     :			int  							=      temp_rs
          __UpperCAmelCase     :			Union[str, Any]  							=      temp_rr

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




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      list(range(10      , 0      , -1  )  )
     print('''Initial List'''  )
     print(*_lowercase  )
     __UpperCAmelCase     :			List[Any]  							=      odd_even_transposition(_lowercase  )
     print('''Sorted List\n'''  )
     print(*_lowercase  )


if __name__ == "__main__":
      main()



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "RagModel",
          "RagPreTrainedModel",
          "RagSequenceForGeneration",
          "RagTokenForGeneration",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "TFRagModel",
          "TFRagPreTrainedModel",
          "TFRagSequenceForGeneration",
          "TFRagTokenForGeneration",
      ]


if TYPE_CHECKING:
      from .configuration_rag import RagConfig
      from .retrieval_rag import RagRetriever
      from .tokenization_rag import RagTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rag import (
                TFRagModel,
                TFRagPreTrainedModel,
                TFRagSequenceForGeneration,
                TFRagTokenForGeneration,
            )

else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)