

from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_SCREAMING_SNAKE_CASE					:  List[str]             =      Lock()
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A				,		_A				,		_A    ):





     '''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(_A    )
               process_lock.release()

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

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

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

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





     '''simple docstring'''




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

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

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





     '''simple docstring'''




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


if __name__ == "__main__":
       main()





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


