'''simple docstring'''





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[int]          =  (UnCLIPScheduler,)



    def 					_lowercase						(self    :    str   ,			**__a    :    Optional[Any] ):
         UpperCAmelCase_           =   {
             "num_train_timesteps": 1000,
             "variance_type": "fixed_small_log",
             "clip_sample": True,
             "clip_sample_range": 1.0,
             "prediction_type": "epsilon",
         }

         config.update(**__a )
         return config



    def 					_lowercase						(self    :    Any ):
         for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__a )



    def 					_lowercase						(self    :    List[Any] ):
         for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                   if prev_timestep is not None and prev_timestep >= time_step:
                        continue

                   self.check_over_forward(time_step=__a   ,			prev_timestep=__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(variance_type="fixed_small_log" )
         UpperCAmelCase_           =   scheduler_class(**__a )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000E-10 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_54_96_25 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_99_49_87 ) ) < 1E-5



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config(variance_type="learned_range" )
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   0.5

         assert scheduler._get_variance(1   ,			predicted_variance=__a ) - -10.1_71_27_90 < 1E-5
         assert scheduler._get_variance(487   ,			predicted_variance=__a ) - -5.7_99_80_52 < 1E-5
         assert scheduler._get_variance(999   ,			predicted_variance=__a ) - -0.0_01_00_11 < 1E-5



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config()
         UpperCAmelCase_           =   scheduler_class(**__a )

         UpperCAmelCase_           =   scheduler.timesteps

         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter
         UpperCAmelCase_           =   torch.manual_seed(0 )

         for i, t in enumerate(__a ):
              # 1. predict noise residual
              UpperCAmelCase_           =   model(__a   ,			__a )

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_           =   scheduler.step(__a   ,			__a   ,			__a   ,			generator=__a ).prev_sample

              UpperCAmelCase_           =   pred_prev_sample

         UpperCAmelCase_           =   torch.sum(torch.abs(__a ) )
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2
         assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.scheduler_classes[0]
         UpperCAmelCase_           =   self.get_scheduler_config()
         UpperCAmelCase_           =   scheduler_class(**__a )

         scheduler.set_timesteps(25 )

         UpperCAmelCase_           =   scheduler.timesteps

         UpperCAmelCase_           =   self.dummy_model()
         UpperCAmelCase_           =   self.dummy_sample_deter
         UpperCAmelCase_           =   torch.manual_seed(0 )

         for i, t in enumerate(__a ):
              # 1. predict noise residual
              UpperCAmelCase_           =   model(__a   ,			__a )

              if i + 1 == timesteps.shape[0]:
                   UpperCAmelCase_           =   None
              else:
                   UpperCAmelCase_           =   timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase_           =   scheduler.step(
                  __a   ,			__a   ,			__a   ,			prev_timestep=__a   ,			generator=__a ).prev_sample

              UpperCAmelCase_           =   pred_prev_sample

         UpperCAmelCase_           =   torch.sum(torch.abs(__a ) )
         UpperCAmelCase_           =   torch.mean(torch.abs(__a ) )

         assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2
         assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3



    def 					_lowercase						(self    :    List[str] ):
         pass






    def 					_lowercase						(self    :    Any ):
         pass





'''simple docstring'''





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_:     Optional[int]  =Lock()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''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 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''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 	lowerCAmelCase_				(					)    ->      str:


     '''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()




