



from multiprocessing import Lock, Pipe, Process

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       List[str]	)->  List[str]:
    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 ,       1_0	):
        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(UpperCamelCase_	)
            process_lock.release()

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

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

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

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any]	)->  Optional[int]:
    A__		 =					[]
    A__		 =					[]
    # 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
    A__		 =					Pipe()
    A__		 =					Pipe()
    process_array_.append(
        Process(
            target=UpperCamelCase_ ,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,       )	)
    A__		 =					temp_rs
    A__		 =					temp_rr

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

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



def 		lowerCAmelCase__    (					)->  Union[str, Any]:
    A__		 =					list(range(1_0 ,       0 ,       -1	)	)
    print('''Initial List'''	)
    print(*UpperCamelCase_	)
    A__		 =					odd_even_transposition(UpperCamelCase_	)
    print('''Sorted List\n'''	)
    print(*UpperCamelCase_	)


if __name__ == "__main__":
   main()





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(DDPMScheduler,)




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,						[0.0_0_2, 0.0_2, 0.2, 2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=a__)




     def 		snake_case_		(				self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a__)




     def 		snake_case_		(				self):
         self.check_over_configs(thresholding=a__)
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=a__ ,						prediction_type=a__ ,						sample_max_value=a__ ,						)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_0_9_7_9)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

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

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

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

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

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

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

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

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=a__)

         A__		 =					scheduler.timesteps

         for i, timestep in enumerate(a__):
             if i == len(a__) - 1:
                 A__		 =					-1
             else:
                 A__		 =					timesteps[i + 1]

             A__		 =					scheduler.previous_timestep(a__)
             A__		 =					prev_t.item()

             self.assertEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(a__ ,						msg='''`custom_timesteps` must be in descending order.'''):
             scheduler.set_timesteps(timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]
         A__		 =					len(a__)

         with self.assertRaises(a__ ,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
             scheduler.set_timesteps(num_inference_steps=a__ ,						timesteps=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             a__ ,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,						):
             scheduler.set_timesteps(timesteps=a__)
