





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


A    :					Optional[Any] =      False


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    str							,	__lowerCAmelCase   :    List[Any]=32   )	->	List[str]:







					"""simple docstring"""






					set_seed(0   )
					A__        =   UNetaDModel(sample_size=__lowerCAmelCase							,	in_channels=3							,	out_channels=3   )
					A__        =   torch.optim.SGD(model.parameters()							,	lr=0.0_0_0_1   )
					return model, optimizer







			@slow
			def    a_       (			self   :    Tuple   )	->	List[str]:







					"""simple docstring"""






					A__        =   """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
					A__        =   DDPMScheduler(
					    num_train_timesteps=10_00							,	beta_start=0.0_0_0_1							,	beta_end=0.0_2							,	beta_schedule="""linear"""							,	clip_sample=__lowerCAmelCase							,	)
					A__        =   DDIMScheduler(
					    num_train_timesteps=10_00							,	beta_start=0.0_0_0_1							,	beta_end=0.0_2							,	beta_schedule="""linear"""							,	clip_sample=__lowerCAmelCase							,	)

					assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

					# shared batches for DDPM and DDIM
					set_seed(0   )
					A__        =   [torch.randn((4, 3, 32, 32)   ).clip(-1							,	1   ).to(__lowerCAmelCase   ) for _ in range(4   )]
					A__        =   [torch.randn((4, 3, 32, 32)   ).to(__lowerCAmelCase   ) for _ in range(4   )]
					A__        =   [torch.randint(0							,	10_00							,	(4,)   ).long().to(__lowerCAmelCase   ) for _ in range(4   )]

					# train with a DDPM scheduler
					A__      ,			A__        =   self.get_model_optimizer(resolution=32   )
					model.train().to(__lowerCAmelCase   )
					for i in range(4   ):
							optimizer.zero_grad()
							A__        =   ddpm_scheduler.add_noise(clean_images[i]							,	noise[i]							,	timesteps[i]   )
							A__        =   model(__lowerCAmelCase							,	timesteps[i]   ).sample
							A__        =   torch.nn.functional.mse_loss(__lowerCAmelCase							,	noise[i]   )
							loss.backward()
							optimizer.step()
					del model, optimizer

					# recreate the model and optimizer, and retry with DDIM
					A__      ,			A__        =   self.get_model_optimizer(resolution=32   )
					model.train().to(__lowerCAmelCase   )
					for i in range(4   ):
							optimizer.zero_grad()
							A__        =   ddim_scheduler.add_noise(clean_images[i]							,	noise[i]							,	timesteps[i]   )
							A__        =   model(__lowerCAmelCase							,	timesteps[i]   ).sample
							A__        =   torch.nn.functional.mse_loss(__lowerCAmelCase							,	noise[i]   )
							loss.backward()
							optimizer.step()
					del model, optimizer

					self.assertTrue(torch.allclose(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-5   )   )
					self.assertTrue(torch.allclose(__lowerCAmelCase							,	__lowerCAmelCase							,	atol=1e-5   )   )










from multiprocessing import Lock, Pipe, Process

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







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :List[str]    ,     __a   :Optional[int]    ,     __a   :Optional[int]    ,     __a   :Optional[Any]    ,     __a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""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    ,     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(__a    )
						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(__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()
						A__        =   lr_cv[0].recv()
						process_lock.release()

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







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""simple docstring"""

		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=__a    ,     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(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     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=__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    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



