



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(KDPMaAncestralDiscreteScheduler,)
			UpperCamelCase      						=	10




			def 			snake_case__					(       self							:			Optional[int]	,	**a_							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     {
					    '''num_train_timesteps''': 11_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**a_			)
					return config




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					for timesteps in [10, 50, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=a_			)




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,	[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
							self.check_over_configs(beta_start=a_	,	beta_end=a_			)




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=a_			)




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					str													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Union[str, Any]													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Any													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__UpperCAmelCase   :					List[str]													=     torch.manual_seed(0			)

					__UpperCAmelCase   :					Dict													=     self.dummy_model()
					__UpperCAmelCase   :					List[Any]													=     self.dummy_sample_deter * scheduler.init_noise_sigma
					__UpperCAmelCase   :					Any													=     sample.to(a_			)

					for i, t in enumerate(scheduler.timesteps			):
							__UpperCAmelCase   :					Dict													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					Optional[Any]													=     model(a_	,	a_			)

							__UpperCAmelCase   :					int													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					Tuple													=     output.prev_sample

					__UpperCAmelCase   :					Optional[int]													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					int													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_38_49.38_77			) < 1e-2
					assert abs(result_mean.item() - 18.03_31			) < 5e-3




			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=a_			)




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					List[str]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Dict													=     self.get_scheduler_config(prediction_type='''v_prediction'''			)
					__UpperCAmelCase   :					Dict													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__UpperCAmelCase   :					Union[str, Any]													=     self.dummy_model()
					__UpperCAmelCase   :					List[str]													=     self.dummy_sample_deter * scheduler.init_noise_sigma
					__UpperCAmelCase   :					str													=     sample.to(a_			)

					__UpperCAmelCase   :					str													=     torch.manual_seed(0			)

					for i, t in enumerate(scheduler.timesteps			):
							__UpperCAmelCase   :					Optional[Any]													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					Dict													=     model(a_	,	a_			)

							__UpperCAmelCase   :					Optional[Any]													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					str													=     output.prev_sample

					__UpperCAmelCase   :					Tuple													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					str													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 3_28.99_70			) < 1e-2
					assert abs(result_mean.item() - 0.4_2_8_4			) < 1e-3




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					Union[str, Any]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Dict													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Tuple													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps	,	device=a_			)
					__UpperCAmelCase   :					Optional[int]													=     torch.manual_seed(0			)

					__UpperCAmelCase   :					Tuple													=     self.dummy_model()
					__UpperCAmelCase   :					Tuple													=     self.dummy_sample_deter.to(a_			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__UpperCAmelCase   :					Tuple													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					int													=     model(a_	,	a_			)

							__UpperCAmelCase   :					Any													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					Union[str, Any]													=     output.prev_sample

					__UpperCAmelCase   :					List[Any]													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Optional[Any]													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_38_49.38_18			) < 1e-1
					assert abs(result_mean.item() - 18.03_31			) < 1e-3










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





