
"""simple docstring"""






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def __magic_name__  (							*_lowerCamelCase:		Union[str, Any] )				->	Dict:

	'''simple docstring'''




	lowerCAmelCase				       =     torch.cuda.device_count()
	print(F"""Device count: {num_processes}""" )
	with patch_environment(
	    world_size=_lowerCamelCase,				master_addr='''127.0.01''',				master_port='''29500''',				mixed_precision=args[1].mixed_precision ):
		lowerCAmelCase				       =     PrepareForLaunch(_lowerCamelCase,				distributed_type='''MULTI_GPU''' )
		torch.multiprocessing.start_processes(_lowerCamelCase,				args=_lowerCamelCase,				nprocs=_lowerCamelCase,				start_method='''spawn''' )


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					UpperCAmelCase          =							rh.cluster(name="""rh-cluster""", instance_type="""V100:4""", provider="""cheapest""", use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					UpperCAmelCase          =							[
					    """pip:./""",
					    """transformers""",
					    """datasets""",
					    """evaluate""",
					    """tqdm""",
					    """scipy""",
					    """scikit-learn""",
					    """tensorboard""",
					    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
					]
					UpperCAmelCase          =							rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

					# Define train args/config, run train function
					UpperCAmelCase          =							argparse.Namespace(cpu=False, mixed_precision="""fp16""")
					UpperCAmelCase          =							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   lowercase__		):
			lowercase			     =    (KDPMaAncestralDiscreteScheduler,)
			lowercase			     =    10






			def 				UpperCAmelCase       (self     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Any )					->  str:





				"""simple docstring"""


				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  int:





				"""simple docstring"""


				for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,[0.00_02, 0.0_02, 0.02] ):
					self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Optional[int]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Dict:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

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






			def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[str]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(prediction_type='''v_prediction''' )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 3_28.99_70 ) < 1e-2
				assert abs(result_mean.item() - 0.42_84 ) < 1e-3






			def 				UpperCAmelCase       (self     :  Dict )					->  Union[str, Any]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps			,device=SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

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


