



import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(VQDiffusionScheduler,)




			def 			snake_case__					(       self							:			Dict	,	**a_							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     {
					    '''num_vec_classes''': 40_97,
					    '''num_train_timesteps''': 1_00,
					}

					config.update(**a_			)
					return config




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





					__UpperCAmelCase   :					Dict													=     4
					__UpperCAmelCase   :					int													=     8
					__UpperCAmelCase   :					int													=     8

					__UpperCAmelCase   :					Optional[int]													=     torch.randint(0	,	a_	,	(batch_size, height * width)			)

					return sample




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





					assert False




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			Dict			):
					'''simple docstring'''





					def model(a_							:			Union[str, Any]	,	a_							:			Optional[int]	,	*a_							:			List[Any]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     sample.shape
							__UpperCAmelCase   :					Optional[Any]													=     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
							__UpperCAmelCase   :					Any													=     F.log_softmax(logits.double()	,	dim=1			).float()
							return return_value

					return model




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





					for timesteps in [2, 5, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=a_			)




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





					for num_vec_classes in [5, 1_00, 10_00, 40_00]:
							self.check_over_configs(num_vec_classes=a_			)




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





					for t in [0, 50, 99]:
							self.check_over_forward(time_step=a_			)




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





					pass










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()





