




"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__						=							logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''encoder-decoder'''
						snake_case__             = True





						def __init__(							self      :	str     ,**a__      :	Union[str, Any])						->       Union[str, Any]:




											"""simple docstring"""







											super().__init__(**a__)
											assert (
											    "encoder" in kwargs and "decoder" in kwargs
											), "Config has to be initialized with encoder and decoder config"
											_lowerCAmelCase:Optional[int]									=  kwargs.pop('''encoder''')
											_lowerCAmelCase:Tuple									=  encoder_config.pop('''model_type''')
											_lowerCAmelCase:int									=  kwargs.pop('''decoder''')
											_lowerCAmelCase:str									=  decoder_config.pop('''model_type''')

											from ..auto.configuration_auto import AutoConfig

											_lowerCAmelCase:Any									=  AutoConfig.for_model(a__     ,**a__)
											_lowerCAmelCase:Union[str, Any]									=  AutoConfig.for_model(a__     ,**a__)
											_lowerCAmelCase:Optional[int]									=  True





						@classmethod
						def       __UpperCamelCase     (							cls      :	Optional[int]     ,a__      :	PretrainedConfig     ,a__      :	PretrainedConfig     ,**a__      :	List[Any])						->       PretrainedConfig:




											"""simple docstring"""







											logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
											_lowerCAmelCase:Dict									=  True
											_lowerCAmelCase:Any									=  True

											return cls(encoder=encoder_config.to_dict()     ,decoder=decoder_config.to_dict()     ,**a__)







						def       __UpperCamelCase     (							self      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  copy.deepcopy(self.__dict__)
											_lowerCAmelCase:List[str]									=  self.encoder.to_dict()
											_lowerCAmelCase:Dict									=  self.decoder.to_dict()
											_lowerCAmelCase:List[Any]									=  self.__class__.model_type
											return output












"""simple docstring"""






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  ArgumentParser(
					    description=(
					        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
					    )    )

					# Optional arguments for the launch helper
					parser.add_argument('''--num_cores'''      ,       type=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=snake_case      ,       help=(
					        '''The full path to the single TPU training '''
					        '''program/script to be launched in parallel, '''
					        '''followed by all the arguments for the '''
					        '''training script'''
					    )      ,       )

					# rest from the training program
					parser.add_argument('''training_script_args'''      ,       nargs=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

					# Import training_script as a module.
					_lowerCAmelCase:Optional[int]									=  Path(args.training_script    )
					sys.path.append(str(script_fpath.parent.resolve()    )    )
					_lowerCAmelCase:Optional[int]									=  script_fpath.stem
					_lowerCAmelCase:Any									=  importlib.import_module(snake_case    )

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores    )]

					xmp.spawn(mod._mp_fn      ,       args=()      ,       nprocs=args.num_cores    )


if __name__ == "__main__":
				main()






