




"""simple docstring"""






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)




class   a__			(       nn.Module  ):





						def __init__(							self      :	Any)						->       str:




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:int									=  nn.Linear(3     ,4)
											_lowerCAmelCase:List[str]									=  nn.BatchNormad(4)
											_lowerCAmelCase:Optional[int]									=  nn.Linear(4     ,5)







						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	Tuple)						->       Tuple:




											"""simple docstring"""







											return self.lineara(self.batchnorm(self.lineara(a__)))




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  ModelForTest()
											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(a__     ,model.state_dict())
																_lowerCAmelCase:List[str]									=  os.path.join(a__     ,'''index.json''')
																self.assertTrue(os.path.isfile(a__))
																# TODO: add tests on what is inside the index

																for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
																					_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,F'{key}.dat')
																					self.assertTrue(os.path.isfile(a__))
																					# TODO: add tests on the fact weights are properly loaded





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




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  [torch.floataa, torch.floataa, torch.bfloataa]

											for dtype in dtypes:
																_lowerCAmelCase:Optional[Any]									=  torch.randn(2     ,3     ,dtype=a__)
																with TemporaryDirectory() as tmp_dir:
																					_lowerCAmelCase:int									=  offload_weight(a__     ,'''weight'''     ,a__     ,{})
																					_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''weight.dat''')
																					self.assertTrue(os.path.isfile(a__))
																					self.assertDictEqual(a__     ,{'''weight''': {'''shape''': [2, 3], '''dtype''': str(a__).split('''.''')[1]}})

																					_lowerCAmelCase:int									=  load_offloaded_weight(a__     ,index['''weight'''])
																					self.assertTrue(torch.equal(a__     ,a__))





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  ModelForTest()
											_lowerCAmelCase:Dict									=  model.state_dict()
											_lowerCAmelCase:Tuple									=  {k: v for k, v in state_dict.items() if '''linear2''' not in k}
											_lowerCAmelCase:List[str]									=  {k: v for k, v in state_dict.items() if '''linear2''' in k}

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(a__     ,a__)
																_lowerCAmelCase:int									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

																# Every key is there with the right value
																self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(a__     ,weight_map[key]))

											_lowerCAmelCase:Any									=  {k: v for k, v in state_dict.items() if '''weight''' in k}
											_lowerCAmelCase:str									=  {k: v for k, v in state_dict.items() if '''weight''' not in k}

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(a__     ,a__)
																_lowerCAmelCase:Any									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

																# Every key is there with the right value
																self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(a__     ,weight_map[key]))

											with TemporaryDirectory() as tmp_dir:
																offload_state_dict(a__     ,a__)
																# Duplicates are removed
																_lowerCAmelCase:Dict									=  OffloadedWeightsLoader(state_dict=a__     ,save_folder=a__)

																# Every key is there with the right value
																self.assertEqual(sorted(a__)     ,sorted(state_dict.keys()))
																for key, param in state_dict.items():
																					self.assertTrue(torch.allclose(a__     ,weight_map[key]))







						def       __UpperCamelCase     (							self      :	List[str])						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
											_lowerCAmelCase:str									=  extract_submodules_state_dict(a__     ,['''a.1''', '''a.2'''])
											self.assertDictEqual(a__     ,{'''a.1''': 0, '''a.2''': 2})

											_lowerCAmelCase:Union[str, Any]									=  {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
											_lowerCAmelCase:str									=  extract_submodules_state_dict(a__     ,['''a.1''', '''a.2'''])
											self.assertDictEqual(a__     ,{'''a.1.a''': 0, '''a.2.a''': 2})












"""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()






