




import operator as op


_A     =			'''scaler.pt'''
_A     =			'''pytorch_model'''
_A     =			'''random_states'''
_A     =			'''optimizer'''
_A     =			'''scheduler'''
_A     =			'''pytorch_model.bin'''
_A     =			'''pytorch_model.bin.index.json'''
_A     =			'''model.safetensors'''
_A     =			'''model.safetensors.index.json'''
_A     =			'''1.10.2'''
_A     =			'''py38'''
_A     =			'''4.17.0'''
_A     =			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
_A     =			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
_A     =			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
_A     =			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
_A     =			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
_A     =			'''2.0.1'''
_A     =			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
_A     =			['''default''', '''reduce-overhead''', '''max-autotune''']

_A     =			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
_A     =			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

_A     =			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
_A     =			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']







def    __UpperCAmelCase (	__a      :			str	)			->     int:

					"""simple docstring"""

					assert column_title.isupper()
					_a	:    Optional[Any]         =		0
					_a	:    List[Any]         =		len(__a	) - 1
					_a	:    List[str]         =		0

					while index >= 0:
										_a	:    Dict         =		(ord(column_title[index]	) - 64) * pow(26		,__a	)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()



