


'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[int]=1024  ,lowerCamelCase					:      List[Any]=1024  ,lowerCamelCase					:      Dict=False  ,**lowerCamelCase					:      Optional[Any]       ):
       _A					:     Any     = AutoTokenizer.from_pretrained(lowerCamelCase       )
       _A					:     List[Any]     = SeqaSeqDataset(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,type_path='train'  ,**lowerCamelCase       )
       _A					:     Optional[int]     = tok.pad_token_id

       def get_lens(lowerCamelCase					:      Optional[int]       ):
              _A					:     List[Any]     = tqdm(
                  DataLoader(lowerCamelCase  ,batch_size=512  ,num_workers=8  ,shuffle=lowerCamelCase  ,collate_fn=ds.collate_fn       )  ,desc=str(ds.len_file       )  ,)
              _A					:     List[str]     = []
              for batch in dl:
                     _A					:     Optional[Any]     = batch['input_ids'].ne(lowerCamelCase       ).sum(1       ).tolist()
                     _A					:     str     = batch['labels'].ne(lowerCamelCase       ).sum(1       ).tolist()
                     if consider_target:
                            for src, tgt in zip(lowerCamelCase  ,lowerCamelCase       ):
                                   max_lens.append(max(lowerCamelCase  ,lowerCamelCase       )       )
                     else:
                            max_lens.extend(lowerCamelCase       )
              return max_lens

       _A					:     Dict     = get_lens(lowerCamelCase       )
       _A					:     List[str]     = SeqaSeqDataset(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,type_path='val'  ,**lowerCamelCase       )
       _A					:     Optional[Any]     = get_lens(lowerCamelCase       )
       pickle_save(lowerCamelCase  ,train_ds.len_file       )
       pickle_save(lowerCamelCase  ,val_ds.len_file       )


if __name__ == "__main__":
   fire.Fire(save_len_file)










'''simple docstring'''







import operator as op


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

A				:					List[Any]    						=			{'''>''': 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				:					Tuple    						=			[
    '''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				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






