


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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def      lowerCAmelCase__	(    _a       :			List[str]  ,      _a       :			str  ,      _a       :			List[str]=10_24  ,      _a       :			int=10_24  ,      _a       :			int=False  ,      **_a       :			Optional[Any]    ):
		snake_case_				:							Optional[Any]						 =       AutoTokenizer.from_pretrained(_a    )
		snake_case_				:							List[Any]						 =       SeqaSeqDataset(_a  ,      _a  ,      _a  ,      _a  ,      type_path="train"  ,      **_a    )
		snake_case_				:							Tuple						 =       tok.pad_token_id

		def get_lens(_a       :			str    ):
				snake_case_				:							Optional[int]						 =       tqdm(
				    DataLoader(_a  ,      batch_size=5_12  ,      num_workers=8  ,      shuffle=_a  ,      collate_fn=ds.collate_fn    )  ,      desc=str(ds.len_file    )  ,      )
				snake_case_				:							List[Any]						 =       []
				for batch in dl:
						snake_case_				:							Dict						 =       batch["input_ids"].ne(_a    ).sum(1    ).tolist()
						snake_case_				:							Optional[int]						 =       batch["labels"].ne(_a    ).sum(1    ).tolist()
						if consider_target:
								for src, tgt in zip(_a  ,      _a    ):
										max_lens.append(max(_a  ,      _a    )    )
						else:
								max_lens.extend(_a    )
				return max_lens

		snake_case_				:							str						 =       get_lens(_a    )
		snake_case_				:							Tuple						 =       SeqaSeqDataset(_a  ,      _a  ,      _a  ,      _a  ,      type_path="val"  ,      **_a    )
		snake_case_				:							Optional[int]						 =       get_lens(_a    )
		pickle_save(_a  ,      train_ds.len_file    )
		pickle_save(_a  ,      val_ds.len_file    )


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :							Optional[int]			  =    {
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NllbMoeForConditionalGeneration''',
							    '''NllbMoeModel''',
							    '''NllbMoePreTrainedModel''',
							    '''NllbMoeTop2Router''',
							    '''NllbMoeSparseMLP''',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
