






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       List[str]=1_0_2_4   ,  lowerCamelCase_       :       Optional[Any]=1_0_2_4   ,  lowerCamelCase_       :       Optional[Any]=False   ,  **lowerCamelCase_       :       str				):
			__a						:  Optional[Any]									=     AutoTokenizer.from_pretrained(lowerCamelCase_				)
			__a						:  Tuple									=     SeqaSeqDataset(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  type_path='train'   ,  **lowerCamelCase_				)
			__a						:  Dict									=     tok.pad_token_id

			def get_lens(lowerCamelCase_       :       int				):
						__a						:  Tuple									=     tqdm(
						    DataLoader(lowerCamelCase_   ,  batch_size=5_1_2   ,  num_workers=8   ,  shuffle=lowerCamelCase_   ,  collate_fn=ds.collate_fn				)   ,  desc=str(ds.len_file				)   ,  )
						__a						:  Optional[int]									=     []
						for batch in dl:
									__a						:  int									=     batch['input_ids'].ne(lowerCamelCase_				).sum(1				).tolist()
									__a						:  Optional[Any]									=     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						:  int									=     get_lens(lowerCamelCase_				)
			__a						:  List[Any]									=     SeqaSeqDataset(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  type_path='val'   ,  **lowerCamelCase_				)
			__a						:  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"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """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

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



