
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=1_0_2_4,		__SCREAMING_SNAKE_CASE=1_0_2_4,		__SCREAMING_SNAKE_CASE=False,		**__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						SeqaSeqDataset(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		type_path="train",		**__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Dict     			=						tok.pad_token_id

    def get_lens(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    List[Any]     			=						tqdm(
            DataLoader(__SCREAMING_SNAKE_CASE,		batch_size=5_1_2,		num_workers=8,		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=ds.collate_fn       ),		desc=str(ds.len_file       ),		)
        snake_case_ :    Any     			=						[]
        for batch in dl:
            snake_case_ :    Optional[Any]     			=						batch["input_ids"].ne(__SCREAMING_SNAKE_CASE       ).sum(1       ).tolist()
            snake_case_ :    Union[str, Any]     			=						batch["labels"].ne(__SCREAMING_SNAKE_CASE       ).sum(1       ).tolist()
            if consider_target:
                for src, tgt in zip(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
                    max_lens.append(max(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )
            else:
                max_lens.extend(__SCREAMING_SNAKE_CASE       )
        return max_lens

    snake_case_ :    int     			=						get_lens(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						SeqaSeqDataset(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		type_path="val",		**__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						get_lens(__SCREAMING_SNAKE_CASE       )
    pickle_save(__SCREAMING_SNAKE_CASE,		train_ds.len_file       )
    pickle_save(__SCREAMING_SNAKE_CASE,		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_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




