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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def 		UpperCAmelCase     (      a_						,  a_						,  a_=1_0_2_4						,  a_=1_0_2_4						,  a_=False						,  **a_		)						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					AutoTokenizer.from_pretrained(a_		)
				__A       =					SeqaSeqDataset(a_						,  a_						,  a_						,  a_						,  type_path="train"						,  **a_		)
				__A       =					tok.pad_token_id

				def get_lens(a_		):
								__A       =					tqdm(
								    DataLoader(a_						,  batch_size=5_1_2						,  num_workers=8						,  shuffle=a_						,  collate_fn=ds.collate_fn		)						,  desc=str(ds.len_file		)						,  )
								__A       =					[]
								for batch in dl:
												__A       =					batch["input_ids"].ne(a_		).sum(1		).tolist()
												__A       =					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

				__A       =					get_lens(a_		)
				__A       =					SeqaSeqDataset(a_						,  a_						,  a_						,  a_						,  type_path="val"						,  **a_		)
				__A       =					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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


