




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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_=1024,snake_case_=1024,snake_case_=False,**snake_case_  ):
      _A							:  Tuple    =		AutoTokenizer.from_pretrained(snake_case_  )
      _A							:  Tuple    =		SeqaSeqDataset(snake_case_,snake_case_,snake_case_,snake_case_,type_path="""train""",**snake_case_  )
      _A							:  List[str]    =		tok.pad_token_id

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

      _A							:  List[Any]    =		get_lens(snake_case_  )
      _A							:  Any    =		SeqaSeqDataset(snake_case_,snake_case_,snake_case_,snake_case_,type_path="""val""",**snake_case_  )
      _A							:  Dict    =		get_lens(snake_case_  )
      pickle_save(snake_case_,train_ds.len_file  )
      pickle_save(snake_case_,val_ds.len_file  )


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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

