



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=1024							,   UpperCamelCase=1024							,   UpperCamelCase=False							,   **UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= AutoTokenizer.from_pretrained(__snake_case      )
    lowerCAmelCase__			:  int											= SeqaSeqDataset(__snake_case							,   __snake_case							,   __snake_case							,   __snake_case							,   type_path="""train"""							,   **__snake_case      )
    lowerCAmelCase__			:  Dict											= tok.pad_token_id

    def get_lens(UpperCamelCase      ):
        lowerCAmelCase__			:  int											= tqdm(
            DataLoader(__snake_case							,   batch_size=512							,   num_workers=8							,   shuffle=__snake_case							,   collate_fn=ds.collate_fn      )							,   desc=str(ds.len_file      )							,   )
        lowerCAmelCase__			:  Dict											= []
        for batch in dl:
            lowerCAmelCase__			:  Tuple											= batch["""input_ids"""].ne(__snake_case      ).sum(1      ).tolist()
            lowerCAmelCase__			:  str											= 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

    lowerCAmelCase__			:  Any											= get_lens(__snake_case      )
    lowerCAmelCase__			:  int											= SeqaSeqDataset(__snake_case							,   __snake_case							,   __snake_case							,   __snake_case							,   type_path="""val"""							,   **__snake_case      )
    lowerCAmelCase__			:  Any											= 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)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 _lowerCAmelCase										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


