





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_snake_case  :    List[str]						    =  logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = field(default=_UpperCamelCase		,    metadata={"""help""": """Whether to use SortishSampler or not."""}    )
  a_        = field(
      default=_UpperCamelCase		,    metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}    )
  a_        = field(
      default=_UpperCamelCase		,    metadata={
          """help""": (
              """The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default """
              """to the `max_length` value of the model configuration."""
          )
      }		,    )
  a_        = field(
      default=_UpperCamelCase		,    metadata={
          """help""": (
              """The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default """
              """to the `num_beams` value of the model configuration."""
          )
      }		,    )
  a_        = field(
      default=_UpperCamelCase		,    metadata={
          """help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
      }		,    )






  def 				lowercase		(				self :		Tuple     )							->						Dict:
     __lowerCAmelCase								=	super().to_dict()
     for k, v in d.items():
        if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ):
           __lowerCAmelCase								=	v.to_dict()
     return d








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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	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

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	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)

