

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__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=1_0_2_4	, lowerCamelCase__=1_0_2_4	, lowerCamelCase__=False	, **lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				Optional[Any]					       = AutoTokenizer.from_pretrained(lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = SeqaSeqDataset(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, type_path='train'	, **lowerCamelCase__			)
	__lowerCamelCase				:				Union[str, Any]					       = tok.pad_token_id

	def get_lens(lowerCamelCase__			):
		__lowerCamelCase				:				Union[str, Any]					       = tqdm(
		    DataLoader(lowerCamelCase__	, batch_size=5_1_2	, num_workers=8	, shuffle=lowerCamelCase__	, collate_fn=ds.collate_fn			)	, desc=str(ds.len_file			)	, )
		__lowerCamelCase				:				Any					       = []
		for batch in dl:
			__lowerCamelCase				:				int					       = batch['input_ids'].ne(lowerCamelCase__			).sum(1			).tolist()
			__lowerCamelCase				:				Union[str, Any]					       = 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				:				Any					       = get_lens(lowerCamelCase__			)
	__lowerCamelCase				:				Union[str, Any]					       = SeqaSeqDataset(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, type_path='val'	, **lowerCamelCase__			)
	__lowerCamelCase				:				Tuple					       = 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)








'''simple docstring'''




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    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	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.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	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.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





