


'''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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]=1_024						,      _SCREAMING_SNAKE_CASE      :   Optional[int]=1_024						,      _SCREAMING_SNAKE_CASE      :   Tuple=False						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Dict					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  SeqaSeqDataset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      type_path='train'						,      **_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  tok.pad_token_id

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

					__a			:       Optional[int]					    =  get_lens(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  SeqaSeqDataset(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      type_path='val'						,      **_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  get_lens(_SCREAMING_SNAKE_CASE   )
					pickle_save(_SCREAMING_SNAKE_CASE						,      train_ds.len_file   )
					pickle_save(_SCREAMING_SNAKE_CASE						,      val_ds.len_file   )


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







'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



