




'''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 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]					,     _a			:							Optional[int]=1_0_2_4					,     _a			:							Dict=1_0_2_4					,     _a			:							str=False					,     **_a			:							Union[str, Any]  ):
    UpperCAmelCase     =	AutoTokenizer.from_pretrained(_a  )
    UpperCAmelCase     =	SeqaSeqDataset(_a					,     _a					,     _a					,     _a					,     type_path='''train'''					,     **_a  )
    UpperCAmelCase     =	tok.pad_token_id

    def get_lens(_a			:							Optional[Any]  ):
        UpperCAmelCase     =	tqdm(
            DataLoader(_a					,     batch_size=5_1_2					,     num_workers=8					,     shuffle=_a					,     collate_fn=ds.collate_fn  )					,     desc=str(ds.len_file  )					,     )
        UpperCAmelCase     =	[]
        for batch in dl:
            UpperCAmelCase     =	batch['''input_ids'''].ne(_a  ).sum(1  ).tolist()
            UpperCAmelCase     =	batch['''labels'''].ne(_a  ).sum(1  ).tolist()
            if consider_target:
                for src, tgt in zip(_a					,     _a  ):
                    max_lens.append(max(_a					,     _a  )  )
            else:
                max_lens.extend(_a  )
        return max_lens

    UpperCAmelCase     =	get_lens(_a  )
    UpperCAmelCase     =	SeqaSeqDataset(_a					,     _a					,     _a					,     _a					,     type_path='''val'''					,     **_a  )
    UpperCAmelCase     =	get_lens(_a  )
    pickle_save(_a					,     train_ds.len_file  )
    pickle_save(_a					,     val_ds.len_file  )


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










'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 		snake_case_							(_a			:							List[str]  ):
    for param in module.parameters():
        UpperCAmelCase     =	False





def 		snake_case_							():
    UpperCAmelCase     =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        UpperCAmelCase     =	'''mps'''
    if device == "mps":
        print(
            '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
            ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
            ''' with generations.'''  )
    return device





def 		snake_case_							(_a			:							Optional[Any]  ):
    UpperCAmelCase     =	plt.imshow(_a  )
    fig.axes.get_xaxis().set_visible(_a  )
    fig.axes.get_yaxis().set_visible(_a  )
    plt.show()







def 		snake_case_							():
    UpperCAmelCase     =	datetime.now()
    UpperCAmelCase     =	current_time.strftime('''%H:%M:%S'''  )
    return timestamp




