


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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=1024							, lowerCamelCase_=1024							, lowerCamelCase_=False							, **lowerCamelCase_ )    ->						int:
    _lowercase       :							Optional[Any]      		=		AutoTokenizer.from_pretrained(lowerCamelCase_ )
    _lowercase       :							Union[str, Any]      		=		SeqaSeqDataset(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, type_path='train'							, **lowerCamelCase_ )
    _lowercase       :							Tuple      		=		tok.pad_token_id

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

    _lowercase       :							Dict      		=		get_lens(lowerCamelCase_ )
    _lowercase       :							List[Any]      		=		SeqaSeqDataset(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, type_path='val'							, **lowerCamelCase_ )
    _lowercase       :							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)






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

SCREAMING_SNAKE_CASE       :      Tuple												= (3, 9, -11, 0, 7, 5, 1, -1)
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= (4, 6, 2, 0, 8, 10, 3, -2)






@dataclass
class _lowerCamelCase:
       lowercase_ :	int
       lowercase_ :	Node | None






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase)       ->   None:




           """simple docstring"""

           _lowercase       :							Node | None      		=		None
           for i in sorted(lowerCamelCase,      reverse=lowerCamelCase):
               _lowercase       :							Tuple      		=		Node(lowerCamelCase,      self.head)
       def __iter__(					self)       ->   Iterator[int]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.head
           while node:
               yield node.data
               _lowercase       :							int      		=		node.next_node
       def __len__(					self)       ->   int:




           """simple docstring"""

           return sum(1 for _ in self)




       def __str__(					self)       ->   str:




           """simple docstring"""

           return " -> ".join([str(lowerCamelCase) for node in self])



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						SortedLinkedList:
    return SortedLinkedList(list(lowerCamelCase_ ) + list(lowerCamelCase_ ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE       :      int												= SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


