



from __future__ import annotations

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

__snake_case    :		Dict  =(3, 9, -1_1, 0, 7, 5, 1, -1)
__snake_case    :		Optional[int]  =(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class 	lowerCamelCase__      :




		'''simple docstring'''



		snake_case_     =42
		snake_case_     =42



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Node | None												=					None
						for i in sorted(__lowerCamelCase		,reverse=__lowerCamelCase			):
										lowerCAmelCase__					:    Any												=					Node(__lowerCamelCase		,self.head			)



		def __iter__(self			)					->      Iterator[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.head
						while node:
										yield node.data
										lowerCAmelCase__					:    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      lowerCAmelCase__		(   lowerCamelCase_		:		SortedLinkedList	,lowerCamelCase_		:		SortedLinkedList):





				'''simple docstring'''

				return SortedLinkedList(list(lowerCamelCase_) + list(lowerCamelCase_))


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__snake_case    :		Optional[Any]  =SortedLinkedList
							print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

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

