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						(__lowercase ,      __lowercase ,      __lowercase=1_024 ,      __lowercase=1_024 ,      __lowercase=False ,      **__lowercase						)	->	List[str]:

						'''simple docstring'''



						_snake_case						:					int    =   AutoTokenizer.from_pretrained(__lowercase						)
						_snake_case						:					Optional[Any]    =   SeqaSeqDataset(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      type_path="train" ,      **__lowercase						)
						_snake_case						:					Any    =   tok.pad_token_id

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

						_snake_case						:					Any    =   get_lens(__lowercase						)
						_snake_case						:					List[str]    =   SeqaSeqDataset(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      type_path="val" ,      **__lowercase						)
						_snake_case						:					str    =   get_lens(__lowercase						)
						pickle_save(__lowercase ,      train_ds.len_file						)
						pickle_save(__lowercase ,      val_ds.len_file						)


if __name__ == "__main__":
				fire.Fire(save_len_file)
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)