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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       int=1024		,	_lowerCamelCase							:       List[Any]=1024		,	_lowerCamelCase							:       List[Any]=False		,	**_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Optional[Any]    					=				AutoTokenizer.from_pretrained(_lowerCamelCase)
    lowercase__ :   Optional[Any]    					=				SeqaSeqDataset(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	type_path="train"		,	**_lowerCamelCase)
    lowercase__ :   Union[str, Any]    					=				tok.pad_token_id

    def get_lens(_lowerCamelCase							:       Optional[Any]):
        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__ :   List[str]    					=				[]
        for batch in dl:
            lowercase__ :   Any    					=				batch["input_ids"].ne(_lowerCamelCase).sum(1).tolist()
            lowercase__ :   List[str]    					=				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__ :   List[Any]    					=				get_lens(_lowerCamelCase)
    lowercase__ :   List[Any]    					=				SeqaSeqDataset(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase		,	type_path="val"		,	**_lowerCamelCase)
    lowercase__ :   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)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





