

'''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 				_lowerCAmelCase			(   __magic_name__						:     Dict   , __magic_name__						:     Dict   , __magic_name__						:     Optional[Any]=1024   , __magic_name__						:     Dict=1024   , __magic_name__						:     Dict=False   , **__magic_name__						:     List[Any]						)				->  str:
						lowercase :							int    		=AutoTokenizer.from_pretrained(__magic_name__						)
						lowercase :							int    		=SeqaSeqDataset(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , type_path='''train'''   , **__magic_name__						)
						lowercase :							List[Any]    		=tok.pad_token_id

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

						lowercase :							int    		=get_lens(__magic_name__						)
						lowercase :							List[str]    		=SeqaSeqDataset(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , type_path='''val'''   , **__magic_name__						)
						lowercase :							Dict    		=get_lens(__magic_name__						)
						pickle_save(__magic_name__   , train_ds.len_file						)
						pickle_save(__magic_name__   , val_ds.len_file						)


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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




