'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_						:       Tuple      			=				logging.get_logger(__name__)

a_						:       List[str]      			=				{
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"audio-spectrogram-transformer"






  def __init__(					self    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-12    ,						UpperCamelCase=16    ,						UpperCamelCase=True    ,						UpperCamelCase=10    ,						UpperCamelCase=10    ,						UpperCamelCase=1024    ,						UpperCamelCase=128    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )

   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						patch_size
   lowerCamelCase_  				=						qkv_bias
   lowerCamelCase_  				=						frequency_stride
   lowerCamelCase_  				=						time_stride
   lowerCamelCase_  				=						max_length
   lowerCamelCase_  				=						num_mel_bins







'''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 __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

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

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


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



