






"""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       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=1024	,      lowerCAmelCase=1024	,      lowerCAmelCase=False	,      **lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			AutoTokenizer.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			SeqaSeqDataset(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      type_path="""train"""	,      **lowerCAmelCase		)
       UpperCAmelCase	    =			tok.pad_token_id

       def get_lens(lowerCAmelCase		):
              UpperCAmelCase	    =			tqdm(
                  DataLoader(lowerCAmelCase	,      batch_size=512	,      num_workers=8	,      shuffle=lowerCAmelCase	,      collate_fn=ds.collate_fn		)	,      desc=str(ds.len_file		)	,      )
              UpperCAmelCase	    =			[]
              for batch in dl:
                     UpperCAmelCase	    =			batch["""input_ids"""].ne(lowerCAmelCase		).sum(1		).tolist()
                     UpperCAmelCase	    =			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

       UpperCAmelCase	    =			get_lens(lowerCAmelCase		)
       UpperCAmelCase	    =			SeqaSeqDataset(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      type_path="""val"""	,      **lowerCAmelCase		)
       UpperCAmelCase	    =			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)








"""simple docstring"""







import math
from typing import Optional

import numpy as np

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


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[Any]            =      {
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			target_bandwidths
         UpperCAmelCase	    =			sampling_rate
         UpperCAmelCase	    =			audio_channels
         UpperCAmelCase	    =			normalize
         UpperCAmelCase	    =			chunk_length_s
         UpperCAmelCase	    =			overlap
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_filters
         UpperCAmelCase	    =			num_residual_layers
         UpperCAmelCase	    =			upsampling_ratios
         UpperCAmelCase	    =			norm_type
         UpperCAmelCase	    =			kernel_size
         UpperCAmelCase	    =			last_kernel_size
         UpperCAmelCase	    =			residual_kernel_size
         UpperCAmelCase	    =			dilation_growth_rate
         UpperCAmelCase	    =			use_causal_conv
         UpperCAmelCase	    =			pad_mode
         UpperCAmelCase	    =			compress
         UpperCAmelCase	    =			num_lstm_layers
         UpperCAmelCase	    =			trim_right_ratio
         UpperCAmelCase	    =			codebook_size
         UpperCAmelCase	    =			codebook_dim if codebook_dim is not None else hidden_size
         UpperCAmelCase	    =			use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
                raise ValueError(
                    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''				)

         super().__init__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         if self.chunk_length_s is None:
                return None
         else:
                return int(self.chunk_length_s * self.sampling_rate				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         if self.chunk_length_s is None or self.overlap is None:
                return None
         else:
                return max(1 ,		int((1.0 - self.overlap) * self.chunk_length				)				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			np.prod(self.upsampling_ratios				)
         return math.ceil(self.sampling_rate / hop_length				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)
