


'''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     UpperCamelCase_      (   A__ :       int							,   A__ :       List[Any]							,   A__ :       Tuple=10_24							,   A__ :       List[Any]=10_24							,   A__ :       Optional[int]=False							,   **A__ :       Optional[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       AutoTokenizer.from_pretrained(A__ )
  lowerCAmelCase_      :				List[Any]      =       SeqaSeqDataset(A__							,   A__							,   A__							,   A__							,   type_path="""train"""							,   **A__ )
  lowerCAmelCase_      :				str      =       tok.pad_token_id

  def get_lens(A__ :       Dict ):
    lowerCAmelCase_      :				Union[str, Any]      =       tqdm(
        DataLoader(A__							,   batch_size=5_12							,   num_workers=8							,   shuffle=A__							,   collate_fn=ds.collate_fn )							,   desc=str(ds.len_file )							,   )
    lowerCAmelCase_      :				Dict      =       []
    for batch in dl:
      lowerCAmelCase_      :				str      =       batch["""input_ids"""].ne(A__ ).sum(1 ).tolist()
      lowerCAmelCase_      :				Optional[int]      =       batch["""labels"""].ne(A__ ).sum(1 ).tolist()
      if consider_target:
        for src, tgt in zip(A__							,   A__ ):
          max_lens.append(max(A__							,   A__ ) )
      else:
        max_lens.extend(A__ )
    return max_lens

  lowerCAmelCase_      :				Union[str, Any]      =       get_lens(A__ )
  lowerCAmelCase_      :				int      =       SeqaSeqDataset(A__							,   A__							,   A__							,   A__							,   type_path="""val"""							,   **A__ )
  lowerCAmelCase_      :				Dict      =       get_lens(A__ )
  pickle_save(A__							,   train_ds.len_file )
  pickle_save(A__							,   val_ds.len_file )


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







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



