





'''simple docstring'''







import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def    __UpperCamelCase	(	a    : Optional[Any]					,   a    : int					,   a    : List[Any]					,   a    : Union[str, Any]=1024     ) ->Tuple:
 snake_case				,		snake_case				=			[], []

 snake_case				=			list(zip(a					,   a     )     )
 snake_case				,		snake_case				=			sorted_examples[0]

 def is_too_big(a    : Tuple     ):
  return tok(a					,   return_tensors='''pt'''     ).input_ids.shape[1] > max_tokens

 for src, tgt in tqdm(sorted_examples[1:]     ):
  snake_case				=			new_src + ''' ''' + src
  snake_case				=			new_tgt + ''' ''' + tgt
  if is_too_big(a     ) or is_too_big(a     ):  # cant fit, finalize example
   finished_src.append(a     )
   finished_tgt.append(a     )
   snake_case				,		snake_case				=			src, tgt
  else:  # can fit, keep adding
   snake_case				,		snake_case				=			cand_src, cand_tgt

    # cleanup
 if new_src:
  assert new_tgt
  finished_src.append(a     )
  finished_tgt.append(a     )
 return finished_src, finished_tgt

def    __UpperCamelCase	(	a    : List[str]					,   a    : Path					,   a    : Union[str, Any]					,   a    : Optional[int]     ) ->Optional[Any]:
 snake_case				=			Path(a     )
 save_path.mkdir(exist_ok=a     )
 for split in ["train"]:
  snake_case				,		snake_case				=			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
  snake_case				=			[x.rstrip() for x in Path(a     ).open().readlines()]
  snake_case				=			[x.rstrip() for x in Path(a     ).open().readlines()]
  snake_case				,		snake_case				=			pack_examples(a					,   a					,   a					,   a     )
  print(f"""packed {split} split from {len(a     )} examples -> {len(a     )}."""     )
  Path(save_path / f"""{split}.source"""     ).open('''w'''     ).write('''\n'''.join(a     )     )
  Path(save_path / f"""{split}.target"""     ).open('''w'''     ).write('''\n'''.join(a     )     )
 for split in ["val", "test"]:
  snake_case				,		snake_case				=			data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
  shutil.copyfile(a					,   save_path / f"""{split}.source"""     )
  shutil.copyfile(a					,   save_path / f"""{split}.target"""     )

def    __UpperCamelCase	(	) ->Any:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''--tok_name'''					,   type=a					,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     )
 parser.add_argument('''--max_seq_len'''					,   type=a					,   default=128     )
 parser.add_argument('''--data_dir'''					,   type=a     )
 parser.add_argument('''--save_path'''					,   type=a     )
 snake_case				=			parser.parse_args()
 snake_case				=			AutoTokenizer.from_pretrained(args.tok_name     )
 return pack_data_dir(a					,   Path(args.data_dir     )					,   args.max_seq_len					,   args.save_path     )


if __name__ == "__main__":
   packer_cli()








'''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

