

'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    SMALL_MODEL_IDENTIFIER
      __magic_name__						       =    '''pt'''
      __magic_name__						       =    '''tf'''



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Optional[int]      ):
      __magic_name__						       =    AutoModel.from_pretrained(self.test_model      )
      model_pt.save_pretrained(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   str      ):
      __magic_name__						       =    TFAutoModel.from_pretrained(self.test_model			,				from_pt=a__      )
      model_tf.save_pretrained(a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    '''mock_framework'''

      # Framework provided - return whatever the user provides
      __magic_name__						       =    FeaturesManager.determine_framework(self.test_model			,				a__      )
      self.assertEqual(a__			,				a__      )

      # Local checkpoint and framework provided - return provided framework
      # PyTorch checkpoint
      with TemporaryDirectory() as local_pt_ckpt:
           self._setup_pt_ckpt(a__      )
           __magic_name__						       =    FeaturesManager.determine_framework(a__			,				a__      )
           self.assertEqual(a__			,				a__      )

      # TensorFlow checkpoint
      with TemporaryDirectory() as local_tf_ckpt:
           self._setup_tf_ckpt(a__      )
           __magic_name__						       =    FeaturesManager.determine_framework(a__			,				a__      )
           self.assertEqual(a__			,				a__      )



 def 			snake_case__  (   self		:   Dict      ):
      # PyTorch checkpoint
      with TemporaryDirectory() as local_pt_ckpt:
           self._setup_pt_ckpt(a__      )
           __magic_name__						       =    FeaturesManager.determine_framework(a__      )
           self.assertEqual(a__			,				self.framework_pt      )

      # TensorFlow checkpoint
      with TemporaryDirectory() as local_tf_ckpt:
           self._setup_tf_ckpt(a__      )
           __magic_name__						       =    FeaturesManager.determine_framework(a__      )
           self.assertEqual(a__			,				self.framework_tf      )

      # Invalid local checkpoint
      with TemporaryDirectory() as local_invalid_ckpt:
           with self.assertRaises(a__      ):
                __magic_name__						       =    FeaturesManager.determine_framework(a__      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    MagicMock(return_value=a__      )
      with patch('''transformers.onnx.features.is_tf_available'''			,				a__      ):
           __magic_name__						       =    FeaturesManager.determine_framework(self.test_model      )
           self.assertEqual(a__			,				self.framework_pt      )

      # PyTorch not in environment -> use TensorFlow
      __magic_name__						       =    MagicMock(return_value=a__      )
      with patch('''transformers.onnx.features.is_torch_available'''			,				a__      ):
           __magic_name__						       =    FeaturesManager.determine_framework(self.test_model      )
           self.assertEqual(a__			,				self.framework_tf      )

      # Both in environment -> use PyTorch
      __magic_name__						       =    MagicMock(return_value=a__      )
      __magic_name__						       =    MagicMock(return_value=a__      )
      with patch('''transformers.onnx.features.is_tf_available'''			,				a__      ), patch(
          '''transformers.onnx.features.is_torch_available'''			,				a__      ):
           __magic_name__						       =    FeaturesManager.determine_framework(self.test_model      )
           self.assertEqual(a__			,				self.framework_pt      )

      # Both not in environment -> raise error
      __magic_name__						       =    MagicMock(return_value=a__      )
      __magic_name__						       =    MagicMock(return_value=a__      )
      with patch('''transformers.onnx.features.is_tf_available'''			,				a__      ), patch(
          '''transformers.onnx.features.is_torch_available'''			,				a__      ):
           with self.assertRaises(a__      ):
                __magic_name__						       =    FeaturesManager.determine_framework(self.test_model      )









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

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

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

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    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			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    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		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    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"]:
          __magic_name__       ,  __magic_name__						       =    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	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    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			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    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()






