

'''simple docstring'''




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm


_lowerCAmelCase         =						logging.get_logger(__name__)






@dataclass
class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					[
     """no_inference""",
     """no_cuda""",
     """no_tpu""",
     """no_speed""",
     """no_memory""",
     """no_env_print""",
     """no_multi_process""",
 ]



 def __init__(   self		:   Optional[int]			,				**a__		:   Optional[int]      ):
      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                __magic_name__						       =    deprecated_arg[3:]
                setattr(self			,				a__			,				not kwargs.pop(a__      )      )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''      )

      __magic_name__						       =    kwargs.pop('''torchscript'''			,				self.torchscript      )
      __magic_name__						       =    kwargs.pop('''torch_xla_tpu_print_metrics'''			,				self.torch_xla_tpu_print_metrics      )
      __magic_name__						       =    kwargs.pop('''fp16_opt_level'''			,				self.fpaa_opt_level      )
      super().__init__(**a__      )

 __SCREAMING_SNAKE_CASE					:bool      	=					field(default=__a		,metadata={"""help""": """Trace the models using torchscript"""}     )
 __SCREAMING_SNAKE_CASE					:bool      	=					field(default=__a		,metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
 __SCREAMING_SNAKE_CASE					:str      	=					field(
     default="""O1"""		,metadata={
         """help""": (
             """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
             """See details at https://nvidia.github.io/apex/amp.html"""
         )
     }		,)



 @cached_property
 def 			snake_case__  (   self		:   int      ):
      requires_backends(self			,				['''torch''']      )
      logger.info('''PyTorch: setting up devices'''      )
      if not self.cuda:
           __magic_name__						       =    torch.device('''cpu'''      )
           __magic_name__						       =    0
      elif is_torch_tpu_available():
           __magic_name__						       =    xm.xla_device()
           __magic_name__						       =    0
      else:
           __magic_name__						       =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )
           __magic_name__						       =    torch.cuda.device_count()
      return device, n_gpu



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      return is_torch_tpu_available() and self.tpu



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      requires_backends(self			,				['''torch''']      )
      # TODO(PVP): currently only single GPU is supported
      return torch.cuda.current_device()



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      requires_backends(self			,				['''torch''']      )
      return self._setup_devices[0]



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      requires_backends(self			,				['''torch''']      )
      return self._setup_devices[1]



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      return self.n_gpu > 0









'''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()






