


"""simple docstring"""







import argparse
import os

from accelerate.test_utils import execute_subprocess_async


def 					lowercase      (    A_=None	)->				str:
  '''simple docstring'''







  if subparsers is not None:
    a						:		Dict											=      subparsers.add_parser("test"	)
  else:
    a						:		Optional[int]											=      argparse.ArgumentParser("Accelerate test command"	)

  parser.add_argument(
      "--config_file"  ,       default=A_  ,       help=(
          "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
          "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
          "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
          "with 'huggingface'."
      )  ,       )

  if subparsers is not None:
    parser.set_defaults(func=A_	)
  return parser


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      os.path.sep.join(__file__.split(os.path.sep	)[:-2] + ["test_utils", "scripts", "test_script.py"]	)

  if args.config_file is None:
    a						:		Tuple											=      script_name
  else:
    a						:		List[Any]											=      F'''--config_file={args.config_file} {script_name}'''

  a						:		str											=      ["accelerate-launch"] + test_args.split()
  a						:		Optional[int]											=      execute_subprocess_async(A_  ,       env=os.environ.copy()	)
  if result.returncode == 0:
    print("Test is a success! You are ready for your distributed training!"	)


def 					lowercase      (    )->				Any:
  '''simple docstring'''







  a						:		Optional[int]											=      test_command_parser()
  a						:		Optional[int]											=      parser.parse_args()
  test_command(A_	)


if __name__ == "__main__":
 main()










"""simple docstring"""







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	"""char"""
    UpperCAmelCase	:							Optional[Any]     =	"""bpe"""
    UpperCAmelCase	:							Optional[Any]     =	"""wp"""






__lowercase    =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	["""image_processor""", """char_tokenizer"""]
    UpperCAmelCase	:							Optional[Any]     =	"""ViTImageProcessor"""
    UpperCAmelCase	:							List[Any]     =	"""MgpstrTokenizer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   Dict=None    ,			**__UpperCAmelCase    :   str):
      a						:		Tuple											=      None
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,			__UpperCAmelCase    ,			)
        a						:		List[str]											=      kwargs.pop("feature_extractor")

      a						:		Dict											=      image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You need to specify an `image_processor`.")
      if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`.")

      a						:		Union[str, Any]											=      tokenizer
      a						:		int											=      AutoTokenizer.from_pretrained("gpt2")
      a						:		str											=      AutoTokenizer.from_pretrained("bert-base-uncased")

      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Dict=None    ,			__UpperCAmelCase    :   Tuple=None    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			**__UpperCAmelCase    :   int):
      if images is None and text is None:
        raise ValueError("You need to specify either an `images` or `text` input to process.")

      if images is not None:
        a						:		List[str]											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)
      if text is not None:
        a						:		Optional[Any]											=      self.char_tokenizer(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        a						:		Any											=      encodings["input_ids"]
        return inputs






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[str]):
      a							,		a							,		a						:		Tuple											=      sequences
      a						:		Optional[int]											=      char_preds.size(0)

      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"char")
      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"bpe")
      a							,		a						:		Union[str, Any]											=      self._decode_helper(__UpperCAmelCase    ,			"wp")

      a						:		Any											=      []
      a						:		Union[str, Any]											=      []
      for i in range(__UpperCAmelCase):
        a						:		Any											=      [char_scores[i], bpe_scores[i], wp_scores[i]]
        a						:		Optional[Any]											=      [char_strs[i], bpe_strs[i], wp_strs[i]]
        a						:		List[str]											=      scores.index(max(__UpperCAmelCase))
        final_strs.append(strs[max_score_index])
        final_scores.append(scores[max_score_index])

      a						:		Dict											=      {}
      a						:		List[str]											=      final_strs
      a						:		str											=      final_scores
      a						:		int											=      char_strs
      a						:		int											=      bpe_strs
      a						:		Tuple											=      wp_strs
      return out






    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[str]):
      if format == DecodeType.CHARACTER:
        a						:		int											=      self.char_decode
        a						:		int											=      1
        a						:		Dict											=      "[s]"
      elif format == DecodeType.BPE:
        a						:		List[str]											=      self.bpe_decode
        a						:		List[str]											=      2
        a						:		int											=      "#"
      elif format == DecodeType.WORDPIECE:
        a						:		Union[str, Any]											=      self.wp_decode
        a						:		List[str]											=      102
        a						:		int											=      "[SEP]"
      else:
        raise ValueError(f'''Format {format} is not supported.''')

      a							,		a						:		str											=      [], []
      a						:		Optional[int]											=      pred_logits.size(0)
      a						:		List[str]											=      pred_logits.size(1)
      a							,		a						:		Tuple											=      pred_logits.topk(1    ,			dim=-1    ,			largest=__UpperCAmelCase    ,			sorted=__UpperCAmelCase)
      a						:		List[str]											=      preds_index.view(-1    ,			__UpperCAmelCase)[:, 1:]
      a						:		Any											=      decoder(__UpperCAmelCase)
      a							,		a						:		Union[str, Any]											=      torch.nn.functional.softmax(__UpperCAmelCase    ,			dim=2).max(dim=2)
      a						:		Union[str, Any]											=      preds_max_prob[:, 1:]

      for index in range(__UpperCAmelCase):
        a						:		str											=      preds_str[index].find(__UpperCAmelCase)
        a						:		Optional[Any]											=      preds_str[index][:pred_eos]
        a						:		Optional[int]											=      preds_index[index].cpu().tolist()
        a						:		Optional[int]											=      pred_index.index(__UpperCAmelCase) if eos_token in pred_index else -1
        a						:		List[str]											=      preds_max_prob[index][: pred_eos_index + 1]
        a						:		int											=      pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(__UpperCAmelCase)
        conf_scores.append(__UpperCAmelCase)

      return dec_strs, conf_scores






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Any):
      a						:		Dict											=      [seq.replace(" "    ,			"") for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]):
      return self.bpe_tokenizer.batch_decode(__UpperCAmelCase)






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		Any											=      [seq.replace(" "    ,			"") for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






