





UpperCAmelCase     :     Union[str, Any]							=	"""
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

UpperCAmelCase     :     Dict							=	[{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase     :     Optional[int]							=	{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}













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     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      """char"""
      _lowercase    : str            =      """bpe"""
      _lowercase    : List[Any]            =      """wp"""
UpperCAmelCase     :     List[Any]							=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Tuple            =      ["""image_processor""", """char_tokenizer"""]
      _lowercase    : Any            =      """ViTImageProcessor"""
      _lowercase    : Optional[Any]            =      """MgpstrTokenizer"""







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =None
        if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."		,				lowerCAmelCase__		,				)
          a__     : List[str]  =kwargs.pop("feature_extractor"							)

        a__     : List[str]  =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__     : str  =tokenizer
        a__     : List[str]  =AutoTokenizer.from_pretrained("gpt2"							)
        a__     : Optional[int]  =AutoTokenizer.from_pretrained("bert-base-uncased"							)

        super().__init__(lowerCAmelCase__		,				lowerCAmelCase__							)







      def __call__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        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__     : Union[str, Any]  =self.image_processor(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)
        if text is not None:
          a__     : int  =self.char_tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

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







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__ ,							a__ ,							a__     : Any  =sequences
        a__     : Union[str, Any]  =char_preds.size(0							)

        a__ ,							a__     : Dict  =self._decode_helper(lowerCAmelCase__		,				"char"							)
        a__ ,							a__     : List[Any]  =self._decode_helper(lowerCAmelCase__		,				"bpe"							)
        a__ ,							a__     : Optional[int]  =self._decode_helper(lowerCAmelCase__		,				"wp"							)

        a__     : List[Any]  =[]
        a__     : Dict  =[]
        for i in range(lowerCAmelCase__							):
          a__     : int  =[char_scores[i], bpe_scores[i], wp_scores[i]]
          a__     : Tuple  =[char_strs[i], bpe_strs[i], wp_strs[i]]
          a__     : Any  =scores.index(max(lowerCAmelCase__							)							)
          final_strs.append(strs[max_score_index]							)
          final_scores.append(scores[max_score_index]							)

        a__     : Dict  ={}
        a__     : str  =final_strs
        a__     : Optional[int]  =final_scores
        a__     : Union[str, Any]  =char_strs
        a__     : List[str]  =bpe_strs
        a__     : Union[str, Any]  =wp_strs
        return out







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        if format == DecodeType.CHARACTER:
          a__     : Optional[Any]  =self.char_decode
          a__     : Dict  =1
          a__     : Tuple  ="[s]"
        elif format == DecodeType.BPE:
          a__     : str  =self.bpe_decode
          a__     : Dict  =2
          a__     : Optional[int]  ="#"
        elif format == DecodeType.WORDPIECE:
          a__     : Union[str, Any]  =self.wp_decode
          a__     : List[Any]  =1_0_2
          a__     : Dict  ="[SEP]"
        else:
          raise ValueError(F'''Format {format} is not supported.'''							)

        a__ ,							a__     : Any  =[], []
        a__     : str  =pred_logits.size(0							)
        a__     : Optional[Any]  =pred_logits.size(1							)
        a__ ,							a__     : Optional[int]  =pred_logits.topk(1		,				dim=-1		,				largest=lowerCAmelCase__		,				sorted=lowerCAmelCase__							)
        a__     : Optional[Any]  =preds_index.view(-1		,				lowerCAmelCase__							)[:, 1:]
        a__     : Dict  =decoder(lowerCAmelCase__							)
        a__ ,							a__     : Any  =torch.nn.functional.softmax(lowerCAmelCase__		,				dim=2							).max(dim=2							)
        a__     : int  =preds_max_prob[:, 1:]

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

        return dec_strs, conf_scores







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =[seq.replace(" "		,				""							) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        return self.bpe_tokenizer.batch_decode(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =[seq.replace(" "		,				""							) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs






