"""simple docstring"""



import numpy as np

from transformers import Pipeline



def 					_lowerCamelCase(					a							):
       __a     				=  np.max(a ,  axis=-1 ,  keepdims=a							)
       __a     				=  np.exp(outputs - maxes							)
       return shifted_exp / shifted_exp.sum(axis=-1 ,  keepdims=a							)






class 				snake_case__     (   snake_case_   ):


 def 		a__ (				self  ,						**lowerCamelCase		):
        __a     				=  {}
        if "second_text" in kwargs:
               __a     				=  kwargs["second_text"]
        return preprocess_kwargs, {}, {}


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        return self.tokenizer(lowerCamelCase  ,						text_pair=lowerCamelCase  ,						return_tensors=self.framework		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.model(**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  model_outputs.logits[0].numpy()
        __a     				=  softmax(lowerCamelCase		)

        __a     				=  np.argmax(lowerCamelCase		)
        __a     				=  self.model.config.idalabel[best_class]
        __a     				=  probabilities[best_class].item()
        __a     				=  logits.tolist()
        return {"label": label, "score": score, "logits": logits}

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
