



'''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 	UpperCAmelCase	(		__lowerCamelCase):


 '''simple docstring'''




 __magic_name__					: Dict  =      'char'
 __magic_name__					: Union[str, Any]  =      'bpe'
 __magic_name__					: str  =      'wp'



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







class 	UpperCAmelCase	(		__lowerCamelCase):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      ['image_processor', 'char_tokenizer']
 __magic_name__					: int  =      'ViTImageProcessor'
 __magic_name__					: Any  =      'MgpstrTokenizer'




 def __init__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=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_       		=kwargs.pop("feature_extractor")

      a_       		=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_       		=tokenizer
      a_       		=AutoTokenizer.from_pretrained("gpt2")
      a_       		=AutoTokenizer.from_pretrained("bert-base-uncased")

      super().__init__(UpperCamelCase_							,  UpperCamelCase_)




 def __call__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  **lowerCAmelCase_) ->      str:



      """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_       		=self.image_processor(UpperCamelCase_							,  return_tensors=UpperCamelCase_							,  **UpperCamelCase_)
      if text is not None:
           a_       		=self.char_tokenizer(UpperCamelCase_							,  return_tensors=UpperCamelCase_							,  **UpperCamelCase_)

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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_		,						a_		,						a_       		=sequences
      a_       		=char_preds.size(0)

      a_		,						a_       		=self._decode_helper(UpperCamelCase_							,  "char")
      a_		,						a_       		=self._decode_helper(UpperCamelCase_							,  "bpe")
      a_		,						a_       		=self._decode_helper(UpperCamelCase_							,  "wp")

      a_       		=[]
      a_       		=[]
      for i in range(UpperCamelCase_):
           a_       		=[char_scores[i], bpe_scores[i], wp_scores[i]]
           a_       		=[char_strs[i], bpe_strs[i], wp_strs[i]]
           a_       		=scores.index(max(UpperCamelCase_))
           final_strs.append(strs[max_score_index])
           final_scores.append(scores[max_score_index])

      a_       		={}
      a_       		=final_strs
      a_       		=final_scores
      a_       		=char_strs
      a_       		=bpe_strs
      a_       		=wp_strs
      return out




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







      if format == DecodeType.CHARACTER:
           a_       		=self.char_decode
           a_       		=1
           a_       		="[s]"
      elif format == DecodeType.BPE:
           a_       		=self.bpe_decode
           a_       		=2
           a_       		="#"
      elif format == DecodeType.WORDPIECE:
           a_       		=self.wp_decode
           a_       		=1_0_2
           a_       		="[SEP]"
      else:
           raise ValueError(f"""Format {format} is not supported.""")

      a_		,						a_       		=[], []
      a_       		=pred_logits.size(0)
      a_       		=pred_logits.size(1)
      a_		,						a_       		=pred_logits.topk(1							,  dim=-1							,  largest=UpperCamelCase_							,  sorted=UpperCamelCase_)
      a_       		=preds_index.view(-1							,  UpperCamelCase_)[:, 1:]
      a_       		=decoder(UpperCamelCase_)
      a_		,						a_       		=torch.nn.functional.softmax(UpperCamelCase_							,  dim=2).max(dim=2)
      a_       		=preds_max_prob[:, 1:]

      for index in range(UpperCamelCase_):
           a_       		=preds_str[index].find(UpperCamelCase_)
           a_       		=preds_str[index][:pred_eos]
           a_       		=preds_index[index].cpu().tolist()
           a_       		=pred_index.index(UpperCamelCase_) if eos_token in pred_index else -1
           a_       		=preds_max_prob[index][: pred_eos_index + 1]
           a_       		=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       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=[seq.replace(" "							,  "") for seq in self.char_tokenizer.batch_decode(UpperCamelCase_)]
      return decode_strs




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      return self.bpe_tokenizer.batch_decode(UpperCamelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=[seq.replace(" "							,  "") for seq in self.wp_tokenizer.batch_decode(UpperCamelCase_)]
      return decode_strs







'''simple docstring'''


import os
from math import logaa






def  UpperCAmelCase_       (		lowercase__ = "base_exp.txt"       ):


     '''simple docstring'''







     a_       		=0
     a_       		=0
     for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__       )       ,	lowercase__       )       )       ):
          a_		,						a_       		=list(map(lowercase__       ,	line.split(","       )       )       )
          if x * logaa(lowercase__       ) > largest:
               a_       		=x * logaa(lowercase__       )
               a_       		=i + 1
     return result


if __name__ == "__main__":
  print(solution())


