





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__       (	__snake_case      ):
      _UpperCAmelCase     :List[str]						   =       'char'
      _UpperCAmelCase     :List[str]						   =       'bpe'
      _UpperCAmelCase     :List[Any]						   =       'wp'


__lowerCamelCase	:					List[str]			  =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Any						   =       ['image_processor', 'char_tokenizer']
      _UpperCAmelCase     :Optional[Any]						   =       'ViTImageProcessor'
      _UpperCAmelCase     :List[Any]						   =       'MgpstrTokenizer'





      def __init__(			self	,							A_=None	,							A_=None	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : 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."	,							A_	,							)
        UpperCamelCase     : Optional[Any]	     =       kwargs.pop("feature_extractor"       )

       UpperCamelCase     : Tuple	     =       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`."       )

       UpperCamelCase     : Dict	     =       tokenizer
       UpperCamelCase     : List[str]	     =       AutoTokenizer.from_pretrained("gpt2"       )
       UpperCamelCase     : int	     =       AutoTokenizer.from_pretrained("bert-base-uncased"       )

       super().__init__(A_	,							A_       )





      def __call__(			self	,							A_=None	,							A_=None	,							A_=None	,							**A_       ):
       '''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:
        UpperCamelCase     : Tuple	     =       self.image_processor(A_	,							return_tensors=A_	,							**A_       )
       if text is not None:
        UpperCamelCase     : Optional[Any]	     =       self.char_tokenizer(A_	,							return_tensors=A_	,							**A_       )

       if text is None:
        return inputs
       elif images is None:
        return encodings
       else:
        UpperCamelCase     : Union[str, Any]	     =       encodings["input_ids"]
        return inputs





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Dict	     =       sequences
       UpperCamelCase     : Dict	     =       char_preds.size(0       )

       UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       self._decode_helper(A_	,							"char"       )
       UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       self._decode_helper(A_	,							"bpe"       )
       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       self._decode_helper(A_	,							"wp"       )

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

       UpperCamelCase     : List[Any]	     =       {}
       UpperCamelCase     : Dict	     =       final_strs
       UpperCamelCase     : List[str]	     =       final_scores
       UpperCamelCase     : str	     =       char_strs
       UpperCamelCase     : Union[str, Any]	     =       bpe_strs
       UpperCamelCase     : Tuple	     =       wp_strs
       return out





      def 	__UpperCamelCase(			self	,							A_	,							A_       ):
       '''simple docstring'''




       if format == DecodeType.CHARACTER:
        UpperCamelCase     : Tuple	     =       self.char_decode
        UpperCamelCase     : str	     =       1
        UpperCamelCase     : List[Any]	     =       "[s]"
       elif format == DecodeType.BPE:
        UpperCamelCase     : Any	     =       self.bpe_decode
        UpperCamelCase     : Any	     =       2
        UpperCamelCase     : Optional[int]	     =       "#"
       elif format == DecodeType.WORDPIECE:
        UpperCamelCase     : Tuple	     =       self.wp_decode
        UpperCamelCase     : Any	     =       102
        UpperCamelCase     : Any	     =       "[SEP]"
       else:
        raise ValueError(F"""Format {format} is not supported."""       )

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

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

       return dec_strs, conf_scores





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [seq.replace(" "	,							""       ) for seq in self.char_tokenizer.batch_decode(A_       )]
       return decode_strs





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return self.bpe_tokenizer.batch_decode(A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [seq.replace(" "	,							""       ) for seq in self.wp_tokenizer.batch_decode(A_       )]
       return decode_strs







def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
