

'''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 							_snake_case (       _a						):
   _A       :     List[str]       =					'''char'''
   _A       :     List[str]       =					'''bpe'''
   _A       :     Optional[int]       =					'''wp'''



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





class 							_snake_case (       _a						):
   _A       :     int       =					['''image_processor''', '''char_tokenizer''']
   _A       :     Optional[int]       =					'''ViTImageProcessor'''
   _A       :     str       =					'''MgpstrTokenizer'''
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE: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."     ,SCREAMING_SNAKE_CASE__     ,)
       SCREAMING_SNAKE_CASE:List[str]						      =					kwargs.pop("feature_extractor"							)

     SCREAMING_SNAKE_CASE:Any						      =					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`."							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					tokenizer
     SCREAMING_SNAKE_CASE:Optional[int]						      =					AutoTokenizer.from_pretrained("gpt2"							)
     SCREAMING_SNAKE_CASE:int						      =					AutoTokenizer.from_pretrained("bert-base-uncased"							)

     super().__init__(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def __call__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,**SCREAMING_SNAKE_CASE__			:      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:
       SCREAMING_SNAKE_CASE:Any						      =					self.image_processor(SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     if text is not None:
       SCREAMING_SNAKE_CASE:List[str]						      =					self.char_tokenizer(SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       SCREAMING_SNAKE_CASE:int						      =					encodings["input_ids"]
       return inputs
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					sequences
     SCREAMING_SNAKE_CASE:int						      =					char_preds.size(0							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self._decode_helper(SCREAMING_SNAKE_CASE__     ,"char"							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self._decode_helper(SCREAMING_SNAKE_CASE__     ,"bpe"							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self._decode_helper(SCREAMING_SNAKE_CASE__     ,"wp"							)

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

     SCREAMING_SNAKE_CASE:List[Any]						      =					{}
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					final_strs
     SCREAMING_SNAKE_CASE:List[Any]						      =					final_scores
     SCREAMING_SNAKE_CASE:str						      =					char_strs
     SCREAMING_SNAKE_CASE:List[str]						      =					bpe_strs
     SCREAMING_SNAKE_CASE:List[Any]						      =					wp_strs
     return out
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     if format == DecodeType.CHARACTER:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					self.char_decode
       SCREAMING_SNAKE_CASE:int						      =					1
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"[s]"
     elif format == DecodeType.BPE:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.bpe_decode
       SCREAMING_SNAKE_CASE:List[str]						      =					2
       SCREAMING_SNAKE_CASE:Any						      =					"#"
     elif format == DecodeType.WORDPIECE:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					self.wp_decode
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					102
       SCREAMING_SNAKE_CASE:str						      =					"[SEP]"
     else:
       raise ValueError(F'''Format {format} is not supported.'''							)

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

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

     return dec_strs, conf_scores
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Any							):
     SCREAMING_SNAKE_CASE:Any						      =					[seq.replace(" "     ,""							) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)]
     return decode_strs
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Any							):
     return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE:str						      =					[seq.replace(" "     ,""							) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)]
     return decode_strs









'''simple docstring'''

from ..utils import DummyObject, requires_backends





class 							_snake_case (       metaclass=_a						):
   _A       :     Any       =					['''torch''', '''torchsde''']
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     requires_backends(self     ,["torch", "torchsde"]							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)







   @classmethod
   def     __UpperCamelCase						( cls			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)






