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  __magic_name__  (		UpperCamelCase_    ):





   """simple docstring"""




   lowerCAmelCase      :    Any  			=   '''char'''
   lowerCAmelCase      :    Dict  			=   '''bpe'''
   lowerCAmelCase      :    List[str]  			=   '''wp'''




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




class  __magic_name__  (		UpperCamelCase_    ):





   """simple docstring"""




   lowerCAmelCase      :    List[Any]  			=   ['''image_processor''', '''char_tokenizer''']
   lowerCAmelCase      :    List[Any]  			=   '''ViTImageProcessor'''
   lowerCAmelCase      :    int  			=   '''MgpstrTokenizer'''



   def __init__(	self					:  List[str]		,  _lowercase					:  str=None		,  _lowercase					:  Dict=None		,  **_lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= 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:     List[Any]							= kwargs.pop('''feature_extractor'''							)

    _UpperCamelCase:     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`.'''							)

    _UpperCamelCase:     Tuple							= tokenizer
    _UpperCamelCase:     Any							= AutoTokenizer.from_pretrained('''gpt2'''							)
    _UpperCamelCase:     List[str]							= AutoTokenizer.from_pretrained('''bert-base-uncased'''							)

    super().__init__(__a		,  __a							)



   def __call__(	self					:  Dict		,  _lowercase					:  int=None		,  _lowercase					:  Optional[Any]=None		,  _lowercase					:  Optional[Any]=None		,  **_lowercase					:  Optional[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:
     _UpperCamelCase:     Union[str, Any]							= self.image_processor(__a		,  return_tensors=__a		,  **__a							)
    if text is not None:
     _UpperCamelCase:     Dict							= 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   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= sequences
    _UpperCamelCase:     int							= char_preds.size(0							)

    _UpperCamelCase:     str							= self._decode_helper(__a		,  '''char'''							)
    _UpperCamelCase:     List[str]							= self._decode_helper(__a		,  '''bpe'''							)
    _UpperCamelCase:     List[str]							= self._decode_helper(__a		,  '''wp'''							)

    _UpperCamelCase:     Any							= []
    _UpperCamelCase:     List[str]							= []
    for i in range(__a							):
     _UpperCamelCase:     Union[str, Any]							= [char_scores[i], bpe_scores[i], wp_scores[i]]
     _UpperCamelCase:     Any							= [char_strs[i], bpe_strs[i], wp_strs[i]]
     _UpperCamelCase:     List[str]							= scores.index(max(__a							)							)
     final_strs.append(strs[max_score_index]							)
     final_scores.append(scores[max_score_index]							)

    _UpperCamelCase:     str							= {}
    _UpperCamelCase:     Optional[Any]							= final_strs
    _UpperCamelCase:     str							= final_scores
    _UpperCamelCase:     Tuple							= char_strs
    _UpperCamelCase:     str							= bpe_strs
    _UpperCamelCase:     Any							= wp_strs
    return out



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  int		,  _lowercase					:  int							):



    """simple docstring"""



    if format == DecodeType.CHARACTER:
     _UpperCamelCase:     str							= self.char_decode
     _UpperCamelCase:     Union[str, Any]							= 1
     _UpperCamelCase:     Union[str, Any]							= '[s]'
    elif format == DecodeType.BPE:
     _UpperCamelCase:     Tuple							= self.bpe_decode
     _UpperCamelCase:     str							= 2
     _UpperCamelCase:     Any							= '#'
    elif format == DecodeType.WORDPIECE:
     _UpperCamelCase:     Any							= self.wp_decode
     _UpperCamelCase:     Tuple							= 102
     _UpperCamelCase:     Dict							= '[SEP]'
    else:
     raise ValueError(f"""Format {format} is not supported."""							)

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

    for index in range(__a							):
     _UpperCamelCase:     List[Any]							= preds_str[index].find(__a							)
     _UpperCamelCase:     List[Any]							= preds_str[index][:pred_eos]
     _UpperCamelCase:     Union[str, Any]							= preds_index[index].cpu().tolist()
     _UpperCamelCase:     str							= pred_index.index(__a							) if eos_token in pred_index else -1
     _UpperCamelCase:     Any							= preds_max_prob[index][: pred_eos_index + 1]
     _UpperCamelCase:     Dict							= 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   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= [seq.replace(''' '''		,  ''''''							) for seq in self.char_tokenizer.batch_decode(__a							)]
    return decode_strs



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.bpe_tokenizer.batch_decode(__a							)



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= [seq.replace(''' '''		,  ''''''							) for seq in self.wp_tokenizer.batch_decode(__a							)]
    return decode_strs





from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()