


"""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     __lowerCamelCase  (							__lowercase				):

     '''simple docstring'''
     A_       :	Optional[Any]    =		'char'
     A_       :	Any    =		'bpe'
     A_       :	List[Any]    =		'wp'







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


class     __lowerCamelCase  (							__lowercase				):

     '''simple docstring'''
     A_       :	Dict    =		['image_processor', 'char_tokenizer']
     A_       :	Dict    =		'ViTImageProcessor'
     A_       :	Optional[int]    =		'MgpstrTokenizer'


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          _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    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[Any]:
          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Union[str, Any]:
          _a    	=				sequences
          _a    	=				char_preds.size(0							)

          _a    	=				self._decode_helper(__UpperCAmelCase    ,				'''char'''							)
          _a    	=				self._decode_helper(__UpperCAmelCase    ,				'''bpe'''							)
          _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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          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    	=				102
               _a    	=				"[SEP]"
          else:
               raise ValueError(F'Format {format} is not supported.'							)

          _a    	=				[], []
          _a    	=				pred_logits.size(0							)
          _a    	=				pred_logits.size(1							)
          _a    	=				pred_logits.topk(1    ,				dim=-1    ,				largest=__UpperCAmelCase    ,				sorted=__UpperCAmelCase							)
          _a    	=				preds_index.view(-1    ,				__UpperCAmelCase							)[:, 1:]
          _a    	=				decoder(__UpperCAmelCase							)
          _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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				[seq.replace(''' '''    ,				''''''							) for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase							)]
          return decode_strs


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          return self.bpe_tokenizer.batch_decode(__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Union[str, Any]:
          _a    	=				[seq.replace(''' '''    ,				''''''							) for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase							)]
          return decode_strs



"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['image_processor', 'tokenizer']
     A_       :	Union[str, Any]    =		'ChineseCLIPImageProcessor'
     A_       :	Union[str, Any]    =		('BertTokenizer', 'BertTokenizerFast')


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:
          _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`.'''							)

          super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				self.image_processor


     def __call__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							)       ->							List[str]:

          if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

          if text is not None:
               _a    	=				self.tokenizer(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if images is not None:
               _a    	=				self.image_processor(__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          if text is not None and images is not None:
               _a    	=				image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**__UpperCAmelCase							)    ,				tensor_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[str]:
          return self.tokenizer.batch_decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							Optional[int]:
          return self.tokenizer.decode(*__UpperCAmelCase    ,				**__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.tokenizer.model_input_names
          _a    	=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





     @property
     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,				__UpperCAmelCase    ,				)
          return self.image_processor_class