


'''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_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'char'
    lowerCamelCase    :   Dict					=     'bpe'
    lowerCamelCase    :   int					=     'wp'







A__						:   List[Any]				=    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






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







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         __lowerCamelCase  :      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_     ,	)
              __lowerCamelCase  :      Dict														=						kwargs.pop('feature_extractor'      )

         __lowerCamelCase  :      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`.'      )

         __lowerCamelCase  :      List[Any]														=						tokenizer
         __lowerCamelCase  :      int														=						AutoTokenizer.from_pretrained('gpt2'      )
         __lowerCamelCase  :      Union[str, Any]														=						AutoTokenizer.from_pretrained('bert-base-uncased'      )

         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	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:
              __lowerCamelCase  :      Tuple														=						self.image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         if text is not None:
              __lowerCamelCase  :      int														=						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:
              __lowerCamelCase  :      Union[str, Any]														=						encodings['input_ids']
              return inputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						sequences
         __lowerCamelCase  :      Optional[int]														=						char_preds.size(0      )

         __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						self._decode_helper(SCREAMING_SNAKE_CASE_     ,	'char'      )
         __lowerCamelCase							,			__lowerCamelCase  :      str														=						self._decode_helper(SCREAMING_SNAKE_CASE_     ,	'bpe'      )
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._decode_helper(SCREAMING_SNAKE_CASE_     ,	'wp'      )

         __lowerCamelCase  :      Optional[Any]														=						[]
         __lowerCamelCase  :      Union[str, Any]														=						[]
         for i in range(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						[char_scores[i], bpe_scores[i], wp_scores[i]]
              __lowerCamelCase  :      str														=						[char_strs[i], bpe_strs[i], wp_strs[i]]
              __lowerCamelCase  :      Dict														=						scores.index(max(SCREAMING_SNAKE_CASE_      )      )
              final_strs.append(strs[max_score_index]      )
              final_scores.append(scores[max_score_index]      )

         __lowerCamelCase  :      List[str]														=						{}
         __lowerCamelCase  :      Optional[Any]														=						final_strs
         __lowerCamelCase  :      Union[str, Any]														=						final_scores
         __lowerCamelCase  :      Optional[int]														=						char_strs
         __lowerCamelCase  :      Tuple														=						bpe_strs
         __lowerCamelCase  :      Optional[int]														=						wp_strs
         return out







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         if format == DecodeType.CHARACTER:
              __lowerCamelCase  :      int														=						self.char_decode
              __lowerCamelCase  :      str														=						1
              __lowerCamelCase  :      Optional[int]														=						'[s]'
         elif format == DecodeType.BPE:
              __lowerCamelCase  :      Optional[Any]														=						self.bpe_decode
              __lowerCamelCase  :      int														=						2
              __lowerCamelCase  :      Union[str, Any]														=						'#'
         elif format == DecodeType.WORDPIECE:
              __lowerCamelCase  :      Optional[Any]														=						self.wp_decode
              __lowerCamelCase  :      Optional[Any]														=						1_02
              __lowerCamelCase  :      Any														=						'[SEP]'
         else:
              raise ValueError(f'Format {format} is not supported.'      )

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

         for index in range(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						preds_str[index].find(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      int														=						preds_str[index][:pred_eos]
              __lowerCamelCase  :      Tuple														=						preds_index[index].cpu().tolist()
              __lowerCamelCase  :      Any														=						pred_index.index(SCREAMING_SNAKE_CASE_      ) if eos_token in pred_index else -1
              __lowerCamelCase  :      Union[str, Any]														=						preds_max_prob[index][: pred_eos_index + 1]
              __lowerCamelCase  :      Union[str, 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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      Any														=						[seq.replace(' '     ,	''      ) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )]
         return decode_strs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Tuple:
         return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         __lowerCamelCase  :      Any														=						[seq.replace(' '     ,	''      ) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )]
         return decode_strs










'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__						:   Tuple				=    logging.get_logger(__name__)

A__						:   Dict				=    {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     'rwkv'
    lowerCamelCase    :   Any					=     {'max_position_embeddings': 'context_length'}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_77     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=40_96     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						vocab_size
         __lowerCamelCase  :      Tuple														=						context_length
         __lowerCamelCase  :      str														=						hidden_size
         __lowerCamelCase  :      List[str]														=						num_hidden_layers
         __lowerCamelCase  :      Any														=						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __lowerCamelCase  :      Optional[int]														=						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __lowerCamelCase  :      Optional[Any]														=						layer_norm_epsilon
         __lowerCamelCase  :      int														=						rescale_every
         __lowerCamelCase  :      Tuple														=						use_cache

         __lowerCamelCase  :      int														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						eos_token_id

         super().__init__(
             tie_word_embeddings=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






