






"""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_			(__A	):
	__magic_name__       =							'''char'''
	__magic_name__       =							'''bpe'''
	__magic_name__       =							'''wp'''





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







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''char_tokenizer''']
	__magic_name__       =							'''ViTImageProcessor'''
	__magic_name__       =							'''MgpstrTokenizer'''





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					Any=None      ,      **lowerCAmelCase_   :					str		)		->     Optional[int]:
				UpperCAmelCase_	:					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."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Union[str, Any]     	=   kwargs.pop("feature_extractor"		)

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

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def __call__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					List[Any]=None      ,      **lowerCAmelCase_   :					Tuple		)		->     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:
							UpperCAmelCase_	:					Tuple     	=   self.image_processor(lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_		)
				if text is not None:
							UpperCAmelCase_	:					str     	=   self.char_tokenizer(lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_		)

				if text is None:
							return inputs
				elif images is None:
							return encodings
				else:
							UpperCAmelCase_	:					List[str]     	=   encodings["input_ids"]
							return inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   sequences
				UpperCAmelCase_	:					Tuple     	=   char_preds.size(0		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   self._decode_helper(lowerCAmelCase_      ,      "char"		)
				UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   self._decode_helper(lowerCAmelCase_      ,      "bpe"		)
				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   self._decode_helper(lowerCAmelCase_      ,      "wp"		)

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

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     str:
				if format == DecodeType.CHARACTER:
							UpperCAmelCase_	:					Dict     	=   self.char_decode
							UpperCAmelCase_	:					Union[str, Any]     	=   1
							UpperCAmelCase_	:					Optional[Any]     	=   "[s]"
				elif format == DecodeType.BPE:
							UpperCAmelCase_	:					int     	=   self.bpe_decode
							UpperCAmelCase_	:					Optional[Any]     	=   2
							UpperCAmelCase_	:					Any     	=   "#"
				elif format == DecodeType.WORDPIECE:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.wp_decode
							UpperCAmelCase_	:					Optional[int]     	=   102
							UpperCAmelCase_	:					Union[str, Any]     	=   "[SEP]"
				else:
							raise ValueError(f"""Format {format} is not supported."""		)

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

				for index in range(lowerCAmelCase_		):
							UpperCAmelCase_	:					Union[str, Any]     	=   preds_str[index].find(lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   preds_str[index][:pred_eos]
							UpperCAmelCase_	:					Tuple     	=   preds_index[index].cpu().tolist()
							UpperCAmelCase_	:					Optional[int]     	=   pred_index.index(lowerCAmelCase_		) if eos_token in pred_index else -1
							UpperCAmelCase_	:					str     	=   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(lowerCAmelCase_		)
							conf_scores.append(lowerCAmelCase_		)

				return dec_strs, conf_scores





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Any		)		->     List[str]:
				UpperCAmelCase_	:					List[Any]     	=   [seq.replace(" "      ,      ""		) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase_		)]
				return decode_strs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Any		)		->     List[str]:
				return self.bpe_tokenizer.batch_decode(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Any		)		->     List[Any]:
				UpperCAmelCase_	:					Dict     	=   [seq.replace(" "      ,      ""		) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase_		)]
				return decode_strs











def        _snake_case   (_snake_case				:       list      ,    _snake_case				:       int      ,    _snake_case				:       int = 0      ,    _snake_case				:       int = 0)					->	int:
   _lowercase              =right or len(_snake_case) - 1
   if left > right:
      return -1
   elif list_data[left] == key:
      return left
   elif list_data[right] == key:
      return right
   else:
      return search(_snake_case      ,    _snake_case      ,    left + 1      ,    right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


