



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 SCREAMING_SNAKE_CASE__					(		UpperCAmelCase_   ):




				'''simple docstring'''






				__lowerCamelCase      :			Any =				"""char"""
				__lowerCamelCase      :			Any =				"""bpe"""
				__lowerCamelCase      :			Dict =				"""wp"""


SCREAMING_SNAKE_CASE_:Tuple					=						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class SCREAMING_SNAKE_CASE__					(		UpperCAmelCase_   ):




				'''simple docstring'''






				__lowerCamelCase      :			Tuple =				["""image_processor""", """char_tokenizer"""]
				__lowerCamelCase      :			List[Any] =				"""ViTImageProcessor"""
				__lowerCamelCase      :			Optional[Any] =				"""MgpstrTokenizer"""






				def __init__(	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
								A			:	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.""",		__lowercase,		)
												A			:	int										=       kwargs.pop("""feature_extractor"""		)

								A			:	int										=       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			:	str										=       tokenizer
								A			:	Dict										=       AutoTokenizer.from_pretrained("""gpt2"""		)
								A			:	Any										=       AutoTokenizer.from_pretrained("""bert-base-uncased"""		)

								super().__init__(__lowercase,		__lowercase		)






				def __call__(	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
								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			:	str										=       self.image_processor(__lowercase,		return_tensors=__lowercase,		**__lowercase		)
								if text is not None:
												A			:	Union[str, Any]										=       self.char_tokenizer(__lowercase,		return_tensors=__lowercase,		**__lowercase		)

								if text is None:
												return inputs
								elif images is None:
												return encodings
								else:
												A			:	Optional[int]										=       encodings['''input_ids''']
												return inputs






				def       _lowerCAmelCase (	self,		lowerCamelCase__		):
								A			:	Tuple										=       sequences
								A			:	Union[str, Any]										=       char_preds.size(0		)

								A			:	str										=       self._decode_helper(__lowercase,		"""char"""		)
								A			:	Dict										=       self._decode_helper(__lowercase,		"""bpe"""		)
								A			:	Optional[int]										=       self._decode_helper(__lowercase,		"""wp"""		)

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

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






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

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

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

								return dec_strs, conf_scores






				def       _lowerCAmelCase (	self,		lowerCamelCase__		):
								A			:	str										=       [seq.replace(""" """,		""""""		) for seq in self.char_tokenizer.batch_decode(__lowercase		)]
								return decode_strs






				def       _lowerCAmelCase (	self,		lowerCamelCase__		):
								return self.bpe_tokenizer.batch_decode(__lowercase		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__		):
								A			:	str										=       [seq.replace(""" """,		""""""		) for seq in self.wp_tokenizer.batch_decode(__lowercase		)]
								return decode_strs







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


