


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       A    (   A_ ):
							UpperCamelCase_   :							List[Any]       ='''char'''
							UpperCamelCase_   :							Union[str, Any]       ='''bpe'''
							UpperCamelCase_   :							Optional[Any]       ='''wp'''


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





class       A    (   A_ ):
							UpperCamelCase_   :							Optional[int]       =['''image_processor''', '''char_tokenizer''']
							UpperCamelCase_   :							List[Any]       ='''ViTImageProcessor'''
							UpperCamelCase_   :							Dict       ='''MgpstrTokenizer'''


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				**lowerCAmelCase					):
									__lowercase=			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      ,				)
											__lowercase=			kwargs.pop('feature_extractor'					)

									__lowercase=			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`.'					)

									__lowercase=			tokenizer
									__lowercase=			AutoTokenizer.from_pretrained('gpt2'					)
									__lowercase=			AutoTokenizer.from_pretrained('bert-base-uncased'					)

									super().__init__(lowerCAmelCase      ,				lowerCAmelCase					)


							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:
											__lowercase=			self.image_processor(lowerCAmelCase      ,				return_tensors=lowerCAmelCase      ,				**lowerCAmelCase					)
									if text is not None:
											__lowercase=			self.char_tokenizer(lowerCAmelCase      ,				return_tensors=lowerCAmelCase      ,				**lowerCAmelCase					)

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


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase,							__lowercase,							__lowercase=			sequences
									__lowercase=			char_preds.size(0					)

									__lowercase,							__lowercase=			self._decode_helper(lowerCAmelCase      ,				'char'					)
									__lowercase,							__lowercase=			self._decode_helper(lowerCAmelCase      ,				'bpe'					)
									__lowercase,							__lowercase=			self._decode_helper(lowerCAmelCase      ,				'wp'					)

									__lowercase=			[]
									__lowercase=			[]
									for i in range(lowerCAmelCase					):
											__lowercase=			[char_scores[i], bpe_scores[i], wp_scores[i]]
											__lowercase=			[char_strs[i], bpe_strs[i], wp_strs[i]]
											__lowercase=			scores.index(max(lowerCAmelCase					)					)
											final_strs.append(strs[max_score_index]					)
											final_scores.append(scores[max_score_index]					)

									__lowercase=			{}
									__lowercase=			final_strs
									__lowercase=			final_scores
									__lowercase=			char_strs
									__lowercase=			bpe_strs
									__lowercase=			wp_strs
									return out


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if format == DecodeType.CHARACTER:
											__lowercase=			self.char_decode
											__lowercase=			1
											__lowercase=			'[s]'
									elif format == DecodeType.BPE:
											__lowercase=			self.bpe_decode
											__lowercase=			2
											__lowercase=			'#'
									elif format == DecodeType.WORDPIECE:
											__lowercase=			self.wp_decode
											__lowercase=			1_0_2
											__lowercase=			'[SEP]'
									else:
											raise ValueError(f'Format {format} is not supported.'					)

									__lowercase,							__lowercase=			[], []
									__lowercase=			pred_logits.size(0					)
									__lowercase=			pred_logits.size(1					)
									__lowercase,							__lowercase=			pred_logits.topk(1      ,				dim=-1      ,				largest=lowerCAmelCase      ,				sorted=lowerCAmelCase					)
									__lowercase=			preds_index.view(-1      ,				lowerCAmelCase					)[:, 1:]
									__lowercase=			decoder(lowerCAmelCase					)
									__lowercase,							__lowercase=			torch.nn.functional.softmax(lowerCAmelCase      ,				dim=2					).max(dim=2					)
									__lowercase=			preds_max_prob[:, 1:]

									for index in range(lowerCAmelCase					):
											__lowercase=			preds_str[index].find(lowerCAmelCase					)
											__lowercase=			preds_str[index][:pred_eos]
											__lowercase=			preds_index[index].cpu().tolist()
											__lowercase=			pred_index.index(lowerCAmelCase					) if eos_token in pred_index else -1
											__lowercase=			preds_max_prob[index][: pred_eos_index + 1]
											__lowercase=			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 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[seq.replace(' '      ,				''					) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase					)]
									return decode_strs


							def 			_A       (self      ,				lowerCAmelCase					):
									return self.bpe_tokenizer.batch_decode(lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[seq.replace(' '      ,				''					) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase					)]
									return decode_strs










def        _lowerCamelCase(	lowercase__   , lowercase__ = " "	)						->	list:

		'''simple docstring'''







		__lowercase=			[]

		__lowercase=			0
		for index, char in enumerate(lowercase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						__lowercase=			index + 1
				elif index + 1 == len(lowercase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()






