
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   (      __a			):
				_lowercase     						='''char'''
				_lowercase     						='''bpe'''
				_lowercase     						='''wp'''


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





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''image_processor''', '''char_tokenizer''']
				_lowercase     						='''ViTImageProcessor'''
				_lowercase     						='''MgpstrTokenizer'''




				def __init__(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							**_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      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							,							)
										lowerCAmelCase_         =      kwargs.pop("feature_extractor"	)

							lowerCAmelCase_         =      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_         =      tokenizer
							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("gpt2"	)
							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("bert-base-uncased"	)

							super().__init__(_UpperCamelCase							,							_UpperCamelCase	)




				def __call__(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							**_UpperCamelCase	)							-> 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_         =      self.image_processor(_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	)
							if text is not None:
										lowerCAmelCase_         =      self.char_tokenizer(_UpperCamelCase							,							return_tensors=_UpperCamelCase							,							**_UpperCamelCase	)

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




				def 	__a		(   self							,							_UpperCamelCase	)							-> Tuple:
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      sequences
							lowerCAmelCase_         =      char_preds.size(0	)

							lowerCAmelCase_      ,    lowerCAmelCase_         =      self._decode_helper(_UpperCamelCase							,							"char"	)
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self._decode_helper(_UpperCamelCase							,							"bpe"	)
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self._decode_helper(_UpperCamelCase							,							"wp"	)

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

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




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

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

							for index in range(_UpperCamelCase	):
										lowerCAmelCase_         =      preds_str[index].find(_UpperCamelCase	)
										lowerCAmelCase_         =      preds_str[index][:pred_eos]
										lowerCAmelCase_         =      preds_index[index].cpu().tolist()
										lowerCAmelCase_         =      pred_index.index(_UpperCamelCase	) if eos_token in pred_index else -1
										lowerCAmelCase_         =      preds_max_prob[index][: pred_eos_index + 1]
										lowerCAmelCase_         =      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 	__a		(   self							,							_UpperCamelCase	)							-> Optional[int]:
							lowerCAmelCase_         =      [seq.replace(" "							,							""	) for seq in self.char_tokenizer.batch_decode(_UpperCamelCase	)]
							return decode_strs




				def 	__a		(   self							,							_UpperCamelCase	)							-> Any:
							return self.bpe_tokenizer.batch_decode(_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      [seq.replace(" "							,							""	) for seq in self.wp_tokenizer.batch_decode(_UpperCamelCase	)]
							return decode_strs





from math import loga






def   lowerCamelCase__							(							__lowerCAmelCase    :    int							):




			"""simple docstring"""


			if a < 0:
						raise ValueError("Input value must be a positive integer"							)
			elif isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						raise TypeError("Input value must be a 'int' type"							)
			return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



