





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			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   int		      =     '''char'''
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''bpe'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''wp'''




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





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     ['''image_processor''', '''char_tokenizer''']
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     '''ViTImageProcessor'''
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     '''MgpstrTokenizer'''






				def __init__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				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			, )
																__SCREAMING_SNAKE_CASE:				Any           =   kwargs.pop('''feature_extractor'''					)

										__SCREAMING_SNAKE_CASE:				Union[str, 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`.'''					)

										__SCREAMING_SNAKE_CASE:				Any           =   tokenizer
										__SCREAMING_SNAKE_CASE:				List[str]           =   AutoTokenizer.from_pretrained('''gpt2'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   AutoTokenizer.from_pretrained('''bert-base-uncased'''					)

										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






				def __call__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, **_lowerCAmelCase					):

										"""simple docstring"""




										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:
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.image_processor(_lowerCAmelCase			, return_tensors=_lowerCAmelCase			, **_lowerCAmelCase					)
										if text is not None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.char_tokenizer(_lowerCAmelCase			, return_tensors=_lowerCAmelCase			, **_lowerCAmelCase					)

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






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[str]           =   sequences
										__SCREAMING_SNAKE_CASE:				str           =   char_preds.size(0					)

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   self._decode_helper(_lowerCAmelCase			, '''char'''					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self._decode_helper(_lowerCAmelCase			, '''bpe'''					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   self._decode_helper(_lowerCAmelCase			, '''wp'''					)

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

										__SCREAMING_SNAKE_CASE:				str           =   {}
										__SCREAMING_SNAKE_CASE:				Tuple           =   final_strs
										__SCREAMING_SNAKE_CASE:				List[Any]           =   final_scores
										__SCREAMING_SNAKE_CASE:				Dict           =   char_strs
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   bpe_strs
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   wp_strs
										return out






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




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

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

										for index in range(_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   preds_str[index].find(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Dict           =   preds_str[index][:pred_eos]
																__SCREAMING_SNAKE_CASE:				Dict           =   preds_index[index].cpu().tolist()
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   pred_index.index(_lowerCAmelCase					) if eos_token in pred_index else -1
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   preds_max_prob[index][: pred_eos_index + 1]
																__SCREAMING_SNAKE_CASE:				Any           =   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 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   [seq.replace(''' '''			, ''''''					) for seq in self.char_tokenizer.batch_decode(_lowerCAmelCase					)]
										return decode_strs






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self.bpe_tokenizer.batch_decode(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   [seq.replace(''' '''			, ''''''					) for seq in self.wp_tokenizer.batch_decode(_lowerCAmelCase					)]
										return decode_strs










import re






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    bool:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   re.compile(
						    R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$'''					)

						return bool(re.search(UpperCamelCase__       ,			UpperCamelCase__					)					)


if __name__ == "__main__":
							lowerCAmelCase							:       str					 =	"""0094702343221"""

							print(is_sri_lankan_phone_number(phone))



