


"""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      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		List[str]							      =    """char"""
							__lowercase:		Any							      =    """bpe"""
							__lowercase:		Dict							      =    """wp"""


__SCREAMING_SNAKE_CASE							:    Any		=    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		str							      =    ["""image_processor""", """char_tokenizer"""]
							__lowercase:		Union[str, Any]							      =    """ViTImageProcessor"""
							__lowercase:		Any							      =    """MgpstrTokenizer"""



							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      int=None		,       **UpperCAmelCase_							:      Dict       )					->Dict:



														"""simple docstring"""



														snake_case_           =		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_		,       )
																					snake_case_           =		kwargs.pop("""feature_extractor"""       )

														snake_case_           =		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`."""       )

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

														super().__init__(UpperCAmelCase_		,       UpperCAmelCase_       )



							def __call__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      int=None		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      Union[str, Any]=None		,       **UpperCAmelCase_							:      Any       )					->Any:



														"""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:
																					snake_case_           =		self.image_processor(UpperCAmelCase_		,       return_tensors=UpperCAmelCase_		,       **UpperCAmelCase_       )
														if text is not None:
																					snake_case_           =		self.char_tokenizer(UpperCAmelCase_		,       return_tensors=UpperCAmelCase_		,       **UpperCAmelCase_       )

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



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_       ,						snake_case_           =		sequences
														snake_case_           =		char_preds.size(0       )

														snake_case_       ,						snake_case_           =		self._decode_helper(UpperCAmelCase_		,       """char"""       )
														snake_case_       ,						snake_case_           =		self._decode_helper(UpperCAmelCase_		,       """bpe"""       )
														snake_case_       ,						snake_case_           =		self._decode_helper(UpperCAmelCase_		,       """wp"""       )

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

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



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														if format == DecodeType.CHARACTER:
																					snake_case_           =		self.char_decode
																					snake_case_           =		1
																					snake_case_           =		"""[s]"""
														elif format == DecodeType.BPE:
																					snake_case_           =		self.bpe_decode
																					snake_case_           =		2
																					snake_case_           =		"""#"""
														elif format == DecodeType.WORDPIECE:
																					snake_case_           =		self.wp_decode
																					snake_case_           =		102
																					snake_case_           =		"""[SEP]"""
														else:
																					raise ValueError(F"""Format {format} is not supported."""       )

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

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



														"""simple docstring"""



														snake_case_           =		[seq.replace(""" """		,       """"""       ) for seq in self.char_tokenizer.batch_decode(UpperCAmelCase_       )]
														return decode_strs



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]       )					->str:



														"""simple docstring"""



														return self.bpe_tokenizer.batch_decode(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->str:



														"""simple docstring"""



														snake_case_           =		[seq.replace(""" """		,       """"""       ) for seq in self.wp_tokenizer.batch_decode(UpperCAmelCase_       )]
														return decode_strs







"""simple docstring"""
from __future__ import annotations







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				list[int]:
							snake_case_           =		0
							snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1

							while i < j:
														if nums[i] + nums[j] == target:
																					return [i, j]
														elif nums[i] + nums[j] < target:
																					snake_case_           =		i + 1
														else:
																					snake_case_           =		j - 1

							return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")



