
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(				_UpperCamelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''char'''
					SCREAMING_SNAKE_CASE			    =    '''bpe'''
					SCREAMING_SNAKE_CASE			    =    '''wp'''
lowercase        =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class       __A(				_UpperCamelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''char_tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''ViTImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''MgpstrTokenizer'''

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     int=None   ,					**__UpperCamelCase					:     str   ):
												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					:     Any   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     str=None   ,					**__UpperCamelCase					:     int   ):
												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    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple   ):
												lowerCamelCase_							  =					sequences
												lowerCamelCase_							  =					char_preds.size(0   )

												lowerCamelCase_							  =					self._decode_helper(__UpperCamelCase   ,					"""char"""   )
												lowerCamelCase_							  =					self._decode_helper(__UpperCamelCase   ,					"""bpe"""   )
												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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[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_							  =					1_0_2
																			lowerCamelCase_							  =					"[SEP]"
												else:
																			raise ValueError(F'''Format {format} is not supported.'''   )

												lowerCamelCase_							  =					[], []
												lowerCamelCase_							  =					pred_logits.size(0   )
												lowerCamelCase_							  =					pred_logits.size(1   )
												lowerCamelCase_							  =					pred_logits.topk(1   ,					dim=-1   ,					largest=__UpperCamelCase   ,					sorted=__UpperCamelCase   )
												lowerCamelCase_							  =					preds_index.view(-1   ,					__UpperCamelCase   )[:, 1:]
												lowerCamelCase_							  =					decoder(__UpperCamelCase   )
												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    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[seq.replace(""" """   ,					""""""   ) for seq in self.char_tokenizer.batch_decode(__UpperCamelCase   )]
												return decode_strs

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Any   ):
												return self.bpe_tokenizer.batch_decode(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[seq.replace(""" """   ,					""""""   ) for seq in self.wp_tokenizer.batch_decode(__UpperCamelCase   )]
												return decode_strs







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = 4_0_0_0_0_0_0       )  ->    int:
							lowerCamelCase_							  =					[]
							lowerCamelCase_  ,   lowerCamelCase_							  =					0, 1
							while b <= n:
														if b % 2 == 0:
																					even_fibs.append(UpperCAmelCase__       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					b, a + b
							return sum(UpperCAmelCase__       )


if __name__ == "__main__":
		print(F"""{solution() = }""")





