



'''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 _UpperCamelCase		(     __UpperCAmelCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    '''char'''
				lowerCAmelCase__					    =    '''bpe'''
				lowerCAmelCase__					    =    '''wp'''

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





class _UpperCamelCase		(     __UpperCAmelCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ['''image_processor''', '''char_tokenizer''']
				lowerCAmelCase__					    =    '''ViTImageProcessor'''
				lowerCAmelCase__					    =    '''MgpstrTokenizer'''

				def __init__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Any=None  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       **_lowerCAmelCase  :  str):







											'''simple docstring'''

											__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.'  ,       __SCREAMING_SNAKE_CASE  ,       )
																		__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__(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE)

				def __call__(							self  :  Tuple  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  List[str]=None  ,       _lowerCAmelCase  :  int=None  ,       **_lowerCAmelCase  :  Optional[int]):







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

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

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

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

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

											__lowercase       	=[]
											__lowercase       	=[]
											for i in range(__SCREAMING_SNAKE_CASE):
																		__lowercase       	=[char_scores[i], bpe_scores[i], wp_scores[i]]
																		__lowercase       	=[char_strs[i], bpe_strs[i], wp_strs[i]]
																		__lowercase       	=scores.index(max(__SCREAMING_SNAKE_CASE))
																		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 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[int]  ,       _lowerCAmelCase  :  List[Any]):







											'''simple docstring'''

											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=__SCREAMING_SNAKE_CASE  ,       sorted=__SCREAMING_SNAKE_CASE)
											__lowercase       	=preds_index.view(-1  ,       __SCREAMING_SNAKE_CASE)[:, 1:]
											__lowercase       	=decoder(__SCREAMING_SNAKE_CASE)
											__lowercase	,				__lowercase       	=torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE  ,       dim=2).max(dim=2)
											__lowercase       	=preds_max_prob[:, 1:]

											for index in range(__SCREAMING_SNAKE_CASE):
																		__lowercase       	=preds_str[index].find(__SCREAMING_SNAKE_CASE)
																		__lowercase       	=preds_str[index][:pred_eos]
																		__lowercase       	=preds_index[index].cpu().tolist()
																		__lowercase       	=pred_index.index(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE)
																		conf_scores.append(__SCREAMING_SNAKE_CASE)

											return dec_strs, conf_scores

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  List[str]):







											'''simple docstring'''

											__lowercase       	=[seq.replace(' '  ,       '') for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)]
											return decode_strs

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Tuple):







											'''simple docstring'''

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








'''simple docstring'''



from functools import lru_cache



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2
							__lowercase       	=set()
							while i * i <= n:
														if n % i:
																					i += 1
														else:
																					n //= i
																					factors.add(_lowerCAmelCase					)
							if n > 1:
														factors.add(_lowerCAmelCase					)
							return factors



@lru_cache
def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(unique_prime_factors(_lowerCAmelCase					)					)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(set(_lowerCAmelCase					)					) in (0, 1)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2

							while True:
														# Increment each value of a generated range
														__lowercase       	=[base + i for i in range(_lowerCAmelCase					)]

														# Run elements through out unique_prime_factors function
														# Append our target number to the end.
														__lowercase       	=[upf_len(_lowerCAmelCase					) for x in group]
														checker.append(_lowerCAmelCase					)

														# If all numbers in the list are equal, return the group variable.
														if equality(_lowerCAmelCase					):
																					return group

														# Increment our base variable by 1
														base += 1



def   _A       (   _lowerCAmelCase = 4					):



							"""simple docstring"""



							__lowercase       	=run(_lowerCAmelCase					)
							return results[0] if len(_lowerCAmelCase					) else None


if __name__ == "__main__":
		print(solution())



