
import torch

from transformers import AutoTokenizer







class     __A(				A__    ):




				def __init__(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								__a    							=					AutoTokenizer.from_pretrained(_lowerCAmelCase       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   List[str]:


								'''simple docstring'''




								if isinstance(_lowerCAmelCase	,  _lowerCAmelCase       ) and all(isinstance(_x	,  _lowerCAmelCase       ) for _x in x       ):
												__a    							=					None
												for l in x:
																__a    							=					self.tokenizer(
																    _lowerCAmelCase	,  padding='''max_length'''	,  max_length=384	,  truncation=_lowerCAmelCase	,  return_tensors='''pt'''	,  )
																__a    							=					torch.tensor([len(_lowerCAmelCase       )]       )
																if d is not None:
																				for k in d.keys():
																								__a    							=					torch.cat((d[k], t[k])	,  0       )
																else:
																				__a    							=					t

												__a    							=					torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''       )       )
												__a    							=					torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''       )       )

								elif isinstance(_lowerCAmelCase	,  _lowerCAmelCase       ) and all(isinstance(_x	,  _lowerCAmelCase       ) for _x in x       ):
												__a    							=					self.tokenizer(
												    _lowerCAmelCase	,  padding='''max_length'''	,  max_length=384	,  truncation=_lowerCAmelCase	,  return_tensors='''pt'''	,  )

								else:
												raise Exception(
												    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
												    ''' strings` for supports are supported.'''       )

								return d




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case=0.70       )					->   List[str]:


								'''simple docstring'''




								__a    							=					[]
								for idx in range(len(W_query['''input_ids''']       )       ):
												__a    							=					__a    							=					range(p_start.shape[1]       )

												__a    							=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )       )

												output.sort(key=lambda _snake_case       : (tup[2] * tup[3])	,  reverse=_lowerCAmelCase       )
												__a    							=					[]
												for k in range(len(_lowerCAmelCase       )       ):
																if output[k][2] * output[k][3] >= thresh:
																				__a							,  __a    							=					output[k][0], output[k][1]
																				__a    							=					self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]       )
																				temp.append((decoded, output[k][2] * output[k][3])       )

												final_outputs.append(_lowerCAmelCase       )

								return final_outputs

def 					__lowerCAmelCase    (    a__ = 99_8001						) ->						int:
				__a    							=					0
				for i in range(999		,					99		,					-1						):  # 3 digit numbers range from 999 down to 100
								for j in range(999		,					99		,					-1						):
												__a    							=					str(i * j						)
												if product_string == product_string[::-1] and i * j < n:
																__a    							=					max(a__		,					i * j						)
				return answer


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