






'''simple docstring'''


import torch

from transformers import AutoTokenizer
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			def __init__( self				:    Optional[Any]			,   lowercase				:    str      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			AutoTokenizer.from_pretrained(lowercase      )






			def 					A   ( self				:    List[Any]			,   lowercase				:    Any      )				->     Tuple:

									'''simple docstring'''







									if isinstance(lowercase			,   lowercase      ) and all(isinstance(_x			,   lowercase      ) for _x in x      ):
															UpperCamelCase__       =			None
															for l in x:
																					UpperCamelCase__       =			self.tokenizer(
																					    lowercase			,   padding="""max_length"""			,   max_length=3_8_4			,   truncation=lowercase			,   return_tensors="""pt"""			,   )
																					UpperCamelCase__       =			torch.tensor([len(lowercase      )]      )
																					if d is not None:
																											for k in d.keys():
																																	UpperCamelCase__       =			torch.cat((d[k], t[k])			,   0      )
																					else:
																											UpperCamelCase__       =			t

															UpperCamelCase__       =			torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""      )      )
															UpperCamelCase__       =			torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""      )      )

									elif isinstance(lowercase			,   lowercase      ) and all(isinstance(_x			,   lowercase      ) for _x in x      ):
															UpperCamelCase__       =			self.tokenizer(
															    lowercase			,   padding="""max_length"""			,   max_length=3_8_4			,   truncation=lowercase			,   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 					A   ( self				:    Dict			,   lowercase				:    List[Any]			,   lowercase				:    Optional[Any]			,   lowercase				:    List[str]			,   lowercase				:    Optional[int]			,   lowercase				:    Any=0.7_0      )				->     List[Any]:

									'''simple docstring'''







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

															UpperCamelCase__       =			[]
															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 lowercase      : (tup[2] * tup[3])			,   reverse=lowercase      )
															UpperCamelCase__       =			[]
															for k in range(len(lowercase      )      ):
																					if output[k][2] * output[k][3] >= thresh:
																											UpperCamelCase__     , UpperCamelCase__       =			output[k][0], output[k][1]
																											UpperCamelCase__       =			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(lowercase      )

									return final_outputs








'''simple docstring'''


def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						UpperCamelCase__       =			1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												UpperCamelCase__       =			int(gap / shrink       )

												# Sort the elements using insertion sort
												for i in range(_A    ,   len(_A       )       ):
																		UpperCamelCase__       =			collection[i]
																		UpperCamelCase__       =			i
																		while j >= gap and collection[j - gap] > temp:
																								UpperCamelCase__       =			collection[j - gap]
																								j -= gap
																		UpperCamelCase__       =			temp

						return collection


if __name__ == "__main__":
					import doctest

					doctest.testmod()
