


"""simple docstring"""






class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__=None  ):
								SCREAMING_SNAKE_CASE__  				=					data
								SCREAMING_SNAKE_CASE__  				=					None






				def __repr__(    self  ):
								SCREAMING_SNAKE_CASE__  				=					""
								SCREAMING_SNAKE_CASE__  				=					self
								while temp:
												string_rep += f'''<{temp.data}> ---> '''
												SCREAMING_SNAKE_CASE__  				=					temp.next
								string_rep += "<END>"
								return string_rep


def __lowercase  (   lowerCamelCase_	:   Optional[int]	):

				# if elements_list is empty
				if not elements_list:
								raise Exception("The Elements List is empty"	)

				# Set first element as Head
				SCREAMING_SNAKE_CASE__  				=					Node(elements_list[0]	)
				SCREAMING_SNAKE_CASE__  				=					head
				# Loop through elements from position 1
				for data in elements_list[1:]:
								SCREAMING_SNAKE_CASE__  				=					Node(lowerCamelCase_	)
								SCREAMING_SNAKE_CASE__  				=					current.next
				return head


_lowerCamelCase  =	[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print('Creating Linked List from List.')
_lowerCamelCase  =	make_linked_list(list_data)
print('Linked List:')

print(linked_list)




"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

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

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

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		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 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

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

								return final_outputs
