
'''simple docstring'''







from random import randint, random
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: bool = False      ,      __SCREAMING_SNAKE_CASE					: bool = False      ,      __SCREAMING_SNAKE_CASE					: int = 5      ,      ):



						"""simple docstring"""





						lowercase_	:     Dict													=       [[-1] * number_of_cells]  # Create a highway without any car
						lowercase_	:     Union[str, Any]													=       0
						lowercase_	:     Union[str, Any]													=       max(__SCREAMING_SNAKE_CASE      ,      0							)
						while i < number_of_cells:
												lowercase_	:     List[Any]													=       (
												    randint(0      ,      __SCREAMING_SNAKE_CASE							) if random_speed else initial_speed
												)  # Place the cars
												i += (
												    randint(1      ,      max_speed * 2							) if random_frequency else frequency
												)  # Arbitrary number, may need tuning
						return highway
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       0
						lowercase_	:     Dict													=       highway_now[car_index + 1 :]
						for cell in range(len(__SCREAMING_SNAKE_CASE							)							):  # May need a better name for this
												if cells[cell] != -1:  # If the cell is not empty then
																		return distance  # we have the distance we wanted
												distance += 1
						# Here if the car is near the end of the highway
						return distance + get_distance(__SCREAMING_SNAKE_CASE      ,      -1							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: float      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     str													=       len(__SCREAMING_SNAKE_CASE							)
						# Beforce calculations, the highway is empty
						lowercase_	:     List[str]													=       [-1] * number_of_cells

						for car_index in range(__SCREAMING_SNAKE_CASE							):
												if highway_now[car_index] != -1:
																		# Add 1 to the current speed of the car and cap the speed
																		lowercase_	:     Dict													=       min(highway_now[car_index] + 1      ,      __SCREAMING_SNAKE_CASE							)
																		# Number of empty cell before the next car
																		lowercase_	:     Optional[Any]													=       get_distance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							) - 1
																		# We can't have the car causing an accident
																		lowercase_	:     Optional[Any]													=       min(next_highway[car_index]      ,      __SCREAMING_SNAKE_CASE							)
																		if random() < probability:
																								# Randomly, a driver will slow down
																								lowercase_	:     Dict													=       max(next_highway[car_index] - 1      ,      0							)
						return next_highway
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: float      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       len(highway[0]							)

						for i in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       update(highway[i]      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       [-1] * number_of_cells

												for car_index in range(__SCREAMING_SNAKE_CASE							):
																		lowercase_	:     str													=       next_speeds_calculated[car_index]
																		if speed != -1:
																								# Change the position based on the speed (with % to create the loop)
																								lowercase_	:     Union[str, Any]													=       (car_index + speed) % number_of_cells
																								# Commit the change of position
																								lowercase_	:     Optional[Any]													=       speed
												highway.append(__SCREAMING_SNAKE_CASE							)

						return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()





'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





											return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

											del W_supports["sizes"]
											del W_supports["start_token_id"]
											del W_supports["end_token_id"]

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



