





from random import randint, random







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a = False   ,		a = False   ,		a = 5   ,		) -> list:
					__A      :		List[str]         =							[[-1] * number_of_cells]  # Create a highway without any car
					__A      :		List[Any]         =							0
					__A      :		Optional[Any]         =							max(UpperCAmelCase__   ,		0    )
					while i < number_of_cells:
										__A      :		Union[str, Any]         =							(
										    randint(0   ,		UpperCAmelCase__    ) 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 					_SCREAMING_SNAKE_CASE							(  a   ,		a    ) -> int:
					__A      :		str         =							0
					__A      :		int         =							highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__    )    ):  # 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(UpperCAmelCase__   ,		-1    )







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a    ) -> list:
					__A      :		str         =							len(UpperCAmelCase__    )
					# Beforce calculations, the highway is empty
					__A      :		Optional[Any]         =							[-1] * number_of_cells

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







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a    ) -> list:
					__A      :		int         =							len(highway[0]    )

					for i in range(UpperCAmelCase__    ):
										__A      :		Optional[int]         =							update(highway[i]   ,		UpperCAmelCase__   ,		UpperCAmelCase__    )
										__A      :		Optional[Any]         =							[-1] * number_of_cells

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

					return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





