




"""simple docstring"""





from random import randint, random

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase = False				,       UpperCamelCase = False				,       UpperCamelCase = 5				,       )			->			list:


			"""simple docstring"""
			__UpperCAmelCase	: str		=					[[-1] * number_of_cells]  # Create a highway without any car
			__UpperCAmelCase	: int		=					0
			__UpperCAmelCase	: str		=					max(UpperCamelCase				,       0							)
			while i < number_of_cells:
						__UpperCAmelCase	: Tuple		=					(
						    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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: List[str]		=					0
			__UpperCAmelCase	: Dict		=					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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					len(UpperCamelCase							)
			# Beforce calculations, the highway is empty
			__UpperCAmelCase	: int		=					[-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
									__UpperCAmelCase	: int		=					min(highway_now[car_index] + 1				,       UpperCamelCase							)
									# Number of empty cell before the next car
									__UpperCAmelCase	: Optional[int]		=					get_distance(UpperCamelCase				,       UpperCamelCase							) - 1
									# We can't have the car causing an accident
									__UpperCAmelCase	: int		=					min(next_highway[car_index]				,       UpperCamelCase							)
									if random() < probability:
												# Randomly, a driver will slow down
												__UpperCAmelCase	: Optional[Any]		=					max(next_highway[car_index] - 1				,       0							)
			return next_highway

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					len(highway[0]							)

			for i in range(UpperCamelCase							):
						__UpperCAmelCase	: List[str]		=					update(highway[i]				,       UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: Any		=					[-1] * number_of_cells

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

			return highway


if __name__ == "__main__":
					import doctest

					doctest.testmod()












"""simple docstring"""





from ...processing_utils import ProcessorMixin



class      a__				(	__magic_name__   ):
			lowercase_    =	["image_processor", "feature_extractor"]
			lowercase_    =	"TvltImageProcessor"
			lowercase_    =	"TvltFeatureExtractor"


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						super().__init__(image_processor=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					image_processor
						__UpperCAmelCase	: Dict		=					feature_extractor


			def __call__(      self      :    Tuple      ,  UpperCamelCase_      :    str=None      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    str=None      ,  UpperCamelCase_      :    List[str]=False      ,  UpperCamelCase_      :    Optional[int]=False      ,  *UpperCamelCase_      :    List[str]      ,  **UpperCamelCase_      :    str      ,  ):


						"""simple docstring"""




						if images is None and audio is None:
									raise ValueError("You need to specify either an `images` or `audio` input to process.")

						__UpperCAmelCase	: Optional[Any]		=					None
						if images is not None:
									__UpperCAmelCase	: int		=					self.image_processor(UpperCamelCase_      ,  mask_pixel=UpperCamelCase_      ,  *UpperCamelCase_      ,  **UpperCamelCase_)
						if images_mixed is not None:
									__UpperCAmelCase	: int		=					self.image_processor(UpperCamelCase_      ,  is_mixed=UpperCamelCase_      ,  *UpperCamelCase_      ,  **UpperCamelCase_)
						if audio is not None:
									__UpperCAmelCase	: List[Any]		=					self.feature_extractor(
									    UpperCamelCase_      ,  *UpperCamelCase_      ,  sampling_rate=UpperCamelCase_      ,  mask_audio=UpperCamelCase_      ,  **UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					{}
						if audio is not None:
									output_dict.update(UpperCamelCase_)
						if images is not None:
									output_dict.update(UpperCamelCase_)
						if images_mixed_dict is not None:
									output_dict.update(UpperCamelCase_)
						return output_dict


			@property
			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					self.image_processor.model_input_names
						__UpperCAmelCase	: List[Any]		=					self.feature_extractor.model_input_names
						return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))


