




'''simple docstring'''



from random import randint, random




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					int      ,__UpperCamelCase:					int      ,__UpperCamelCase:					bool = False      ,__UpperCamelCase:					bool = False      ,__UpperCamelCase:					int = 5      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[[-1] * number_of_cells]  # Create a highway without any car
      SCREAMING_SNAKE_CASE   :      int    					=						0
      SCREAMING_SNAKE_CASE   :      Dict    					=						max(__UpperCamelCase      ,0    )
      while i < number_of_cells:
            SCREAMING_SNAKE_CASE   :      int    					=						(
                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   lowercase__(			__UpperCamelCase:					list      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						0
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						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   lowercase__(			__UpperCamelCase:					list      ,__UpperCamelCase:					float      ,__UpperCamelCase:					int    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					list      ,__UpperCamelCase:					int      ,__UpperCamelCase:					float      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						len(highway[0]    )

      for i in range(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						update(highway[i]      ,__UpperCamelCase      ,__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Tuple    					=						[-1] * number_of_cells

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

      return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()








'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


