




import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")





class 					snake_case_   :



				'''simple docstring'''





				def __init__(      self							:   Union[str, Any]      ,							__magic_name__							:   Union[str, Any]      ,							__magic_name__							:   List[str]      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   bool = False							)							->			int:
											lowerCamelCase_			:    List[str]   						=      scheduler
											lowerCamelCase_			:    List[str]   						=      optimizers if isinstance(__a      ,							(list, tuple)							) else [optimizers]
											lowerCamelCase_			:    Union[str, Any]   						=      split_batches
											lowerCamelCase_			:    Dict   						=      step_with_optimizer
											lowerCamelCase_			:    Optional[Any]   						=      GradientState()





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]      ,							*__magic_name__							:   Dict      ,							**__magic_name__							:   List[Any]							)							->			Optional[int]:
											if not self.step_with_optimizer:
																		# No link between scheduler and optimizer -> just step
																		self.scheduler.step(*__a      ,							**__a							)
																		return

											# Otherwise, first make sure the optimizer was stepped.
											if not self.gradient_state.sync_gradients:
																		if self.gradient_state.adjust_scheduler:
																									self.scheduler._step_count += 1
																		return

											for opt in self.optimizers:
																		if opt.step_was_skipped:
																									return
											if self.split_batches:
																		# Split batches -> the training dataloader batch size is not changed so one step per training step
																		self.scheduler.step(*__a      ,							**__a							)
											else:
																		# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																		# num_processes steps per training step
																		lowerCamelCase_			:    List[Any]   						=      AcceleratorState().num_processes
																		for _ in range(__a							):
																									# Special case when using OneCycle and `drop_last` was not used
																									if hasattr(self.scheduler      ,							"total_steps"							):
																																if self.scheduler._step_count <= self.scheduler.total_steps:
																																							self.scheduler.step(*__a      ,							**__a							)
																									else:
																																self.scheduler.step(*__a      ,							**__a							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			str:
											return self.scheduler.get_last_lr()





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			Union[str, Any]:
											return self.scheduler.state_dict()





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple      ,							__magic_name__							:   Any							)							->			Union[str, Any]:
											self.scheduler.load_state_dict(__a							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			Any:
											return self.scheduler.get_lr()





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]      ,							*__magic_name__							:   Optional[Any]      ,							**__magic_name__							:   Tuple							)							->			Optional[int]:
											return self.scheduler.print_lr(*__a      ,							**__a							)


'''simple docstring'''


from random import randint, random





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: int = 5     ,       ):


	"""simple docstring"""
	__UpperCAmelCase      					=      [[-1] * number_of_cells]  # Create a highway without any car
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      max(UpperCamelCase__     ,       0							)
	while i < number_of_cells:
		__UpperCAmelCase      					=      (
		    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    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      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    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


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

	for i in range(UpperCamelCase__							):
		__UpperCAmelCase      					=      update(highway[i]     ,       UpperCamelCase__     ,       UpperCamelCase__							)
		__UpperCAmelCase      					=      [-1] * number_of_cells

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

	return highway


if __name__ == "__main__":
					import doctest

					doctest.testmod()
