



from random import randint, random






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      bool = False      ,			UpperCamelCase		:      bool = False      ,			UpperCamelCase		:      int = 5      ,			)  ->list:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		[[-1] * number_of_cells]  # Create a highway without any car
			lowerCamelCase__ :      int								=		0
			lowerCamelCase__ :      str								=		max(UpperCamelCase      ,			0 )
			while i < number_of_cells:
						lowerCamelCase__ :      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      _A				(UpperCamelCase		:      list      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		0
			lowerCamelCase__ :      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      _A				(UpperCamelCase		:      list      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      int )  ->list:



			'''simple docstring'''







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






def      _A				(UpperCamelCase		:      list      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      float      ,			UpperCamelCase		:      int )  ->list:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		len(highway[0] )

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

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

			return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}" )
			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



