
from random import randint, random




def 							a		(				snake_case__:   int		,  snake_case__:   int		,  snake_case__:   int		,  snake_case__:   bool = False		,  snake_case__:   bool = False		,  snake_case__:   int = 5		,  ):


				'''simple docstring'''
				lowercase_	    =   [[-1] * number_of_cells]  # Create a highway without any car
				lowercase_	    =   0
				lowercase_	    =   max(_lowerCamelCase		,  0   )
				while i < number_of_cells:
								lowercase_	    =   (
								    randint(0		,  _lowerCamelCase   ) 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		(				snake_case__:   list		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   0
				lowercase_	    =   highway_now[car_index + 1 :]
				for cell in range(len(_lowerCamelCase   )   ):  # 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(_lowerCamelCase		,  -1   )




def 							a		(				snake_case__:   list		,  snake_case__:   float		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   len(_lowerCamelCase   )
				# Beforce calculations, the highway is empty
				lowercase_	    =   [-1] * number_of_cells

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




def 							a		(				snake_case__:   list		,  snake_case__:   int		,  snake_case__:   float		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   len(highway[0]   )

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

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

				return highway


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






