





from random import randint, random







def 					lowerCAmelCase__(		lowercase				:			Tuple      ,   lowercase				:			Dict      ,   lowercase				:			Union[str, Any]      ,   lowercase				:			Tuple = False      ,   lowercase				:			List[Any] = False      ,   lowercase				:			Tuple = 5      ,   )       ->			list:
  __snake_case      : Tuple						=				[[-1] * number_of_cells]  # Create a highway without any car
  __snake_case      : Dict						=				0
  __snake_case      : Any						=				max(__a      ,   0 )
  while i < number_of_cells:
    __snake_case      : str						=				(
        randint(0      ,   __a ) 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__(		lowercase				:			Optional[Any]      ,   lowercase				:			Any )       ->			int:
  __snake_case      : Optional[int]						=				0
  __snake_case      : Optional[int]						=				highway_now[car_index + 1 :]
  for cell in range(len(__a ) ):  # 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(__a      ,   -1 )







def 					lowerCAmelCase__(		lowercase				:			int      ,   lowercase				:			Tuple      ,   lowercase				:			Optional[Any] )       ->			list:
  __snake_case      : Optional[int]						=				len(__a )
  # Beforce calculations, the highway is empty
  __snake_case      : Dict						=				[-1] * number_of_cells

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







def 					lowerCAmelCase__(		lowercase				:			Tuple      ,   lowercase				:			Dict      ,   lowercase				:			Dict      ,   lowercase				:			Optional[int] )       ->			list:
  __snake_case      : Dict						=				len(highway[0] )

  for i in range(__a ):
    __snake_case      : Dict						=				update(highway[i]      ,   __a      ,   __a )
    __snake_case      : List[str]						=				[-1] * number_of_cells

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

  return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

