
"""simple docstring"""





from random import randint, random



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case = False,	__snake_case = False,	__snake_case = 5,	)				->						list:


					"""simple docstring"""





					_UpperCamelCase									=    [[-1] * number_of_cells]  # Create a highway without any car
					_UpperCamelCase									=    0
					_UpperCamelCase									=    max(__snake_case,	0      )
					while i < number_of_cells:
										_UpperCamelCase									=    (
										    randint(0,	__snake_case      ) 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__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    0
					_UpperCamelCase									=    highway_now[car_index + 1 :]
					for cell in range(len(__snake_case      )      ):  # 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(__snake_case,	-1      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						list:


					"""simple docstring"""





					_UpperCamelCase									=    len(__snake_case      )
					# Beforce calculations, the highway is empty
					_UpperCamelCase									=    [-1] * number_of_cells

					for car_index in range(__snake_case      ):
										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,	__snake_case      )
															# Number of empty cell before the next car
															_UpperCamelCase									=    get_distance(__snake_case,	__snake_case      ) - 1
															# We can't have the car causing an accident
															_UpperCamelCase									=    min(next_highway[car_index],	__snake_case      )
															if random() < probability:
																				# Randomly, a driver will slow down
																				_UpperCamelCase									=    max(next_highway[car_index] - 1,	0      )
					return next_highway



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						list:


					"""simple docstring"""





					_UpperCamelCase									=    len(highway[0]      )

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

										for car_index in range(__snake_case      ):
															_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(__snake_case      )

					return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


