


from random import randint, random
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False						,    SCREAMING_SNAKE_CASE = False						,    SCREAMING_SNAKE_CASE = 5						,    ):


		"""simple docstring"""




		lowercase__						=				[[-1] * number_of_cells]  # Create a highway without any car
		lowercase__						=				0
		lowercase__						=				max(SCREAMING_SNAKE_CASE						,    0					)
		while i < number_of_cells:
				lowercase__						=				(
				    randint(0						,    SCREAMING_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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				0
		lowercase__						=				highway_now[car_index + 1 :]
		for cell in range(len(SCREAMING_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(SCREAMING_SNAKE_CASE						,    -1					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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

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


		"""simple docstring"""




		lowercase__						=				len(highway[0]					)

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

				for car_index in range(SCREAMING_SNAKE_CASE					):
						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(SCREAMING_SNAKE_CASE					)

		return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''lxmert'''
							_lowercase     :      Any  				=			{}

							def __init__(							self:     Any   ,						UpperCamelCase_:     List[Any]=30_522   ,						UpperCamelCase_:     int=768   ,						UpperCamelCase_:     Optional[Any]=12   ,						UpperCamelCase_:     Dict=9_500   ,						UpperCamelCase_:     List[Any]=1_600   ,						UpperCamelCase_:     List[Any]=400   ,						UpperCamelCase_:     List[str]=3_072   ,						UpperCamelCase_:     int="gelu"   ,						UpperCamelCase_:     Any=0.1   ,						UpperCamelCase_:     int=0.1   ,						UpperCamelCase_:     List[str]=512   ,						UpperCamelCase_:     Optional[int]=2   ,						UpperCamelCase_:     Union[str, Any]=0.02   ,						UpperCamelCase_:     Dict=1E-1_2   ,						UpperCamelCase_:     List[Any]=9   ,						UpperCamelCase_:     List[Any]=5   ,						UpperCamelCase_:     Optional[Any]=5   ,						UpperCamelCase_:     str=2_048   ,						UpperCamelCase_:     Dict=4   ,						UpperCamelCase_:     Any=6.67   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     Any=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     Any=True   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				num_qa_labels
									lowercase__						=				num_object_labels
									lowercase__						=				num_attr_labels
									lowercase__						=				l_layers
									lowercase__						=				x_layers
									lowercase__						=				r_layers
									lowercase__						=				visual_feat_dim
									lowercase__						=				visual_pos_dim
									lowercase__						=				visual_loss_normalizer
									lowercase__						=				task_matched
									lowercase__						=				task_mask_lm
									lowercase__						=				task_obj_predict
									lowercase__						=				task_qa
									lowercase__						=				visual_obj_loss
									lowercase__						=				visual_attr_loss
									lowercase__						=				visual_feat_loss
									lowercase__						=				{'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
									super().__init__(**UpperCamelCase_							)


