from random import randint, random

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = False       ,					lowerCAmelCase_ = False       ,					lowerCAmelCase_ = 5       ,					)		->						list:




       '''simple docstring'''






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




       '''simple docstring'''






       _A=	0
       _A=	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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						list:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						list:




       '''simple docstring'''






       _A=	len(highway[0]   )

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

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

       return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()







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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""speech_to_text_2"""
     _SCREAMING_SNAKE_CASE     :     str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(					self				,      lowerCAmelCase__=10000				,      lowerCAmelCase__=6				,      lowerCAmelCase__=2048				,      lowerCAmelCase__=4				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=256				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=2				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1024				,      **lowerCAmelCase__				,      ):
            _A=	vocab_size
            _A=	d_model
            _A=	decoder_ffn_dim
            _A=	decoder_layers
            _A=	decoder_attention_heads
            _A=	dropout
            _A=	attention_dropout
            _A=	activation_dropout
            _A=	activation_function
            _A=	init_std
            _A=	decoder_layerdrop
            _A=	use_cache
            _A=	decoder_layers
            _A=	scale_embedding  # scale factor will be sqrt(d_model) if True
            _A=	max_target_positions

            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      decoder_start_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )