


from random import randint, random






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase = False					,  _lowercase = False					,  _lowercase = 5					,  )					-> list:


     """simple docstring"""


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


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> list:


     """simple docstring"""


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

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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> list:


     """simple docstring"""


     __UpperCamelCase =			len(highway[0] )

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

          for car_index in range(_lowercase ):
               __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(_lowercase )

     return highway


if __name__ == "__main__":
    import doctest

    doctest.testmod()




"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



