


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					:     Dict       =     logging.get_logger(__name__)




def    a__    (		snake_case__					:							str						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							List[Any]							=     RobertaPreLayerNormConfig.from_pretrained(
      snake_case__						,   architectures=["""RobertaPreLayerNormForMaskedLM"""]     )

  # convert state_dict
  _UpperCAmelCase		:							Tuple							=     torch.load(hf_hub_download(repo_id=snake_case__						,   filename="""pytorch_model.bin"""     )     )
  _UpperCAmelCase		:							List[Any]							=     {}
  for tensor_key, tensor_value in original_state_dict.items():
    # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
    if tensor_key.startswith("""roberta."""     ):
      _UpperCAmelCase		:							List[Any]							=     """roberta_prelayernorm.""" + tensor_key[len("""roberta."""     ) :]

    # The original implementation contains weights which are not used, remove them from the state_dict
    if tensor_key.endswith(""".self.LayerNorm.weight"""     ) or tensor_key.endswith(""".self.LayerNorm.bias"""     ):
      continue

    _UpperCAmelCase		:							int							=     tensor_value

  _UpperCAmelCase		:							Any							=     RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=snake_case__						,   config=snake_case__						,   state_dict=snake_case__     )
  model.save_pretrained(snake_case__     )

  # convert tokenizer
  _UpperCAmelCase		:							Union[str, Any]							=     AutoTokenizer.from_pretrained(snake_case__     )
  tokenizer.save_pretrained(snake_case__     )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint-repo',
     default=None,
     type=str,
     required=True,
     help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        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    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     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    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-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		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

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

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





