





"""simple docstring"""




from queue import PriorityQueue
from typing import Any

import numpy as np





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					):






  """simple docstring"""





  for nxt, d in graph[v]:
    if nxt in visited_forward:
      continue
    A_  :       Optional[int]										=  cst_fwd.get(_UpperCAmelCase ,					np.inf					)
    A_  :       Any										=  cst_fwd[v] + d
    if new_cost_f < old_cost_f:
      queue.put((new_cost_f, nxt)					)
      A_  :       Optional[Any]										=  new_cost_f
      A_  :       str										=  v
    if nxt in visited_backward:
      if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
        A_  :       Any										=  cst_fwd[v] + d + cst_bwd[nxt]
  return shortest_distance





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  -1

  A_  :       Optional[Any]										=  set()
  A_  :       Any										=  set()
  A_  :       Any										=  {source: 0}
  A_  :       Optional[int]										=  {destination: 0}
  A_  :       Dict										=  {source: None}
  A_  :       Optional[int]										=  {destination: None}
  A_  :       PriorityQueue[Any]										=  PriorityQueue()
  A_  :       PriorityQueue[Any]										=  PriorityQueue()

  A_  :       Optional[Any]										=  np.inf

  queue_forward.put((0, source)					)
  queue_backward.put((0, destination)					)

  if source == destination:
    return 0

  while not queue_forward.empty() and not queue_backward.empty():
    A_						,       A_  :       Tuple										=  queue_forward.get()
    visited_forward.add(_UpperCAmelCase					)

    A_						,       A_  :       Any										=  queue_backward.get()
    visited_backward.add(_UpperCAmelCase					)

    A_  :       Optional[Any]										=  pass_and_relaxation(
        _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					)

    A_  :       Optional[Any]										=  pass_and_relaxation(
        _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					)

    if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
      break

  if shortest_distance != np.inf:
    A_  :       List[str]										=  shortest_distance
  return shortest_path_distance


lowerCamelCase_     :						int						=					{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCamelCase_     :						List[str]						=					{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)