





"""simple docstring"""




from heapq import heappop, heappush

import numpy as np





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






  """simple docstring"""





  A_						,       A_  :       List[str]										=  grid.shape
  A_  :       Optional[int]										=  [-1, 1, 0, 0]
  A_  :       str										=  [0, 0, -1, 1]
  if allow_diagonal:
    dx += [-1, -1, 1, 1]
    dy += [-1, 1, -1, 1]

  A_						,       A_  :       List[Any]										=  [(0, source)], set()
  A_  :       Optional[Any]										=  np.full((rows, cols) ,					np.inf					)
  A_  :       int										=  0
  A_  :       Optional[int]										=  np.empty((rows, cols) ,					dtype=_UpperCAmelCase					)
  A_  :       Optional[int]										=  None

  while queue:
    ((A_)						,       (A_))  :       str										=  heappop(_UpperCAmelCase					)
    if (x, y) in visited:
      continue
    visited.add((x, y)					)

    if (x, y) == destination:
      A_  :       int										=  []
      while (x, y) != source:
        path.append((x, y)					)
        A_						,       A_  :       List[Any]										=  predecessors[x, y]
      path.append(_UpperCAmelCase					)  # add the source manually
      path.reverse()
      return matrix[destination], path

    for i in range(len(_UpperCAmelCase					)					):
      A_						,       A_  :       Tuple										=  x + dx[i], y + dy[i]
      if 0 <= nx < rows and 0 <= ny < cols:
        A_  :       Union[str, Any]										=  grid[nx][ny]
        if next_node == 1 and matrix[nx, ny] > dist + 1:
          heappush(_UpperCAmelCase ,					(dist + 1, (nx, ny))					)
          A_  :       Optional[Any]										=  dist + 1
          A_  :       Optional[Any]										=  (x, y)

  return np.inf, []


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)