




from __future__ import annotations





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	int       ,       _lowerCAmelCase						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    vertices
       SCREAMING_SNAKE_CASE_					=    [[0] * vertices for _ in range(lowercase_		)]



 def    lowerCAmelCase_						(							self						:	Any       ,       _lowerCAmelCase						:	Optional[int]		):
       print('Vertex \t Distance from Source'		)
       for vertex in range(self.vertices		):
             print(lowercase_       ,       '\t\t'       ,       distances_from_source[vertex]		)



 def    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	Any		):
       SCREAMING_SNAKE_CASE_					=    1E7
       SCREAMING_SNAKE_CASE_					=    0

       # Search not nearest vertex not in the shortest path tree
       for vertex in range(self.vertices		):
             if distances_from_source[vertex] < minimum and visited[vertex] is False:
                   SCREAMING_SNAKE_CASE_					=    distances_from_source[vertex]
                   SCREAMING_SNAKE_CASE_					=    vertex
       return min_index




 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	List[Any]		):
       SCREAMING_SNAKE_CASE_					=    [int(1E7		)] * self.vertices  # distances from the source
       SCREAMING_SNAKE_CASE_					=    0
       SCREAMING_SNAKE_CASE_					=    [False] * self.vertices

       for _ in range(self.vertices		):
             SCREAMING_SNAKE_CASE_					=    self.minimum_distance(lowercase_       ,       lowercase_		)
             SCREAMING_SNAKE_CASE_					=    True

             # Update dist value of the adjacent vertices
             # of the picked vertex only if the current
             # distance is greater than new distance and
             # the vertex in not in the shortest path tree
             for v in range(self.vertices		):
                   if (
                       self.graph[u][v] > 0
                       and visited[v] is False
                       and distances[v] > distances[u] + self.graph[u][v]
                   ):
                         SCREAMING_SNAKE_CASE_					=    distances[u] + self.graph[u][v]

       self.print_solution(lowercase_		)


if __name__ == "__main__":
   lowerCamelCase__				:     str			       =							Graph(9)
   lowerCamelCase__				:     Optional[int]			       =							[
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 11, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 14, 0, 0, 0],
       [0, 0, 0, 9, 0, 10, 0, 0, 0],
       [0, 0, 4, 14, 10, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 11, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				List[Any]:
      return {remove_prefix(__UpperCAmelCase    ,    'model.'							): v for k, v in sd.items()}

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    list(Path(__UpperCAmelCase							).glob('*.ckpt'							)							)
            assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

      if len(__UpperCAmelCase							) > 1:
            logger.info(f"averaging the weights of {ckpt_files}"							)

      SCREAMING_SNAKE_CASE_					=    [sanitize(torch.load(__UpperCAmelCase    ,    map_location='cpu'							)['state_dict']							) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_					=    average_state_dicts(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(__UpperCAmelCase							)
      try:
            SCREAMING_SNAKE_CASE_					=    AutoTokenizer.from_pretrained(__UpperCAmelCase							)
            tok.save_pretrained(__UpperCAmelCase							)
      except Exception:
            pass
            # dont copy tokenizer if cant


if __name__ == "__main__":
   fire.Fire(convert_pl_to_hf)