

from __future__ import annotations

lowercase_   							=  {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					None:
        '''simple docstring'''






        _lowercase      			=graph
        # mapping node to its parent in resulting breadth first tree
        _lowercase      			={}
        _lowercase      			=source_vertex




  def     A__	(							self   )    ->					None:
        '''simple docstring'''






        _lowercase      			={self.source_vertex}
        _lowercase      			=None
        _lowercase      			=[self.source_vertex]  # first in first out queue

        while queue:
              _lowercase      			=queue.pop(0   )
              for adjacent_vertex in self.graph[vertex]:
                    if adjacent_vertex not in visited:
                          visited.add(lowerCAmelCase   )
                          _lowercase      			=vertex
                          queue.append(lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        if target_vertex == self.source_vertex:
              return self.source_vertex

        _lowercase      			=self.parent.get(lowerCAmelCase   )
        if target_vertex_parent is None:
              _lowercase      			=(
                  F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
              )
              raise ValueError(lowerCAmelCase   )

        return self.shortest_path(lowerCAmelCase   ) + F'''->{target_vertex}'''


if __name__ == "__main__":
  lowercase_   							=  Graph(graph, 'G')
  g.breath_first_search()
  print(g.shortest_path('D'))
  print(g.shortest_path('G'))
  print(g.shortest_path('Foo'))





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    '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)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

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


