





snake_case_           =      {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}






def lowerCamelCase__   (							snake_case_     :  dict	,				snake_case_     :  Any	,				snake_case_     :  Union[str, Any]	)				->    Optional[int]:
   __snake_case								=       set()
   # keep track of all the paths to be checked
   __snake_case								=       [[start]]

   # return path if start is goal
   if start == goal:
      return [start]

   # keeps looping until all possible paths have been checked
   while queue:
      # pop the first path from the queue
      __snake_case								=       queue.pop(0	)
      # get the last node from the path
      __snake_case								=       path[-1]
      if node not in explored:
         __snake_case								=       graph[node]
         # go through all neighbour nodes, construct a new path and
         # push it into the queue
         for neighbour in neighbours:
            __snake_case								=       list(A__	)
            new_path.append(A__	)
            queue.append(A__	)
            # return path if neighbour is goal
            if neighbour == goal:
               return new_path

                 # mark node as explored
         explored.add(A__	)

      # in case there's no path between the 2 nodes
   return []






def lowerCamelCase__   (							snake_case_     :  dict	,				snake_case_     :  Tuple	,				snake_case_     :  Any	)				->    List[str]:





   if not graph or start not in graph or target not in graph:
      return -1
   if start == target:
      return 0
   __snake_case								=       [start]
   __snake_case								=       set(A__	)
   # Keep tab on distances from `start` node.
   __snake_case								=       {start: 0, target: -1}
   while queue:
      __snake_case								=       queue.pop(0	)
      if node == target:
         __snake_case								=       (
             dist[node] if dist[target] == -1 else min(dist[target]	,				dist[node]	)
         )
      for adjacent in graph[node]:
         if adjacent not in visited:
            visited.add(A__	)
            queue.append(A__	)
            __snake_case								=       dist[node] + 1
   return dist[target]


if __name__ == "__main__":
      print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
      print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4








import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_           =      data_utils.TransfoXLTokenizer
snake_case_           =      data_utils.TransfoXLCorpus
snake_case_           =      data_utils
snake_case_           =      data_utils






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  int	)				->    Dict:
   if transfo_xl_dataset_file:
      # Convert a pre-processed corpus (see original TensorFlow repo)
      with open(snake_case_	,				'''rb'''	) as fp:
         __snake_case								=       pickle.load(snake_case_	,				encoding='''latin1'''	)
      # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
      print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""	)
      __snake_case								=       corpus.vocab.__dict__
      torch.save(snake_case_	,				snake_case_	)

      __snake_case								=       corpus.__dict__
      corpus_dict_no_vocab.pop('''vocab'''	,				snake_case_	)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + CORPUS_NAME
      print(f"""Save dataset to {pytorch_dataset_dump_path}"""	)
      torch.save(snake_case_	,				snake_case_	)

   if tf_checkpoint_path:
      # Convert a pre-trained TensorFlow model
      __snake_case								=       os.path.abspath(snake_case_	)
      __snake_case								=       os.path.abspath(snake_case_	)

      print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""	)
      # Initialise PyTorch model
      if transfo_xl_config_file == "":
         __snake_case								=       TransfoXLConfig()
      else:
         __snake_case								=       TransfoXLConfig.from_json_file(snake_case_	)
      print(f"""Building PyTorch model from configuration: {config}"""	)
      __snake_case								=       TransfoXLLMHeadModel(snake_case_	)

      __snake_case								=       load_tf_weights_in_transfo_xl(snake_case_	,				snake_case_	,				snake_case_	)
      # Save pytorch-model
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      print(f"""Save PyTorch model to {os.path.abspath(snake_case_	)}"""	)
      torch.save(model.state_dict()	,				snake_case_	)
      print(f"""Save configuration file to {os.path.abspath(snake_case_	)}"""	)
      with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as f:
         f.write(config.to_json_string()	)


if __name__ == "__main__":
    snake_case_           =      argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--tf_checkpoint_path',
        default='',
        type=str,
        help='An optional path to a TensorFlow checkpoint path to be converted.',
    )
    parser.add_argument(
        '--transfo_xl_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--transfo_xl_dataset_file',
        default='',
        type=str,
        help='An optional dataset file to be converted in a vocabulary.',
    )
    snake_case_           =      parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )

