






"""simple docstring"""






from __future__ import annotations

UpperCAmelCase_     :				List[Any]         =				{
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Any		,			lowercase_  :						dict[str, list[str]]		,			lowercase_  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	graph
          # mapping node to its parent in resulting breadth first tree
          SCREAMING_SNAKE_CASE_   :						dict[str, str | None]							=	{}
          SCREAMING_SNAKE_CASE_   :						Dict							=	source_vertex
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	{self.source_vertex}
          SCREAMING_SNAKE_CASE_   :						Dict							=	None
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[self.source_vertex]  # first in first out queue

          while queue:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	queue.pop(0)
              for adjacent_vertex in self.graph[vertex]:
                  if adjacent_vertex not in visited:
                      visited.add(lowercase_)
                      SCREAMING_SNAKE_CASE_   :						Dict							=	vertex
                      queue.append(lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						str):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.parent.get(lowercase_)
          if target_vertex_parent is None:
              SCREAMING_SNAKE_CASE_   :						int							=	(
                  F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
              )
              raise ValueError(lowercase_)

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


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










"""simple docstring"""






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCAmelCase_     :				int         =				{
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def       _A    (__a					,       __a					,       __a					,       __a=None							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	XLNetConfig.from_json_file(__a							)

    SCREAMING_SNAKE_CASE_   :						List[Any]							=	finetuning_task.lower() if finetuning_task is not None else ''''''
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'							)
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	GLUE_TASKS_NUM_LABELS[finetuning_task]
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForSequenceClassification(__a							)
    elif "squad" in finetuning_task:
        SCREAMING_SNAKE_CASE_   :						str							=	finetuning_task
        SCREAMING_SNAKE_CASE_   :						Dict							=	XLNetForQuestionAnswering(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	XLNetLMHeadModel(__a							)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__a					,       __a					,       __a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	os.path.join(__a					,       __a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(__a					,       __a							)
    print(f'Save PyTorch model to {os.path.abspath(__a							)}'							)
    torch.save(model.state_dict()					,       __a							)
    print(f'Save configuration file to {os.path.abspath(__a							)}'							)
    with open(__a					,       '''w'''					,       encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_     :				str         =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--xlnet_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained XLNet model. \n"""
          """This specifies the model architecture."""
      ),
  )
  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(
      """--finetuning_task""",
      default=None,
      type=str,
      help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
  )
  UpperCAmelCase_     :				int         =				parser.parse_args()
  print(args)

  convert_xlnet_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
  )


