



'''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


a_					=	{
    "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(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  int,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[Any]=None							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=XLNetConfig.from_json_file(A__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, 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__       :			Any													=finetuning_task
            SCREAMING_SNAKE_CASE__       :			int													=GLUE_TASKS_NUM_LABELS[finetuning_task]
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=XLNetForSequenceClassification(A__							)
      elif "squad" in finetuning_task:
            SCREAMING_SNAKE_CASE__       :			Dict													=finetuning_task
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=XLNetForQuestionAnswering(A__							)
      else:
            SCREAMING_SNAKE_CASE__       :			Dict													=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__       :			Optional[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__":
  a_					=	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',
  )
  a_					=	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
  )







'''simple docstring'''
from __future__ import annotations

import os
from collections.abc import Mapping

a_					=	tuple[int, int]






class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							Any   , __lowercase		:							set[int]   , __lowercase		:							Mapping[EdgeT, int] )		->  None:
       SCREAMING_SNAKE_CASE__       :			set[int]													=vertices
       SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={
           (min(__lowercase ), max(__lowercase )): weight for edge, weight in edges.items()
       }




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							EdgeT   , __lowercase		:							int )		->  None:
       self.vertices.add(edge[0] )
       self.vertices.add(edge[1] )
       SCREAMING_SNAKE_CASE__       :			List[str]													=weight




 def        __magic_name__		( self		:							Optional[Any] )		->  Graph:
       SCREAMING_SNAKE_CASE__       :			Graph													=Graph({min(self.vertices )}   , {} )
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int

       while len(subgraph.vertices ) < len(self.vertices ):
             SCREAMING_SNAKE_CASE__       :			Any													=max(self.edges.values() ) + 1
             for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                         if weight < min_weight:
                               SCREAMING_SNAKE_CASE__       :			List[str]													=edge
                               SCREAMING_SNAKE_CASE__       :			Any													=weight

             subgraph.add_edge(__lowercase   , __lowercase )

       return subgraph





def 	_a(					UpperCamelCase__			:  str = "p107_network.txt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
      SCREAMING_SNAKE_CASE__       :			str													=os.path.join(UpperCamelCase__,		UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={}
      SCREAMING_SNAKE_CASE__       :			list[str]
      SCREAMING_SNAKE_CASE__       :			int
      SCREAMING_SNAKE_CASE__       :			int

      with open(UpperCamelCase__							) as f:
            SCREAMING_SNAKE_CASE__       :			Any													=f.read().strip().split('''\n'''							)

      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[line.split(''','''							) for line in data]

      for edgea in range(1,		len(UpperCamelCase__							)							):
            for edgea in range(UpperCamelCase__							):
                  if adjaceny_matrix[edgea][edgea] != "-":
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=int(adjaceny_matrix[edgea][edgea]							)

      SCREAMING_SNAKE_CASE__       :			Graph													=Graph(set(range(len(UpperCamelCase__							)							)							),		UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Graph													=graph.prims_algorithm()

      SCREAMING_SNAKE_CASE__       :			int													=sum(graph.edges.values()							)
      SCREAMING_SNAKE_CASE__       :			int													=sum(subgraph.edges.values()							)

      return initial_total - optimal_total


if __name__ == "__main__":
  print(F'''{solution() = }''')