from __future__ import annotations

import os
from collections.abc import Mapping

UpperCAmelCase_           =				tuple[int, int]




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	List[str]		,	_lowercase       :	set[int]		,	_lowercase       :	Mapping[EdgeT, int]				):



    """simple docstring"""



    _UpperCamelCase:  set[int]    						=			vertices
    _UpperCamelCase:  dict[EdgeT, int]    						=			{
        (min(_lowercase				), max(_lowercase				)): weight for edge, weight in edges.items()
    }



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	EdgeT		,	_lowercase       :	int				):



    """simple docstring"""



    self.vertices.add(edge[0]				)
    self.vertices.add(edge[1]				)
    _UpperCamelCase:  Optional[int]    						=			weight



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Graph    						=			Graph({min(self.vertices				)}		,	{}				)
    _UpperCamelCase:  EdgeT
    _UpperCamelCase:  int
    _UpperCamelCase:  EdgeT
    _UpperCamelCase:  int

    while len(subgraph.vertices				) < len(self.vertices				):
     _UpperCamelCase:  str    						=			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:
        _UpperCamelCase:  Union[str, Any]    						=			edge
        _UpperCamelCase:  Optional[Any]    						=			weight

     subgraph.add_edge(_lowercase		,	_lowercase				)

    return subgraph




def    lowerCAmelCase_  (		lowercase:				str = "p107_network.txt"		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			os.path.abspath(os.path.dirname(lowercase		)		)
 _UpperCamelCase:  str    						=			os.path.join(lowercase	,       lowercase		)
 _UpperCamelCase:  dict[EdgeT, int]    						=			{}
 _UpperCamelCase:  list[str]
 _UpperCamelCase:  int
 _UpperCamelCase:  int

 with open(lowercase		) as f:
  _UpperCamelCase:  List[Any]    						=			f.read().strip().split('''\n'''		)

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

 for edgea in range(1	,       len(lowercase		)		):
  for edgea in range(lowercase		):
   if adjaceny_matrix[edgea][edgea] != "-":
    _UpperCamelCase:  int    						=			int(adjaceny_matrix[edgea][edgea]		)

 _UpperCamelCase:  Graph    						=			Graph(set(range(len(lowercase		)		)		)	,       lowercase		)

 _UpperCamelCase:  Graph    						=			graph.prims_algorithm()

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

 return initial_total - optimal_total


if __name__ == "__main__":
  print(f"""{solution() = }""")
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
UpperCAmelCase_           =				data_utils.TransfoXLTokenizer
UpperCAmelCase_           =				data_utils.TransfoXLCorpus
UpperCAmelCase_           =				data_utils
UpperCAmelCase_           =				data_utils




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				Union[str, Any]	,       lowercase:				Any	,       lowercase:				List[Any]		)     ->					List[Any]:







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

  _UpperCamelCase:  str    						=			corpus.__dict__
  corpus_dict_no_vocab.pop('''vocab'''	,       lowercase		)
  _UpperCamelCase:  str    						=			pytorch_dump_folder_path + '''/''' + CORPUS_NAME
  print(F"""Save dataset to {pytorch_dataset_dump_path}"""		)
  torch.save(lowercase	,       lowercase		)

 if tf_checkpoint_path:
  # Convert a pre-trained TensorFlow model
  _UpperCamelCase:  Optional[Any]    						=			os.path.abspath(lowercase		)
  _UpperCamelCase:  List[str]    						=			os.path.abspath(lowercase		)

  print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""		)
  # Initialise PyTorch model
  if transfo_xl_config_file == "":
   _UpperCamelCase:  Tuple    						=			TransfoXLConfig()
  else:
   _UpperCamelCase:  Union[str, Any]    						=			TransfoXLConfig.from_json_file(lowercase		)
  print(F"""Building PyTorch model from configuration: {config}"""		)
  _UpperCamelCase:  Optional[int]    						=			TransfoXLLMHeadModel(lowercase		)

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


if __name__ == "__main__":
  UpperCAmelCase_           =				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.''',
  )
  UpperCAmelCase_           =				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,
  )