


import heapq


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	list[list]						     =						[]

						# for each node and his adjacency list add them and the rank of the node to queue
						# using heapq module the queue will be filled like a Priority Queue
						# heapq works with a min priority queue, so I used -1*len(v) to build it
						for key, value in graph.items():
												# O(log(n))
												heapq.heappush(lowercase__       ,			[-1 * len(lowercase__ ), (key, value)] )

						# chosen_vertices = set of chosen vertices
						__SCREAMING_SNAKE_CASE		:	List[str]						     =						set()

						# while queue isn't empty and there are still edges
						#   (queue[0][0] is the rank of the node with max rank)
						while queue and queue[0][0] != 0:
												# extract vertex with max rank from queue and add it to chosen_vertices
												__SCREAMING_SNAKE_CASE		:	List[Any]						     =						heapq.heappop(lowercase__ )[1][0]
												chosen_vertices.add(lowercase__ )

												# Remove all arcs adjacent to argmax
												for elem in queue:
																		# if v haven't adjacent node, skip
																		if elem[0] == 0:
																								continue
																		# if argmax is reachable from elem
																		# remove argmax from elem's adjacent list and update his rank
																		if argmax in elem[1][1]:
																								__SCREAMING_SNAKE_CASE		:	List[Any]						     =						elem[1][1].index(lowercase__ )
																								del elem[1][1][index]
																								elem[0] += 1
																								    # re-order the queue
												heapq.heapify(lowercase__ )
						return chosen_vertices


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__lowerCAmelCase      :					Union[str, Any]    							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
						print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")

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,
  )