






'''simple docstring'''
import heapq




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    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(_UpperCamelCase						,		[-1 * len(_UpperCamelCase							), (key, value)]							)

   # chosen_vertices = set of chosen vertices
   lowercase_	:    Optional[Any]           =     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
      lowercase_	:    Any           =     heapq.heappop(_UpperCamelCase							)[1][0]
      chosen_vertices.add(_UpperCamelCase							)

      # 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]:
            lowercase_	:    str           =     elem[1][1].index(_UpperCamelCase							)
            del elem[1][1][index]
            elem[0] += 1
        # re-order the queue
      heapq.heapify(_UpperCamelCase							)
   return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCamelCase__ =			{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)}""")












'''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__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Tuple           =     XLNetConfig.from_json_file(_UpperCamelCase							)

   lowercase_	:    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}"""							)
      lowercase_	:    Dict           =     finetuning_task
      lowercase_	:    Any           =     GLUE_TASKS_NUM_LABELS[finetuning_task]
      lowercase_	:    Any           =     XLNetForSequenceClassification(_UpperCamelCase							)
   elif "squad" in finetuning_task:
      lowercase_	:    Optional[int]           =     finetuning_task
      lowercase_	:    Optional[int]           =     XLNetForQuestionAnswering(_UpperCamelCase							)
   else:
      lowercase_	:    Union[str, Any]           =     XLNetLMHeadModel(_UpperCamelCase							)

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

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


if __name__ == "__main__":
 UpperCamelCase__ =			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__ =			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
 )




