



def 		UpperCamelCase	(   snake_case__       :    dict	)	->     set:
   UpperCamelCase    :  List[str]							=	set()
   # edges = list of graph's edges
   UpperCamelCase    :  int							=	get_edges(snake_case__	)

   # While there are still elements in edges list, take an arbitrary edge
   # (from_node, to_node) and add his extremity to chosen_vertices and then
   # remove all arcs adjacent to the from_node and to_node
   while edges:
      UpperCamelCase	,     UpperCamelCase    :  Any							=	edges.pop()
      chosen_vertices.add(snake_case__	)
      chosen_vertices.add(snake_case__	)
      for edge in edges.copy():
         if from_node in edge or to_node in edge:
            edges.discard(snake_case__	)
   return chosen_vertices
def 		UpperCamelCase	(   snake_case__       :    dict	)	->     set:
   UpperCamelCase    :  Dict							=	set()
   for from_node, to_nodes in graph.items():
      for to_node in to_nodes:
         edges.add((from_node, to_node)	)
   return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


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(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



