





'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class     lowerCAmelCase__ :

       '''simple docstring'''
       _lowerCamelCase								=42
       _lowerCamelCase								=42






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Union[str, Any]       ,     a__					:				int						):
              UpperCAmelCase  =						[[] for _ in range(a__						)]
              UpperCAmelCase  =						size

       def __getitem__( self					:				Any       ,     a__					:				int						):
              return iter(self._graph[vertex]						)

       @property
       def 			__snake_case      ( self					:				List[str]						):
              return self._size

       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				int       ,     a__					:				int       ,     a__					:				int						):
              if weight not in (0, 1):
                     raise ValueError('''Edge weight must be either 0 or 1.'''						)

              if to_vertex < 0 or to_vertex >= self.size:
                     raise ValueError('''Vertex indexes must be in [0; size).'''						)

              self._graph[from_vertex].append(Edge(a__       ,     a__						)						)



       def 			__snake_case      ( self					:				Dict       ,     a__					:				int       ,     a__					:				int						):
              UpperCAmelCase  =						deque([start_vertex]						)
              UpperCAmelCase  =						[None] * self.size
              UpperCAmelCase  =						0

              while queue:
                     UpperCAmelCase  =						queue.popleft()
                     UpperCAmelCase  =						distances[current_vertex]
                     if current_distance is None:
                            continue

                     for edge in self[current_vertex]:
                            UpperCAmelCase  =						current_distance + edge.weight
                            UpperCAmelCase  =						distances[edge.destination_vertex]
                            if (
                                isinstance(a__       ,     a__						)
                                and new_distance >= dest_vertex_distance
                            ):
                                   continue
                            UpperCAmelCase  =						new_distance
                            if edge.weight == 0:
                                   queue.appendleft(edge.destination_vertex						)
                            else:
                                   queue.append(edge.destination_vertex						)

              if distances[finish_vertex] is None:
                     raise ValueError('''No path from start_vertex to finish_vertex.'''						)

              return distances[finish_vertex]


if __name__ == "__main__":
    import doctest

    doctest.testmod()







import argparse

import torch

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


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)