
"""simple docstring"""






from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

UpperCamelCase_						   =   {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->list[str]:

   """simple docstring"""



   a_						   =     {start}
   a_						   =     [start]
   a_						   =     Queue()
   queue.put(UpperCAmelCase     )
   while not queue.empty():
      a_						   =     queue.get()
      for w in graph[v]:
         if w not in explored:
            explored.add(UpperCAmelCase     )
            result.append(UpperCAmelCase     )
            queue.put(UpperCAmelCase     )
   return result
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->list[str]:

   """simple docstring"""



   a_						   =     {start}
   a_						   =     [start]
   a_						   =     deque([start]     )
   while queue:
      a_						   =     queue.popleft()
      for child in graph[v]:
         if child not in visited:
            visited.add(UpperCAmelCase     )
            result.append(UpperCAmelCase     )
            queue.append(UpperCAmelCase     )
   return result
def    UpperCamelCase						(					UpperCAmelCase     )						->None:

   """simple docstring"""



   a_						   =     F'''from __main__ import G, {name}'''
   a_						   =     10_000
   a_						   =     timeit(F'''{name}(G, \'A\')''' ,   setup=UpperCAmelCase ,   number=UpperCAmelCase     )
   print(F'''{name:<35} finished {number} runs in {res:.5f} seconds'''     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      benchmark_function('breadth_first_search')
      benchmark_function('breadth_first_search_with_deque')
      # breadth_first_search                finished 10000 runs in 0.20999 seconds
      # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds

"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


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(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT 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.bert_config_file, args.pytorch_dump_path)