



'''simple docstring'''






from collections import deque




def     snake_case_					(     lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = len(lowerCAmelCase_		)
       _UpperCAmelCase  :						Optional[int]			     = deque()
       _UpperCAmelCase  :						Any			     = [False for _ in range(lowerCAmelCase_		)]
       _UpperCAmelCase  :						Optional[Any]			     = [-1 for _ in range(lowerCAmelCase_		)]
       _UpperCAmelCase  :						Optional[int]			     = index_of[:]

       def strong_connect(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		):
              _UpperCAmelCase  :						List[str]			     = index  # the number when this node is seen
              _UpperCAmelCase  :						Optional[Any]			     = index  # lowest rank node reachable from here
              index += 1
              stack.append(lowerCAmelCase_		)
              _UpperCAmelCase  :						str			     = True

              for w in g[v]:
                     if index_of[w] == -1:
                            _UpperCAmelCase  :						str			     = strong_connect(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
                            _UpperCAmelCase  :						Optional[int]			     = (
                                lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                            )
                     elif on_stack[w]:
                            _UpperCAmelCase  :						str			     = (
                                lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                            )

              if lowlink_of[v] == index_of[v]:
                     _UpperCAmelCase  :						str			     = []
                     _UpperCAmelCase  :						List[Any]			     = stack.pop()
                     _UpperCAmelCase  :						List[str]			     = False
                     component.append(lowerCAmelCase_		)
                     while w != v:
                            _UpperCAmelCase  :						List[str]			     = stack.pop()
                            _UpperCAmelCase  :						Optional[int]			     = False
                            component.append(lowerCAmelCase_		)
                     components.append(lowerCAmelCase_		)
              return index

       _UpperCAmelCase  :						Union[str, Any]			     = []
       for v in range(lowerCAmelCase_		):
              if index_of[v] == -1:
                     strong_connect(lowerCAmelCase_       ,   0       ,   lowerCAmelCase_		)

       return components




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = [[] for _ in range(lowerCAmelCase_		)]
       for u, v in edges:
              g[u].append(lowerCAmelCase_		)
       return g


if __name__ == "__main__":
   # Test
   A_							: List[Any]						 =	7
   A_							: Optional[Any]						 =	[0, 0, 1, 2, 3, 3, 4, 4, 6]
   A_							: Tuple						 =	[1, 3, 2, 0, 1, 4, 5, 6, 5]
   A_							: Union[str, Any]						 =	[(u, v) for u, v in zip(source, target)]
   A_							: str						 =	create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)








'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Save pytorch-model
       print("""Save PyTorch model to {}""".format(lowerCAmelCase_		)		)
       torch.save(model.state_dict()       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



