
"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]



def 							a__ (				lowerCAmelCase	:       dict[int, list[int]]		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Dict 							=						len(lowerCAmelCase		)  # No of vertices in graph
     UpperCAmelCase__							: str 							=						[0] * n
     UpperCAmelCase__							: Optional[Any] 							=						[False] * n

     def dfs(lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       List[str]		):
          UpperCAmelCase__							: int 							=						True
          UpperCAmelCase__							: List[Any] 							=						id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    id_		)
                    UpperCAmelCase__							: Any 							=						min(low[at]       ,    low[to]		)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)		)
               else:
                    # This edge is a back edge and cannot be a bridge
                    UpperCAmelCase__							: int 							=						min(low[at]       ,    low[to]		)

     UpperCAmelCase__							: list[tuple[int, int]] 							=						[]
     for i in range(lowerCAmelCase		):
          if not visited[i]:
               dfs(lowerCAmelCase       ,    -1       ,    lowerCAmelCase       ,    id_		)
     return bridges


if __name__ == "__main__":
    import doctest

    doctest.testmod()





"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



