

def 						lowerCamelCase__      ( __A    :list[list[int]]	,__A    :int	,__A    :int	,__A    :list[int]		):


       """simple docstring"""




       if graph[path[curr_ind - 1]][next_ver] == 0:
              return False

       # 2. Validate that next vertex is not already in path
       return not any(vertex == next_ver for vertex in path		)







def 						lowerCamelCase__      ( __A    :list[list[int]]	,__A    :list[int]	,__A    :int		):


       """simple docstring"""




       if curr_ind == len(UpperCamelCase__		):
              # return whether path exists between current and starting vertices
              return graph[path[curr_ind - 1]][path[0]] == 1

       # Recursive Step
       for next_ver in range(0	,len(UpperCamelCase__		)		):
              if valid_connection(UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__		):
                     # Insert current vertex  into path as next transition
                     __snake_case       	=							next_ver
                     # Validate created path
                     if util_hamilton_cycle(UpperCamelCase__	,UpperCamelCase__	,curr_ind + 1		):
                            return True
                     # Backtrack
                     __snake_case       	=							-1
       return False







def 						lowerCamelCase__      ( __A    :list[list[int]]	,__A    :int = 0		):


       """simple docstring"""




       __snake_case       	=							[-1] * (len(UpperCamelCase__		) + 1)
       # initialize start and end of path with starting index
       __snake_case       	=							__snake_case       	=							start_index
       # evaluate and if we find answer return path either return empty array
       return path if util_hamilton_cycle(UpperCamelCase__	,UpperCamelCase__	,1		) else []








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_big_bird(UpperCamelCase__					,	UpperCamelCase__					,	is_trivia_qa=UpperCamelCase__    )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"    )
       model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						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(
           '--big_bird_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.'
       )
       parser.add_argument(
           '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





