





from __future__ import annotations

from typing import Generic, TypeVar

a__: List[Any]							   =						TypeVar('T')




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self,__lowerCamelCase					):
        A__		   =		data
        A__		   =		self
        A__		   =		0




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        # map from node name to the node object
        A__		   =		{}



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # create a new set with x as its member
        A__		   =		DisjointSetTreeNode(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # find the set x belongs to (with path-compression)
        A__		   =		self.map[data]
        if elem_ref != elem_ref.parent:
               A__		   =		self.find_set(elem_ref.parent.data					)
        return elem_ref.parent



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # helper function for union operation
        if nodea.rank > nodea.rank:
               A__		   =		nodea
        else:
               A__		   =		nodea
               if nodea.rank == nodea.rank:
                      nodea.rank += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # merge 2 disjoint sets
        self.link(self.find_set(__lowerCamelCase					),self.find_set(__lowerCamelCase					)					)




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        # connections: map from the node to the neighbouring nodes (with weights)
        A__		   =		{}



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # add a node ONLY if its not present in the graph
        if node not in self.connections:
               A__		   =		{}



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # add an edge with the given weight
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight




 def 							UpperCamelCase     (				self					):
        A__		   =		[]
        A__		   =		set()
        for start in self.connections:
               for end in self.connections[start]:
                      if (start, end) not in seen:
                             seen.add((end, start)					)
                             edges.append((start, end, self.connections[start][end])					)
        edges.sort(key=lambda __lowerCamelCase					: x[2]					)

        # creating the disjoint set
        A__		   =		DisjointSetTree[T]()
        for node in self.connections:
               disjoint_set.make_set(__lowerCamelCase					)

        # MST generation
        A__		   =		0
        A__		   =		0
        A__		   =		GraphUndirectedWeighted[T]()
        while num_edges < len(self.connections					) - 1:
               A__   ,      A__   ,      A__		   =		edges[index]
               index += 1
               A__		   =		disjoint_set.find_set(__lowerCamelCase					)
               A__		   =		disjoint_set.find_set(__lowerCamelCase					)
               if parent_u != parent_v:
                      num_edges += 1
                      graph.add_edge(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
                      disjoint_set.union(__lowerCamelCase,__lowerCamelCase					)
        return graph












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
       )





