



"""simple docstring"""



from __future__ import annotations

from collections import Counter
from random import random




class 		a_						:







    def __init__(			self			:       List[str]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   {}







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       str     )		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   {}







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       float     )		->None:






        '''simple docstring'''






        if nodea not in self.connections:
            self.add_node(__UpperCamelCase     )
        if nodea not in self.connections:
            self.add_node(__UpperCamelCase     )
        _UpperCAmelCase          =   probability







    def      _snake_case					(			self			:       Dict     )		->list[str]:






        '''simple docstring'''






        return list(self.connections     )







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   random()

        for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                return dest
        return ""







def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  dict[str, int]:





    """simple docstring"""

    _UpperCAmelCase          =   MarkovChainGraphUndirectedUnweighted()

    for nodea, nodea, probability in transitions:
        graph.add_transition_probability(_A					,      _A					,      _A      )

    _UpperCAmelCase          =   Counter(graph.get_nodes()      )
    _UpperCAmelCase          =   start

    for _ in range(_A      ):
        _UpperCAmelCase          =   graph.transition(_A      )
        visited[node] += 1

    return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)