





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 # Initialise PyTorch model
 UpperCamelCase	:					Any       	=   AlbertConfig.from_json_file(_lowerCAmelCase     )
 print(F"""Building PyTorch model from configuration: {config}"""     )
 UpperCamelCase	:					int       	=   AlbertForPreTraining(_lowerCAmelCase     )

 # Load weights from tf checkpoint
 load_tf_weights_in_albert(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

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


if __name__ == "__main__":
    __lowerCamelCase       :     str   =     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(
        """--albert_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained ALBERT 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."""
    )
    __lowerCamelCase       :     int   =     parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)







class 					A__   :





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   set_counts
       UpperCamelCase	:					int       	=   max(A_   )
       UpperCamelCase	:					Optional[Any]       	=   len(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   [1] * num_sets
       UpperCamelCase	:					Union[str, Any]       	=   list(range(A_   )   )





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.get_parent(A_   )
       UpperCamelCase	:					Optional[int]       	=   self.get_parent(A_   )

       if src_parent == dst_parent:
        return False

       if self.ranks[dst_parent] >= self.ranks[src_parent]:
        self.set_counts[dst_parent] += self.set_counts[src_parent]
        UpperCamelCase	:					int       	=   0
        UpperCamelCase	:					Dict       	=   dst_parent
        if self.ranks[dst_parent] == self.ranks[src_parent]:
         self.ranks[dst_parent] += 1
        UpperCamelCase	:					Optional[int]       	=   self.set_counts[dst_parent]
       else:
        self.set_counts[src_parent] += self.set_counts[dst_parent]
        UpperCamelCase	:					Any       	=   0
        UpperCamelCase	:					Optional[int]       	=   src_parent
        UpperCamelCase	:					int       	=   self.set_counts[src_parent]

       UpperCamelCase	:					Any       	=   max(self.max_set       ,							A_   )
       return True





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if self.parents[disj_set] == disj_set:
        return disj_set
       UpperCamelCase	:					Optional[int]       	=   self.get_parent(self.parents[disj_set]   )
       return self.parents[disj_set]
