


import argparse

import torch

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


logging.set_verbosity_info()

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     # Initialise PyTorch model
     __UpperCamelCase    							=AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
     print(F'Building PyTorch model from configuration: {config}'				)
     __UpperCamelCase    							=AlbertForPreTraining(SCREAMING_SNAKE_CASE__				)

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

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


if __name__ == "__main__":
   _A		       =						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.'
   )
   _A		       =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)










'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



