

from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class   lowercase_   (  __lowercase       ,							__lowercase					):



      @register_to_config
      def __init__(      self				:    Optional[Any]      , A__				:    int = 768      , )   ->					Any:
        super().__init__()

        _snake_case						=							nn.Parameter(torch.zeros(1      , A__		)		)
        _snake_case						=							nn.Parameter(torch.ones(1      , A__		)		)



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[Union[str, torch.device]] = None      , A__				:    Optional[torch.dtype] = None      , )   ->					Union[str, Any]:
        _snake_case						=							nn.Parameter(self.mean.to(A__		).to(A__		)		)
        _snake_case						=							nn.Parameter(self.std.to(A__		).to(A__		)		)
        return self



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    Optional[Any]		)   ->					List[Any]:
        _snake_case						=							(embeds - self.mean) * 1.0 / self.std
        return embeds







      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    List[str]		)   ->					Optional[Any]:
        _snake_case						=							(embeds * self.std) + self.mean
        return embeds




import argparse

import torch

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


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

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

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


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)

