






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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_						):





					"""simple docstring"""






					@register_to_config
					def __init__( self					:  Dict							,		__A					:  int = 7_6_8							,		):
						super().__init__()

						snake_case__			:				Union[str, Any]       =  nn.Parameter(torch.zeros(1							,		__A	)	)
						snake_case__			:				List[str]       =  nn.Parameter(torch.ones(1							,		__A	)	)



					def 	_lowercase					( self					:  List[str]							,		__A					:  Optional[Union[str, torch.device]] = None							,		__A					:  Optional[torch.dtype] = None							,		):
						snake_case__			:				List[str]       =  nn.Parameter(self.mean.to(__A	).to(__A	)	)
						snake_case__			:				Optional[int]       =  nn.Parameter(self.std.to(__A	).to(__A	)	)
						return self



					def 	_lowercase					( self					:  Dict							,		__A					:  Union[str, Any]	):
						snake_case__			:				List[str]       =  (embeds - self.mean) * 1.0 / self.std
						return embeds






					def 	_lowercase					( self					:  Tuple							,		__A					:  Any	):
						snake_case__			:				Optional[int]       =  (embeds * self.std) + self.mean
						return embeds











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				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(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT 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.mobilebert_config_file, args.pytorch_dump_path)



