






from ...configuration_utils import PretrainedConfig


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """bert-generation"""




  def __init__(  self		,		_UpperCAmelCase=50358		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=24		,		_UpperCAmelCase=16		,		_UpperCAmelCase=4096		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=True		,		**_UpperCAmelCase		,		)      ->			Optional[int]:
     super().__init__(pad_token_id=_UpperCAmelCase		,		bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		**_UpperCAmelCase			)

     UpperCamelCase_						=  vocab_size
     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_hidden_layers
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  hidden_act
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  max_position_embeddings
     UpperCamelCase_						=  initializer_range
     UpperCamelCase_						=  layer_norm_eps
     UpperCamelCase_						=  position_embedding_type
     UpperCamelCase_						=  use_cache













import argparse

import torch

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


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   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."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





