"""simple docstring"""




import logging

from transformers import PretrainedConfig


__lowerCAmelCase    :							List[str]													=						logging.getLogger(__name__)


__lowerCAmelCase    :							Optional[Any]													=						{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Tuple			=    "bertabs"




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     Dict=30522  ,						snake_case__						:     List[str]=512  ,						snake_case__						:     Any=6  ,						snake_case__						:     Optional[Any]=512  ,						snake_case__						:     Optional[Any]=8  ,						snake_case__						:     Union[str, Any]=512  ,						snake_case__						:     int=0.2  ,						snake_case__						:     Any=6  ,						snake_case__						:     str=768  ,						snake_case__						:     Optional[Any]=8  ,						snake_case__						:     Dict=2048  ,						snake_case__						:     Union[str, Any]=0.2  ,						**snake_case__						:     Optional[int]  ,						):
							super().__init__(**snake_case__     )

							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   max_pos

							lowerCAmelCase__        =   enc_layers
							lowerCAmelCase__        =   enc_hidden_size
							lowerCAmelCase__        =   enc_heads
							lowerCAmelCase__        =   enc_ff_size
							lowerCAmelCase__        =   enc_dropout

							lowerCAmelCase__        =   dec_layers
							lowerCAmelCase__        =   dec_hidden_size
							lowerCAmelCase__        =   dec_heads
							lowerCAmelCase__        =   dec_ff_size
							lowerCAmelCase__        =   dec_dropout





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(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    :							List[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(
			    "--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    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




