
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'''
     # Initialise PyTorch model
     lowercase					=		MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE	)
     print(f'Building PyTorch model from configuration: {config}'	)
     lowercase					=		MobileBertForPreTraining(__SCREAMING_SNAKE_CASE	)
     # Load weights from tf checkpoint
     lowercase					=		load_tf_weights_in_mobilebert(__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__":
       lowercase__	:Dict										=					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."
       )
       lowercase__	:Optional[int]										=					parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




