

import argparse

import torch

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


logging.set_verbosity_info()







def       lowercase					(	__A :					Union[str, Any] ,    __A :					Optional[Any] ,    __A :					List[str]		)				->			Dict:





    '''simple docstring'''




    snake_case							:		str        =		MobileBertConfig.from_json_file(__lowercase		)
    print(f"""Building PyTorch model from configuration: {config}"""		)
    snake_case							:		Any        =		MobileBertForPreTraining(__lowercase		)
    # Load weights from tf checkpoint
    snake_case							:		Optional[int]        =		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__":
     __lowercase     : 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.'''
     )
     __lowercase     : Optional[Any]       =						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_torch_available


snake_case__   :						Optional[int]													=   {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

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





