






from typing import TYPE_CHECKING

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


snake_case__   :						Tuple													=   {
    """configuration_audio_spectrogram_transformer""": [
        """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ASTConfig""",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Union[str, Any]													=   [
     """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ASTForAudioClassification""",
     """ASTModel""",
     """ASTPreTrainedModel""",
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Any													=   ["""ASTFeatureExtractor"""]

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

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













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)





