




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  # Initialise PyTorch model
  lowerCAmelCase__										=      AlbertConfig.from_json_file(__lowerCAmelCase				)
  print(F"""Building PyTorch model from configuration: {config}"""				)
  lowerCAmelCase__										=      AlbertForPreTraining(__lowerCAmelCase				)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
  torch.save(model.state_dict()   , __lowerCAmelCase				)


if __name__ == "__main__":
      __magic_name__  :  Any					=       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(
          """--albert_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained ALBERT 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."""
      )
      __magic_name__  :  int					=       parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)







from typing import TYPE_CHECKING

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


__magic_name__  :  List[str]					=       {
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Union[str, Any]					=       [
          """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """M2M100ForConditionalGeneration""",
          """M2M100Model""",
          """M2M100PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  List[str]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

