





'''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 lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Tuple    )						->Optional[int]:
    # Initialise PyTorch model
    _SCREAMING_SNAKE_CASE									=			MobileBertConfig.from_json_file(__lowerCamelCase    )
    print(F'Building PyTorch model from configuration: {config}'    )
    _SCREAMING_SNAKE_CASE									=			MobileBertForPreTraining(__lowerCamelCase    )
    # Load weights from tf checkpoint
    _SCREAMING_SNAKE_CASE									=			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__":
      lowercase_					= 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_					= parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




'''simple docstring'''

from typing import TYPE_CHECKING

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


__a: Dict          =   {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

  __a: Dict          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

