





"""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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       MobileBertConfig.from_json_file(lowercase_			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      _UpperCamelCase      :	Tuple						=       MobileBertForPreTraining(lowercase_			)
      # Load weights from tf checkpoint
      _UpperCamelCase      :	Dict						=       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__":
  lowerCamelCase__				  =   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."
  )
  lowerCamelCase__				  =   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
