



import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class 				lowerCAmelCase_		:
       UpperCAmelCase__				:					Optional[Any]        =  None
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Optional[int]							=	self.feature_extraction_class(**self.feat_extract_dict       )
          UpperCamelCase    :  List[Any]							=	json.loads(feat_extract.to_json_string()       )
          for key, value in self.feat_extract_dict.items():
             self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  List[Any]							=	self.feature_extraction_class(**self.feat_extract_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  str							=	os.path.join(SCREAMING_SNAKE_CASE_,			'feat_extract.json'       )
             feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Optional[Any]							=	self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_       )

          self.assertEqual(feat_extract_second.to_dict(),			feat_extract_first.to_dict()       )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[str]							=	self.feature_extraction_class(**self.feat_extract_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  Any							=	feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_       )[0]
             check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Any							=	self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_       )

          self.assertEqual(feat_extract_second.to_dict(),			feat_extract_first.to_dict()       )






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	self.feature_extraction_class()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )








import argparse

import torch

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


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



