



import unittest

from transformers import DonutProcessor


a_    :  List[Any]						  =		'naver-clova-ix/donut-base'





class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		DonutProcessor.from_pretrained(__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		{
               "name": "John Doe",
               "age": "99",
               "city": "Atlanta",
               "state": "GA",
               "zip": "30301",
               "phone": "123-4567",
               "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
           }

           lowerCamelCase             =		(
               "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
               "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
               "<s_nicknames><s_nickname>Johnny</s_nickname>"
               "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
           )
           lowerCamelCase             =		self.processor.tokenajson(__a     )

           self.assertDictEqual(__a					,      __a     )




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(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  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(
         '--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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)