





"""simple docstring"""





import unittest

from knapsack import greedy_knapsack as kp




class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						[10, 20, 30, 40, 50, 60]
        __UpperCAmelCase    :						str       =						[2, 4, 6, 8, 10, 12]
        __UpperCAmelCase    :						Union[str, Any]       =						100
        self.assertEqual(kp.calc_profit(A__						,				A__						,				A__ )						,				210 )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        self.assertRaisesRegex(A__						,				'''max_weight must greater than zero.''' )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        self.assertRaisesRegex(A__						,				'''Weight can not be negative.''' )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        self.assertRaisesRegex(A__						,				'''Profit can not be negative.''' )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        self.assertRaisesRegex(A__						,				'''max_weight must greater than zero.''' )







      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        self.assertRaisesRegex(
            A__						,				'''The length of profit and weight must be same.''' )


if __name__ == "__main__":
  unittest.main()






"""simple docstring"""





import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						LxmertConfig.from_json_file(UpperCAmelCase_  )
  print(f'''Building PyTorch model from configuration: {config}'''  )
  __UpperCAmelCase    :						Tuple       =						LxmertForPreTraining(UpperCAmelCase_  )

  # Load weights from tf checkpoint
  load_tf_weights_in_lxmert(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
  torch.save(model.state_dict()  ,			UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Optional[int]				   =       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(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase__				:Union[str, Any]				   =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)