



def 	UpperCAmelCase__      (      UpperCAmelCase__ :list[int] ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		len(UpperCAmelCase__					)
 a						=		[[False] * (required_sum + 1) for _ in range(arr_len + 1					)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1					):
  a						=		True

 # sum is not zero and set is empty then false
 for i in range(1 ,					required_sum + 1					):
  a						=		False

 for i in range(1 ,					arr_len + 1					):
  for j in range(1 ,					required_sum + 1					):
   if arr[i - 1] > j:
    a						=		subset[i - 1][j]
   if arr[i - 1] <= j:
    a						=		subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, 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(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 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.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

