





'''simple docstring'''





import argparse

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


logging.set_verbosity_info()





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




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

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

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


if __name__ == "__main__":
   UpperCamelCase_						:      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.'''
   )
   UpperCamelCase_						:      Union[str, Any]       =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''





from __future__ import annotations

from typing import Any





def      __a     (       _UpperCamelCase:   list[Any]   )						->			None:

     """simple docstring"""




     create_state_space_tree(_UpperCamelCase ,						[] ,						0   )





def      __a     (       _UpperCamelCase:   list[Any] ,						_UpperCamelCase:   list[Any] ,						_UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     if index == len(_UpperCamelCase   ):
          print(_UpperCamelCase   )
          return

     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.append(sequence[index]   )
     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.pop()


if __name__ == "__main__":
   UpperCamelCase_						:      list[Any]       =						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['''A''', '''B''', '''C'''])
   generate_all_subsequences(seq)


