




"""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_			(		snake_case_     :       Optional[Any]						,snake_case_     :       int						,snake_case_     :       Dict    ):







    '''simple docstring'''
    UpperCamelCase				:   List[Any]          =							TaConfig.from_json_file(snake_case_    )
    print(f'Building PyTorch model from configuration: {config}'    )
    UpperCamelCase				:   List[Any]          =							TaForConditionalGeneration(snake_case_    )

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

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'    )
    model.save_pretrained(snake_case_    )


if __name__ == "__main__":
  __A :		List[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 :		Optional[int]      							=    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












"""simple docstring"""




class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
       UpperCamelCase				:   Optional[Any]          =							data
       UpperCamelCase				:   str          =							None







   def __repr__(							self ):
       UpperCamelCase				:   str          =							""""""
       UpperCamelCase				:   Any          =							self
       while temp:
           string_rep += f'<{temp.data}> ---> '
           UpperCamelCase				:   Optional[int]          =							temp.next
       string_rep += "<END>"
       return string_rep







def 					A_			(		snake_case_     :       Tuple    ):







    '''simple docstring'''

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""    )

    # Set first element as Head
    UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
    UpperCamelCase				:   str          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
        UpperCamelCase				:   Optional[Any]          =							current.next
    return head


__A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('''Creating Linked List from List.''')
__A :		int      							=    make_linked_list(list_data)
print('''Linked List:''')


print(linked_list)




