import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Optional[Any]):
    # Initialise PyTorch model
    lowercase__					:  Tuple          =							BertConfig.from_pretrained(
        "bert-large-cased"						,      vocab_size=_lowerCamelCase						,      max_position_embeddings=512						,      is_decoder=_lowerCamelCase						,      add_cross_attention=_lowerCamelCase						,      )
    lowercase__					:  Optional[Any]          =							bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    lowercase__					:  int          =							BertGenerationConfig(**_lowerCamelCase)
    if is_encoder:
        lowercase__					:  int          =							BertGenerationEncoder(_lowerCamelCase)
    else:
        lowercase__					:  Union[str, Any]          =							BertGenerationDecoder(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowerCamelCase						,      _lowerCamelCase						,      model_class="bert"						,      is_encoder_named_decoder=_lowerCamelCase						,      is_encoder=_lowerCamelCase						,      )

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


if __name__ == "__main__":
      UpperCamelCase    				=		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_named_decoder''',
          action='''store_true''',
          help='''If decoder has to be renamed to encoder in PyTorch model.''',
      )
      parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
      parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
      UpperCamelCase    				=		parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )




class     snake_case_			:






   def __init__( self							:	Optional[int]      ,		lowercase_							:	Optional[int]=None    )   ->		Optional[int]:
       lowercase__					:  List[Any]          =							data
       lowercase__					:  Any          =							None







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



def      lowercase_							(     _lowerCamelCase				:			List[Any]):

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

    # Set first element as Head
    lowercase__					:  List[str]          =							Node(elements_list[0])
    lowercase__					:  Tuple          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        lowercase__					:  Tuple          =							Node(_lowerCamelCase)
        lowercase__					:  Union[str, Any]          =							current.next
    return head


UpperCamelCase    				=		[1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print('''Creating Linked List from List.''')
UpperCamelCase    				=		make_linked_list(list_data)
print('''Linked List:''')

print(linked_list)



