

'''simple docstring'''



import argparse

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


logging.set_verbosity_info()

def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__			,      a__)		->    Dict:



    """simple docstring"""







    _snake_case : Union[str, Any]												=		BertConfig.from_pretrained(
        'bert-large-cased'			,      vocab_size=a__			,      max_position_embeddings=5_1_2			,      is_decoder=a__			,      add_cross_attention=a__			,      )
    _snake_case : int												=		bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    _snake_case : str												=		BertGenerationConfig(**a__)
    if is_encoder:
        _snake_case : List[Any]												=		BertGenerationEncoder(a__)
    else:
        _snake_case : Union[str, Any]												=		BertGenerationDecoder(a__)
    print(F"""Building PyTorch model from configuration: {config}""")

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        a__			,      a__			,      model_class='bert'			,      is_encoder_named_decoder=a__			,      is_encoder=a__			,      )

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    =   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=50_358, type=int, help="Vocab size of model")
   SCREAMING_SNAKE_CASE_    =   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,
   )






'''simple docstring'''



from __future__ import annotations

def   lowerCamelCase__       (      a__)		->    int | float:



    """simple docstring"""







    _snake_case : int												=		sorted(a__)
    _snake_case : List[str]												=		len(a__)
    _snake_case : Union[str, Any]												=		length >> 1
    return (
        (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
        if length % 2 == 0
        else sorted_list[mid_index]
    )

def   lowerCamelCase__       (      )		->    Union[str, Any]:



    """simple docstring"""







    import doctest

    doctest.testmod()


if __name__ == "__main__":
   main()



