
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  ):
  """simple docstring"""




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

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

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


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=50358, 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,
  )









from .prime_check import Test

Test()

