




'''simple docstring'''
import argparse

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


logging.set_verbosity_info()
def 							snake_case_    (  a__						:      Optional[int]       ,a__						:      str       ,a__						:      Union[str, Any]       ,a__						:      Optional[Any]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		BertConfig.from_pretrained(
        """bert-large-cased"""       ,vocab_size=a__       ,max_position_embeddings=5_12       ,is_decoder=a__       ,add_cross_attention=a__       ,)
    __lowercase     							=		bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    __lowercase     							=		BertGenerationConfig(**a__     )
    if is_encoder:
        __lowercase     							=		BertGenerationEncoder(a__     )
    else:
        __lowercase     							=		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__":
  A				: int					      =			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""")
  A				: str					      =			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'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

    for k in range(t - 1     ):
        # select a column of modified matrix A':
        __lowercase     							=		r[k:, [k]]
        # construct first basis vector
        __lowercase     							=		np.zeros_like(a__     )
        __lowercase     							=		1.0
        # determine scaling factor
        __lowercase     							=		np.linalg.norm(a__     )
        # construct vector v for Householder reflection
        __lowercase     							=		x + np.sign(x[0]     ) * alpha * ea
        v /= np.linalg.norm(a__     )

        # construct the Householder matrix
        __lowercase     							=		np.eye(m - k     ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __lowercase     							=		np.block([[np.eye(a__     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




