
import argparse

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


logging.set_verbosity_info()


def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     int	):
    # Initialise PyTorch model
    UpperCamelCase	:str     							=       BertConfig.from_pretrained(
        '''bert-large-cased'''					,  vocab_size=_lowerCamelCase					,  max_position_embeddings=512					,  is_decoder=_lowerCamelCase					,  add_cross_attention=_lowerCamelCase					,  )
    UpperCamelCase	:Any     							=       bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase	:List[Any]     							=       BertGenerationConfig(**_lowerCamelCase	)
    if is_encoder:
        UpperCamelCase	:int     							=       BertGenerationEncoder(_lowerCamelCase	)
    else:
        UpperCamelCase	:int     							=       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__":
 __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=5_03_58, type=int, help="""Vocab size of model""")
 __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,
 )













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





