





"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:	int							      =logging.get_logger(__name__)

lowerCAmelCase:	Optional[int]							      ={
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""prophetnet"""
   __UpperCAmelCase        =					["""past_key_values"""]
   __UpperCAmelCase        =					{
       """num_attention_heads""": """num_encoder_attention_heads""",
   }



   def __init__(       self      ,   snake_case = 0.1      ,   snake_case = "gelu"      ,   snake_case = 3_0_5_2_2      ,   snake_case = 1_0_2_4      ,   snake_case = 4_0_9_6      ,   snake_case = 1_2      ,   snake_case = 1_6      ,   snake_case = 4_0_9_6      ,   snake_case = 1_2      ,   snake_case = 1_6      ,   snake_case = 0.1      ,   snake_case = 0.1      ,   snake_case = 5_1_2      ,   snake_case = 0.02      ,   snake_case = True      ,   snake_case = True      ,   snake_case = 0      ,   snake_case = 2      ,   snake_case = 3_2      ,   snake_case = 1_2_8      ,   snake_case = False      ,   snake_case = 0.0      ,   snake_case = True      ,   snake_case = 0      ,   snake_case = 1      ,   snake_case = 2      ,   **snake_case      ,   ) ->    int:







      """simple docstring"""
      lowercase    :			Tuple  =       vocab_size
      lowercase    :			Tuple  =       hidden_size
      lowercase    :			Union[str, Any]  =       encoder_ffn_dim
      lowercase    :			Tuple  =       num_encoder_layers
      lowercase    :			Optional[Any]  =       num_encoder_attention_heads
      lowercase    :			Tuple  =       decoder_ffn_dim
      lowercase    :			str  =       num_decoder_layers
      lowercase    :			Optional[Any]  =       num_decoder_attention_heads
      lowercase    :			Optional[int]  =       max_position_embeddings
      lowercase    :			List[str]  =       init_std  # Normal(0, this parameter)
      lowercase    :			Any  =       activation_function

      # parameters for prophetnet
      lowercase    :			Union[str, Any]  =       ngram
      lowercase    :			Union[str, Any]  =       num_buckets
      lowercase    :			int  =       relative_max_distance
      lowercase    :			Tuple  =       disable_ngram_loss
      lowercase    :			List[Any]  =       eps

      # 3 Types of Dropout
      lowercase    :			Dict  =       attention_dropout
      lowercase    :			Any  =       activation_dropout
      lowercase    :			Tuple  =       dropout

      lowercase    :			Tuple  =       use_cache

      super().__init__(
          pad_token_id=snake_case      ,   bos_token_id=snake_case      ,   eos_token_id=snake_case      ,   is_encoder_decoder=snake_case      ,   add_cross_attention=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )



   @property
   def    _UpperCAmelCase	(       self	) ->    int:







      """simple docstring"""
      return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Tuple:







      """simple docstring"""
      raise NotImplementedError(
          """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
          """ `num_decoder_layers`."""	)









"""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 ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       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__":
   lowerCAmelCase:	Dict							      =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")
   lowerCAmelCase:	Optional[int]							      =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,
   )


