'''simple docstring'''




import argparse

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


logging.set_verbosity_info()

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Tuple					)					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					BertConfig.from_pretrained(
           "bert-large-cased"    ,			vocab_size=_UpperCAmelCase    ,			max_position_embeddings=512    ,			is_decoder=_UpperCAmelCase    ,			add_cross_attention=_UpperCAmelCase    ,			)
       _UpperCAmelCase  :						Union[str, Any]							   =					bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       _UpperCAmelCase  :						List[Any]							   =					BertGenerationConfig(**_UpperCAmelCase					)
       if is_encoder:
              _UpperCAmelCase  :						Optional[int]							   =					BertGenerationEncoder(_UpperCAmelCase					)
       else:
              _UpperCAmelCase  :						List[Any]							   =					BertGenerationDecoder(_UpperCAmelCase					)
       print(F"""Building PyTorch model from configuration: {config}"""					)

       # Load weights from tf checkpoint
       load_tf_weights_in_bert_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			model_class="bert"    ,			is_encoder_named_decoder=_UpperCAmelCase    ,			is_encoder=_UpperCAmelCase    ,			)

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE :  str  =						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 :  List[Any]  =						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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             if "encoder" not in kwargs or "decoder" not in kwargs:
                    raise ValueError(
                        F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
                        F""" `decoder` sub-configurations are passed, but only {kwargs}""" )

             _UpperCAmelCase  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

             return cls(encoder=encoder_config.to_dict()		,      decoder=decoder_config.to_dict()		,      **A )





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

