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


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {
    'google/pegasus-x-base': 'https://huggingface.co/google/pegasus-x-base/resolve/main/config.json',
    'google/pegasus-x-large': 'https://huggingface.co/google/pegasus-x-large/resolve/main/config.json',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}







class        UpperCAmelCase_					(				_a     ):
    UpperCAmelCase_      :							Optional[Any]    			=				'pegasus_x'
    UpperCAmelCase_      :							List[str]    			=				['past_key_values']
    UpperCAmelCase_      :							str    			=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(       self	: List[Any]   ,   lowerCAmelCase	: Any=96103   ,   lowerCAmelCase	: int=16384   ,   lowerCAmelCase	: int=16   ,   lowerCAmelCase	: int=4096   ,   lowerCAmelCase	: List[Any]=16   ,   lowerCAmelCase	: Optional[int]=16   ,   lowerCAmelCase	: int=4096   ,   lowerCAmelCase	: Dict=16   ,   lowerCAmelCase	: Any=0.0   ,   lowerCAmelCase	: str=0.0   ,   lowerCAmelCase	: str=True   ,   lowerCAmelCase	: Tuple=True   ,   lowerCAmelCase	: Optional[Any]="gelu"   ,   lowerCAmelCase	: Any=1024   ,   lowerCAmelCase	: Any=0.1   ,   lowerCAmelCase	: Tuple=0.0   ,   lowerCAmelCase	: str=0.0   ,   lowerCAmelCase	: Union[str, Any]=0.02   ,   lowerCAmelCase	: Optional[Any]=0   ,   lowerCAmelCase	: Union[str, Any]=True   ,   lowerCAmelCase	: List[Any]=0   ,   lowerCAmelCase	: int=1   ,   lowerCAmelCase	: int=1   ,   lowerCAmelCase	: List[str]=32   ,   lowerCAmelCase	: Tuple=512   ,   lowerCAmelCase	: Tuple=True   ,   **lowerCAmelCase	: List[Any]   ,   )						->			str:
      a__  		=  vocab_size
      a__  		=  max_position_embeddings
      a__  		=  d_model
      a__  		=  encoder_ffn_dim
      a__  		=  encoder_layers
      a__  		=  encoder_attention_heads
      a__  		=  decoder_ffn_dim
      a__  		=  decoder_layers
      a__  		=  decoder_attention_heads
      a__  		=  dropout
      a__  		=  attention_dropout
      a__  		=  activation_dropout
      a__  		=  activation_function
      a__  		=  init_std
      a__  		=  encoder_layerdrop
      a__  		=  decoder_layerdrop
      a__  		=  use_cache
      a__  		=  encoder_layers
      a__  		=  scale_embedding  # scale factor will be sqrt(d_model) if True

      a__  		=  num_global_tokens
      a__  		=  block_size
      a__  		=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A   ,   eos_token_id=_A   ,   is_encoder_decoder=_A   ,   decoder_start_token_id=_A   ,   forced_eos_token_id=_A   ,   **_A   ,   )




    @property
    def     __lowerCamelCase			(       self	: str )						->			int:
      return self.encoder_attention_heads




    @property
    def     __lowerCamelCase			(       self	: Any )						->			Any:
      return self.d_model






import argparse

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


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			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__":
		snake_case     :    List[Any]						=    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_0_3_5_8, type=int, help='Vocab size of model')
		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,
		)