
"""simple docstring"""




import argparse

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


logging.set_verbosity_info()


def   __magic_name__     (				__snake_case    :	Optional[int]					,			__snake_case    :	Union[str, Any]					,			__snake_case    :	str					,			__snake_case    :	Dict					,			__snake_case    :	List[Any]  )			->    str:
       # Initialise PyTorch model
       lowercase   :    Tuple			=							BertConfig.from_pretrained(
           "bert-large-cased"					,			vocab_size=snake_case__					,			max_position_embeddings=512					,			is_decoder=snake_case__					,			add_cross_attention=snake_case__					,			)
       lowercase   :    str			=							bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       lowercase   :    int			=							BertGenerationConfig(**snake_case__  )
       if is_encoder:
              lowercase   :    Optional[Any]			=							BertGenerationEncoder(snake_case__  )
       else:
              lowercase   :    Any			=							BertGenerationDecoder(snake_case__  )
       print(f"""Building PyTorch model from configuration: {config}"""  )

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

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


if __name__ == "__main__":
   _A      :	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=5_03_58, type=int, help="""Vocab size of model""")
   _A      :	List[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"""




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


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _a = 0.1     ,   _a = 0.1     ,   _a = 0.0     ,   _a = False     ,   _a = False     ,   _a = False     ,   _a = "gelu"     ,   _a = None     ,   _a = False     ,   _a = 0     ,   _a = False     ,   _a = 0.0     ,   _a = 8     ,   _a = None     ,   _a = None     ,   _a = True     ,   _a = True     ,   _a=0     ,   _a=1     ,   _a=2     ,   **_a     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          lowercase   :    Optional[int]			=							kdim
          lowercase   :    str			=							vdim
          lowercase   :    List[Any]			=							self_attention
          lowercase   :    Any			=							bias

          super().__init__(
              pad_token_id=_a     ,   bos_token_id=_a     ,   eos_token_id=_a     ,   **_a     ,   )


