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


_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={
    # 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__		(     UpperCamelCase_):
      _UpperCAmelCase    :   Optional[int]   	=      """graphormer"""
      _UpperCAmelCase    :   Tuple   	=      ["""past_key_values"""]
      def __init__(				self   ,  __magic_name__ = 1   ,  __magic_name__ = 5_1_2 * 9   ,  __magic_name__ = 5_1_2 * 3   ,  __magic_name__ = 5_1_2   ,  __magic_name__ = 5_1_2   ,  __magic_name__ = 5_1_2   ,  __magic_name__ = 1_2_8   ,  __magic_name__ = 5   ,  __magic_name__ = 1_0_2_4   ,  __magic_name__ = "multi_hop"   ,  __magic_name__ = 5_1_2   ,  __magic_name__ = False   ,  __magic_name__ = 1_2   ,  __magic_name__ = 7_6_8   ,  __magic_name__ = 7_6_8   ,  __magic_name__ = 3_2   ,  __magic_name__ = 0.1   ,  __magic_name__ = 0.1   ,  __magic_name__ = 0.0   ,  __magic_name__ = False   ,  __magic_name__ = False   ,  __magic_name__ = False   ,  __magic_name__ = "gelu"   ,  __magic_name__ = None   ,  __magic_name__ = False   ,  __magic_name__ = 0   ,  __magic_name__ = False   ,  __magic_name__ = 0.0   ,  __magic_name__ = 8   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = True   ,  __magic_name__ = True   ,  __magic_name__=0   ,  __magic_name__=1   ,  __magic_name__=2   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     Any          =				num_classes
       lowerCamelCase		:     Dict          =				num_atoms
       lowerCamelCase		:     Dict          =				num_in_degree
       lowerCamelCase		:     str          =				num_out_degree
       lowerCamelCase		:     Optional[Any]          =				num_edges
       lowerCamelCase		:     List[str]          =				num_spatial
       lowerCamelCase		:     Optional[Any]          =				num_edge_dis
       lowerCamelCase		:     Tuple          =				edge_type
       lowerCamelCase		:     List[str]          =				multi_hop_max_dist
       lowerCamelCase		:     List[Any]          =				spatial_pos_max
       lowerCamelCase		:     int          =				max_nodes
       lowerCamelCase		:     str          =				num_hidden_layers
       lowerCamelCase		:     Tuple          =				embedding_dim
       lowerCamelCase		:     Union[str, Any]          =				embedding_dim
       lowerCamelCase		:     List[str]          =				ffn_embedding_dim
       lowerCamelCase		:     List[str]          =				num_attention_heads
       lowerCamelCase		:     Optional[int]          =				dropout
       lowerCamelCase		:     str          =				attention_dropout
       lowerCamelCase		:     Union[str, Any]          =				layerdrop
       lowerCamelCase		:     Dict          =				encoder_normalize_before
       lowerCamelCase		:     Optional[Any]          =				pre_layernorm
       lowerCamelCase		:     Optional[int]          =				apply_graphormer_init
       lowerCamelCase		:     Optional[Any]          =				activation_fn
       lowerCamelCase		:     Optional[int]          =				embed_scale
       lowerCamelCase		:     Optional[int]          =				freeze_embeddings
       lowerCamelCase		:     Tuple          =				num_trans_layers_to_freeze
       lowerCamelCase		:     List[Any]          =				share_input_output_embed
       lowerCamelCase		:     Dict          =				traceable
       lowerCamelCase		:     int          =				q_noise
       lowerCamelCase		:     List[str]          =				qn_block_size

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

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






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
              """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
              """Not needed for `l0`"""
          ),
      )
      parser.add_argument(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




