
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def    _a (						lowerCamelCase							):
 if "model" in orig_key:
  lowerCamelCase		:     Dict          =				orig_key.replace("""model.""",     """"""							)
 if "norm1" in orig_key:
  lowerCamelCase		:     List[str]          =				orig_key.replace("""norm1""",     """attention.output.LayerNorm"""							)
 if "norm2" in orig_key:
  lowerCamelCase		:     Optional[Any]          =				orig_key.replace("""norm2""",     """output.LayerNorm"""							)
 if "norm" in orig_key:
  lowerCamelCase		:     str          =				orig_key.replace("""norm""",     """LayerNorm"""							)
 if "transformer" in orig_key:
  lowerCamelCase		:     Union[str, Any]          =				orig_key.split("""."""							)[0].split("""_"""							)[-1]
  lowerCamelCase		:     Dict          =				orig_key.replace(F'''transformer_{layer_num}''',     F'''encoder.layer.{layer_num}'''							)
 if "mha.attn" in orig_key:
  lowerCamelCase		:     List[Any]          =				orig_key.replace("""mha.attn""",     """attention.self"""							)
 if "mha" in orig_key:
  lowerCamelCase		:     List[Any]          =				orig_key.replace("""mha""",     """attention"""							)
 if "W_q" in orig_key:
  lowerCamelCase		:     Optional[Any]          =				orig_key.replace("""W_q""",     """self.query"""							)
 if "W_k" in orig_key:
  lowerCamelCase		:     Optional[Any]          =				orig_key.replace("""W_k""",     """self.key"""							)
 if "W_v" in orig_key:
  lowerCamelCase		:     List[str]          =				orig_key.replace("""W_v""",     """self.value"""							)
 if "ff1" in orig_key:
  lowerCamelCase		:     Tuple          =				orig_key.replace("""ff1""",     """intermediate.dense"""							)
 if "ff2" in orig_key:
  lowerCamelCase		:     List[Any]          =				orig_key.replace("""ff2""",     """output.dense"""							)
 if "ff" in orig_key:
  lowerCamelCase		:     Tuple          =				orig_key.replace("""ff""",     """output.dense"""							)
 if "mlm_class" in orig_key:
  lowerCamelCase		:     Dict          =				orig_key.replace("""mlm.mlm_class""",     """cls.predictions.decoder"""							)
 if "mlm" in orig_key:
  lowerCamelCase		:     Any          =				orig_key.replace("""mlm""",     """cls.predictions.transform"""							)
 if "cls" not in orig_key:
  lowerCamelCase		:     str          =				"""nystromformer.""" + orig_key

 return orig_key





def    _a (						lowerCamelCase,     lowerCamelCase							):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase		:     List[str]          =				orig_state_dict.pop(lowerCamelCase							)

  if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
   continue
  else:
   lowerCamelCase		:     str          =				val

 lowerCamelCase		:     Tuple          =				orig_state_dict["""cls.predictions.decoder.bias"""]
 lowerCamelCase		:     List[Any]          =				(
     torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
 )

 return orig_state_dict





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Tuple          =				torch.load(lowerCamelCase,     map_location="""cpu"""							)["""model_state_dict"""]
 lowerCamelCase		:     str          =				NystromformerConfig.from_json_file(lowerCamelCase							)
 lowerCamelCase		:     Tuple          =				NystromformerForMaskedLM(lowerCamelCase							)

 lowerCamelCase		:     int          =				convert_checkpoint_helper(lowerCamelCase,     lowerCamelCase							)

 model.load_state_dict(lowerCamelCase							)
 model.eval()
 model.save_pretrained(lowerCamelCase							)

 print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Nystromformer model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _lowerCamelCase        =parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






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)




