





"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional

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


lowerCAmelCase:						List[str]				   =logging.get_logger(__name__)

# TODO Update this
lowerCAmelCase:						int				   ={
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}
class lowerCamelCase__	(     __UpperCamelCase					):
   __UpperCAmelCase										=       """esm"""



   def __init__(							self   , snake_case=None   , snake_case=None   , snake_case=None   , snake_case=7_6_8   , snake_case=1_2   , snake_case=1_2   , snake_case=3_0_7_2   , snake_case=0.1   , snake_case=0.1   , snake_case=1_0_2_6   , snake_case=0.02   , snake_case=1E-12   , snake_case="absolute"   , snake_case=True   , snake_case=None   , snake_case=False   , snake_case=False   , snake_case=None   , snake_case=None   , **snake_case   , )			->     Any:







      """simple docstring"""
      super().__init__(pad_token_id=snake_case__   , mask_token_id=snake_case__   , **snake_case__						)

      lowercase     :      List[str]	=							vocab_size
      lowercase     :      str	=							hidden_size
      lowercase     :      Union[str, Any]	=							num_hidden_layers
      lowercase     :      Optional[int]	=							num_attention_heads
      lowercase     :      int	=							intermediate_size
      lowercase     :      Any	=							hidden_dropout_prob
      lowercase     :      Dict	=							attention_probs_dropout_prob
      lowercase     :      Union[str, Any]	=							max_position_embeddings
      lowercase     :      Union[str, Any]	=							initializer_range
      lowercase     :      Tuple	=							layer_norm_eps
      lowercase     :      List[Any]	=							position_embedding_type
      lowercase     :      Any	=							use_cache
      lowercase     :      Optional[int]	=							emb_layer_norm_before
      lowercase     :      int	=							token_dropout
      lowercase     :      Dict	=							is_folding_model
      if is_folding_model:
         if esmfold_config is None:
            logger.info("""No esmfold_config supplied for folding model, using default values."""						)
            lowercase     :      Optional[Any]	=							EsmFoldConfig()
         elif isinstance(snake_case__   , snake_case__						):
            lowercase     :      int	=							EsmFoldConfig(**snake_case__						)
         lowercase     :      Tuple	=							esmfold_config
         if vocab_list is None:
            logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""						)
            lowercase     :      Optional[int]	=							get_default_vocab_list()
         else:
            lowercase     :      Optional[int]	=							vocab_list
      else:
         lowercase     :      Optional[Any]	=							None
         lowercase     :      Any	=							None
      if self.esmfold_config is not None and getattr(self.esmfold_config   , """use_esm_attn_map"""   , snake_case__						):
         raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""						)





   def  _UpperCAmelCase						(							self						)			->     Optional[Any]:







      """simple docstring"""
      lowercase     :      List[str]	=							super().to_dict()
      if isinstance(self.esmfold_config   , snake_case__						):
         lowercase     :      int	=							self.esmfold_config.to_dict()
      return output




@dataclass
class lowerCamelCase__	:
   __UpperCAmelCase										=       None
   __UpperCAmelCase										=       True
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       0

   __UpperCAmelCase										=       True
   __UpperCAmelCase										=       False

   __UpperCAmelCase										=       128
   __UpperCAmelCase										=       None



   def  _UpperCAmelCase						(							self						)			->     Any:







      """simple docstring"""






      if self.trunk is None:
         lowercase     :      Dict	=							TrunkConfig()
      elif isinstance(self.trunk   , snake_case__						):
         lowercase     :      Any	=							TrunkConfig(**self.trunk						)





   def  _UpperCAmelCase						(							self						)			->     List[Any]:







      """simple docstring"""
      lowercase     :      Optional[int]	=							asdict(self						)
      lowercase     :      Dict	=							self.trunk.to_dict()
      return output




@dataclass
class lowerCamelCase__	:
   __UpperCAmelCase										=       48
   __UpperCAmelCase										=       1024
   __UpperCAmelCase										=       128
   __UpperCAmelCase										=       32
   __UpperCAmelCase										=       32
   __UpperCAmelCase										=       32
   __UpperCAmelCase										=       0
   __UpperCAmelCase										=       0
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       4
   __UpperCAmelCase										=       128
   __UpperCAmelCase										=       None



   def  _UpperCAmelCase						(							self						)			->     List[str]:







      """simple docstring"""






      if self.structure_module is None:
         lowercase     :      Dict	=							StructureModuleConfig()
      elif isinstance(self.structure_module   , snake_case__						):
         lowercase     :      str	=							StructureModuleConfig(**self.structure_module						)

      if self.max_recycles <= 0:
         raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.'''						)
      if self.sequence_state_dim % self.sequence_state_dim != 0:
         raise ValueError(
             """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
             f''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''						)
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
         raise ValueError(
             """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
             f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''						)

      lowercase     :      Optional[int]	=							self.sequence_state_dim // self.sequence_head_width
      lowercase     :      int	=							self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
         raise ValueError(
             """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
             f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''						)
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
         raise ValueError(
             """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
             f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''						)
      if self.pairwise_state_dim % 2 != 0:
         raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''						)

      if self.dropout >= 0.4:
         raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.'''						)





   def  _UpperCAmelCase						(							self						)			->     int:







      """simple docstring"""
      lowercase     :      int	=							asdict(self						)
      lowercase     :      Tuple	=							self.structure_module.to_dict()
      return output




@dataclass
class lowerCamelCase__	:
   __UpperCAmelCase										=       384
   __UpperCAmelCase										=       128
   __UpperCAmelCase										=       16
   __UpperCAmelCase										=       128
   __UpperCAmelCase										=       12
   __UpperCAmelCase										=       4
   __UpperCAmelCase										=       8
   __UpperCAmelCase										=       0.1
   __UpperCAmelCase										=       8
   __UpperCAmelCase										=       1
   __UpperCAmelCase										=       2
   __UpperCAmelCase										=       7
   __UpperCAmelCase										=       10
   __UpperCAmelCase										=       1E-8
   __UpperCAmelCase										=       1E5



   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""
      return asdict(self						)




def       __snake_case    (   )		->					Any:
   return (
       "<cls>",
       "<pad>",
       "<eos>",
       "<unk>",
       "L",
       "A",
       "G",
       "V",
       "S",
       "E",
       "R",
       "T",
       "I",
       "D",
       "P",
       "K",
       "Q",
       "N",
       "F",
       "Y",
       "M",
       "H",
       "W",
       "C",
       "X",
       "B",
       "U",
       "Z",
       "O",
       ".",
       "-",
       "<null_1>",
       "<mask>",
   )





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

 SCREAMING_SNAKE_CASE		    =      args.model_name_or_path.rstrip('/'						)
 SCREAMING_SNAKE_CASE		    =      args.target_model_path

 print(f"""Load fine-pruned model from {model_name_or_path}"""						)
 SCREAMING_SNAKE_CASE		    =      torch.load(os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 SCREAMING_SNAKE_CASE		    =      {}

 for name, tensor in model.items():
  if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  elif "classifier" in name or "qa_output" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  elif "bias" in name:
   SCREAMING_SNAKE_CASE		    =      tensor
   print(f"""Copied layer {name}"""						)
  else:
   if pruning_method == "magnitude":
    SCREAMING_SNAKE_CASE		    =      MagnitudeBinarizer.apply(inputs=_UpperCamelCase		,				threshold=_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "topK":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE		    =      TopKBinarizer.apply(_UpperCamelCase		,				_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "sigmoied_threshold":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE		    =      ThresholdBinarizer.apply(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   elif pruning_method == "l0":
    if "mask_scores" in name:
     continue
    SCREAMING_SNAKE_CASE		    =      name[:-6]
    SCREAMING_SNAKE_CASE		    =      model[f"""{prefix_}mask_scores"""]
    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      -0.1, 1.1
    SCREAMING_SNAKE_CASE		    =      torch.sigmoid(_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      s * (r - l) + l
    SCREAMING_SNAKE_CASE		    =      s_bar.clamp(min=0.0		,				max=1.0						)
    SCREAMING_SNAKE_CASE		    =      tensor * mask
    print(f"""Pruned layer {name}"""						)
   else:
    raise ValueError('Unknown pruning method'						)

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

 if not os.path.isdir(_UpperCamelCase						):
  shutil.copytree(_UpperCamelCase		,				_UpperCamelCase						)
  print(f"""\nCreated folder {target_model_path}"""						)

 torch.save(_UpperCamelCase		,				os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 print('\nPruned model saved! See you later!'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()

      parser.add_argument(
          "--pruning_method",
          choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
          type=str,
          required=True,
          help=(
              "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
              " sigmoied_threshold = Soft movement pruning)"
          ),
      )
      parser.add_argument(
          "--threshold",
          type=float,
          required=False,
          help=(
              "For `magnitude` and `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(
          "--model_name_or_path",
          type=str,
          required=True,
          help="Folder containing the model that was previously fine-pruned",
      )
      parser.add_argument(
          "--target_model_path",
          default=None,
          type=str,
          required=False,
          help="Folder containing the model that was previously fine-pruned",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

