



"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser(
       description=(
           """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
           """ Distillation"""
       )
   )
   parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
   parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
   parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
   parser.add_argument("""--vocab_transform""", action="""store_true""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

   for w in ["word_embeddings", "position_embeddings"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _UpperCAmelCase     =			0
   for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)








"""simple docstring"""
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: Any     			=[]

   for rt in rc.restypes:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
      restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
      SCREAMING_SNAKE_CASE_: Any     			={name: i for i, name in enumerate(lowercase )}
      restype_atomaa_to_atomaa_list.append(
          [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )

      restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )

   # Add dummy mapping for restype 'UNK'
   restype_atomaa_to_atomaa_list.append([0] * 14 )
   restype_atomaa_to_atomaa_list.append([0] * 37 )
   restype_atomaa_mask_list.append([0.0] * 14 )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor(
       lowercase							,			dtype=torch.floataa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=protein["""aatype"""].to(torch.long )

   # create the mapping for (residx, atom14) --> atom37, i.e. an array
   # with shape (num_res, 14) containing the atom37 indices for this protein
   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Any     			=restype_atomaa_mask[protein_aatype]

   SCREAMING_SNAKE_CASE_: Tuple     			=residx_atomaa_mask
   SCREAMING_SNAKE_CASE_: Dict     			=residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   SCREAMING_SNAKE_CASE_: Dict     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=residx_atomaa_to_atomaa.long()

   # create the corresponding mask
   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.zeros([21, 37]							,			dtype=torch.floataa							,			device=protein["""aatype"""].device )
   for restype, restype_letter in enumerate(rc.restypes ):
      SCREAMING_SNAKE_CASE_: int     			=rc.restype_atoa[restype_letter]
      SCREAMING_SNAKE_CASE_: Any     			=rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=rc.atom_order[atom_name]
         SCREAMING_SNAKE_CASE_: Dict     			=1

   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_mask[protein_aatype]
   SCREAMING_SNAKE_CASE_: List[Any]     			=residx_atomaa_mask

   return protein





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=tree_map(lambda lowercase : torch.tensor(lowercase							,			device=batch["""aatype"""].device )							,			lowercase							,			np.ndarray )
   SCREAMING_SNAKE_CASE_: int     			=tensor_tree_map(lambda lowercase : np.array(lowercase )							,			make_atomaa_masks(lowercase ) )
   return out



