





'''simple docstring'''




import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


lowercase__       : List[str]         =       Mapping[str, np.ndarray]
lowercase__       : Dict         =       Mapping[str, Any]  # Is a nested dict.
lowercase__       : Dict         =       0.01


@dataclasses.dataclass(frozen=a__				)
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			42  # [num_res, num_atom_type, 3]

     # Amino-acid type for each residue represented as an integer between 0 and
     # 20, where 20 is 'X'.
     lowerCAmelCase						     =			42  # [num_res]

     # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
     # is present and 0.0 if not. This should be used for loss masking.
     lowerCAmelCase						     =			42  # [num_res, num_atom_type]

     # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
     lowerCAmelCase						     =			42  # [num_res]

     # B-factors, or temperature factors, of each residue (in sq. angstroms units),
     # representing the displacement of the residue from its ground truth mean
     # value.
     lowerCAmelCase						     =			42  # [num_res, num_atom_type]

     # Chain indices for multi-chain predictions
     lowerCAmelCase						     =			None

     # Optional remark about the protein. Included as a comment in output PDB
     # files
     lowerCAmelCase						     =			None

     # Templates used to generate this protein (prediction-only)
     lowerCAmelCase						     =			None

     # Chain corresponding to each parent
     lowerCAmelCase						     =			None






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Protein:
     __A		:					Optional[Any]						=			r'(\[[A-Z]+\]\n)'
     __A		:					List[str]						=			[tag.strip() for tag in re.split(__snake_case					,				__snake_case     ) if len(__snake_case     ) > 0]
     __A		:					Iterator[Tuple[str, List[str]]]						=			zip(tags[0::2]					,				[l.split('\n'     ) for l in tags[1::2]]     )

     __A		:					List[str]						=			["N", "CA", "C"]
     __A		:					int						=			None
     __A		:					Dict						=			None
     __A		:					Tuple						=			None
     for g in groups:
          if "[PRIMARY]" == g[0]:
               __A		:					Optional[int]						=			g[1][0].strip()
               for i in range(len(__snake_case     )     ):
                    if seq[i] not in residue_constants.restypes:
                         __A		:					Optional[int]						=			'X'  # FIXME: strings are immutable
               __A		:					int						=			np.array(
                   [residue_constants.restype_order.get(__snake_case					,				residue_constants.restype_num     ) for res_symbol in seq]     )
          elif "[TERTIARY]" == g[0]:
               __A		:					List[List[float]]						=			[]
               for axis in range(3     ):
                    tertiary.append(list(map(__snake_case					,				g[1][axis].split()     )     )     )
               __A		:					List[str]						=			np.array(__snake_case     )
               __A		:					Tuple						=			np.zeros((len(tertiary[0]     ) // 3, residue_constants.atom_type_num, 3)     ).astype(np.floataa     )
               for i, atom in enumerate(__snake_case     ):
                    __A		:					Dict						=			np.transpose(tertiary_np[:, i::3]     )
               atom_positions *= PICO_TO_ANGSTROM
          elif "[MASK]" == g[0]:
               __A		:					Dict						=			np.array(list(map({'-': 0, '+': 1}.get					,				g[1][0].strip()     )     )     )
               __A		:					Any						=			np.zeros(
                   (
                       len(__snake_case     ),
                       residue_constants.atom_type_num,
                   )     ).astype(np.floataa     )
               for i, atom in enumerate(__snake_case     ):
                    __A		:					Tuple						=			1
               atom_mask *= mask[..., None]

     assert aatype is not None

     return Protein(
         atom_positions=__snake_case					,				atom_mask=__snake_case					,				aatype=__snake_case					,				residue_index=np.arange(len(__snake_case     )     )					,				b_factors=__snake_case					,				)






def 			_lowerCAmelCase     (    __snake_case    :					Protein					,				__snake_case    :					int = 0     )	->	List[str]:
     __A		:					List[str]						=			[]

     __A		:					Dict						=			prot.remark
     if remark is not None:
          pdb_headers.append(f'REMARK {remark}'     )

     __A		:					Optional[int]						=			prot.parents
     __A		:					List[Any]						=			prot.parents_chain_index
     if parents is not None and parents_chain_index is not None:
          __A		:					Tuple						=			[p for i, p in zip(__snake_case					,				__snake_case     ) if i == chain_id]

     if parents is None or len(__snake_case     ) == 0:
          __A		:					List[str]						=			['N/A']

     pdb_headers.append(f'PARENT {" ".join(__snake_case     )}'     )

     return pdb_headers






def 			_lowerCAmelCase     (    __snake_case    :					Protein					,				__snake_case    :					str     )	->	str:
     __A		:					List[str]						=			[]
     __A		:					Union[str, Any]						=			pdb_str.split('\n'     )

     __A		:					Tuple						=			prot.remark
     if remark is not None:
          out_pdb_lines.append(f'REMARK {remark}'     )

     __A		:					List[List[str]]
     if prot.parents is not None and len(prot.parents     ) > 0:
          __A		:					List[Any]						=			[]
          if prot.parents_chain_index is not None:
               __A		:					Dict[str, List[str]]						=			{}
               for p, i in zip(prot.parents					,				prot.parents_chain_index     ):
                    parent_dict.setdefault(str(__snake_case     )					,				[]     )
                    parent_dict[str(__snake_case     )].append(__snake_case     )

               __A		:					Dict						=			max([int(__snake_case     ) for chain_idx in parent_dict]     )
               for i in range(max_idx + 1     ):
                    __A		:					Any						=			parent_dict.get(str(__snake_case     )					,				['N/A']     )
                    parents_per_chain.append(__snake_case     )
          else:
               parents_per_chain.append(list(prot.parents     )     )
     else:
          __A		:					Any						=			[['N/A']]

     def make_parent_line(__snake_case    :					Sequence[str]     ) -> str:
          return f'PARENT {" ".join(__snake_case     )}'

     out_pdb_lines.append(make_parent_line(parents_per_chain[0]     )     )

     __A		:					Any						=			0
     for i, l in enumerate(__snake_case     ):
          if "PARENT" not in l and "REMARK" not in l:
               out_pdb_lines.append(__snake_case     )
          if "TER" in l and "END" not in lines[i + 1]:
               chain_counter += 1
               if not chain_counter >= len(__snake_case     ):
                    __A		:					Union[str, Any]						=			parents_per_chain[chain_counter]
               else:
                    __A		:					List[Any]						=			['N/A']

               out_pdb_lines.append(make_parent_line(__snake_case     )     )

     return "\n".join(__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Protein     )	->	str:
     __A		:					List[str]						=			residue_constants.restypes + ['X']

     def res_atoa(__snake_case    :					int     ) -> str:
          return residue_constants.restype_atoa.get(restypes[r]					,				'UNK'     )

     __A		:					List[str]						=			residue_constants.atom_types

     __A		:					List[str]						=			[]

     __A		:					Dict						=			prot.atom_mask
     __A		:					Any						=			prot.aatype
     __A		:					Tuple						=			prot.atom_positions
     __A		:					Any						=			prot.residue_index.astype(np.intaa     )
     __A		:					List[Any]						=			prot.b_factors
     __A		:					Any						=			prot.chain_index

     if np.any(aatype > residue_constants.restype_num     ):
          raise ValueError('Invalid aatypes.'     )

     __A		:					str						=			get_pdb_headers(__snake_case     )
     if len(__snake_case     ) > 0:
          pdb_lines.extend(__snake_case     )

     __A		:					int						=			aatype.shape[0]
     __A		:					Tuple						=			1
     __A		:					int						=			0
     __A		:					Union[str, Any]						=			string.ascii_uppercase
     __A		:					Optional[Any]						=			None
     # Add all atom sites.
     for i in range(__snake_case     ):
          __A		:					Union[str, Any]						=			res_atoa(aatype[i]     )
          for atom_name, pos, mask, b_factor in zip(__snake_case					,				atom_positions[i]					,				atom_mask[i]					,				b_factors[i]     ):
               if mask < 0.5:
                    continue

               __A		:					Any						=			'ATOM'
               __A		:					Tuple						=			atom_name if len(__snake_case     ) == 4 else f' {atom_name}'
               __A		:					Tuple						=			''
               __A		:					Tuple						=			''
               __A		:					Any						=			1.00
               __A		:					Any						=			atom_name[0]  # Protein supports only C, N, O, S, this works.
               __A		:					int						=			''

               __A		:					Tuple						=			'A'
               if chain_index is not None:
                    __A		:					Tuple						=			chain_tags[chain_index[i]]

               # PDB is a columnar format, every space matters here!
               __A		:					Any						=			(
                   f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
                   f'{res_name_a:>3} {chain_tag:>1}'
                   f'{residue_index[i]:>4}{insertion_code:>1}   '
                   f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
                   f'{occupancy:>6.2f}{b_factor:>6.2f}          '
                   f'{element:>2}{charge:>2}'
               )
               pdb_lines.append(__snake_case     )
               atom_index += 1

          __A		:					str						=			i == n - 1
          if chain_index is not None:
               if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                    __A		:					str						=			True
                    __A		:					List[Any]						=			chain_index[i + 1]

          if should_terminate:
               # Close the chain.
               __A		:					Any						=			'TER'
               __A		:					List[Any]						=			(
                   f'{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]     ):>3} {chain_tag:>1}{residue_index[i]:>4}'
               )
               pdb_lines.append(__snake_case     )
               atom_index += 1

               if i != n - 1:
                    # "prev" is a misnomer here. This happens at the beginning of
                    # each new chain.
                    pdb_lines.extend(get_pdb_headers(__snake_case					,				__snake_case     )     )

     pdb_lines.append('END'     )
     pdb_lines.append(''     )
     return "\n".join(__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Protein     )	->	np.ndarray:
     return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def 			_lowerCAmelCase     (    __snake_case    :					FeatureDict					,				__snake_case    :					ModelOutput					,				__snake_case    :					Optional[np.ndarray] = None					,				__snake_case    :					Optional[np.ndarray] = None					,				__snake_case    :					Optional[str] = None					,				__snake_case    :					Optional[Sequence[str]] = None					,				__snake_case    :					Optional[Sequence[int]] = None					,				)	->	Protein:
     return Protein(
         aatype=features['aatype']					,				atom_positions=result['final_atom_positions']					,				atom_mask=result['final_atom_mask']					,				residue_index=features['residue_index'] + 1					,				b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask']     )					,				chain_index=__snake_case					,				remark=__snake_case					,				parents=__snake_case					,				parents_chain_index=__snake_case					,				)






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)