





'''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


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




@dataclasses.dataclass(frozen=snake_case_   )
class  UpperCAmelCase     :
      _lowercase:			np.ndarray  # [num_res, num_atom_type, 3]

      # Amino-acid type for each residue represented as an integer between 0 and
      # 20, where 20 is 'X'.
      _lowercase:			np.ndarray  # [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.
      _lowercase:			np.ndarray  # [num_res, num_atom_type]

      # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
      _lowercase:			np.ndarray  # [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.
      _lowercase:			np.ndarray  # [num_res, num_atom_type]

      # Chain indices for multi-chain predictions
      _lowercase:			Optional[np.ndarray]						     =   None

      # Optional remark about the protein. Included as a comment in output PDB
      # files
      _lowercase:			Optional[str]						     =   None

      # Templates used to generate this protein (prediction-only)
      _lowercase:			Optional[Sequence[str]]						     =   None

      # Chain corresponding to each parent
      _lowercase:			Optional[Sequence[int]]						     =   None




def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							r"""(\[[A-Z]+\]\n)"""
      _lowerCAmelCase							     =							[tag.strip() for tag in re.split(lowerCAmelCase							,	lowerCAmelCase  ) if len(lowerCAmelCase  ) > 0]
      _lowerCAmelCase							     =							zip(tags[0::2]							,	[l.split("""\n"""  ) for l in tags[1::2]]  )

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

      assert aatype is not None

      return Protein(
          atom_positions=lowerCAmelCase							,	atom_mask=lowerCAmelCase							,	aatype=lowerCAmelCase							,	residue_index=np.arange(len(lowerCAmelCase  )  )							,	b_factors=lowerCAmelCase							,	)
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase = 0  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]

      _lowerCAmelCase							     =							prot.remark
      if remark is not None:
            pdb_headers.append(f"REMARK {remark}"  )

      _lowerCAmelCase							     =							prot.parents
      _lowerCAmelCase							     =							prot.parents_chain_index
      if parents is not None and parents_chain_index is not None:
            _lowerCAmelCase							     =							[p for i, p in zip(lowerCAmelCase							,	lowerCAmelCase  ) if i == chain_id]

      if parents is None or len(lowerCAmelCase  ) == 0:
            _lowerCAmelCase							     =							["""N/A"""]

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

      return pdb_headers
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							pdb_str.split("""\n"""  )

      _lowerCAmelCase							     =							prot.remark
      if remark is not None:
            out_pdb_lines.append(f"REMARK {remark}"  )

      _lowerCAmelCase							     =							42
      if prot.parents is not None and len(prot.parents  ) > 0:
            _lowerCAmelCase							     =							[]
            if prot.parents_chain_index is not None:
                  _lowerCAmelCase							     =							{}
                  for p, i in zip(prot.parents							,	prot.parents_chain_index  ):
                        parent_dict.setdefault(str(lowerCAmelCase  )							,	[]  )
                        parent_dict[str(lowerCAmelCase  )].append(lowerCAmelCase  )

                  _lowerCAmelCase							     =							max([int(lowerCAmelCase  ) for chain_idx in parent_dict]  )
                  for i in range(max_idx + 1  ):
                        _lowerCAmelCase							     =							parent_dict.get(str(lowerCAmelCase  )							,	["""N/A"""]  )
                        parents_per_chain.append(lowerCAmelCase  )
            else:
                  parents_per_chain.append(list(prot.parents  )  )
      else:
            _lowerCAmelCase							     =							[["""N/A"""]]

      def make_parent_line(lowerCAmelCase  ) -> str:
            return f"PARENT {' '.join(lowerCAmelCase  )}"

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

      _lowerCAmelCase							     =							0
      for i, l in enumerate(lowerCAmelCase  ):
            if "PARENT" not in l and "REMARK" not in l:
                  out_pdb_lines.append(lowerCAmelCase  )
            if "TER" in l and "END" not in lines[i + 1]:
                  chain_counter += 1
                  if not chain_counter >= len(lowerCAmelCase  ):
                        _lowerCAmelCase							     =							parents_per_chain[chain_counter]
                  else:
                        _lowerCAmelCase							     =							["""N/A"""]

                  out_pdb_lines.append(make_parent_line(lowerCAmelCase  )  )

      return "\n".join(lowerCAmelCase  )
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							residue_constants.restypes + ["""X"""]

      def res_atoa(lowerCAmelCase  ) -> str:
            return residue_constants.restype_atoa.get(restypes[r]							,	"""UNK"""  )

      _lowerCAmelCase							     =							residue_constants.atom_types

      _lowerCAmelCase							     =							[]

      _lowerCAmelCase							     =							prot.atom_mask
      _lowerCAmelCase							     =							prot.aatype
      _lowerCAmelCase							     =							prot.atom_positions
      _lowerCAmelCase							     =							prot.residue_index.astype(np.intaa  )
      _lowerCAmelCase							     =							prot.b_factors
      _lowerCAmelCase							     =							prot.chain_index

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

      _lowerCAmelCase							     =							get_pdb_headers(lowerCAmelCase  )
      if len(lowerCAmelCase  ) > 0:
            pdb_lines.extend(lowerCAmelCase  )

      _lowerCAmelCase							     =							aatype.shape[0]
      _lowerCAmelCase							     =							1
      _lowerCAmelCase							     =							0
      _lowerCAmelCase							     =							string.ascii_uppercase
      _lowerCAmelCase							     =							None
      # Add all atom sites.
      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase							     =							res_atoa(aatype[i]  )
            for atom_name, pos, mask, b_factor in zip(lowerCAmelCase							,	atom_positions[i]							,	atom_mask[i]							,	b_factors[i]  ):
                  if mask < 0.5:
                        continue

                  _lowerCAmelCase							     =							"""ATOM"""
                  _lowerCAmelCase							     =							atom_name if len(lowerCAmelCase  ) == 4 else f" {atom_name}"
                  _lowerCAmelCase							     =							""""""
                  _lowerCAmelCase							     =							""""""
                  _lowerCAmelCase							     =							1.00
                  _lowerCAmelCase							     =							atom_name[0]  # Protein supports only C, N, O, S, this works.
                  _lowerCAmelCase							     =							""""""

                  _lowerCAmelCase							     =							"""A"""
                  if chain_index is not None:
                        _lowerCAmelCase							     =							chain_tags[chain_index[i]]

                  # PDB is a columnar format, every space matters here!
                  _lowerCAmelCase							     =							(
                      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(lowerCAmelCase  )
                  atom_index += 1

            _lowerCAmelCase							     =							i == n - 1
            if chain_index is not None:
                  if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                        _lowerCAmelCase							     =							True
                        _lowerCAmelCase							     =							chain_index[i + 1]

            if should_terminate:
                  # Close the chain.
                  _lowerCAmelCase							     =							"""TER"""
                  _lowerCAmelCase							     =							(
                      f"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]  ):>3} {chain_tag:>1}{residue_index[i]:>4}"
                  )
                  pdb_lines.append(lowerCAmelCase  )
                  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(lowerCAmelCase							,	lowerCAmelCase  )  )

      pdb_lines.append("""END"""  )
      pdb_lines.append(""""""  )
      return "\n".join(lowerCAmelCase  )
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase = None							,	lowerCAmelCase = None							,	lowerCAmelCase = None							,	lowerCAmelCase = None							,	lowerCAmelCase = None							,	):





      """simple docstring"""


      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=lowerCAmelCase							,	remark=lowerCAmelCase							,	parents=lowerCAmelCase							,	parents_chain_index=lowerCAmelCase							,	)













import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{val_avg_em:.4f}-{step_count}'
   else:
      raise NotImplementedError(
          F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
          ' function.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   return EarlyStopping(
       monitor=F"""val_{metric}""",	mode='min' if 'loss' in metric else 'max',	patience=lowerCAmelCase_,	verbose=lowerCAmelCase_,	)




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
     # Log results
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	od / 'test_generations.txt'
     else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
        generations_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
     with open(lowerCAmelCase_					,  'a+'     ) as writer:
        for key in sorted(lowerCAmelCase_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

     if "preds" in metrics:
        __lowerCAmelCase								=	'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	count_trainable_parameters(lowerCAmelCase_     )
     # mp stands for million parameters
     trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}     )






  @rank_zero_only
  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

