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


__lowerCamelCase    :				Union[str, Any]							     =	Mapping[str, np.ndarray]
__lowerCamelCase    :				Any							     =	Mapping[str, Any]  # Is a nested dict.
__lowerCamelCase    :				int							     =	0.01







@dataclasses.dataclass(frozen=lowerCamelCase_	)
class __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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Protein:







  """simple docstring"""





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

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

  assert aatype is not None

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Protein				,  __UpperCamelCase					: int = 0			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []

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

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

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

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

  return pdb_headers



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Protein				,  __UpperCamelCase					: str			)							->      str:







  """simple docstring"""





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

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

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

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

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

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

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

      out_pdb_lines.append(make_parent_line(__UpperCamelCase			)			)

  return "\n".join(__UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Protein			)							->      str:







  """simple docstring"""





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

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

  SCREAMING_SNAKE_CASE__    							=   residue_constants.atom_types

  SCREAMING_SNAKE_CASE__    							=   []

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

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

  SCREAMING_SNAKE_CASE__    							=   get_pdb_headers(__UpperCamelCase			)
  if len(__UpperCamelCase			) > 0:
    pdb_lines.extend(__UpperCamelCase			)

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

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

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

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

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

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

  pdb_lines.append("""END"""			)
  pdb_lines.append(""""""			)
  return "\n".join(__UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Protein			)							->      np.ndarray:







  """simple docstring"""





  return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: FeatureDict				,  __UpperCamelCase					: ModelOutput				,  __UpperCamelCase					: Optional[np.ndarray] = None				,  __UpperCamelCase					: Optional[np.ndarray] = None				,  __UpperCamelCase					: Optional[str] = None				,  __UpperCamelCase					: Optional[Sequence[str]] = None				,  __UpperCamelCase					: Optional[Sequence[int]] = None				,  )							->      Protein:







  """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=__UpperCamelCase				,  remark=__UpperCamelCase				,  parents=__UpperCamelCase				,  parents_chain_index=__UpperCamelCase				,  )

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
