

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           =					Mapping[str, np.ndarray]
lowerCamelCase           =					Mapping[str, Any]  # Is a nested dict.
lowerCamelCase           =					0.01







@dataclasses.dataclass(frozen=lowerCamelCase__							)
class 						__magic_name__			:

   '''simple docstring'''




   lowerCamelCase__ :	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'.
   lowerCamelCase__ :	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.
   lowerCamelCase__ :	np.ndarray  # [num_res, num_atom_type]

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

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

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

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

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





def 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







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

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

  assert aatype is not None

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




def 			UpperCAmelCase__				(			_A      : Protein	,      _A      : int = 0							):





  '''simple docstring'''







  a__  =[]

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

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

  if parents is None or len(_A							) == 0:
    a__  =['''N/A''']

  pdb_headers.append(F"""PARENT {" ".join(_A							)}"""							)

  return pdb_headers




def 			UpperCAmelCase__				(			_A      : Protein	,      _A      : str							):





  '''simple docstring'''







  a__  =[]
  a__  =pdb_str.split('''\n'''							)

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

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

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

  def make_parent_line(_A      : Sequence[str]							) -> str:
    return F"""PARENT {" ".join(_A							)}"""

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

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

      out_pdb_lines.append(make_parent_line(_A							)							)

  return "\n".join(_A							)




def 			UpperCAmelCase__				(			_A      : Protein							):





  '''simple docstring'''







  a__  =residue_constants.restypes + ['''X''']

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

  a__  =residue_constants.atom_types

  a__  =[]

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

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

  a__  =get_pdb_headers(_A							)
  if len(_A							) > 0:
    pdb_lines.extend(_A							)

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

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

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

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

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

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

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




def 			UpperCAmelCase__				(			_A      : Protein							):





  '''simple docstring'''







  return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def 			UpperCAmelCase__				(			_A      : FeatureDict	,      _A      : ModelOutput	,      _A      : Optional[np.ndarray] = None	,      _A      : Optional[np.ndarray] = None	,      _A      : Optional[str] = None	,      _A      : Optional[Sequence[str]] = None	,      _A      : Optional[Sequence[int]] = 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=_A	,      remark=_A	,      parents=_A	,      parents_chain_index=_A	,      )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
