




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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict[str, torch.Tensor]							)					-> Dict[str, torch.Tensor]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[]

      for rt in rc.restypes:
            SCREAMING_SNAKE_CASE_							=			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_							=			{name: i for i, name in enumerate(__UpperCAmelCase							)}
            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_							=			torch.tensor(
          __UpperCAmelCase			,			dtype=torch.intaa			,			device=protein['aatype'].device			,			)
      SCREAMING_SNAKE_CASE_							=			torch.tensor(
          __UpperCAmelCase			,			dtype=torch.intaa			,			device=protein['aatype'].device			,			)
      SCREAMING_SNAKE_CASE_							=			torch.tensor(
          __UpperCAmelCase			,			dtype=torch.floataa			,			device=protein['aatype'].device			,			)
      SCREAMING_SNAKE_CASE_							=			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_							=			restype_atomaa_to_atomaa[protein_aatype]
      SCREAMING_SNAKE_CASE_							=			restype_atomaa_mask[protein_aatype]

      SCREAMING_SNAKE_CASE_							=			residx_atomaa_mask
      SCREAMING_SNAKE_CASE_							=			residx_atomaa_to_atomaa.long()

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

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

      SCREAMING_SNAKE_CASE_							=			restype_atomaa_mask[protein_aatype]
      SCREAMING_SNAKE_CASE_							=			residx_atomaa_mask

      return protein





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict[str, torch.Tensor]							)					-> Dict[str, np.ndarray]:
      SCREAMING_SNAKE_CASE_							=			tree_map(lambda __UpperCAmelCase							: torch.tensor(__UpperCAmelCase			,			device=batch['aatype'].device							)			,			__UpperCAmelCase			,			np.ndarray							)
      SCREAMING_SNAKE_CASE_							=			tensor_tree_map(lambda __UpperCAmelCase							: np.array(__UpperCAmelCase							)			,			make_atomaa_masks(__UpperCAmelCase							)							)
      return out





import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)