

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       lowercase					(	__A :					Dict[str, torch.Tensor]		)				->			Dict[str, torch.Tensor]:





    '''simple docstring'''




    snake_case							:		str        =		[]
    snake_case							:		Optional[int]        =		[]
    snake_case							:		str        =		[]

    for rt in rc.restypes:
        snake_case							:		List[Any]        =		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]		)
        snake_case							:		List[str]        =		{name: i for i, name in enumerate(__A		)}
        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		)

    snake_case							:		List[Any]        =		torch.tensor(
        __A ,    dtype=torch.intaa ,    device=protein["""aatype"""].device ,    )
    snake_case							:		int        =		torch.tensor(
        __A ,    dtype=torch.intaa ,    device=protein["""aatype"""].device ,    )
    snake_case							:		Tuple        =		torch.tensor(
        __A ,    dtype=torch.floataa ,    device=protein["""aatype"""].device ,    )
    snake_case							:		List[str]        =		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
    snake_case							:		Any        =		restype_atomaa_to_atomaa[protein_aatype]
    snake_case							:		Dict        =		restype_atomaa_mask[protein_aatype]

    snake_case							:		str        =		residx_atomaa_mask
    snake_case							:		Optional[int]        =		residx_atomaa_to_atomaa.long()

    # create the gather indices for mapping back
    snake_case							:		Tuple        =		restype_atomaa_to_atomaa[protein_aatype]
    snake_case							:		Optional[Any]        =		residx_atomaa_to_atomaa.long()

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

    snake_case							:		Any        =		restype_atomaa_mask[protein_aatype]
    snake_case							:		Union[str, Any]        =		residx_atomaa_mask

    return protein







def       lowercase					(	__A :					Dict[str, torch.Tensor]		)				->			Dict[str, np.ndarray]:





    '''simple docstring'''




    snake_case							:		Tuple        =		tree_map(lambda __A		: torch.tensor(__A ,    device=batch["""aatype"""].device		) ,    __A ,    np.ndarray		)
    snake_case							:		int        =		tensor_tree_map(lambda __A		: np.array(__A		) ,    make_atomaa_masks(__A		)		)
    return out




'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


UpperCamelCase__  :						str					=      [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

   for issue in open_issues:
      UpperCAmelCase__				:					int 					=	sorted(issue.get_comments()	,      key=lambda _A     : i.created_at	,      reverse=_A     )
      UpperCAmelCase__				:					int 					=	comments[0] if len(_A     ) > 0 else None
      if (
          last_comment is not None
          and last_comment.user.login == "github-actions[bot]"
          and (dt.utcnow() - issue.updated_at).days > 7
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Closes the issue after 7 days of inactivity since the Stalebot notification.
         issue.edit(state='''closed'''     )
      elif (
          "stale" in issue.get_labels()
          and last_comment is not None
          and last_comment.user.login != "github-actions[bot]"
      ):
         # Opens the issue if someone other than Stalebot commented.
         issue.edit(state='''open'''     )
         issue.remove_from_labels('''stale'''     )
      elif (
          (dt.utcnow() - issue.updated_at).days > 23
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Post a Stalebot notification after 23 days of inactivity.
         issue.create_comment(
             '''This issue has been automatically marked as stale because it has not had '''
             '''recent activity. If you think this still needs to be addressed '''
             '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
             '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
             '''are likely to be ignored.'''     )
         issue.add_to_labels('''stale'''     )


if __name__ == "__main__":
  main()




