




'''simple docstring'''



from datetime import datetime as dt
import os

from github import Github


UpperCamelCase_    	=				[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Github(os.environ['GITHUB_TOKEN']    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						g.get_repo('huggingface/transformers'    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						repo.get_issues(state='open'    )

      for issue in open_issues:
            SCREAMING_SNAKE_CASE   :      Any    					=						sorted([comment for comment in issue.get_comments()]      ,key=lambda __UpperCamelCase    : i.created_at      ,reverse=__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						comments[0] if len(__UpperCamelCase    ) > 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()    )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed'    )
            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()    )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.'    )


if __name__ == "__main__":
      main()








'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







       def        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

             logger.info(F"{len(A  )} controlnets loaded from {pretrained_model_path}."  )

             if len(A  ) == 0:
                   raise ValueError(
                       F"No ControlNets found under {os.path.dirname(A  )}. Expected at least {pretrained_model_path + '_0'}."  )

             return cls(A  )


