






"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class      _lowercase (     lowerCAmelCase						):
						_a   :     List[Any]								=   ['''image_processor''', '''tokenizer''']
						_a   :     int								=   '''BlipImageProcessor'''
						_a   :     Optional[Any]								=   '''AutoTokenizer'''


						def __init__(    self				:	int							,   a				:	int							,   a				:	Dict							,   a				:	List[str]						):





												"""simple docstring"""







												super().__init__(_lowerCAmelCase							,   _lowerCAmelCase						)

												# add QFormer tokenizer
												__snake_case      :    Union[str, Any]  			=qformer_tokenizer


						def __call__(    self				:	Dict							,   a				:	Optional[Any] = None							,   a				:	List[Any] = None							,   a				:	List[str] = True							,   a				:	Optional[Any] = False							,   a				:	Union[str, Any] = None							,   a				:	str = None							,   a				:	str = 0							,   a				:	Optional[int] = None							,   a				:	List[str] = None							,   a				:	List[str] = False							,   a				:	List[Any] = False							,   a				:	int = False							,   a				:	Optional[int] = False							,   a				:	Any = False							,   a				:	int = True							,   a				:	Tuple = None							,   **a				:	str							,   ):





												"""simple docstring"""







												if images is None and text is None:
																		raise ValueError('''You have to specify at least images or text.'''						)

												__snake_case      :    List[Any]  			=BatchFeature()

												if text is not None:
																		__snake_case      :    Optional[int]  			=self.tokenizer(
																		    text=_lowerCAmelCase							,   add_special_tokens=_lowerCAmelCase							,   padding=_lowerCAmelCase							,   truncation=_lowerCAmelCase							,   max_length=_lowerCAmelCase							,   stride=_lowerCAmelCase							,   pad_to_multiple_of=_lowerCAmelCase							,   return_attention_mask=_lowerCAmelCase							,   return_overflowing_tokens=_lowerCAmelCase							,   return_special_tokens_mask=_lowerCAmelCase							,   return_offsets_mapping=_lowerCAmelCase							,   return_token_type_ids=_lowerCAmelCase							,   return_length=_lowerCAmelCase							,   verbose=_lowerCAmelCase							,   return_tensors=_lowerCAmelCase							,   **_lowerCAmelCase							,   )
																		encoding.update(_lowerCAmelCase						)
																		__snake_case      :    List[Any]  			=self.qformer_tokenizer(
																		    text=_lowerCAmelCase							,   add_special_tokens=_lowerCAmelCase							,   padding=_lowerCAmelCase							,   truncation=_lowerCAmelCase							,   max_length=_lowerCAmelCase							,   stride=_lowerCAmelCase							,   pad_to_multiple_of=_lowerCAmelCase							,   return_attention_mask=_lowerCAmelCase							,   return_overflowing_tokens=_lowerCAmelCase							,   return_special_tokens_mask=_lowerCAmelCase							,   return_offsets_mapping=_lowerCAmelCase							,   return_token_type_ids=_lowerCAmelCase							,   return_length=_lowerCAmelCase							,   verbose=_lowerCAmelCase							,   return_tensors=_lowerCAmelCase							,   **_lowerCAmelCase							,   )
																		__snake_case      :    int  			=qformer_text_encoding.pop('''input_ids'''						)
																		__snake_case      :    Any  			=qformer_text_encoding.pop('''attention_mask'''						)

												if images is not None:
																		__snake_case      :    Tuple  			=self.image_processor(_lowerCAmelCase							,   return_tensors=_lowerCAmelCase						)
																		encoding.update(_lowerCAmelCase						)

												return encoding


						def _UpperCamelCase						(    self				:	Dict							,   *a				:	Dict							,   **a				:	Dict						):





												"""simple docstring"""







												return self.tokenizer.batch_decode(*_lowerCAmelCase							,   **_lowerCAmelCase						)


						def _UpperCamelCase						(    self				:	int							,   *a				:	str							,   **a				:	Optional[Any]						):





												"""simple docstring"""







												return self.tokenizer.decode(*_lowerCAmelCase							,   **_lowerCAmelCase						)


						@property
						# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
						def _UpperCamelCase						(    self				:	str						):





												"""simple docstring"""







												__snake_case      :    Any  			=self.tokenizer.model_input_names
												__snake_case      :    Union[str, Any]  			=self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


						def _UpperCamelCase						(    self				:	Dict							,   a				:	List[Any]							,   **a				:	int						):





												"""simple docstring"""







												if os.path.isfile(_lowerCAmelCase						):
																		raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''						)
												os.makedirs(_lowerCAmelCase							,   exist_ok=_lowerCAmelCase						)
												__snake_case      :    List[str]  			=os.path.join(_lowerCAmelCase							,   '''qformer_tokenizer'''						)
												self.qformer_tokenizer.save_pretrained(_lowerCAmelCase						)
												return super().save_pretrained(_lowerCAmelCase							,   **_lowerCAmelCase						)







						@classmethod
						def _UpperCamelCase						(    cls				:	Dict							,   a				:	int							,   **a				:	Tuple						):





												"""simple docstring"""







												__snake_case      :    Optional[Any]  			=AutoTokenizer.from_pretrained(_lowerCAmelCase							,   subfolder='''qformer_tokenizer'''						)
												__snake_case      :    Union[str, Any]  			=cls._get_arguments_from_pretrained(_lowerCAmelCase							,   **_lowerCAmelCase						)
												args.append(_lowerCAmelCase						)
												return cls(*_lowerCAmelCase						)










"""simple docstring"""





import os
from datetime import datetime as dt

from github import Github


UpperCamelCase_	:				List[str]										= [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def     __lowercase					(    )					->					Optional[Any]:
      __snake_case      :    List[Any]  			=Github(os.environ['''GITHUB_TOKEN'''] )
      __snake_case      :    int  			=g.get_repo('''huggingface/diffusers''' )
      __snake_case      :    Any  			=repo.get_issues(state='''open''' )

      for issue in open_issues:
            __snake_case      :    Dict  			=sorted(issue.get_comments()       ,		key=lambda a : i.created_at       ,		reverse=a )
            __snake_case      :    Dict  			=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()


