
"""simple docstring"""


from datetime import datetime as dt
import os

from github import Github


_lowercase				 =					[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]






def    _snake_case					(		):
	A     = Github(os.environ['GITHUB_TOKEN']  )
	A     = g.get_repo('huggingface/transformers'  )
	A     = repo.get_issues(state='open'  )

	for issue in open_issues:
		A     = sorted([comment for comment in issue.get_comments()]						,   key=lambda snake_case__  : i.created_at						,   reverse=snake_case__  )
		A     = comments[0] if len(snake_case__  ) > 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'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['image_processor', 'tokenizer']
       _snake_case      :	int									=       'BlipImageProcessor'
       _snake_case      :	Tuple									=       ('BertTokenizer', 'BertTokenizerFast')





       def __init__(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			False
            super().__init__(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			self.image_processor





       def __call__(				self  :						int			,					lowerCAmelCase__  :						ImageInput = None			,					lowerCAmelCase__  :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Union[bool, str, PaddingStrategy] = False			,					lowerCAmelCase__  :						Union[bool, str, TruncationStrategy] = None			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						int = 0			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						Optional[bool] = None			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Union[str, TensorType]] = None			,					**lowerCAmelCase__  :						Optional[Any]			,					)       ->	BatchEncoding:

            '''simple docstring'''




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

            # Get only text
            if images is None:
                 _UpperCamelCase							       =			self.tokenizer
                 _UpperCamelCase							       =			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__			,					)
                 return text_encoding

            # add pixel_values
            _UpperCamelCase							       =			self.image_processor(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__	)

            if text is not None:
                 _UpperCamelCase							       =			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__			,					)
            else:
                 _UpperCamelCase							       =			None

            if text_encoding is not None:
                 encoding_image_processor.update(lowerCAmelCase__	)

            return encoding_image_processor





       def snake_case__		(				self  :						Any			,					*lowerCAmelCase__  :						Union[str, Any]			,					**lowerCAmelCase__  :						Dict	)       ->	List[str]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Tuple			,					*lowerCAmelCase__  :						int			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




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







       @property
       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.model_input_names
            _UpperCamelCase							       =			self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



