


'''simple docstring'''



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 						_snake_case   (       tf.keras.layers.Layer		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								super().__init__()
								UpperCAmelCase__					:   int       				=       pad_token_id
								UpperCAmelCase__					:   List[Any]       				=       max_length
								UpperCAmelCase__					:   Optional[int]       				=       vocab
								UpperCAmelCase__					:   str       				=       merges
								UpperCAmelCase__					:   Dict       				=       BytePairTokenizer(_lowerCamelCase							,							_lowerCamelCase							,							sequence_length=_lowerCamelCase)




					@classmethod
					def 			snake_case__  ( cls							,							_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       [""" """.join(_lowerCamelCase) for m in tokenizer.bpe_ranks.keys()]
								UpperCAmelCase__					:   Tuple       				=       tokenizer.get_vocab()
								return cls(_lowerCamelCase							,							_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase)




					@classmethod
					def 			snake_case__  ( cls							,							_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Tuple       				=       GPTaTokenizer.from_pretrained(_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase)
								return cls.from_tokenizer(_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase)




					@classmethod
					def 			snake_case__  ( cls							,							_lowerCamelCase):
								return cls(**_lowerCamelCase)




					def 			snake_case__  ( self):
								return {
								    "vocab": self.vocab,
								    "merges": self.merges,
								    "max_length": self.max_length,
								    "pad_token_id": self.pad_token_id,
								}




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.tf_tokenizer(_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       tf.ones_like(_lowerCamelCase)

								if self.pad_token_id is not None:
											# pad the tokens up to max length
											UpperCAmelCase__					:   str       				=       max_length if max_length is not None else self.max_length

											if max_length is not None:
														UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       pad_model_inputs(
														    _lowerCamelCase							,							max_seq_length=_lowerCamelCase							,							pad_value=self.pad_token_id)

								return {"attention_mask": attention_mask, "input_ids": input_ids}



'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


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







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       Github(os.environ["""GITHUB_TOKEN"""]   )
			UpperCAmelCase__					:   Any       				=       g.get_repo("""huggingface/diffusers"""   )
			UpperCAmelCase__					:   Optional[int]       				=       repo.get_issues(state="""open"""   )

			for issue in open_issues:
						UpperCAmelCase__					:   Any       				=       sorted(issue.get_comments()	, key=lambda UpperCamelCase__   : i.created_at	, reverse=UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       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 >= 3_0
						    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 > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    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()