



import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					'''▁'''

UpperCAmelCase							 =					{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

UpperCAmelCase							 =					{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

UpperCAmelCase							 =					{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

UpperCAmelCase							 =					{
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

UpperCAmelCase							 =					{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}

class  __snake_case      (   a__):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					["input_ids"]

							UpperCamelCase__				:      Union[str, Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Dict         =					PRETRAINED_INIT_CONFIGURATION
							UpperCamelCase__				:      List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      Optional[Any]         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      Dict         =					RESOURCE_FILES_NAMES


							def __init__(							self  ,  a_  ,  a_=None  ,  a_=False  ,  a_="utf8"  ,  a_="[UNK]"  ,  a_="[SEP]"  ,  a_="[PAD]"  ,  a_="[CLS]"  ,  a_="[MASK]"  ,  a_ = None  ,  **a_  ,  ):
											# Mask token behave like a normal word, i.e. include the space before it and
											# is included in the raw text, there should be a match in a non-normalized sentence.

											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs
											super().__init__(
											    do_lower_case=lowerCAmelCase__  ,  unk_token=lowerCAmelCase__  ,  sep_token=lowerCAmelCase__  ,  pad_token=lowerCAmelCase__  ,  cls_token=lowerCAmelCase__  ,  mask_token=lowerCAmelCase__  ,  vocab_file=lowerCAmelCase__  ,  encoding=lowerCAmelCase__  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **lowerCAmelCase__  ,  )
											a__				      =		do_lower_case
											a__				      =		sentencepiece_model_ckpt
											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(lowerCAmelCase__		)

											# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
											if vocab_file is not None:
															a__				      =		self.load_vocab(filepath=lowerCAmelCase__		)
											else:
															a__				      =		{self.sp_model.id_to_piece(lowerCAmelCase__		): id for id in range(self.sp_model.get_piece_size()		)}
											a__				      =		{v: k for k, v in self.vocab.items()}


							def _a      (							self  ,  a_		):
											if text is None:
															return None

											a__				      =		self.tokenize(lowerCAmelCase__		)
											a__				      =		"", []

											for i, ch in enumerate(lowerCAmelCase__		):
															if ch in self.SP_CHAR_MAPPING:
																			a__				      =		self.SP_CHAR_MAPPING.get(lowerCAmelCase__		)
															else:
																			a__				      =		unicodedata.normalize("""NFKC"""  ,  lowerCAmelCase__		)
															if self.is_whitespace(lowerCAmelCase__		):
																			continue
															normalized_text += ch
															char_mapping.extend([i] * len(lowerCAmelCase__		)		)

											a__				      =		normalized_text, [], 0

											if self.do_lower_case:
															a__				      =		text.lower()

											for token in split_tokens:
															if token[:1] == "▁":
																			a__				      =		token[1:]
															a__				      =		text[offset:].index(lowerCAmelCase__		) + offset
															a__				      =		start + len(lowerCAmelCase__		)

															token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)		)
															a__				      =		end
											return token_mapping


							@property
							def _a      (							self		):
											return len(self.vocab		)


							def _a      (							self		):
											return dict(self.vocab  ,  **self.added_tokens_encoder		)


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

											# for backward compatibility
											if not hasattr(self  ,  """sp_model_kwargs"""		):
															a__				      =		{}

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.sentencepiece_model_ckpt		)


							def _a      (							self  ,  a_		):
											return "".join((self.SP_CHAR_MAPPING.get(lowerCAmelCase__  ,  lowerCAmelCase__		) for c in text)		)


							def _a      (							self  ,  a_  ,  a_=False  ,  a_=64  ,  a_=0.1		):

											if self.sp_model_kwargs.get("""enable_sampling"""		) is True:
															a__				      =		True
											if self.sp_model_kwargs.get("""alpha"""		) is not None:
															a__				      =		self.sp_model_kwargs.get("""alpha"""		)
											if self.sp_model_kwargs.get("""nbest_size"""		) is not None:
															a__				      =		self.sp_model_kwargs.get("""nbest_size"""		)

											if not enable_sampling:
															a__				      =		self.sp_model.EncodeAsPieces(lowerCAmelCase__		)
											else:
															a__				      =		self.sp_model.SampleEncodeAsPieces(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__		)
											a__				      =		[]
											for pi, piece in enumerate(lowerCAmelCase__		):
															if piece == SPIECE_UNDERLINE:
																			if not pieces[pi + 1].startswith(lowerCAmelCase__		) and pi != 0:
																							new_pieces.append(lowerCAmelCase__		)
																							continue
																			else:
																							continue
															a__				      =		0
															for i, chunk in enumerate(lowerCAmelCase__		):
																			if chunk == SPIECE_UNDERLINE:
																							continue
																			if self.is_ch_char(lowerCAmelCase__		) or self.is_punct(lowerCAmelCase__		):
																							if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																											new_pieces.append(piece[lst_i:i]		)
																							new_pieces.append(lowerCAmelCase__		)
																							a__				      =		i + 1
																			elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
																							if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																											new_pieces.append(piece[lst_i:i]		)
																							a__				      =		i
																			elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
																							if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																											new_pieces.append(piece[lst_i:i]		)
																							a__				      =		i
															if len(lowerCAmelCase__		) > lst_i:
																			new_pieces.append(piece[lst_i:]		)
											return new_pieces


							def _a      (							self  ,  a_		):
											a__				      =		"".join(lowerCAmelCase__		).replace(lowerCAmelCase__  ,  """ """		).strip()
											return out_string


							def _a      (							self  ,  a_		):
											a__				      =		self.convert_ids_to_tokens(lowerCAmelCase__		)
											a__				      =		"".join(lowerCAmelCase__		).replace(lowerCAmelCase__  ,  """ """		).strip()
											return out_string


							def _a      (							self  ,  a_		):
											return self.vocab.get(lowerCAmelCase__  ,  self.vocab.get(self.unk_token		)		)


							def _a      (							self  ,  a_		):
											return self.reverse_vocab.get(lowerCAmelCase__  ,  self.unk_token		)


							def _a      (							self  ,  a_  ,  a_=None		):
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											a__				      =		[self.cls_token_id]
											a__				      =		[self.sep_token_id]
											return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


							def _a      (							self  ,  a_  ,  a_=None		):
											if offset_mapping_a is None:
															return [(0, 0)] + offset_mapping_a + [(0, 0)]

											return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]


							def _a      (							self  ,  a_  ,  a_=None  ,  a_=False		):

											if already_has_special_tokens:
															if token_ids_a is not None:
																			raise ValueError(
																			    """You should not supply a second sequence if the provided sequence of """
																			    """ids is already formatted with special tokens for the model."""		)
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
															return [1] + ([0] * len(lowerCAmelCase__		)) + [1, 1] + ([0] * len(lowerCAmelCase__		)) + [1]
											return [1] + ([0] * len(lowerCAmelCase__		)) + [1]


							def _a      (							self  ,  a_  ,  a_ = None		):
											# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
											if token_ids_a is None:
															# [CLS] X [SEP]
															return (len(lowerCAmelCase__		) + 2) * [0]

											# [CLS] A [SEP] [SEP] B [SEP]
											return [0] * (len(lowerCAmelCase__		) + 1) + [1] * (len(lowerCAmelCase__		) + 3)


							def _a      (							self  ,  a_		):
											if "\u4e00" <= char <= "\u9fff":
															return True
											return False


							def _a      (							self  ,  a_		):
											if ("a" <= char <= "z") or ("A" <= char <= "Z"):
															return True
											return False


							def _a      (							self  ,  a_		):
											if char in ",;:.?!~，；：。？！《》【】":
															return True
											return False


							def _a      (							self  ,  a_		):
											if char == " " or char == "\t" or char == "\n" or char == "\r":
															return True
											if len(lowerCAmelCase__		) == 1:
															a__				      =		unicodedata.category(lowerCAmelCase__		)
															if cat == "Zs":
																			return True
											return False


							def _a      (							self  ,  a_		):
											a__				      =		{}
											with io.open(lowerCAmelCase__  ,  """r"""  ,  encoding="""utf-8"""		) as f:
															for index, line in enumerate(lowerCAmelCase__		):
																			a__				      =		line.rstrip("""\n"""		)
																			a__				      =		int(lowerCAmelCase__		)

											return token_to_idx







							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		0
											if os.path.isdir(lowerCAmelCase__		):
															a__				      =		os.path.join(
															    lowerCAmelCase__  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
											else:
															a__				      =		(filename_prefix + "-" if filename_prefix else "") + save_directory
											with open(lowerCAmelCase__  ,  """w"""  ,  encoding="""utf-8"""		) as writer:
															for token, token_index in sorted(self.vocab.items()  ,  key=lambda a_		: kv[1]		):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																							    """ Please check that the vocabulary is not corrupted!"""		)
																							a__				      =		token_index
																			writer.write(token + """\n"""		)
																			index += 1

											a__				      =		os.path.join(lowerCAmelCase__  ,  """sentencepiece.bpe.model"""		)
											with open(lowerCAmelCase__  ,  """wb"""		) as fi:
															a__				      =		self.sp_model.serialized_model_proto()
															fi.write(lowerCAmelCase__		)

											return (vocab_file,)






import os
from datetime import datetime as dt

from github import Github


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

def 			A_      (	):

				"""simple docstring"""
				a__				      =		Github(os.environ["""GITHUB_TOKEN"""]		)
				a__				      =		g.get_repo("""huggingface/diffusers"""		)
				a__				      =		repo.get_issues(state="""open"""		)

				for issue in open_issues:
								a__				      =		sorted(issue.get_comments() ,		key=lambda __a		: i.created_at ,		reverse=__a		)
								a__				      =		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()

