code: 
  
  
  
  """simple docstring"""
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ={
           '''sld_warmup_steps''': 1_5,
           '''sld_guidance_scale''': 2_0,
           '''sld_threshold''': 0.0,
           '''sld_momentum_scale''': 0.0,
           '''sld_mom_beta''': 0.0,
       }
       a__             ={
           '''sld_warmup_steps''': 1_0,
           '''sld_guidance_scale''': 1_0_0_0,
           '''sld_threshold''': 0.01,
           '''sld_momentum_scale''': 0.3,
           '''sld_mom_beta''': 0.4,
       }
       a__             ={
           '''sld_warmup_steps''': 7,
           '''sld_guidance_scale''': 2_0_0_0,
           '''sld_threshold''': 0.025,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
       a__             ={
           '''sld_warmup_steps''': 0,
           '''sld_guidance_scale''': 5_0_0_0,
           '''sld_threshold''': 1.0,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
  
  
  
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =42
       a__             =42
       a__             =42
       a__             =42
  
  
  if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCAmelCase					:Dict  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCAmelCase					:Any  =      {
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCAmelCase					:str  =      {
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_INIT_CONFIGURATION
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =MPNetTokenizer
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
           super().__init__(
               A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)
  
           _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
           if (
               pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
           ):
               _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
               _UpperCAmelCase							:						List[Any]       							=					do_lower_case
               _UpperCAmelCase							:						List[Any]       							=					strip_accents
               _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)
  
           _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           if self._mask_token is None:
               if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''	)
               return None
           return str(self._mask_token	)
  
  
  
  
  
       @mask_token.setter
       def 				__lowerCAmelCase			(      self ,			A	)							->      str:
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
           _UpperCAmelCase							:						List[Any]       							=					value
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
           _UpperCAmelCase							:						List[str]       							=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output
  
           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						int       							=					[self.sep_token_id]
           _UpperCAmelCase							:						int       							=					[self.cls_token_id]
  
           if token_ids_a is None:
               return len(cls + token_ids_a + sep	) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
           return tuple(A	)
  
  
  