



"""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    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			List[str]         =   {
      'sld_warmup_steps': 1_5,
      'sld_guidance_scale': 2_0,
      'sld_threshold': 0.0,
      'sld_momentum_scale': 0.0,
      'sld_mom_beta': 0.0,
  }
  UpperCamelCase		:			Union[str, Any]         =   {
      '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,
  }
  UpperCamelCase		:			Union[str, Any]         =   {
      'sld_warmup_steps': 7,
      'sld_guidance_scale': 2_0_0_0,
      'sld_threshold': 0.0_25,
      'sld_momentum_scale': 0.5,
      'sld_mom_beta': 0.7,
  }
  UpperCamelCase		:			Tuple         =   {
      '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    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			Union[List[PIL.Image.Image], np.ndarray]
  UpperCamelCase		:			Optional[List[bool]]
  UpperCamelCase		:			Optional[Union[List[PIL.Image.Image], np.ndarray]]
  UpperCamelCase		:			Optional[str]


if is_transformers_available() and is_torch_available():
   from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
   from .safety_checker import SafeStableDiffusionSafetyChecker








"""simple docstring"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[str]						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =vocab
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =normalizers.BertNormalizer(
         clean_text=lowerCAmelCase    ,						handle_chinese_chars=lowerCAmelCase    ,						strip_accents=lowerCAmelCase    ,						lowercase=lowerCAmelCase    ,						)
     try:
        import rjieba
     except ImportError:
        raise ImportError(
            """You need to install rjieba to use RoFormerTokenizer. """
            """See https://pypi.org/project/rjieba/ for installation."""						)
     SCREAMING_SNAKE_CASE_:	Dict            =rjieba



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   NormalizedString						)       ->   List[NormalizedString]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]

     # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
     for token, start, end in self.jieba.tokenize(str(lowerCAmelCase						)    ,						hmm=lowerCAmelCase						):
        if token in self.vocab:
           splits.append(normalized_string[start:end]						)
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.normalizers.normalize_str(lowerCAmelCase						).split()
           for token in token_list:
              if token:
                 SCREAMING_SNAKE_CASE_:	Dict            =start + len(lowerCAmelCase						)
                 splits.append(normalized_string[start:end]						)
                 SCREAMING_SNAKE_CASE_:	Optional[Any]            =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

     return splits





  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   PreTokenizedString						)       ->   Tuple:


     '''simple docstring'''




     pretok.split(self.jieba_split						)



