
'''simple docstring'''





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			str						,			lowerCAmelCase_	:			Tuple     ) ->							None:
        snake_case_    							=				vocab
        snake_case_    							=				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."     )
        snake_case_    							=				rjieba

  def 		_lowerCamelCase						(self	:			Optional[Any]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			NormalizedString     ) ->							List[NormalizedString]:
        snake_case_    							=				[]

        # 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:
                    snake_case_    							=				self.normalizers.normalize_str(lowerCAmelCase_     ).split()
                    for token in token_list:
                          if token:
                                snake_case_    							=				start + len(lowerCAmelCase_     )
                                splits.append(normalized_string[start:end]     )
                                snake_case_    							=				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	:			List[Any]						,			lowerCAmelCase_	:			PreTokenizedString     ) ->							Any:
        pretok.split(self.jieba_split     )


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       for t in [0, 50, 99]:
             self.check_over_forward(time_step=snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
