



'''simple docstring'''




from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		vocab
					__SCREAMING_SNAKE_CASE	:		str   	=		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	:		List[Any]   	=		rjieba

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			NormalizedString						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		[]

					# 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	:		Optional[int]   	=		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	:		int   	=		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 UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			PreTokenizedString						):





					"""simple docstring"""






					pretok.split(self.jieba_split						)




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass