


"""simple docstring"""






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       (VQDiffusionScheduler,)
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  {
					    '''num_vec_classes''': 40_97,
					    '''num_train_timesteps''': 1_00,
					}

					config.update(**SCREAMING_SNAKE_CASE__      )
					return config
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  4
					_UpperCamelCase				:Optional[Any]				    =  8
					_UpperCamelCase				:List[str]				    =  8

					_UpperCamelCase				:Optional[Any]				    =  torch.randint(0				,							SCREAMING_SNAKE_CASE__				,							(batch_size, height * width)      )

					return sample
		@property
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					assert False
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  int:

					"""simple docstring"""



					def model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__      ):
								_UpperCamelCase    ,			_UpperCamelCase				:Optional[int]				    =  sample.shape
								_UpperCamelCase				:int				    =  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      )			->  List[str]:

					"""simple docstring"""



					for timesteps in [2, 5, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					for num_vec_classes in [5, 1_00, 10_00, 40_00]:
								self.check_over_configs(num_vec_classes=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



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





		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					pass





"""simple docstring"""






class     A:

		"""simple docstring"""





		def __init__(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  []
					_UpperCamelCase				:Dict				    =  0
					_UpperCamelCase				:List[Any]				    =  0
		def __str__(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  '''<''' + str(self.entries      )[1:-1] + '''>'''
					return printed
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Tuple:

					"""simple docstring"""



					self.entries.append(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  self.length + 1
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  self.length - 1
					_UpperCamelCase				:Optional[int]				    =  self.entries[self.front]
					# self.front-=1
					# self.entries = self.entries[self.front:]
					_UpperCamelCase				:Tuple				    =  self.entries[1:]
					return dequeued
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					for _ in range(SCREAMING_SNAKE_CASE__      ):
								self.put(self.get()      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					return self.entries[0]
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					return self.length

