



"""simple docstring"""

from functools import lru_cache
@lru_cache
def    __snake_case       (  __A			:	int    )    ->					int:






				'''simple docstring'''
				if not isinstance(__A       ,		__A    ):
								raise TypeError('k must be an integer.'    )
				if k < 0:
								raise ValueError('k integer must be greater or equal to zero.'    )
				if k == 0:
								return 0
				elif k == 1:
								return 1
				else:
								return fibonacci(k - 1    ) + fibonacci(k - 2    )
def    __snake_case       (  __A			:	list       ,		__A			:	int    )    ->					int:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    len(__A    )
				# Find m such that F_m >= n where F_i is the i_th fibonacci number.
				SCREAMING_SNAKE_CASE	:       str 							=    0
				while True:
								if fibonacci(__A    ) >= len_list:
												SCREAMING_SNAKE_CASE	:       Any 							=    i
												break
								i += 1
				SCREAMING_SNAKE_CASE	:       int 							=    0
				while fibb_k > 0:
								SCREAMING_SNAKE_CASE	:       str 							=    min(
								    offset + fibonacci(fibb_k - 1    )       ,		len_list - 1    )  # Prevent out of range
								SCREAMING_SNAKE_CASE	:       Tuple 							=    arr[index_k]
								if item_k_1 == val:
												return index_k
								elif val < item_k_1:
												fibb_k -= 1
								elif val > item_k_1:
												offset += fibonacci(fibb_k - 1    )
												fibb_k -= 2
				else:
								return -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


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

					return model





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Dict:





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  int:





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


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







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





