import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 		a__						( A__ ):
					A     	=							(VQDiffusionScheduler,)

					def     __UpperCamelCase       (   self				:	Tuple,**_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   {
							    "num_vec_classes": 4097,
							    "num_train_timesteps": 100,
							}

							config.update(**_A   )
							return config

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   4
							SCREAMING_SNAKE_CASE_  :		Any    				=   8
							SCREAMING_SNAKE_CASE_  :		Dict    				=   8

							SCREAMING_SNAKE_CASE_  :		str    				=   torch.randint(0,_A,(batch_size, height * width)   )

							return sample

					@property
					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							assert False

					def     __UpperCamelCase       (   self				:	Any,_A				:	List[Any]   ):







							"""simple docstring"""






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

							return model

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							for timesteps in [2, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							for num_vec_classes in [5, 100, 1000, 4000]:
									self.check_over_configs(num_vec_classes=_A   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






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






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							pass

from __future__ import annotations

import sys




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n < 0:
				raise ValueError("Negative arguments are not supported"     )
		return _fib(lowerCAmelCase     )[0]




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   _fib(n // 2     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__lowerCamelCase :     str          =  int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')
