
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(CMStochasticIterativeScheduler,)
	_lowerCAmelCase							       =				10





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					A__  :	str         =							{
					    '''num_train_timesteps''': 201,
					    '''sigma_min''': 0.0_0_2,
					    '''sigma_max''': 8_0.0,
					}

					config.update(**UpperCamelCase__		)
					return config





	def   __snake_case				(			self		):
					A__  :	Tuple         =							10

					A__  :	Any         =							self.get_scheduler_config()
					A__  :	Tuple         =							self.scheduler_classes[0](**UpperCamelCase__		)

					scheduler.set_timesteps(UpperCamelCase__		)

					A__  :	Dict         =							scheduler.timesteps[0]
					A__  :	Dict         =							scheduler.timesteps[1]

					A__  :	List[str]         =							self.dummy_sample
					A__  :	str         =							0.1 * sample

					A__  :	Dict         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample
					A__  :	int         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample

					self.assertEqual(output_a.shape				,       sample.shape		)
					self.assertEqual(output_a.shape				,       output_a.shape		)





	def   __snake_case				(			self		):
					for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.scheduler_classes[0]
					A__  :	Dict         =							self.get_scheduler_config()
					A__  :	List[Any]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[Any]         =							1
					scheduler.set_timesteps(UpperCamelCase__		)
					A__  :	Optional[int]         =							scheduler.timesteps

					A__  :	Tuple         =							torch.manual_seed(0		)

					A__  :	Optional[int]         =							self.dummy_model()
					A__  :	Dict         =							self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(UpperCamelCase__		):
									# 1. scale model input
									A__  :	Tuple         =							scheduler.scale_model_input(UpperCamelCase__				,       UpperCamelCase__		)

									# 2. predict noise residual
									A__  :	Dict         =							model(UpperCamelCase__				,       UpperCamelCase__		)

									# 3. predict previous sample x_t-1
									A__  :	Optional[int]         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       generator=UpperCamelCase__		).prev_sample

									A__  :	Any         =							pred_prev_sample

					A__  :	Tuple         =							torch.sum(torch.abs(UpperCamelCase__		)		)
					A__  :	str         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_sum.item() - 1_9_2.7_6_1_4		) < 1e-2
					assert abs(result_mean.item() - 0.2_5_1_0		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.scheduler_classes[0]
					A__  :	Any         =							self.get_scheduler_config()
					A__  :	str         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Any         =							[106, 0]
					scheduler.set_timesteps(timesteps=UpperCamelCase__		)
					A__  :	int         =							scheduler.timesteps

					A__  :	Optional[int]         =							torch.manual_seed(0		)

					A__  :	int         =							self.dummy_model()
					A__  :	str         =							self.dummy_sample_deter * scheduler.init_noise_sigma

					for t in timesteps:
									# 1. scale model input
									A__  :	Any         =							scheduler.scale_model_input(UpperCamelCase__				,       UpperCamelCase__		)

									# 2. predict noise residual
									A__  :	Optional[Any]         =							model(UpperCamelCase__				,       UpperCamelCase__		)

									# 3. predict previous sample x_t-1
									A__  :	Union[str, Any]         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       generator=UpperCamelCase__		).prev_sample

									A__  :	Tuple         =							pred_prev_sample

					A__  :	List[Any]         =							torch.sum(torch.abs(UpperCamelCase__		)		)
					A__  :	str         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_sum.item() - 3_4_7.6_3_5_7		) < 1e-2
					assert abs(result_mean.item() - 0.4_5_2_7		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.scheduler_classes[0]
					A__  :	str         =							self.get_scheduler_config()
					A__  :	int         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[Any]         =							[39, 30, 12, 15, 0]

					with self.assertRaises(UpperCamelCase__				,       msg='''`timesteps` must be in descending order.'''		):
									scheduler.set_timesteps(timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.scheduler_classes[0]
					A__  :	Tuple         =							self.get_scheduler_config()
					A__  :	Optional[int]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[int]         =							[39, 30, 12, 1, 0]
					A__  :	Tuple         =							len(UpperCamelCase__		)

					with self.assertRaises(UpperCamelCase__				,       msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''		):
									scheduler.set_timesteps(num_inference_steps=UpperCamelCase__				,       timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.scheduler_classes[0]
					A__  :	int         =							self.get_scheduler_config()
					A__  :	Union[str, Any]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	str         =							[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    UpperCamelCase__				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
									scheduler.set_timesteps(timesteps=UpperCamelCase__		)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				A__  :	Optional[Any]         =							len(__UpperCamelCase       )

				for i in range(n - 1       ):
								for j in range(i + 1							,						__UpperCamelCase       ):
												if arr[i] > arr[j]:
																num_inversions += 1

				return num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				if len(__UpperCamelCase       ) <= 1:
								return arr, 0
				A__  :	Optional[int]         =							len(__UpperCamelCase       ) // 2
				A__  :	List[str]         =							arr[0:mid]
				A__  :	Union[str, Any]         =							arr[mid:]

				A__       ,							A__  :	List[Any]         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							_count_cross_inversions(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							inversion_p + inversions_q + cross_inversions
				return c, num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							[]
				A__  :	Tuple         =							0
				while i < len(__UpperCamelCase       ) and j < len(__UpperCamelCase       ):
								if p[i] > q[j]:
												# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
												# These are all inversions. The claim emerges from the
												# property that P is sorted.
												num_inversion += len(__UpperCamelCase       ) - i
												r.append(q[j]       )
												j += 1
								else:
												r.append(p[i]       )
												i += 1

				if i < len(__UpperCamelCase       ):
								r.extend(p[i:]       )
				else:
								r.extend(q[j:]       )

				return r, num_inversion

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[str]         =							[10, 2, 1, 5, 5, 2, 11]

				# this arr has 8 inversions:
				# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

				A__  :	int         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 8

				print('''number of inversions = '''							,						__UpperCamelCase       )

				# testing an array with zero inversion (a sorted arr_1)

				arr_a.sort()
				A__  :	Optional[Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )

				# an empty list should also have zero inversions
				A__  :	Union[str, Any]         =							[]
				A__  :	Union[str, Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Any         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()