


"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class      _UpperCAmelCase					(				__lowercase							):






		'''simple docstring'''

		lowerCamelCase__           =(DPMSolverMultistepScheduler,)
		lowerCamelCase__           =(('num_inference_steps', 25),)



		def 							SCREAMING_SNAKE_CASE    (self							,			**a_   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							{
			    '''num_train_timesteps''': 10_00,
			    '''beta_start''': 0.0001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			    '''solver_order''': 2,
			    '''prediction_type''': '''epsilon''',
			    '''thresholding''': False,
			    '''sample_max_value''': 1.0,
			    '''algorithm_type''': '''dpmsolver++''',
			    '''solver_type''': '''midpoint''',
			    '''lower_order_final''': False,
			    '''lambda_min_clipped''': -float('''inf'''   ),
			    '''variance_type''': None,
			}

			config.update(**A_   )
			return config



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=0							,			**a_   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							dict(self.forward_default_kwargs   )
			__snake_case      :		Union[str, Any]         =							kwargs.pop('''num_inference_steps'''							,			A_   )
			__snake_case      :		Tuple         =							self.dummy_sample
			__snake_case      :		str         =							0.1 * sample
			__snake_case      :		Any         =							[residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
				__snake_case      :		Tuple         =							self.get_scheduler_config(**A_   )
				__snake_case      :		int         =							scheduler_class(**A_   )
				scheduler.set_timesteps(A_   )
				# copy over dummy past residuals
				__snake_case      :		int         =							dummy_past_residuals[: scheduler.config.solver_order]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(A_   )
					__snake_case      :		List[str]         =							scheduler_class.from_pretrained(A_   )
					new_scheduler.set_timesteps(A_   )
					# copy over dummy past residuals
					__snake_case      :		Any         =							dummy_past_residuals[: new_scheduler.config.solver_order]

				__snake_case      ,				__snake_case      :		int         =							sample, sample
				for t in range(A_							,			time_step + scheduler.config.solver_order + 1   ):
					__snake_case      :		Any         =							scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample
					__snake_case      :		List[str]         =							new_scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample

					assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=0							,			**a_   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							dict(self.forward_default_kwargs   )
			__snake_case      :		Union[str, Any]         =							kwargs.pop('''num_inference_steps'''							,			A_   )
			__snake_case      :		Dict         =							self.dummy_sample
			__snake_case      :		Tuple         =							0.1 * sample
			__snake_case      :		Any         =							[residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
				__snake_case      :		Any         =							self.get_scheduler_config()
				__snake_case      :		List[str]         =							scheduler_class(**A_   )
				scheduler.set_timesteps(A_   )

				# copy over dummy past residuals (must be after setting timesteps)
				__snake_case      :		Optional[int]         =							dummy_past_residuals[: scheduler.config.solver_order]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(A_   )
					__snake_case      :		Tuple         =							scheduler_class.from_pretrained(A_   )
					# copy over dummy past residuals
					new_scheduler.set_timesteps(A_   )

					# copy over dummy past residual (must be after setting timesteps)
					__snake_case      :		Dict         =							dummy_past_residuals[: new_scheduler.config.solver_order]

				__snake_case      :		Tuple         =							scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample
				__snake_case      :		Optional[Any]         =							new_scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample

				assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler outputs are not identical"



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=None							,			**a_   ):





			'''simple docstring'''

			if scheduler is None:
				__snake_case      :		Optional[Any]         =							self.scheduler_classes[0]
				__snake_case      :		str         =							self.get_scheduler_config(**A_   )
				__snake_case      :		List[Any]         =							scheduler_class(**A_   )

			__snake_case      :		Dict         =							10
			__snake_case      :		Any         =							self.dummy_model()
			__snake_case      :		Any         =							self.dummy_sample_deter
			scheduler.set_timesteps(A_   )

			for i, t in enumerate(scheduler.timesteps   ):
				__snake_case      :		List[Any]         =							model(A_							,			A_   )
				__snake_case      :		Optional[Any]         =							scheduler.step(A_							,			A_							,			A_   ).prev_sample

			return sample



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							dict(self.forward_default_kwargs   )

			__snake_case      :		Dict         =							kwargs.pop('''num_inference_steps'''							,			A_   )

			for scheduler_class in self.scheduler_classes:
				__snake_case      :		str         =							self.get_scheduler_config()
				__snake_case      :		Union[str, Any]         =							scheduler_class(**A_   )

				__snake_case      :		str         =							self.dummy_sample
				__snake_case      :		Union[str, Any]         =							0.1 * sample

				if num_inference_steps is not None and hasattr(A_							,			'''set_timesteps'''   ):
					scheduler.set_timesteps(A_   )
				elif num_inference_steps is not None and not hasattr(A_							,			'''set_timesteps'''   ):
					__snake_case      :		List[Any]         =							num_inference_steps

				# copy over dummy past residuals (must be done after set_timesteps)
				__snake_case      :		int         =							[residual + 0.2, residual + 0.15, residual + 0.10]
				__snake_case      :		Any         =							dummy_past_residuals[: scheduler.config.solver_order]

				__snake_case      :		List[Any]         =							scheduler.timesteps[5]
				__snake_case      :		int         =							scheduler.timesteps[6]

				__snake_case      :		List[str]         =							scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample
				__snake_case      :		List[Any]         =							scheduler.step(A_							,			A_							,			A_							,			**A_   ).prev_sample

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



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			for timesteps in [25, 50, 1_00, 9_99, 10_00]:
				self.check_over_configs(num_train_timesteps=A_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.check_over_configs(thresholding=A_   )
			for order in [1, 2, 3]:
				for solver_type in ["midpoint", "heun"]:
					for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample"]:
							self.check_over_configs(
							    thresholding=A_							,			prediction_type=A_							,			sample_max_value=A_							,			algorithm_type='''dpmsolver++'''							,			solver_order=A_							,			solver_type=A_							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=A_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
				for solver_type in ["midpoint", "heun"]:
					for order in [1, 2, 3]:
						for prediction_type in ["epsilon", "sample"]:
							if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
								if order == 3:
									continue
							else:
								self.check_over_configs(
								    solver_order=A_							,			solver_type=A_							,			prediction_type=A_							,			algorithm_type=A_							,			)
							__snake_case      :		Optional[int]         =							self.full_loop(
							    solver_order=A_							,			solver_type=A_							,			prediction_type=A_							,			algorithm_type=A_							,			)
							assert not torch.isnan(A_   ).any(), "Samples have nan numbers"



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.check_over_configs(lower_order_final=A_   )
			self.check_over_configs(lower_order_final=A_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.check_over_configs(lambda_min_clipped=-float('''inf'''   )   )
			self.check_over_configs(lambda_min_clipped=-5.1   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.check_over_configs(variance_type=A_   )
			self.check_over_configs(variance_type='''learned_range'''   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
				self.check_over_forward(num_inference_steps=A_							,			time_step=0   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.full_loop()
			__snake_case      :		List[Any]         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 0.3301   ) < 1E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							self.full_loop(thresholding=A_							,			dynamic_thresholding_ratio=0.87							,			sample_max_value=0.5   )
			__snake_case      :		List[Any]         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 1.1364   ) < 1E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							self.full_loop(prediction_type='''v_prediction'''   )
			__snake_case      :		Tuple         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 0.2251   ) < 1E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Dict         =							self.full_loop(prediction_type='''v_prediction'''							,			use_karras_sigmas=A_   )
			__snake_case      :		Dict         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 0.2096   ) < 1E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
			__snake_case      :		Union[str, Any]         =							self.full_loop(scheduler=A_   )
			__snake_case      :		List[str]         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 0.3301   ) < 1E-3

			__snake_case      :		Optional[Any]         =							DPMSolverSinglestepScheduler.from_config(scheduler.config   )
			__snake_case      :		Tuple         =							UniPCMultistepScheduler.from_config(scheduler.config   )
			__snake_case      :		str         =							DEISMultistepScheduler.from_config(scheduler.config   )
			__snake_case      :		Optional[Any]         =							DPMSolverMultistepScheduler.from_config(scheduler.config   )

			__snake_case      :		Any         =							self.full_loop(scheduler=A_   )
			__snake_case      :		Optional[int]         =							torch.mean(torch.abs(A_   )   )

			assert abs(result_mean.item() - 0.3301   ) < 1E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.scheduler_classes[0]
			__snake_case      :		Optional[Any]         =							self.get_scheduler_config(thresholding=A_							,			dynamic_thresholding_ratio=0   )
			__snake_case      :		Union[str, Any]         =							scheduler_class(**A_   )

			__snake_case      :		Any         =							10
			__snake_case      :		str         =							self.dummy_model()
			__snake_case      :		Union[str, Any]         =							self.dummy_sample_deter.half()
			scheduler.set_timesteps(A_   )

			for i, t in enumerate(scheduler.timesteps   ):
				__snake_case      :		Union[str, Any]         =							model(A_							,			A_   )
				__snake_case      :		Union[str, Any]         =							scheduler.step(A_							,			A_							,			A_   ).prev_sample

			assert sample.dtype == torch.floataa



		def 							SCREAMING_SNAKE_CASE    (self							,			**a_   ):





			'''simple docstring'''

			for scheduler_class in self.scheduler_classes:
				__snake_case      :		str         =							self.get_scheduler_config(**A_   )
				__snake_case      :		Optional[int]         =							scheduler_class(**A_   )

				scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
				assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps






from hashlib import mda, shaaaa

snake_case__						      =			(shaaaa, mda)



class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_ = 8    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           _lowerCamelCase           =					size



     def 		UpperCamelCase_    (       self					,					A_    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           self.bitarray |= h



     def 		UpperCamelCase_    (       self					,					A_    )			->					bool:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           return (h & self.bitarray) == h



     def __contains__(       self					,					A_    )			->					bool:






           """simple docstring"""
           return self.exists(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					bin(A_    )[2:]
           return res.zfill(self.size    )



     @property
     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           return self.format_bin(self.bitarray    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           for func in HASH_FUNCTIONS:
                 _lowerCamelCase           =					(
                     int.from_bytes(func(value.encode()    ).digest()					,					'''little'''    ) % self.size
                 )
                 res |= 2**position
           return res



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           return self.format_bin(self.hash_(A_    )    )





     @property
     def 		UpperCamelCase_    (       self    )			->					float:






           """simple docstring"""
           _lowerCamelCase           =					bin(self.bitarray    ).count('''1'''    )
           return (n_ones / self.size) ** len(A_    )