






"""simple docstring"""







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCAmelCase(  lowerCamelCase__				):




						"""simple docstring"""




						__lowerCamelCase										=   (DDPMScheduler,)




						def 						UpperCAmelCase_			(  self			,    **snake_case__     ):
												'''simple docstring'''
												lowercase__							: Dict=			{
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.00_01,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**SCREAMING_SNAKE_CASE__     )
												return config




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,    [0.0_02, 0.02, 0.2, 2]     ):
																		self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__			,    beta_end=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__     )
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=SCREAMING_SNAKE_CASE__			,    prediction_type=SCREAMING_SNAKE_CASE__			,    sample_max_value=SCREAMING_SNAKE_CASE__			,    )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



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




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: str=			self.scheduler_classes[0]
												lowercase__							: Any=			self.get_scheduler_config()
												lowercase__							: Optional[Any]=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_09_79     )     ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1e-5




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: int=			self.scheduler_classes[0]
												lowercase__							: List[Any]=			self.get_scheduler_config()
												lowercase__							: Optional[Any]=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: Any=			len(SCREAMING_SNAKE_CASE__     )

												lowercase__							: List[str]=			self.dummy_model()
												lowercase__							: str=			self.dummy_sample_deter
												lowercase__							: List[str]=			torch.manual_seed(0     )

												for t in reversed(range(SCREAMING_SNAKE_CASE__     )     ):
																		# 1. predict noise residual
																		lowercase__							: Tuple=			model(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__     )

																		# 2. predict previous mean of sample x_t-1
																		lowercase__							: int=			scheduler.step(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    generator=SCREAMING_SNAKE_CASE__     ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		lowercase__							: Any=			pred_prev_sample

												lowercase__							: Optional[int]=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
												lowercase__							: List[str]=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

												assert abs(result_sum.item() - 258.9606     ) < 1e-2
												assert abs(result_mean.item() - 0.33_72     ) < 1e-3




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Dict=			self.scheduler_classes[0]
												lowercase__							: Union[str, Any]=			self.get_scheduler_config(prediction_type="v_prediction"     )
												lowercase__							: str=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: List[str]=			len(SCREAMING_SNAKE_CASE__     )

												lowercase__							: int=			self.dummy_model()
												lowercase__							: Any=			self.dummy_sample_deter
												lowercase__							: Tuple=			torch.manual_seed(0     )

												for t in reversed(range(SCREAMING_SNAKE_CASE__     )     ):
																		# 1. predict noise residual
																		lowercase__							: Optional[int]=			model(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__     )

																		# 2. predict previous mean of sample x_t-1
																		lowercase__							: Union[str, Any]=			scheduler.step(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    generator=SCREAMING_SNAKE_CASE__     ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		lowercase__							: Union[str, Any]=			pred_prev_sample

												lowercase__							: Union[str, Any]=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__     )     )
												lowercase__							: Optional[Any]=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__     )     )

												assert abs(result_sum.item() - 202.0296     ) < 1e-2
												assert abs(result_mean.item() - 0.26_31     ) < 1e-3




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[str]=			self.scheduler_classes[0]
												lowercase__							: str=			self.get_scheduler_config()
												lowercase__							: int=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: Optional[int]=			[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )

												lowercase__							: Tuple=			scheduler.timesteps

												for i, timestep in enumerate(SCREAMING_SNAKE_CASE__     ):
																		if i == len(SCREAMING_SNAKE_CASE__     ) - 1:
																								lowercase__							: Optional[Any]=			-1
																		else:
																								lowercase__							: Optional[int]=			timesteps[i + 1]

																		lowercase__							: str=			scheduler.previous_timestep(SCREAMING_SNAKE_CASE__     )
																		lowercase__							: Optional[int]=			prev_t.item()

																		self.assertEqual(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Any=			self.scheduler_classes[0]
												lowercase__							: Union[str, Any]=			self.get_scheduler_config()
												lowercase__							: Dict=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: Union[str, Any]=			[100, 87, 50, 51, 0]

												with self.assertRaises(SCREAMING_SNAKE_CASE__			,    msg="`custom_timesteps` must be in descending order."     ):
																		scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Dict=			self.scheduler_classes[0]
												lowercase__							: int=			self.get_scheduler_config()
												lowercase__							: Dict=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: Optional[int]=			[100, 87, 50, 1, 0]
												lowercase__							: Optional[int]=			len(SCREAMING_SNAKE_CASE__     )

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




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			self.scheduler_classes[0]
												lowercase__							: List[str]=			self.get_scheduler_config()
												lowercase__							: Optional[Any]=			scheduler_class(**SCREAMING_SNAKE_CASE__     )

												lowercase__							: Optional[int]=			[scheduler.config.num_train_timesteps]

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










"""simple docstring"""






UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""prompt""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset([])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""image"""])

UpperCamelCase__				:str			   =	frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""image"""])

UpperCamelCase__				:Optional[int]			   =	frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:int			   =	frozenset(["""prompt""", """image""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

UpperCamelCase__				:Dict			   =	frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Dict			   =	frozenset(["""image""", """mask_image"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""example_image""", """image""", """mask_image"""])

UpperCamelCase__				:List[str]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Any			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[Any]			   =	frozenset([])

UpperCamelCase__				:Optional[Any]			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[int]			   =	frozenset([])

UpperCamelCase__				:str			   =	frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""prompt""", """negative_prompt"""])
UpperCamelCase__				:Tuple			   =	frozenset(["""input_tokens"""])

UpperCamelCase__				:int			   =	frozenset(["""input_tokens"""])

