

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				(DDPMParallelScheduler,)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      **__A						:     int							):
		__A :			int		    =			{
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0_0_0_1,
		    """beta_end""": 0.0_2,
		    """beta_schedule""": """linear""",
		    """variance_type""": """fixed_small""",
		    """clip_sample""": True,
		}

		config.update(**__A							)
		return config




	def        lowerCAmelCase_       (     self						:     List[str]							):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,      [0.0_0_2, 0.0_2, 0.2, 2]							):
			self.check_over_configs(beta_start=__A					,      beta_end=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for variance in ["fixed_small", "fixed_large", "other"]:
			self.check_over_configs(variance_type=__A							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		self.check_over_configs(thresholding=__A							)
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "sample", "v_prediction"]:
				self.check_over_configs(
				    thresholding=__A					,      prediction_type=__A					,      sample_max_value=__A					,      )




	def        lowerCAmelCase_       (     self						:     int							):
		for prediction_type in ["epsilon", "sample", "v_prediction"]:
			self.check_over_configs(prediction_type=__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		for t in [0, 500, 999]:
			self.check_over_forward(time_step=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			Tuple		    =			self.get_scheduler_config()
		__A :			int		    =			scheduler_class(**__A							)

		assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1e-5




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			List[Any]		    =			scheduler_class(**__A							)

		__A :			Any		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Union[str, Any]		    =			self.dummy_sample_deter
		__A :			List[Any]		    =			self.dummy_sample_deter + 0.1
		__A :			Optional[Any]		    =			self.dummy_sample_deter - 0.1

		__A :			Dict		    =			samplea.shape[0]
		__A :			Optional[int]		    =			torch.stack([samplea, samplea, samplea]					,      dim=0							)
		__A :			Dict		    =			torch.arange(__A							)[0:3, None].repeat(1					,      __A							)

		__A :			Optional[Any]		    =			model(samples.flatten(0					,      1							)					,      timesteps.flatten(0					,      1							)							)
		__A :			Dict		    =			scheduler.batch_step_no_noise(__A					,      timesteps.flatten(0					,      1							)					,      samples.flatten(0					,      1							)							)

		__A :			List[str]		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[str]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1e-2
		assert abs(result_mean.item() - 0.5_0_0_5							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			len(__A							)

		__A :			Tuple		    =			self.dummy_model()
		__A :			Optional[Any]		    =			self.dummy_sample_deter
		__A :			List[str]		    =			torch.manual_seed(0							)

		for t in reversed(range(__A							)							):
			# 1. predict noise residual
			__A :			str		    =			model(__A					,      __A							)

			# 2. predict previous mean of sample x_t-1
			__A :			List[str]		    =			scheduler.step(__A					,      __A					,      __A					,      generator=__A							).prev_sample

			__A :			Union[str, Any]		    =			pred_prev_sample

		__A :			List[Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1e-2
		assert abs(result_mean.item() - 0.3_3_7_2							) < 1e-3




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config(prediction_type="""v_prediction"""							)
		__A :			int		    =			scheduler_class(**__A							)

		__A :			Dict		    =			len(__A							)

		__A :			Any		    =			self.dummy_model()
		__A :			str		    =			self.dummy_sample_deter
		__A :			Union[str, Any]		    =			torch.manual_seed(0							)

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

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

			__A :			Dict		    =			pred_prev_sample

		__A :			Union[str, Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Union[str, Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1e-2
		assert abs(result_mean.item() - 0.2_6_3_1							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Any		    =			self.get_scheduler_config()
		__A :			Optional[Any]		    =			scheduler_class(**__A							)

		__A :			Optional[Any]		    =			[100, 87, 50, 1, 0]

		scheduler.set_timesteps(timesteps=__A							)

		__A :			List[Any]		    =			scheduler.timesteps

		for i, timestep in enumerate(__A							):
			if i == len(__A							) - 1:
				__A :			List[str]		    =			-1
			else:
				__A :			int		    =			timesteps[i + 1]

			__A :			Any		    =			scheduler.previous_timestep(__A							)
			__A :			Any		    =			prev_t.item()

			self.assertEqual(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			str		    =			self.scheduler_classes[0]
		__A :			int		    =			self.get_scheduler_config()
		__A :			Optional[int]		    =			scheduler_class(**__A							)

		__A :			Any		    =			[100, 87, 50, 51, 0]

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




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			List[str]		    =			self.scheduler_classes[0]
		__A :			Dict		    =			self.get_scheduler_config()
		__A :			Tuple		    =			scheduler_class(**__A							)

		__A :			Tuple		    =			[100, 87, 50, 1, 0]
		__A :			Union[str, Any]		    =			len(__A							)

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




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Tuple		    =			self.scheduler_classes[0]
		__A :			Optional[int]		    =			self.get_scheduler_config()
		__A :			Dict		    =			scheduler_class(**__A							)

		__A :			List[Any]		    =			[scheduler.config.num_train_timesteps]

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








UpperCAmelCase_							:					dict[str, float]		 =   {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.35_5818,
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   float       )					->				float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__A :			Optional[int]		    =			(
		    f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Valid values are: {", ".join(a__       )}"""
		)
		raise ValueError(a__       )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





