
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Tuple									=			(DEISMultistepScheduler,)
	_UpperCamelCase	:	List[str]									=			(("num_inference_steps", 25),)





	def       SCREAMING_SNAKE_CASE__		(			self						,       **snake_case			):
		lowercase	    =       {
		    'num_train_timesteps': 1000,
		    'beta_start': 0.0_001,
		    'beta_end': 0.02,
		    'beta_schedule': 'linear',
		    'solver_order': 2,
		}

		config.update(**SCREAMING_SNAKE_CASE__			)
		return config





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0						,       **snake_case			):
		lowercase	    =       dict(self.forward_default_kwargs			)
		lowercase	    =       kwargs.pop('num_inference_steps'						,       SCREAMING_SNAKE_CASE__			)
		lowercase	    =       self.dummy_sample
		lowercase	    =       0.1 * sample
		lowercase	    =       [residual + 0.2, residual + 0.15, residual + 0.10]

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

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(SCREAMING_SNAKE_CASE__			)
				lowercase	    =       scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__			)
				new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__			)
				# copy over dummy past residuals
				lowercase	    =       dummy_past_residuals[: new_scheduler.config.solver_order]

			lowercase    ,   lowercase	    =       sample, sample
			for t in range(SCREAMING_SNAKE_CASE__						,       time_step + scheduler.config.solver_order + 1			):
				lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample
				lowercase	    =       new_scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0						,       **snake_case			):
		lowercase	    =       dict(self.forward_default_kwargs			)
		lowercase	    =       kwargs.pop('num_inference_steps'						,       SCREAMING_SNAKE_CASE__			)
		lowercase	    =       self.dummy_sample
		lowercase	    =       0.1 * sample
		lowercase	    =       [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			lowercase	    =       self.get_scheduler_config()
			lowercase	    =       scheduler_class(**SCREAMING_SNAKE_CASE__			)
			scheduler.set_timesteps(SCREAMING_SNAKE_CASE__			)

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

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

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

			lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample
			lowercase	    =       new_scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=None						,       **snake_case			):



		if scheduler is None:
			lowercase	    =       self.scheduler_classes[0]
			lowercase	    =       self.get_scheduler_config(**SCREAMING_SNAKE_CASE__			)
			lowercase	    =       scheduler_class(**SCREAMING_SNAKE_CASE__			)

		lowercase	    =       self.scheduler_classes[0]
		lowercase	    =       self.get_scheduler_config(**SCREAMING_SNAKE_CASE__			)
		lowercase	    =       scheduler_class(**SCREAMING_SNAKE_CASE__			)

		lowercase	    =       10
		lowercase	    =       self.dummy_model()
		lowercase	    =       self.dummy_sample_deter
		scheduler.set_timesteps(SCREAMING_SNAKE_CASE__			)

		for i, t in enumerate(scheduler.timesteps			):
			lowercase	    =       model(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__			)
			lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__			).prev_sample

		return sample





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       dict(self.forward_default_kwargs			)

		lowercase	    =       kwargs.pop('num_inference_steps'						,       SCREAMING_SNAKE_CASE__			)

		for scheduler_class in self.scheduler_classes:
			lowercase	    =       self.get_scheduler_config()
			lowercase	    =       scheduler_class(**SCREAMING_SNAKE_CASE__			)

			lowercase	    =       self.dummy_sample
			lowercase	    =       0.1 * sample

			if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__						,       'set_timesteps'			):
				scheduler.set_timesteps(SCREAMING_SNAKE_CASE__			)
			elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__						,       'set_timesteps'			):
				lowercase	    =       num_inference_steps

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

			lowercase	    =       scheduler.timesteps[5]
			lowercase	    =       scheduler.timesteps[6]

			lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample
			lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       **SCREAMING_SNAKE_CASE__			).prev_sample

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       DEISMultistepScheduler(**self.get_scheduler_config()			)
		lowercase	    =       self.full_loop(scheduler=SCREAMING_SNAKE_CASE__			)
		lowercase	    =       torch.mean(torch.abs(SCREAMING_SNAKE_CASE__			)			)

		assert abs(result_mean.item() - 0.23_916			) < 1E-3

		lowercase	    =       DPMSolverSinglestepScheduler.from_config(scheduler.config			)
		lowercase	    =       DPMSolverMultistepScheduler.from_config(scheduler.config			)
		lowercase	    =       UniPCMultistepScheduler.from_config(scheduler.config			)
		lowercase	    =       DEISMultistepScheduler.from_config(scheduler.config			)

		lowercase	    =       self.full_loop(scheduler=SCREAMING_SNAKE_CASE__			)
		lowercase	    =       torch.mean(torch.abs(SCREAMING_SNAKE_CASE__			)			)

		assert abs(result_mean.item() - 0.23_916			) < 1E-3





	def       SCREAMING_SNAKE_CASE__		(			self			):



		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__			)
		for order in [1, 2, 3]:
			for solver_type in ["logrho"]:
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    thresholding=SCREAMING_SNAKE_CASE__						,       prediction_type=SCREAMING_SNAKE_CASE__						,       sample_max_value=SCREAMING_SNAKE_CASE__						,       algorithm_type='deis'						,       solver_order=SCREAMING_SNAKE_CASE__						,       solver_type=SCREAMING_SNAKE_CASE__						,       )





	def       SCREAMING_SNAKE_CASE__		(			self			):



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





	def       SCREAMING_SNAKE_CASE__		(			self			):



		for algorithm_type in ["deis"]:
			for solver_type in ["logrho"]:
				for order in [1, 2, 3]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    solver_order=SCREAMING_SNAKE_CASE__						,       solver_type=SCREAMING_SNAKE_CASE__						,       prediction_type=SCREAMING_SNAKE_CASE__						,       algorithm_type=SCREAMING_SNAKE_CASE__						,       )
						lowercase	    =       self.full_loop(
						    solver_order=SCREAMING_SNAKE_CASE__						,       solver_type=SCREAMING_SNAKE_CASE__						,       prediction_type=SCREAMING_SNAKE_CASE__						,       algorithm_type=SCREAMING_SNAKE_CASE__						,       )
						assert not torch.isnan(SCREAMING_SNAKE_CASE__			).any(), "Samples have nan numbers"





	def       SCREAMING_SNAKE_CASE__		(			self			):
		self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__			)
		self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__			)





	def       SCREAMING_SNAKE_CASE__		(			self			):



		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__						,       time_step=0			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.full_loop()
		lowercase	    =       torch.mean(torch.abs(SCREAMING_SNAKE_CASE__			)			)

		assert abs(result_mean.item() - 0.23_916			) < 1E-3





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.full_loop(prediction_type='v_prediction'			)
		lowercase	    =       torch.mean(torch.abs(SCREAMING_SNAKE_CASE__			)			)

		assert abs(result_mean.item() - 0.091			) < 1E-3





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.scheduler_classes[0]
		lowercase	    =       self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__						,       dynamic_thresholding_ratio=0			)
		lowercase	    =       scheduler_class(**SCREAMING_SNAKE_CASE__			)

		lowercase	    =       10
		lowercase	    =       self.dummy_model()
		lowercase	    =       self.dummy_sample_deter.half()
		scheduler.set_timesteps(SCREAMING_SNAKE_CASE__			)

		for i, t in enumerate(scheduler.timesteps			):
			lowercase	    =       model(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__			)
			lowercase	    =       scheduler.step(SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__						,       SCREAMING_SNAKE_CASE__			).prev_sample

		assert sample.dtype == torch.floataa



def  __lowerCamelCase					( _lowercase			)       ->      str:
     return "".join(chr(ord(_lowercase			) - 32			) if 'a' <= char <= 'z' else char for char in word			)


if __name__ == "__main__":
 from doctest import testmod

 testmod()


