



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       (DEISMultistepScheduler,)
	SCREAMING_SNAKE_CASE       =       (("num_inference_steps", 25),)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	**snake_case_							:    Tuple)->							List[str]:
						__lowerCAmelCase	   ={
						    """num_train_timesteps""": 10_00,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """solver_order""": 2,
						}

						config.update(**snake_case_)
						return config



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Optional[Any]=0				,	**snake_case_							:    Tuple)->							int:
						__lowerCAmelCase	   =dict(self.forward_default_kwargs)
						__lowerCAmelCase	   =kwargs.pop("""num_inference_steps"""				,	snake_case_)
						__lowerCAmelCase	   =self.dummy_sample
						__lowerCAmelCase	   =0.1 * sample
						__lowerCAmelCase	   =[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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



	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						pass



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    List[Any]=0				,	**snake_case_							:    List[str])->							Dict:
						__lowerCAmelCase	   =dict(self.forward_default_kwargs)
						__lowerCAmelCase	   =kwargs.pop("""num_inference_steps"""				,	snake_case_)
						__lowerCAmelCase	   =self.dummy_sample
						__lowerCAmelCase	   =0.1 * sample
						__lowerCAmelCase	   =[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
											__lowerCAmelCase	   =self.get_scheduler_config()
											__lowerCAmelCase	   =scheduler_class(**snake_case_)
											scheduler.set_timesteps(snake_case_)

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

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

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

											__lowerCAmelCase	   =scheduler.step(snake_case_				,	snake_case_				,	snake_case_				,	**snake_case_).prev_sample
											__lowerCAmelCase	   =new_scheduler.step(snake_case_				,	snake_case_				,	snake_case_				,	**snake_case_).prev_sample

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



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    Any=None				,	**snake_case_							:    int)->							Union[str, Any]:
						if scheduler is None:
											__lowerCAmelCase	   =self.scheduler_classes[0]
											__lowerCAmelCase	   =self.get_scheduler_config(**snake_case_)
											__lowerCAmelCase	   =scheduler_class(**snake_case_)

						__lowerCAmelCase	   =self.scheduler_classes[0]
						__lowerCAmelCase	   =self.get_scheduler_config(**snake_case_)
						__lowerCAmelCase	   =scheduler_class(**snake_case_)

						__lowerCAmelCase	   =10
						__lowerCAmelCase	   =self.dummy_model()
						__lowerCAmelCase	   =self.dummy_sample_deter
						scheduler.set_timesteps(snake_case_)

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

						return sample



	def 		UpperCamelCase							(						self							:    Tuple)->							Tuple:
						__lowerCAmelCase	   =dict(self.forward_default_kwargs)

						__lowerCAmelCase	   =kwargs.pop("""num_inference_steps"""				,	snake_case_)

						for scheduler_class in self.scheduler_classes:
											__lowerCAmelCase	   =self.get_scheduler_config()
											__lowerCAmelCase	   =scheduler_class(**snake_case_)

											__lowerCAmelCase	   =self.dummy_sample
											__lowerCAmelCase	   =0.1 * sample

											if num_inference_steps is not None and hasattr(snake_case_				,	"""set_timesteps"""):
																scheduler.set_timesteps(snake_case_)
											elif num_inference_steps is not None and not hasattr(snake_case_				,	"""set_timesteps"""):
																__lowerCAmelCase	   =num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowerCAmelCase	   =[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowerCAmelCase	   =dummy_past_residuals[: scheduler.config.solver_order]

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

											__lowerCAmelCase	   =scheduler.step(snake_case_				,	snake_case_				,	snake_case_				,	**snake_case_).prev_sample
											__lowerCAmelCase	   =scheduler.step(snake_case_				,	snake_case_				,	snake_case_				,	**snake_case_).prev_sample

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



	def 		UpperCamelCase							(						self							:    List[str])->							Tuple:
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						__lowerCAmelCase	   =DEISMultistepScheduler(**self.get_scheduler_config())
						__lowerCAmelCase	   =self.full_loop(scheduler=snake_case_)
						__lowerCAmelCase	   =torch.mean(torch.abs(snake_case_))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1e-3

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

						__lowerCAmelCase	   =self.full_loop(scheduler=snake_case_)
						__lowerCAmelCase	   =torch.mean(torch.abs(snake_case_))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1e-3



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Union[str, Any]:
						for timesteps in [25, 50, 1_00, 9_99, 10_00]:
											self.check_over_configs(num_train_timesteps=snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							List[Any]:
						self.check_over_configs(thresholding=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=snake_case_				,	prediction_type=snake_case_				,	sample_max_value=snake_case_				,	algorithm_type="""deis"""				,	solver_order=snake_case_				,	solver_type=snake_case_				,	)



	def 		UpperCamelCase							(						self							:    str)->							int:
						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Union[str, Any]:
						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=snake_case_				,	solver_type=snake_case_				,	prediction_type=snake_case_				,	algorithm_type=snake_case_				,	)
																										__lowerCAmelCase	   =self.full_loop(
																										    solver_order=snake_case_				,	solver_type=snake_case_				,	prediction_type=snake_case_				,	algorithm_type=snake_case_				,	)
																										assert not torch.isnan(snake_case_).any(), "Samples have nan numbers"



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						self.check_over_configs(lower_order_final=snake_case_)
						self.check_over_configs(lower_order_final=snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Any:
						for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
											self.check_over_forward(num_inference_steps=snake_case_				,	time_step=0)



	def 		UpperCamelCase							(						self							:    Optional[int])->							Any:
						__lowerCAmelCase	   =self.full_loop()
						__lowerCAmelCase	   =torch.mean(torch.abs(snake_case_))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1e-3



	def 		UpperCamelCase							(						self							:    Optional[int])->							Dict:
						__lowerCAmelCase	   =self.full_loop(prediction_type="""v_prediction""")
						__lowerCAmelCase	   =torch.mean(torch.abs(snake_case_))

						assert abs(result_mean.item() - 0.0_9_1) < 1e-3



	def 		UpperCamelCase							(						self							:    List[str])->							int:
						__lowerCAmelCase	   =self.scheduler_classes[0]
						__lowerCAmelCase	   =self.get_scheduler_config(thresholding=snake_case_				,	dynamic_thresholding_ratio=0)
						__lowerCAmelCase	   =scheduler_class(**snake_case_)

						__lowerCAmelCase	   =10
						__lowerCAmelCase	   =self.dummy_model()
						__lowerCAmelCase	   =self.dummy_sample_deter.half()
						scheduler.set_timesteps(snake_case_)

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

						assert sample.dtype == torch.floataa









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_	       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EncodecModel''',
				    '''EncodecPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




