


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase			:		Tuple =			{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Dict =			[
	    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MusicgenForConditionalGeneration',
	    'MusicgenForCausalLM',
	    'MusicgenModel',
	    'MusicgenPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_musicgen import (
	    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MusicgenConfig,
	    MusicgenDecoderConfig,
	)
	from .processing_musicgen import MusicgenProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_musicgen import (
		    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MusicgenForCausalLM,
		    MusicgenForConditionalGeneration,
		    MusicgenModel,
		    MusicgenPreTrainedModel,
		)

else:
	import sys

	__lowercase			:		List[Any] =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	A_			    =					(KDPMaAncestralDiscreteScheduler,)
	A_			    =					10




	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





						__a    : Dict      					=						{
						    'num_train_timesteps': 1100,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__a							)
						return config




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
											self.check_over_configs(beta_start=__a		,       beta_end=__a							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





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




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Tuple      					=						self.get_scheduler_config()
						__a    : Optional[int]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : int      					=						torch.manual_seed(0							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : int      					=						sample.to(__a							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : int      					=						model(__a		,       __a							)

											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : int      					=						output.prev_sample

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

						assert abs(result_sum.item() - 13849.3877							) < 1E-2
						assert abs(result_mean.item() - 18.0331							) < 5E-3




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





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




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : Dict      					=						self.scheduler_classes[0]
						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
						__a    : List[str]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps							)

						__a    : Dict      					=						self.dummy_model()
						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
						__a    : List[str]      					=						sample.to(__a							)

						__a    : Tuple      					=						torch.manual_seed(0							)

						for i, t in enumerate(scheduler.timesteps							):
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : Optional[int]      					=						model(__a		,       __a							)

											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Union[str, Any]      					=						output.prev_sample

						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 328.9970							) < 1E-2
						assert abs(result_mean.item() - 0.4284							) < 1E-3





	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if torch_device == "mps":
											return
						__a    : List[str]      					=						self.scheduler_classes[0]
						__a    : Union[str, Any]      					=						self.get_scheduler_config()
						__a    : List[Any]      					=						scheduler_class(**__a							)

						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
						__a    : Union[str, Any]      					=						torch.manual_seed(0							)

						__a    : List[str]      					=						self.dummy_model()
						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)

											__a    : str      					=						model(__a		,       __a							)

											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
											__a    : Any      					=						output.prev_sample

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

						assert abs(result_sum.item() - 13849.3818							) < 1E-1
						assert abs(result_mean.item() - 18.0331							) < 1E-3



