



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      (EulerAncestralDiscreteScheduler,)
     lowerCAmelCase       =      10





     def 						__snake_case     (							self			:				List[str]  ,	**UpperCamelCase			:				int					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						{
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**UpperCamelCase					)
      return config





     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->str:
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]					):
       self.check_over_configs(beta_start=UpperCamelCase  ,	beta_end=UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->Optional[int]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase					)





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





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     str							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __SCREAMING_SNAKE_CASE							:     Any							=						sample.to(UpperCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
       __SCREAMING_SNAKE_CASE							:     List[str]							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model(UpperCamelCase  ,	UpperCamelCase					)

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

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2					) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3					) < 1E-3





     def 						__snake_case     (							self			:				Dict					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.get_scheduler_config(prediction_type="v_prediction"					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
      __SCREAMING_SNAKE_CASE							:     List[str]							=						sample.to(UpperCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
       __SCREAMING_SNAKE_CASE							:     str							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Any							=						model(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						scheduler.step(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	generator=UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						output.prev_sample

      __SCREAMING_SNAKE_CASE							:     str							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_0_8.4_4_3_9					) < 1E-2
      assert abs(result_mean.item() - 0.1_4_1_2					) < 1E-3






     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE							:     int							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps  ,	device=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     str							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sample.to(UpperCamelCase					)

      for t in scheduler.timesteps:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     str							=						model(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scheduler.step(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	generator=UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						output.prev_sample

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2					) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3					) < 1E-3








import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
					)


from PIL import Image  # noqa: E402







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"mel_config.json"





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

					# Pad with silence if necessary.
					if len(self.audio		) < self.x_res * self.hop_length:
									A__	:						List[Any]								=				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(UpperCamelCase__		)					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_mels=self.n_mels		)
					A__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				librosa.feature.inverse.mel_to_audio(
					    UpperCamelCase__					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_iter=self.n_iter		)
					return audio