


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Union[str, Any]				=       (DPMSolverMultistepScheduler,)
       __UpperCamelCase   :  int				=       (("num_inference_steps", 25),)






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		{
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''solver_order''': 2,
                  '''prediction_type''': '''epsilon''',
                  '''thresholding''': False,
                  '''sample_max_value''': 1.0,
                  '''algorithm_type''': '''dpmsolver++''',
                  '''solver_type''': '''midpoint''',
                  '''lower_order_final''': False,
                  '''lambda_min_clipped''': -float('''inf'''    ),
                  '''variance_type''': None,
              }

              config.update(**__SCREAMING_SNAKE_CASE    )
              return config






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		dict(self.forward_default_kwargs    )
              UpperCamelCase     :						Dict 						=		kwargs.pop('''num_inference_steps'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		self.dummy_sample
              UpperCamelCase     :						Union[str, Any] 						=		0.1 * sample
              UpperCamelCase     :						List[Any] 						=		[residual + 0.2, residual + 0.15, residual + 0.10]

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase     :						str 						=		self.get_scheduler_config(**__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Dict 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )
                     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )
                     # copy over dummy past residuals
                     UpperCamelCase     :						List[Any] 						=		dummy_past_residuals[: scheduler.config.solver_order]

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(__SCREAMING_SNAKE_CASE    )
                            UpperCamelCase     :						List[str] 						=		scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE    )
                            new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )
                            # copy over dummy past residuals
                            UpperCamelCase     :						Dict 						=		dummy_past_residuals[: new_scheduler.config.solver_order]

                     UpperCamelCase						,			UpperCamelCase     :						int 						=		sample, sample
                     for t in range(__SCREAMING_SNAKE_CASE			,       time_step + scheduler.config.solver_order + 1    ):
                            UpperCamelCase     :						List[str] 						=		scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ).prev_sample
                            UpperCamelCase     :						List[str] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              pass






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		dict(self.forward_default_kwargs    )
              UpperCamelCase     :						Optional[Any] 						=		kwargs.pop('''num_inference_steps'''			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[Any] 						=		self.dummy_sample
              UpperCamelCase     :						Optional[Any] 						=		0.1 * sample
              UpperCamelCase     :						str 						=		[residual + 0.2, residual + 0.15, residual + 0.10]

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase     :						List[Any] 						=		self.get_scheduler_config()
                     UpperCamelCase     :						Tuple 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )
                     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

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

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(__SCREAMING_SNAKE_CASE    )
                            UpperCamelCase     :						Any 						=		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)
                            UpperCamelCase     :						Tuple 						=		dummy_past_residuals[: new_scheduler.config.solver_order]

                     UpperCamelCase     :						Optional[int] 						=		scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ).prev_sample
                     UpperCamelCase     :						Union[str, Any] 						=		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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=None			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if scheduler is None:
                     UpperCamelCase     :						Dict 						=		self.scheduler_classes[0]
                     UpperCamelCase     :						Dict 						=		self.get_scheduler_config(**__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Any 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[Any] 						=		10
              UpperCamelCase     :						Union[str, Any] 						=		self.dummy_model()
              UpperCamelCase     :						Tuple 						=		self.dummy_sample_deter
              scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

              for i, t in enumerate(scheduler.timesteps    ):
                     UpperCamelCase     :						Any 						=		model(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Tuple 						=		scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).prev_sample

              return sample






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		dict(self.forward_default_kwargs    )

              UpperCamelCase     :						int 						=		kwargs.pop('''num_inference_steps'''			,       __SCREAMING_SNAKE_CASE    )

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase     :						List[str] 						=		self.get_scheduler_config()
                     UpperCamelCase     :						Optional[Any] 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )

                     UpperCamelCase     :						Any 						=		self.dummy_sample
                     UpperCamelCase     :						Optional[int] 						=		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'''    ):
                            UpperCamelCase     :						Optional[int] 						=		num_inference_steps

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

                     UpperCamelCase     :						List[str] 						=		scheduler.timesteps[5]
                     UpperCamelCase     :						Any 						=		scheduler.timesteps[6]

                     UpperCamelCase     :						List[Any] 						=		scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ).prev_sample
                     UpperCamelCase     :						str 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              for timesteps in [25, 50, 100, 999, 1_000]:
                     self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE    )
              for order in [1, 2, 3]:
                     for solver_type in ["midpoint", "heun"]:
                            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='''dpmsolver++'''			,       solver_order=__SCREAMING_SNAKE_CASE			,       solver_type=__SCREAMING_SNAKE_CASE			,       )






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




              for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                     for solver_type in ["midpoint", "heun"]:
                            for order in [1, 2, 3]:
                                   for prediction_type in ["epsilon", "sample"]:
                                          if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                                 if order == 3:
                                                        continue
                                          else:
                                                 self.check_over_configs(
                                                     solver_order=__SCREAMING_SNAKE_CASE			,       solver_type=__SCREAMING_SNAKE_CASE			,       prediction_type=__SCREAMING_SNAKE_CASE			,       algorithm_type=__SCREAMING_SNAKE_CASE			,       )
                                          UpperCamelCase     :						List[Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE    )
              self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              self.check_over_configs(lambda_min_clipped=-float('''inf'''    )    )
              self.check_over_configs(lambda_min_clipped=-5.1    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE    )
              self.check_over_configs(variance_type='''learned_range'''    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                     self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE			,       time_step=0    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self.full_loop()
              UpperCamelCase     :						Dict 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 0.3_301    ) < 1e-3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		self.full_loop(thresholding=__SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0.87			,       sample_max_value=0.5    )
              UpperCamelCase     :						int 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 1.1_364    ) < 1e-3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		self.full_loop(prediction_type='''v_prediction'''    )
              UpperCamelCase     :						List[Any] 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 0.2_251    ) < 1e-3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self.full_loop(prediction_type='''v_prediction'''			,       use_karras_sigmas=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 0.2_096    ) < 1e-3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
              UpperCamelCase     :						int 						=		self.full_loop(scheduler=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 0.3_301    ) < 1e-3

              UpperCamelCase     :						Any 						=		DPMSolverSinglestepScheduler.from_config(scheduler.config    )
              UpperCamelCase     :						Any 						=		UniPCMultistepScheduler.from_config(scheduler.config    )
              UpperCamelCase     :						Optional[Any] 						=		DEISMultistepScheduler.from_config(scheduler.config    )
              UpperCamelCase     :						Tuple 						=		DPMSolverMultistepScheduler.from_config(scheduler.config    )

              UpperCamelCase     :						Dict 						=		self.full_loop(scheduler=__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

              assert abs(result_mean.item() - 0.3_301    ) < 1e-3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		self.scheduler_classes[0]
              UpperCamelCase     :						Union[str, Any] 						=		self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE			,       dynamic_thresholding_ratio=0    )
              UpperCamelCase     :						Tuple 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Tuple 						=		10
              UpperCamelCase     :						Optional[Any] 						=		self.dummy_model()
              UpperCamelCase     :						List[Any] 						=		self.dummy_sample_deter.half()
              scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

              for i, t in enumerate(scheduler.timesteps    ):
                     UpperCamelCase     :						Tuple 						=		model(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		scheduler.step(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).prev_sample

              assert sample.dtype == torch.floataa






       def 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase     :						List[str] 						=		self.get_scheduler_config(**__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Any 						=		scheduler_class(**__SCREAMING_SNAKE_CASE    )

                     scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
                     assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps





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

  __UpperCAmelCase			:			Union[str, Any]			      =					True
  __UpperCAmelCase			:			Optional[int]			      =					""
except Exception as e:
  __UpperCAmelCase			:			Union[str, Any]			      =					False
  __UpperCAmelCase			:			Tuple			      =					(
      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_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "mel_config.json"






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 22_050			,       __SCREAMING_SNAKE_CASE = 2_048			,       __SCREAMING_SNAKE_CASE = 512			,       __SCREAMING_SNAKE_CASE = 80			,       __SCREAMING_SNAKE_CASE = 32			,       ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		hop_length
              UpperCamelCase     :						List[str] 						=		sample_rate
              UpperCamelCase     :						List[Any] 						=		n_fft
              UpperCamelCase     :						List[str] 						=		top_db
              UpperCamelCase     :						Tuple 						=		n_iter
              self.set_resolution(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		None

              if not _librosa_can_be_imported:
                     raise ValueError(_import_error    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		x_res
              UpperCamelCase     :						Union[str, Any] 						=		y_res
              UpperCamelCase     :						Any 						=		self.y_res
              UpperCamelCase     :						Union[str, Any] 						=		self.x_res * self.hop_length - 1






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              if audio_file is not None:
                     UpperCamelCase						,			UpperCamelCase     :						Tuple 						=		librosa.load(__SCREAMING_SNAKE_CASE			,       mono=__SCREAMING_SNAKE_CASE			,       sr=self.sr    )
              else:
                     UpperCamelCase     :						Tuple 						=		raw_audio

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              return len(self.audio    ) // self.slice_size






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = 0    ):




              """simple docstring"""




              return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.sr






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		np.frombuffer(image.tobytes()			,       dtype='''uint8'''    ).reshape((image.height, image.width)    )
              UpperCamelCase     :						Tuple 						=		bytedata.astype('''float'''    ) * self.top_db / 255 - self.top_db
              UpperCamelCase     :						List[Any] 						=		librosa.db_to_power(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		librosa.feature.inverse.mel_to_audio(
                  __SCREAMING_SNAKE_CASE			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_iter=self.n_iter    )
              return audio

