

'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase__     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					(KDPMaDiscreteScheduler,)
 __SCREAMING_SNAKE_CASE					:List[str]      	=					10



 def 			snake_case__  (   self		:   Optional[int]			,				**a__		:   int      ):
      __magic_name__						       =    {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
      }

      config.update(**a__      )
      return config



 def 			snake_case__  (   self		:   Any      ):
      for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=a__      )



 def 			snake_case__  (   self		:   Any      ):
      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]			,				[0.0_002, 0.002, 0.02]      ):
           self.check_over_configs(beta_start=a__			,				beta_end=a__      )



 def 			snake_case__  (   self		:   Any      ):
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=a__      )



 def 			snake_case__  (   self		:   Any      ):
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config(prediction_type='''v_prediction'''      )
      __magic_name__						       =    scheduler_class(**a__      )

      scheduler.set_timesteps(self.num_inference_steps      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__						       =    sample.to(a__      )

      for i, t in enumerate(scheduler.timesteps      ):
           __magic_name__						       =    scheduler.scale_model_input(a__			,				a__      )

           __magic_name__						       =    model(a__			,				a__      )

           __magic_name__						       =    scheduler.step(a__			,				a__			,				a__      )
           __magic_name__						       =    output.prev_sample

      __magic_name__						       =    torch.sum(torch.abs(a__      )      )
      __magic_name__						       =    torch.mean(torch.abs(a__      )      )

      if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934E-07      ) < 1E-2
           assert abs(result_mean.item() - 6.1112E-10      ) < 1E-3
      else:
           # CUDA
           assert abs(result_sum.item() - 4.693428650170972E-07      ) < 1E-2
           assert abs(result_mean.item() - 0.0_002      ) < 1E-3



 def 			snake_case__  (   self		:   Any      ):
      if torch_device == "mps":
           return
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**a__      )

      scheduler.set_timesteps(self.num_inference_steps      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__						       =    sample.to(a__      )

      for i, t in enumerate(scheduler.timesteps      ):
           __magic_name__						       =    scheduler.scale_model_input(a__			,				a__      )

           __magic_name__						       =    model(a__			,				a__      )

           __magic_name__						       =    scheduler.step(a__			,				a__			,				a__      )
           __magic_name__						       =    output.prev_sample

      __magic_name__						       =    torch.sum(torch.abs(a__      )      )
      __magic_name__						       =    torch.mean(torch.abs(a__      )      )

      if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4_125      ) < 1E-2
           assert abs(result_mean.item() - 0.0_266      ) < 1E-3
      else:
           # CUDA
           assert abs(result_sum.item() - 20.4_125      ) < 1E-2
           assert abs(result_mean.item() - 0.0_266      ) < 1E-3



 def 			snake_case__  (   self		:   Optional[Any]      ):
      if torch_device == "mps":
           return
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**a__      )

      scheduler.set_timesteps(self.num_inference_steps			,				device=a__      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter.to(a__      ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
           __magic_name__						       =    scheduler.scale_model_input(a__			,				a__      )

           __magic_name__						       =    model(a__			,				a__      )

           __magic_name__						       =    scheduler.step(a__			,				a__			,				a__      )
           __magic_name__						       =    output.prev_sample

      __magic_name__						       =    torch.sum(torch.abs(a__      )      )
      __magic_name__						       =    torch.mean(torch.abs(a__      )      )

      if str(a__      ).startswith('''cpu'''      ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4_125      ) < 1E-2
           assert abs(result_mean.item() - 0.0_266      ) < 1E-3
      else:
           # CUDA
           assert abs(result_sum.item() - 20.4_125      ) < 1E-2
           assert abs(result_mean.item() - 0.0_266      ) < 1E-3









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =						{
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      _lowerCAmelCase         =						["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






