
'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class     lowercase__						(	__a ):






   '''simple docstring'''







   _snake_case		 =				(UniPCMultistepScheduler,)
   _snake_case		 =				(('''num_inference_steps''', 25),)







   def    UpperCAmelCase					(      self			, **lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	{
          '''num_train_timesteps''': 1_0_0_0,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''solver_order''': 2,
          '''solver_type''': '''bh2''',
      }

      config.update(**lowerCAmelCase__ )
      return config







   def    UpperCAmelCase					(      self			, lowerCamelCase__=0			, **lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	dict(self.forward_default_kwargs )
      UpperCamelCase	  =	kwargs.pop('''num_inference_steps'''			, lowerCAmelCase__ )
      UpperCamelCase	  =	self.dummy_sample
      UpperCamelCase	  =	0.1 * sample
      UpperCamelCase	  =	[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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







   def    UpperCAmelCase					(      self			, lowerCamelCase__=0			, **lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	dict(self.forward_default_kwargs )
      UpperCamelCase	  =	kwargs.pop('''num_inference_steps'''			, lowerCAmelCase__ )
      UpperCamelCase	  =	self.dummy_sample
      UpperCamelCase	  =	0.1 * sample
      UpperCamelCase	  =	[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
         UpperCamelCase	  =	self.get_scheduler_config()
         UpperCamelCase	  =	scheduler_class(**lowerCAmelCase__ )
         scheduler.set_timesteps(lowerCAmelCase__ )

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

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

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

         UpperCamelCase	  =	scheduler.step(lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__			, **lowerCAmelCase__ ).prev_sample
         UpperCamelCase	  =	new_scheduler.step(lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__			, **lowerCAmelCase__ ).prev_sample

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







   def    UpperCAmelCase					(      self			, lowerCamelCase__=None			, **lowerCamelCase__ ):

      '''simple docstring'''







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

      UpperCamelCase	  =	self.scheduler_classes[0]
      UpperCamelCase	  =	self.get_scheduler_config(**lowerCAmelCase__ )
      UpperCamelCase	  =	scheduler_class(**lowerCAmelCase__ )

      UpperCamelCase	  =	1_0
      UpperCamelCase	  =	self.dummy_model()
      UpperCamelCase	  =	self.dummy_sample_deter
      scheduler.set_timesteps(lowerCAmelCase__ )

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

      return sample







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	dict(self.forward_default_kwargs )

      UpperCamelCase	  =	kwargs.pop('''num_inference_steps'''			, lowerCAmelCase__ )

      for scheduler_class in self.scheduler_classes:
         UpperCamelCase	  =	self.get_scheduler_config()
         UpperCamelCase	  =	scheduler_class(**lowerCAmelCase__ )

         UpperCamelCase	  =	self.dummy_sample
         UpperCamelCase	  =	0.1 * sample

         if num_inference_steps is not None and hasattr(lowerCAmelCase__			, '''set_timesteps''' ):
            scheduler.set_timesteps(lowerCAmelCase__ )
         elif num_inference_steps is not None and not hasattr(lowerCAmelCase__			, '''set_timesteps''' ):
            UpperCamelCase	  =	num_inference_steps

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

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

         UpperCamelCase	  =	scheduler.step(lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__			, **lowerCAmelCase__ ).prev_sample
         UpperCamelCase	  =	scheduler.step(lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__			, **lowerCAmelCase__ ).prev_sample

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







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	UniPCMultistepScheduler(**self.get_scheduler_config() )
      UpperCamelCase	  =	self.full_loop(scheduler=lowerCAmelCase__ )
      UpperCamelCase	  =	torch.mean(torch.abs(lowerCAmelCase__ ) )

      assert abs(result_mean.item() - 0.2464 ) < 1e-3

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

      UpperCamelCase	  =	self.full_loop(scheduler=lowerCAmelCase__ )
      UpperCamelCase	  =	torch.mean(torch.abs(lowerCAmelCase__ ) )

      assert abs(result_mean.item() - 0.2464 ) < 1e-3







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      self.check_over_configs(thresholding=lowerCAmelCase__ )
      for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
            for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      thresholding=lowerCAmelCase__			, prediction_type=lowerCAmelCase__			, sample_max_value=lowerCAmelCase__			, solver_order=lowerCAmelCase__			, solver_type=lowerCAmelCase__			, )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







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







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   solver_order=lowerCAmelCase__			, solver_type=lowerCAmelCase__			, prediction_type=lowerCAmelCase__			, )
               UpperCamelCase	  =	self.full_loop(
                   solver_order=lowerCAmelCase__			, solver_type=lowerCAmelCase__			, prediction_type=lowerCAmelCase__			, )
               assert not torch.isnan(lowerCAmelCase__ ).any(), "Samples have nan numbers"







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      self.check_over_configs(lower_order_final=lowerCAmelCase__ )
      self.check_over_configs(lower_order_final=lowerCAmelCase__ )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
         self.check_over_forward(num_inference_steps=lowerCAmelCase__			, time_step=0 )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.full_loop()
      UpperCamelCase	  =	torch.mean(torch.abs(lowerCAmelCase__ ) )

      assert abs(result_mean.item() - 0.2464 ) < 1e-3







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.full_loop(prediction_type='''v_prediction''' )
      UpperCamelCase	  =	torch.mean(torch.abs(lowerCAmelCase__ ) )

      assert abs(result_mean.item() - 0.1014 ) < 1e-3







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.scheduler_classes[0]
      UpperCamelCase	  =	self.get_scheduler_config(thresholding=lowerCAmelCase__			, dynamic_thresholding_ratio=0 )
      UpperCamelCase	  =	scheduler_class(**lowerCAmelCase__ )

      UpperCamelCase	  =	1_0
      UpperCamelCase	  =	self.dummy_model()
      UpperCamelCase	  =	self.dummy_sample_deter.half()
      scheduler.set_timesteps(lowerCAmelCase__ )

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

      assert sample.dtype == torch.floataa







   def    UpperCAmelCase					(      self			, **lowerCamelCase__ ):

      '''simple docstring'''







      for scheduler_class in self.scheduler_classes:
         UpperCamelCase	  =	self.get_scheduler_config(**lowerCAmelCase__ )
         UpperCamelCase	  =	scheduler_class(**lowerCAmelCase__ )

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
