code: 
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Tuple       = (KDPMaAncestralDiscreteScheduler,)
  			UpperCAmelCase       :       int       = 10
  
  			def _lowercase (	self     :     Any							,							**lowerCAmelCase_     :     List[Any]					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       {
  								    '''num_train_timesteps''': 1_100,
  								    '''beta_start''': 0.0_001,
  								    '''beta_end''': 0.02,
  								    '''beta_schedule''': '''linear''',
  								}
  
  								config.update(**lowerCAmelCase_					)
  								return config
  
  			def _lowercase (	self     :     Any					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								for timesteps in [10, 50, 100, 1_000]:
  													self.check_over_configs(num_train_timesteps=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								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=lowerCAmelCase_							,							beta_end=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Optional[Any]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								for schedule in ["linear", "scaled_linear"]:
  													self.check_over_configs(beta_schedule=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     str					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if torch_device == "mps":
  													return
  								SCREAMING_SNAKE_CASE_	      =       self.scheduler_classes[0]
  								SCREAMING_SNAKE_CASE_	      =       self.get_scheduler_config()
  								SCREAMING_SNAKE_CASE_	      =       scheduler_class(**lowerCAmelCase_					)
  
  								scheduler.set_timesteps(self.num_inference_steps					)
  
  								SCREAMING_SNAKE_CASE_	      =       torch.manual_seed(0					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_model()
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_sample_deter * scheduler.init_noise_sigma
  								SCREAMING_SNAKE_CASE_	      =       sample.to(lowerCAmelCase_					)
  
  								for i, t in enumerate(scheduler.timesteps					):
  													SCREAMING_SNAKE_CASE_	      =       scheduler.scale_model_input(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       scheduler.step(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							generator=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       output.prev_sample
  
  								SCREAMING_SNAKE_CASE_	      =       torch.sum(torch.abs(lowerCAmelCase_					)					)
  								SCREAMING_SNAKE_CASE_	      =       torch.mean(torch.abs(lowerCAmelCase_					)					)
  
  								assert abs(result_sum.item() - 13_849.3_877					) < 1e-2
  								assert abs(result_mean.item() - 18.0_331					) < 5e-3
  
  			def _lowercase (	self     :     Dict					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								for prediction_type in ["epsilon", "v_prediction"]:
  													self.check_over_configs(prediction_type=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     List[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if torch_device == "mps":
  													return
  								SCREAMING_SNAKE_CASE_	      =       self.scheduler_classes[0]
  								SCREAMING_SNAKE_CASE_	      =       self.get_scheduler_config(prediction_type='''v_prediction'''					)
  								SCREAMING_SNAKE_CASE_	      =       scheduler_class(**lowerCAmelCase_					)
  
  								scheduler.set_timesteps(self.num_inference_steps					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_model()
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_sample_deter * scheduler.init_noise_sigma
  								SCREAMING_SNAKE_CASE_	      =       sample.to(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       torch.manual_seed(0					)
  
  								for i, t in enumerate(scheduler.timesteps					):
  													SCREAMING_SNAKE_CASE_	      =       scheduler.scale_model_input(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       scheduler.step(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							generator=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       output.prev_sample
  
  								SCREAMING_SNAKE_CASE_	      =       torch.sum(torch.abs(lowerCAmelCase_					)					)
  								SCREAMING_SNAKE_CASE_	      =       torch.mean(torch.abs(lowerCAmelCase_					)					)
  
  								assert abs(result_sum.item() - 328.9_970					) < 1e-2
  								assert abs(result_mean.item() - 0.4_284					) < 1e-3
  
  
  
  
  			def _lowercase (	self     :     Any					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if torch_device == "mps":
  													return
  								SCREAMING_SNAKE_CASE_	      =       self.scheduler_classes[0]
  								SCREAMING_SNAKE_CASE_	      =       self.get_scheduler_config()
  								SCREAMING_SNAKE_CASE_	      =       scheduler_class(**lowerCAmelCase_					)
  
  								scheduler.set_timesteps(self.num_inference_steps							,							device=lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.manual_seed(0					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_model()
  								SCREAMING_SNAKE_CASE_	      =       self.dummy_sample_deter.to(lowerCAmelCase_					) * scheduler.init_noise_sigma
  
  								for t in scheduler.timesteps:
  													SCREAMING_SNAKE_CASE_	      =       scheduler.scale_model_input(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       model(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       scheduler.step(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							generator=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       output.prev_sample
  
  								SCREAMING_SNAKE_CASE_	      =       torch.sum(torch.abs(lowerCAmelCase_					)					)
  								SCREAMING_SNAKE_CASE_	      =       torch.mean(torch.abs(lowerCAmelCase_					)					)
  
  								assert abs(result_sum.item() - 13_849.3_818					) < 1e-1
  								assert abs(result_mean.item() - 18.0_331					) < 1e-3
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  