code: 
  
  
  
  
  import torch
  
  from diffusers import LMSDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = (LMSDiscreteScheduler,)
  	UpperCAmelCase   :							Union[str, Any]         = 10
  
  
  
  
  
  	def     A				(  self				,    **UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	{
  								    """num_train_timesteps""": 1100,
  								    """beta_start""": 0.0001,
  								    """beta_end""": 0.02,
  								    """beta_schedule""": """linear""",
  								}
  
  								config.update(**UpperCamelCase_							)
  								return config
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for timesteps in [10, 50, 100, 1000]:
  															self.check_over_configs(num_train_timesteps=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]				,    [0.0002, 0.002, 0.02]							):
  															self.check_over_configs(beta_start=UpperCamelCase_				,    beta_end=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								for schedule in ["linear", "scaled_linear"]:
  															self.check_over_configs(beta_schedule=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for prediction_type in ["epsilon", "v_prediction"]:
  															self.check_over_configs(prediction_type=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for t in [0, 500, 800]:
  															self.check_over_forward(time_step=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.scheduler_classes[0]
  								a_       :   Tuple			=	self.get_scheduler_config()
  								a_       :   Tuple			=	scheduler_class(**UpperCamelCase_							)
  
  								scheduler.set_timesteps(self.num_inference_steps							)
  
  								a_       :   str			=	self.dummy_model()
  								a_       :   str			=	self.dummy_sample_deter * scheduler.init_noise_sigma
  
  								for i, t in enumerate(scheduler.timesteps							):
  															a_       :   List[str]			=	scheduler.scale_model_input(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   List[str]			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   int			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   Optional[Any]			=	output.prev_sample
  
  								a_       :   int			=	torch.sum(torch.abs(UpperCamelCase_							)							)
  								a_       :   List[str]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_sum.item() - 1006.388							) < 1e-2
  								assert abs(result_mean.item() - 1.31							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.scheduler_classes[0]
  								a_       :   Optional[int]			=	self.get_scheduler_config(prediction_type="""v_prediction"""							)
  								a_       :   List[Any]			=	scheduler_class(**UpperCamelCase_							)
  
  								scheduler.set_timesteps(self.num_inference_steps							)
  
  								a_       :   Any			=	self.dummy_model()
  								a_       :   Dict			=	self.dummy_sample_deter * scheduler.init_noise_sigma
  
  								for i, t in enumerate(scheduler.timesteps							):
  															a_       :   str			=	scheduler.scale_model_input(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Any			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Any			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   Optional[int]			=	output.prev_sample
  
  								a_       :   List[str]			=	torch.sum(torch.abs(UpperCamelCase_							)							)
  								a_       :   List[str]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_sum.item() - 0.0017							) < 1e-2
  								assert abs(result_mean.item() - 2.2676e-06							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	self.scheduler_classes[0]
  								a_       :   Tuple			=	self.get_scheduler_config()
  								a_       :   str			=	scheduler_class(**UpperCamelCase_							)
  
  								scheduler.set_timesteps(self.num_inference_steps				,    device=UpperCamelCase_							)
  
  								a_       :   Dict			=	self.dummy_model()
  								a_       :   Optional[Any]			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  								a_       :   Dict			=	sample.to(UpperCamelCase_							)
  
  								for i, t in enumerate(scheduler.timesteps							):
  															a_       :   int			=	scheduler.scale_model_input(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   str			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   int			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   Optional[int]			=	output.prev_sample
  
  								a_       :   int			=	torch.sum(torch.abs(UpperCamelCase_							)							)
  								a_       :   List[Any]			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_sum.item() - 1006.388							) < 1e-2
  								assert abs(result_mean.item() - 1.31							) < 1e-3
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.scheduler_classes[0]
  								a_       :   Optional[int]			=	self.get_scheduler_config()
  								a_       :   Tuple			=	scheduler_class(**UpperCamelCase_				,    use_karras_sigmas=UpperCamelCase_							)
  
  								scheduler.set_timesteps(self.num_inference_steps				,    device=UpperCamelCase_							)
  
  								a_       :   Dict			=	self.dummy_model()
  								a_       :   int			=	self.dummy_sample_deter.to(UpperCamelCase_							) * scheduler.init_noise_sigma
  								a_       :   int			=	sample.to(UpperCamelCase_							)
  
  								for t in scheduler.timesteps:
  															a_       :   int			=	scheduler.scale_model_input(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Tuple			=	model(UpperCamelCase_				,    UpperCamelCase_							)
  
  															a_       :   Optional[Any]			=	scheduler.step(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
  															a_       :   Dict			=	output.prev_sample
  
  								a_       :   Tuple			=	torch.sum(torch.abs(UpperCamelCase_							)							)
  								a_       :   Tuple			=	torch.mean(torch.abs(UpperCamelCase_							)							)
  
  								assert abs(result_sum.item() - 3812.9927							) < 2e-2
  								assert abs(result_mean.item() - 4.9648							) < 1e-3
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  