

'''simple docstring'''



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 		lowerCAmelCase__     (   __lowerCamelCase      ):



 """simple docstring"""



 lowerCAmelCase__						      =   (LMSDiscreteScheduler,)
 lowerCAmelCase__						      =   10
 def    UpperCAmelCase__	(	self      : Dict      ,							**__SCREAMING_SNAKE_CASE      : int    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**_lowerCAmelCase    )
      return config
 def    UpperCAmelCase__	(	self      : Dict    )						->				Any:


      """simple docstring"""




      for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=_lowerCAmelCase    )
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Any:


      """simple docstring"""




      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,							[0.0002, 0.002, 0.02]    ):
           self.check_over_configs(beta_start=_lowerCAmelCase      ,							beta_end=_lowerCAmelCase    )
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Tuple:


      """simple docstring"""




      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=_lowerCAmelCase    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[int]:


      """simple docstring"""




      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_lowerCAmelCase    )
 def    UpperCAmelCase__	(	self      : Any    )						->				str:


      """simple docstring"""




      for t in [0, 500, 800]:
           self.check_over_forward(time_step=_lowerCAmelCase    )
 def    UpperCAmelCase__	(	self      : Any    )						->				Optional[Any]:


      """simple docstring"""




      __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						     =   self.dummy_model()
      __SCREAMING_SNAKE_CASE						     =   self.dummy_sample_deter * scheduler.init_noise_sigma

      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    )
           __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() - 1_006.388    ) < 1E-2
      assert abs(result_mean.item() - 1.31    ) < 1E-3
 def    UpperCAmelCase__	(	self      : Any    )						->				Optional[int]:


      """simple docstring"""




      __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

      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    )
           __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() - 0.0017    ) < 1E-2
      assert abs(result_mean.item() - 2.2_676E-06    ) < 1E-3
 def    UpperCAmelCase__	(	self      : int    )						->				List[Any]:


      """simple docstring"""




      __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						     =   self.dummy_model()
      __SCREAMING_SNAKE_CASE						     =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __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    )
           __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() - 1_006.388    ) < 1E-2
      assert abs(result_mean.item() - 1.31    ) < 1E-3


 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE						     =   self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE						     =   scheduler_class(**_lowerCAmelCase      ,							use_karras_sigmas=_lowerCAmelCase    )

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

      __SCREAMING_SNAKE_CASE						     =   self.dummy_model()
      __SCREAMING_SNAKE_CASE						     =   self.dummy_sample_deter.to(_lowerCAmelCase    ) * scheduler.init_noise_sigma
      __SCREAMING_SNAKE_CASE						     =   sample.to(_lowerCAmelCase    )

      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    )
           __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() - 3_812.9_927    ) < 2E-2
      assert abs(result_mean.item() - 4.9648    ) < 1E-3









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)

UpperCAmelCase				: Optional[int]         =  {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "qdqbert"
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=30_522      ,							__SCREAMING_SNAKE_CASE      : str=768      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=12      ,							__SCREAMING_SNAKE_CASE      : Any=12      ,							__SCREAMING_SNAKE_CASE      : Any=3_072      ,							__SCREAMING_SNAKE_CASE      : List[str]="gelu"      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Any=0.1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=512      ,							__SCREAMING_SNAKE_CASE      : List[str]=2      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : List[str]=1E-12      ,							__SCREAMING_SNAKE_CASE      : str=True      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=0      ,							__SCREAMING_SNAKE_CASE      : Dict=2      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				str:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE      ,							bos_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   max_position_embeddings
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_hidden_layers
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   intermediate_size
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   type_vocab_size
      __SCREAMING_SNAKE_CASE						     =   layer_norm_eps
      __SCREAMING_SNAKE_CASE						     =   use_cache






