



'''simple docstring'''


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (KDPMaDiscreteScheduler,)
    _SCREAMING_SNAKE_CASE	  =  10






    def  A       (  self      :					int  ,						**UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  {
           'num_train_timesteps': 1_1_0_0,
           'beta_start': 0.0_0_0_1,
           'beta_end': 0.0_2,
           'beta_schedule': 'linear',
       }

       config.update(**UpperCamelCase__ )
       return config






    def  A       (  self      :					Dict ):


       """simple docstring"""







       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
          self.check_over_configs(beta_start=UpperCamelCase__  ,						beta_end=UpperCamelCase__ )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config(prediction_type='v_prediction' )
       UpperCamelCase					=  scheduler_class(**UpperCamelCase__ )

       scheduler.set_timesteps(self.num_inference_steps )

       UpperCamelCase					=  self.dummy_model()
       UpperCamelCase					=  self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase					=  sample.to(UpperCamelCase__ )

       for i, t in enumerate(scheduler.timesteps ):
          UpperCamelCase					=  scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  model(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  output.prev_sample

       UpperCamelCase					=  torch.sum(torch.abs(UpperCamelCase__ ) )
       UpperCamelCase					=  torch.mean(torch.abs(UpperCamelCase__ ) )

       if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_9_3_4E-0_7 ) < 1E-2
          assert abs(result_mean.item() - 6.1_1_1_2E-1_0 ) < 1E-3
       else:
          # CUDA
          assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7 ) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2 ) < 1E-3






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       if torch_device == "mps":
          return
       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**UpperCamelCase__ )

       scheduler.set_timesteps(self.num_inference_steps )

       UpperCamelCase					=  self.dummy_model()
       UpperCamelCase					=  self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCamelCase					=  sample.to(UpperCamelCase__ )

       for i, t in enumerate(scheduler.timesteps ):
          UpperCamelCase					=  scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  model(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  output.prev_sample

       UpperCamelCase					=  torch.sum(torch.abs(UpperCamelCase__ ) )
       UpperCamelCase					=  torch.mean(torch.abs(UpperCamelCase__ ) )

       if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3
       else:
          # CUDA
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3






    def  A       (  self      :					str ):


       """simple docstring"""







       if torch_device == "mps":
          return
       UpperCamelCase					=  self.scheduler_classes[0]
       UpperCamelCase					=  self.get_scheduler_config()
       UpperCamelCase					=  scheduler_class(**UpperCamelCase__ )

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

       UpperCamelCase					=  self.dummy_model()
       UpperCamelCase					=  self.dummy_sample_deter.to(UpperCamelCase__ ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
          UpperCamelCase					=  scheduler.scale_model_input(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  model(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  scheduler.step(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  output.prev_sample

       UpperCamelCase					=  torch.sum(torch.abs(UpperCamelCase__ ) )
       UpperCamelCase					=  torch.mean(torch.abs(UpperCamelCase__ ) )

       if str(UpperCamelCase__ ).startswith('cpu' ):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3
       else:
          # CUDA
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1E-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1E-3






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

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

