




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(EulerDiscreteScheduler,)
    UpperCAmelCase__							  =				10


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			**UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   {
               '''num_train_timesteps''': 1_100,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**UpperCAmelCase__)
           return config


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





           '''simple docstring'''

           for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->List[str]:





           '''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=UpperCAmelCase__ ,			beta_end=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->int:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Any:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

           scheduler.set_timesteps(self.num_inference_steps)

           A__							=   torch.manual_seed(0)

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter * scheduler.init_noise_sigma
           A__							=   sample.to(UpperCAmelCase__)

           for i, t in enumerate(scheduler.timesteps):
                  A__							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__)
                  A__							=   output.prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 10.0807) < 1e-2
           assert abs(result_mean.item() - 0.0131) < 1e-3


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





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config(prediction_type='''v_prediction''')
           A__							=   scheduler_class(**UpperCAmelCase__)

           scheduler.set_timesteps(self.num_inference_steps)

           A__							=   torch.manual_seed(0)

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter * scheduler.init_noise_sigma
           A__							=   sample.to(UpperCAmelCase__)

           for i, t in enumerate(scheduler.timesteps):
                  A__							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__)
                  A__							=   output.prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 0.0002) < 1e-2
           assert abs(result_mean.item() - 2.26_76e-06) < 1e-3


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Dict:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

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

           A__							=   torch.manual_seed(0)

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           A__							=   sample.to(UpperCAmelCase__)

           for t in scheduler.timesteps:
                  A__							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__)
                  A__							=   output.prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 10.0807) < 1e-2
           assert abs(result_mean.item() - 0.0131) < 1e-3





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__ ,			use_karras_sigmas=UpperCAmelCase__)

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

           A__							=   torch.manual_seed(0)

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           A__							=   sample.to(UpperCAmelCase__)

           for t in scheduler.timesteps:
                  A__							=   scheduler.scale_model_input(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)

                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			generator=UpperCAmelCase__)
                  A__							=   output.prev_sample

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 124.52299499511719) < 1e-2
           assert abs(result_mean.item() - 0.16213932633399963) < 1e-3











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

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


