



'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		(DPMSolverSDEScheduler,)
    lowercase					=		10




    def lowerCamelCase       (    self					:     str ,     **snake_case_					:     Union[str, Any]						):
          snake_case__  :    Optional[Any]    						=      {
              """num_train_timesteps""": 1_100,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """noise_sampler_seed""": 0,
          }

          config.update(**snake_case_						)
          return config




    def lowerCamelCase       (    self					:     Any						):
          for timesteps in [10, 50, 100, 1_000]:
                self.check_over_configs(num_train_timesteps=snake_case_						)




    def lowerCamelCase       (    self					:     Tuple						):
          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=snake_case_ ,     beta_end=snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    List[Any]    						=      self.scheduler_classes[0]
          snake_case__  :    List[Any]    						=      self.get_scheduler_config()
          snake_case__  :    List[str]    						=      scheduler_class(**snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps						)

          snake_case__  :    str    						=      self.dummy_model()
          snake_case__  :    Any    						=      self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__  :    List[str]    						=      sample.to(snake_case_						)

          for i, t in enumerate(scheduler.timesteps						):
                snake_case__  :    Optional[Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Union[str, Any]    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    int    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      output.prev_sample

          snake_case__  :    Any    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    Union[str, Any]    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.47821044921875						) < 1E-2
                assert abs(result_mean.item() - 0.2178705964565277						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59352111816406						) < 1E-2
                assert abs(result_mean.item() - 0.22342906892299652						) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    str    						=      self.scheduler_classes[0]
          snake_case__  :    List[Any]    						=      self.get_scheduler_config(prediction_type="""v_prediction"""						)
          snake_case__  :    List[Any]    						=      scheduler_class(**snake_case_						)

          scheduler.set_timesteps(self.num_inference_steps						)

          snake_case__  :    Any    						=      self.dummy_model()
          snake_case__  :    Tuple    						=      self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__  :    List[str]    						=      sample.to(snake_case_						)

          for i, t in enumerate(scheduler.timesteps						):
                snake_case__  :    Union[str, Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    Union[str, Any]    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Optional[Any]    						=      output.prev_sample

          snake_case__  :    Tuple    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    Tuple    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 124.77149200439453						) < 1E-2
                assert abs(result_mean.item() - 0.16226289014816284						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 128.1663360595703						) < 1E-2
                assert abs(result_mean.item() - 0.16688326001167297						) < 1E-3
          else:
                assert abs(result_sum.item() - 119.8487548828125						) < 1E-2
                assert abs(result_mean.item() - 0.1560530662536621						) < 1E-3




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Dict    						=      self.scheduler_classes[0]
          snake_case__  :    List[str]    						=      self.get_scheduler_config()
          snake_case__  :    Any    						=      scheduler_class(**snake_case_						)

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

          snake_case__  :    Tuple    						=      self.dummy_model()
          snake_case__  :    Any    						=      self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                snake_case__  :    Union[str, Any]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Optional[Any]    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Any    						=      output.prev_sample

          snake_case__  :    int    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    str    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.46957397460938						) < 1E-2
                assert abs(result_mean.item() - 0.21805934607982635						) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59353637695312						) < 1E-2
                assert abs(result_mean.item() - 0.22342908382415771						) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Tuple    						=      self.scheduler_classes[0]
          snake_case__  :    Dict    						=      self.get_scheduler_config()
          snake_case__  :    List[str]    						=      scheduler_class(**snake_case_ ,     use_karras_sigmas=snake_case_						)

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

          snake_case__  :    int    						=      self.dummy_model()
          snake_case__  :    List[str]    						=      self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma
          snake_case__  :    Tuple    						=      sample.to(snake_case_						)

          for t in scheduler.timesteps:
                snake_case__  :    Optional[int]    						=      scheduler.scale_model_input(snake_case_ ,     snake_case_						)

                snake_case__  :    Dict    						=      model(snake_case_ ,     snake_case_						)

                snake_case__  :    int    						=      scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_						)
                snake_case__  :    Any    						=      output.prev_sample

          snake_case__  :    Dict    						=      torch.sum(torch.abs(snake_case_						)						)
          snake_case__  :    List[Any]    						=      torch.mean(torch.abs(snake_case_						)						)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 176.66974135742188						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 177.63653564453125						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
          else:
                assert abs(result_sum.item() - 170.3135223388672						) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a          =		{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TimesformerModel",
           "TimesformerForVideoClassification",
           "TimesformerPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timesformer import (
                  TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TimesformerForVideoClassification,
                  TimesformerModel,
                  TimesformerPreTrainedModel,
              )

else:
       import sys

       __a          =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
