


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[str]      =  (DEISMultistepScheduler,)
    UpperCAmelCase__						:		List[Any]      =  (("num_inference_steps", 2_5),)






    def _a					(			self					,   **A_    )  ->       Dict:
         __UpperCamelCase    							={
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**A_    )
         return config






    def _a					(			self					,   A_=0					,   **A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=dict(self.forward_default_kwargs    )
         __UpperCamelCase    							=kwargs.pop('num_inference_steps'					,   A_    )
         __UpperCamelCase    							=self.dummy_sample
         __UpperCamelCase    							=0.1 * sample
         __UpperCamelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              __UpperCamelCase    							=self.get_scheduler_config(**A_    )
              __UpperCamelCase    							=scheduler_class(**A_    )
              scheduler.set_timesteps(A_    )
              # copy over dummy past residuals
              __UpperCamelCase    							=dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(A_    )
                   __UpperCamelCase    							=scheduler_class.from_pretrained(A_    )
                   new_scheduler.set_timesteps(A_    )
                   # copy over dummy past residuals
                   __UpperCamelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]

              __UpperCamelCase							,      __UpperCamelCase    							=sample, sample
              for t in range(A_					,   time_step + scheduler.config.solver_order + 1    ):
                   __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample
                   __UpperCamelCase    							=new_scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample

                   assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"






    def _a					(			self    )  ->       Dict:
         pass






    def _a					(			self					,   A_=0					,   **A_    )  ->       List[str]:
         __UpperCamelCase    							=dict(self.forward_default_kwargs    )
         __UpperCamelCase    							=kwargs.pop('num_inference_steps'					,   A_    )
         __UpperCamelCase    							=self.dummy_sample
         __UpperCamelCase    							=0.1 * sample
         __UpperCamelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              __UpperCamelCase    							=self.get_scheduler_config()
              __UpperCamelCase    							=scheduler_class(**A_    )
              scheduler.set_timesteps(A_    )

              # copy over dummy past residuals (must be after setting timesteps)
              __UpperCamelCase    							=dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(A_    )
                   __UpperCamelCase    							=scheduler_class.from_pretrained(A_    )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(A_    )

                   # copy over dummy past residual (must be after setting timesteps)
                   __UpperCamelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]

              __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample
              __UpperCamelCase    							=new_scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample

              assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"






    def _a					(			self					,   A_=None					,   **A_    )  ->       Any:
         if scheduler is None:
              __UpperCamelCase    							=self.scheduler_classes[0]
              __UpperCamelCase    							=self.get_scheduler_config(**A_    )
              __UpperCamelCase    							=scheduler_class(**A_    )

         __UpperCamelCase    							=self.scheduler_classes[0]
         __UpperCamelCase    							=self.get_scheduler_config(**A_    )
         __UpperCamelCase    							=scheduler_class(**A_    )

         __UpperCamelCase    							=10
         __UpperCamelCase    							=self.dummy_model()
         __UpperCamelCase    							=self.dummy_sample_deter
         scheduler.set_timesteps(A_    )

         for i, t in enumerate(scheduler.timesteps    ):
              __UpperCamelCase    							=model(A_					,   A_    )
              __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_    ).prev_sample

         return sample






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=dict(self.forward_default_kwargs    )

         __UpperCamelCase    							=kwargs.pop('num_inference_steps'					,   A_    )

         for scheduler_class in self.scheduler_classes:
              __UpperCamelCase    							=self.get_scheduler_config()
              __UpperCamelCase    							=scheduler_class(**A_    )

              __UpperCamelCase    							=self.dummy_sample
              __UpperCamelCase    							=0.1 * sample

              if num_inference_steps is not None and hasattr(A_					,   'set_timesteps'    ):
                   scheduler.set_timesteps(A_    )
              elif num_inference_steps is not None and not hasattr(A_					,   'set_timesteps'    ):
                   __UpperCamelCase    							=num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              __UpperCamelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
              __UpperCamelCase    							=dummy_past_residuals[: scheduler.config.solver_order]

              __UpperCamelCase    							=scheduler.timesteps[5]
              __UpperCamelCase    							=scheduler.timesteps[6]

              __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample
              __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_					,   **A_    ).prev_sample

              self.assertEqual(output_a.shape					,   sample.shape    )
              self.assertEqual(output_a.shape					,   output_a.shape    )






    def _a					(			self    )  ->       List[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         __UpperCamelCase    							=DEISMultistepScheduler(**self.get_scheduler_config()    )
         __UpperCamelCase    							=self.full_loop(scheduler=A_    )
         __UpperCamelCase    							=torch.mean(torch.abs(A_    )    )

         assert abs(result_mean.item() - 0.2_3916    ) < 1E-3

         __UpperCamelCase    							=DPMSolverSinglestepScheduler.from_config(scheduler.config    )
         __UpperCamelCase    							=DPMSolverMultistepScheduler.from_config(scheduler.config    )
         __UpperCamelCase    							=UniPCMultistepScheduler.from_config(scheduler.config    )
         __UpperCamelCase    							=DEISMultistepScheduler.from_config(scheduler.config    )

         __UpperCamelCase    							=self.full_loop(scheduler=A_    )
         __UpperCamelCase    							=torch.mean(torch.abs(A_    )    )

         assert abs(result_mean.item() - 0.2_3916    ) < 1E-3






    def _a					(			self    )  ->       Dict:
         for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=A_    )






    def _a					(			self    )  ->       Tuple:
         self.check_over_configs(thresholding=A_    )
         for order in [1, 2, 3]:
              for solver_type in ["logrho"]:
                   for threshold in [0.5, 1.0, 2.0]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 thresholding=A_					,   prediction_type=A_					,   sample_max_value=A_					,   algorithm_type='deis'					,   solver_order=A_					,   solver_type=A_					,   )






    def _a					(			self    )  ->       Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=A_    )






    def _a					(			self    )  ->       Any:
         for algorithm_type in ["deis"]:
              for solver_type in ["logrho"]:
                   for order in [1, 2, 3]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 solver_order=A_					,   solver_type=A_					,   prediction_type=A_					,   algorithm_type=A_					,   )
                             __UpperCamelCase    							=self.full_loop(
                                 solver_order=A_					,   solver_type=A_					,   prediction_type=A_					,   algorithm_type=A_					,   )
                             assert not torch.isnan(A_    ).any(), "Samples have nan numbers"






    def _a					(			self    )  ->       int:
         self.check_over_configs(lower_order_final=A_    )
         self.check_over_configs(lower_order_final=A_    )






    def _a					(			self    )  ->       int:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=A_					,   time_step=0    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.full_loop()
         __UpperCamelCase    							=torch.mean(torch.abs(A_    )    )

         assert abs(result_mean.item() - 0.2_3916    ) < 1E-3






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.full_loop(prediction_type='v_prediction'    )
         __UpperCamelCase    							=torch.mean(torch.abs(A_    )    )

         assert abs(result_mean.item() - 0.091    ) < 1E-3






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.scheduler_classes[0]
         __UpperCamelCase    							=self.get_scheduler_config(thresholding=A_					,   dynamic_thresholding_ratio=0    )
         __UpperCamelCase    							=scheduler_class(**A_    )

         __UpperCamelCase    							=10
         __UpperCamelCase    							=self.dummy_model()
         __UpperCamelCase    							=self.dummy_sample_deter.half()
         scheduler.set_timesteps(A_    )

         for i, t in enumerate(scheduler.timesteps    ):
              __UpperCamelCase    							=model(A_					,   A_    )
              __UpperCamelCase    							=scheduler.step(A_					,   A_					,   A_    ).prev_sample

         assert sample.dtype == torch.floataa









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





