


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					(DPMSolverMultistepScheduler,)
      snake_case__       =					(('''num_inference_steps''', 2_5),)




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						={
             "num_train_timesteps": 10_00,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
             "prediction_type": "epsilon",
             "thresholding": False,
             "sample_max_value": 1.0,
             "algorithm_type": "dpmsolver++",
             "solver_type": "midpoint",
             "lower_order_final": False,
             "lambda_min_clipped": -float("inf"),
             "variance_type": None,
         }

         config.update(**__SCREAMING_SNAKE_CASE)
         return config




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=0   ,			**__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=dict(self.forward_default_kwargs)
         UpperCamelCase__ :			int 						=kwargs.pop("num_inference_steps"   ,			__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			int 						=self.dummy_sample
         UpperCamelCase__ :			Union[str, Any] 						=0.1 * sample
         UpperCamelCase__ :			str 						=[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            UpperCamelCase__ :			int 						=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			List[str] 						=scheduler_class(**__SCREAMING_SNAKE_CASE)
            scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
            # copy over dummy past residuals
            UpperCamelCase__ :			str 						=dummy_past_residuals[: scheduler.config.solver_order]

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

            UpperCamelCase__						,	UpperCamelCase__ :			Tuple 						=sample, sample
            for t in range(__SCREAMING_SNAKE_CASE   ,			time_step + scheduler.config.solver_order + 1):
               UpperCamelCase__ :			Any 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample
               UpperCamelCase__ :			List[Any] 						=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample

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




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         pass




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=0   ,			**__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=dict(self.forward_default_kwargs)
         UpperCamelCase__ :			Any 						=kwargs.pop("num_inference_steps"   ,			__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			str 						=self.dummy_sample
         UpperCamelCase__ :			int 						=0.1 * sample
         UpperCamelCase__ :			Union[str, Any] 						=[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            UpperCamelCase__ :			Tuple 						=self.get_scheduler_config()
            UpperCamelCase__ :			List[Any] 						=scheduler_class(**__SCREAMING_SNAKE_CASE)
            scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

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

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

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

            UpperCamelCase__ :			Tuple 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample
            UpperCamelCase__ :			Optional[int] 						=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=None   ,			**__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         if scheduler is None:
            UpperCamelCase__ :			int 						=self.scheduler_classes[0]
            UpperCamelCase__ :			Tuple 						=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Optional[Any] 						=scheduler_class(**__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Optional[Any] 						=10
         UpperCamelCase__ :			List[str] 						=self.dummy_model()
         UpperCamelCase__ :			int 						=self.dummy_sample_deter
         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

         for i, t in enumerate(scheduler.timesteps):
            UpperCamelCase__ :			int 						=model(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Optional[int] 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE).prev_sample

         return sample




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=dict(self.forward_default_kwargs)

         UpperCamelCase__ :			str 						=kwargs.pop("num_inference_steps"   ,			__SCREAMING_SNAKE_CASE)

         for scheduler_class in self.scheduler_classes:
            UpperCamelCase__ :			str 						=self.get_scheduler_config()
            UpperCamelCase__ :			str 						=scheduler_class(**__SCREAMING_SNAKE_CASE)

            UpperCamelCase__ :			str 						=self.dummy_sample
            UpperCamelCase__ :			str 						=0.1 * sample

            if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE   ,			"set_timesteps"):
               scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
            elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE   ,			"set_timesteps"):
               UpperCamelCase__ :			Optional[Any] 						=num_inference_steps

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

            UpperCamelCase__ :			str 						=scheduler.timesteps[5]
            UpperCamelCase__ :			int 						=scheduler.timesteps[6]

            UpperCamelCase__ :			Union[str, Any] 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample
            UpperCamelCase__ :			Optional[Any] 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE).prev_sample

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




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         for timesteps in [25, 50, 1_00, 9_99, 10_00]:
            self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=__SCREAMING_SNAKE_CASE   ,			prediction_type=__SCREAMING_SNAKE_CASE   ,			sample_max_value=__SCREAMING_SNAKE_CASE   ,			algorithm_type="dpmsolver++"   ,			solver_order=__SCREAMING_SNAKE_CASE   ,			solver_type=__SCREAMING_SNAKE_CASE   ,			)




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                        if order == 3:
                           continue
                     else:
                        self.check_over_configs(
                            solver_order=__SCREAMING_SNAKE_CASE   ,			solver_type=__SCREAMING_SNAKE_CASE   ,			prediction_type=__SCREAMING_SNAKE_CASE   ,			algorithm_type=__SCREAMING_SNAKE_CASE   ,			)
                     UpperCamelCase__ :			str 						=self.full_loop(
                         solver_order=__SCREAMING_SNAKE_CASE   ,			solver_type=__SCREAMING_SNAKE_CASE   ,			prediction_type=__SCREAMING_SNAKE_CASE   ,			algorithm_type=__SCREAMING_SNAKE_CASE   ,			)
                     assert not torch.isnan(__SCREAMING_SNAKE_CASE).any(), "Samples have nan numbers"




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)
         self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         self.check_over_configs(lambda_min_clipped=-float("inf"))
         self.check_over_configs(lambda_min_clipped=-5.1)




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)
         self.check_over_configs(variance_type="learned_range")




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
            self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE   ,			time_step=0)




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.full_loop()
         UpperCamelCase__ :			int 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 0.33_01) < 1E-3




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,			dynamic_thresholding_ratio=0.87   ,			sample_max_value=0.5)
         UpperCamelCase__ :			List[str] 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 1.13_64) < 1E-3




      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=self.full_loop(prediction_type="v_prediction")
         UpperCamelCase__ :			Any 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 0.22_51) < 1E-3




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=self.full_loop(prediction_type="v_prediction"   ,			use_karras_sigmas=__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Any 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 0.20_96) < 1E-3




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=DPMSolverMultistepScheduler(**self.get_scheduler_config())
         UpperCamelCase__ :			List[Any] 						=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			List[str] 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 0.33_01) < 1E-3

         UpperCamelCase__ :			List[Any] 						=DPMSolverSinglestepScheduler.from_config(scheduler.config)
         UpperCamelCase__ :			str 						=UniPCMultistepScheduler.from_config(scheduler.config)
         UpperCamelCase__ :			Optional[Any] 						=DEISMultistepScheduler.from_config(scheduler.config)
         UpperCamelCase__ :			Dict 						=DPMSolverMultistepScheduler.from_config(scheduler.config)

         UpperCamelCase__ :			Dict 						=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[Any] 						=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

         assert abs(result_mean.item() - 0.33_01) < 1E-3




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=self.scheduler_classes[0]
         UpperCamelCase__ :			Tuple 						=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,			dynamic_thresholding_ratio=0)
         UpperCamelCase__ :			List[str] 						=scheduler_class(**__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Tuple 						=10
         UpperCamelCase__ :			List[str] 						=self.dummy_model()
         UpperCamelCase__ :			Optional[Any] 						=self.dummy_sample_deter.half()
         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

         for i, t in enumerate(scheduler.timesteps):
            UpperCamelCase__ :			Dict 						=model(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Tuple 						=scheduler.step(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE).prev_sample

         assert sample.dtype == torch.floataa




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         for scheduler_class in self.scheduler_classes:
            UpperCamelCase__ :			Optional[Any] 						=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			str 						=scheduler_class(**__SCREAMING_SNAKE_CASE)

            scheduler.set_timesteps(scheduler.config.num_train_timesteps)
            assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps





from typing import TYPE_CHECKING

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


__UpperCAmelCase      			=  {
    """configuration_switch_transformers""": [
        """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwitchTransformersConfig""",
        """SwitchTransformersOnnxConfig""",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SwitchTransformersEncoderModel""",
         """SwitchTransformersForConditionalGeneration""",
         """SwitchTransformersModel""",
         """SwitchTransformersPreTrainedModel""",
         """SwitchTransformersTop1Router""",
         """SwitchTransformersSparseMLP""",
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

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

