




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 		__lowercase						(__a				):




      """simple docstring"""





      _snake_case					=		(DPMSolverSinglestepScheduler,)
      _snake_case					=		(("""num_inference_steps""", 25),)

      def       UpperCAmelCase ( self							, **A      )						->							int:
         snake_case :							int           =			{
             """num_train_timesteps""": 1_0_0_0,
             """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""",
             """lambda_min_clipped""": -float("""inf"""      ),
             """variance_type""": None,
         }

         config.update(**_UpperCamelCase      )
         return config

      def       UpperCAmelCase ( self							, A=0							, **A      )						->							Tuple:
         snake_case :							Optional[int]           =			dict(self.forward_default_kwargs      )
         snake_case :							Any           =			kwargs.pop("""num_inference_steps"""							, _UpperCamelCase      )
         snake_case :							Dict           =			self.dummy_sample
         snake_case :							str           =			0.1 * sample
         snake_case :							List[Any]           =			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            snake_case :							Any           =			self.get_scheduler_config(**_UpperCamelCase      )
            snake_case :							List[str]           =			scheduler_class(**_UpperCamelCase      )
            scheduler.set_timesteps(_UpperCamelCase      )
            # copy over dummy past residuals
            snake_case :							Optional[Any]           =			dummy_past_residuals[: scheduler.config.solver_order]

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

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

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

      def       UpperCAmelCase ( self      )						->							str:
         pass

      def       UpperCAmelCase ( self							, A=0							, **A      )						->							int:
         snake_case :							Any           =			dict(self.forward_default_kwargs      )
         snake_case :							Optional[int]           =			kwargs.pop("""num_inference_steps"""							, _UpperCamelCase      )
         snake_case :							Optional[int]           =			self.dummy_sample
         snake_case :							int           =			0.1 * sample
         snake_case :							Optional[int]           =			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            snake_case :							str           =			self.get_scheduler_config()
            snake_case :							Tuple           =			scheduler_class(**_UpperCamelCase      )
            scheduler.set_timesteps(_UpperCamelCase      )

            # copy over dummy past residuals (must be after setting timesteps)
            snake_case :							Union[str, Any]           =			dummy_past_residuals[: scheduler.config.solver_order]

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

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

            snake_case :							int           =			scheduler.step(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, **_UpperCamelCase      ).prev_sample
            snake_case :							str           =			new_scheduler.step(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, **_UpperCamelCase      ).prev_sample

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

      def       UpperCAmelCase ( self							, A=None							, **A      )						->							Dict:
         if scheduler is None:
            snake_case :							List[Any]           =			self.scheduler_classes[0]
            snake_case :							int           =			self.get_scheduler_config(**_UpperCamelCase      )
            snake_case :							int           =			scheduler_class(**_UpperCamelCase      )

         snake_case :							Optional[int]           =			self.scheduler_classes[0]
         snake_case :							Dict           =			self.get_scheduler_config(**_UpperCamelCase      )
         snake_case :							Dict           =			scheduler_class(**_UpperCamelCase      )

         snake_case :							Tuple           =			1_0
         snake_case :							Union[str, Any]           =			self.dummy_model()
         snake_case :							List[Any]           =			self.dummy_sample_deter
         scheduler.set_timesteps(_UpperCamelCase      )

         for i, t in enumerate(scheduler.timesteps      ):
            snake_case :							Tuple           =			model(_UpperCamelCase							, _UpperCamelCase      )
            snake_case :							List[Any]           =			scheduler.step(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase      ).prev_sample

         return sample

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         snake_case :							str           =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
         snake_case :							Tuple           =			5_0
         snake_case :							List[Any]           =			self.dummy_model()
         snake_case :							Optional[int]           =			self.dummy_sample_deter
         scheduler.set_timesteps(_UpperCamelCase      )

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]      ):
            snake_case :							Optional[int]           =			model(_UpperCamelCase							, _UpperCamelCase      )
            snake_case :							str           =			scheduler.step(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase      ).prev_sample

         snake_case :							List[str]           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.25_74      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							str:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         snake_case :							str           =			DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
         snake_case :							str           =			self.full_loop(scheduler=_UpperCamelCase      )
         snake_case :							str           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.27_91      ) < 1e-3

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

         snake_case :							int           =			self.full_loop(scheduler=_UpperCamelCase      )
         snake_case :							str           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.27_91      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							Any:
         self.check_over_configs(thresholding=_UpperCamelCase      )
         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=_UpperCamelCase							, prediction_type=_UpperCamelCase							, sample_max_value=_UpperCamelCase							, algorithm_type="""dpmsolver++"""							, solver_order=_UpperCamelCase							, solver_type=_UpperCamelCase							, )

      def       UpperCAmelCase ( self      )						->							List[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							int:
         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_UpperCamelCase							, solver_type=_UpperCamelCase							, prediction_type=_UpperCamelCase							, algorithm_type=_UpperCamelCase							, )
                     snake_case :							Optional[int]           =			self.full_loop(
                         solver_order=_UpperCamelCase							, solver_type=_UpperCamelCase							, prediction_type=_UpperCamelCase							, algorithm_type=_UpperCamelCase							, )
                     assert not torch.isnan(_UpperCamelCase      ).any(), "Samples have nan numbers"

      def       UpperCAmelCase ( self      )						->							Optional[int]:
         self.check_over_configs(lower_order_final=_UpperCamelCase      )
         self.check_over_configs(lower_order_final=_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							str:
         self.check_over_configs(lambda_min_clipped=-float("""inf"""      )      )
         self.check_over_configs(lambda_min_clipped=-5.1      )

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         self.check_over_configs(variance_type=_UpperCamelCase      )
         self.check_over_configs(variance_type="""learned_range"""      )

      def       UpperCAmelCase ( self      )						->							int:
         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=_UpperCamelCase							, time_step=0      )

      def       UpperCAmelCase ( self      )						->							List[Any]:
         snake_case :							Optional[Any]           =			self.full_loop()
         snake_case :							Any           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.27_91      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							Optional[int]:
         snake_case :							Optional[int]           =			self.full_loop(use_karras_sigmas=_UpperCamelCase      )
         snake_case :							int           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.22_48      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							Any           =			self.full_loop(prediction_type="""v_prediction"""      )
         snake_case :							int           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.14_53      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							List[Any]           =			self.full_loop(prediction_type="""v_prediction"""							, use_karras_sigmas=_UpperCamelCase      )
         snake_case :							int           =			torch.mean(torch.abs(_UpperCamelCase      )      )

         assert abs(result_mean.item() - 0.06_49      ) < 1e-3

      def       UpperCAmelCase ( self      )						->							List[str]:
         snake_case :							Optional[int]           =			self.scheduler_classes[0]
         snake_case :							Optional[Any]           =			self.get_scheduler_config(thresholding=_UpperCamelCase							, dynamic_thresholding_ratio=0      )
         snake_case :							Dict           =			scheduler_class(**_UpperCamelCase      )

         snake_case :							List[Any]           =			1_0
         snake_case :							Union[str, Any]           =			self.dummy_model()
         snake_case :							Optional[Any]           =			self.dummy_sample_deter.half()
         scheduler.set_timesteps(_UpperCamelCase      )

         for i, t in enumerate(scheduler.timesteps      ):
            snake_case :							Tuple           =			model(_UpperCamelCase							, _UpperCamelCase      )
            snake_case :							List[str]           =			scheduler.step(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase      ).prev_sample

         assert sample.dtype == torch.floataa







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

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



