

"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 		_A       (			__a					):
      __a								=	(DPMSolverSinglestepScheduler,)
      __a								=	(('num_inference_steps', 25),)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			{
             "num_train_timesteps": 1000,
             "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(**SCREAMING_SNAKE_CASE__		)
         return config





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0    ,						**SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			dict(self.forward_default_kwargs		)
         lowerCamelCase__						=			kwargs.pop("num_inference_steps"    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.dummy_sample
         lowerCamelCase__						=			0.1 * sample
         lowerCamelCase__						=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

            lowerCamelCase__					,       lowerCamelCase__						=			sample, sample
            for t in range(SCREAMING_SNAKE_CASE__    ,						time_step + scheduler.config.solver_order + 1		):
               lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		).prev_sample
               lowerCamelCase__						=			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 					_lowerCamelCase  (  self		)   ->					Tuple:
         pass





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			dict(self.forward_default_kwargs		)
         lowerCamelCase__						=			kwargs.pop("num_inference_steps"    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.dummy_sample
         lowerCamelCase__						=			0.1 * sample
         lowerCamelCase__						=			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            lowerCamelCase__						=			self.get_scheduler_config()
            lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)
            scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			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)
               lowerCamelCase__						=			dummy_past_residuals[: new_scheduler.config.solver_order]

            lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		).prev_sample
            lowerCamelCase__						=			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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         if scheduler is None:
            lowerCamelCase__						=			self.scheduler_classes[0]
            lowerCamelCase__						=			self.get_scheduler_config(**SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config(**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			10
         lowerCamelCase__						=			self.dummy_model()
         lowerCamelCase__						=			self.dummy_sample_deter
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

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

         return sample





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
         lowerCamelCase__						=			50
         lowerCamelCase__						=			self.dummy_model()
         lowerCamelCase__						=			self.dummy_sample_deter
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]		):
            lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			scheduler.step(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		).prev_sample

         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__		)





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

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

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

         lowerCamelCase__						=			self.full_loop(scheduler=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         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 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         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=SCREAMING_SNAKE_CASE__    ,						solver_type=SCREAMING_SNAKE_CASE__    ,						prediction_type=SCREAMING_SNAKE_CASE__    ,						algorithm_type=SCREAMING_SNAKE_CASE__    ,						)
                     lowerCamelCase__						=			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 					_lowerCamelCase  (  self		)   ->					Any:
         self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__		)
         self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
         self.check_over_configs(lambda_min_clipped=-5.1		)





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





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__    ,						time_step=0		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			self.full_loop()
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			self.full_loop(use_karras_sigmas=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.full_loop(prediction_type="v_prediction"		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			self.full_loop(prediction_type="v_prediction"    ,						use_karras_sigmas=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

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





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__    ,						dynamic_thresholding_ratio=0		)
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			10
         lowerCamelCase__						=			self.dummy_model()
         lowerCamelCase__						=			self.dummy_sample_deter.half()
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

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

         assert sample.dtype == torch.floataa







"""simple docstring"""




from __future__ import annotations

from typing import Any



def 	UpperCAmelCase__   (				A__   )      ->      None:




   """simple docstring"""
   create_state_space_tree(A__	,    []	,    0   )



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      None:




   """simple docstring"""

   if index == len(A__   ):
      print(A__   )
      return

   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.append(sequence[index]   )
   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.pop()


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			list[Any]	  =			[3, 1, 2, 4]
     generate_all_subsequences(seq)

     seq.clear()
     seq.extend(['''A''', '''B''', '''C'''])
     generate_all_subsequences(seq)




