




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 	__lowerCAmelCase			(			lowerCAmelCase_				):
       UpperCamelCase__        =						(DPMSolverMultistepScheduler,)
       UpperCamelCase__        =						(('num_inference_steps', 25),)
       def    lowerCamelCase__							(		self		:Tuple	,   **__magic_name__		:Tuple       ):







        '''simple docstring'''







        a		       =							{
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """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(**lowerCamelCase_       )
        return config
       def    lowerCamelCase__							(		self		:Tuple	,   __magic_name__		:Union[str, Any]=0	,   **__magic_name__		:Optional[int]       ):







        '''simple docstring'''







        a		       =							dict(self.forward_default_kwargs       )
        a		       =							kwargs.pop("""num_inference_steps"""	,   lowerCamelCase_       )
        a		       =							self.dummy_sample
        a		       =							0.1 * sample
        a		       =							[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

          assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        pass
       def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Any=0	,   **__magic_name__		:str       ):







        '''simple docstring'''







        a		       =							dict(self.forward_default_kwargs       )
        a		       =							kwargs.pop("""num_inference_steps"""	,   lowerCamelCase_       )
        a		       =							self.dummy_sample
        a		       =							0.1 * sample
        a		       =							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
         a		       =							self.get_scheduler_config()
         a		       =							scheduler_class(**lowerCamelCase_       )
         scheduler.set_timesteps(lowerCamelCase_       )

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

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

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

         a		       =							scheduler.step(lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_	,   **lowerCamelCase_       ).prev_sample
         a		       =							new_scheduler.step(lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_	,   **lowerCamelCase_       ).prev_sample

         assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
       def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:Any=None	,   **__magic_name__		:int       ):







        '''simple docstring'''







        if scheduler is None:
         a		       =							self.scheduler_classes[0]
         a		       =							self.get_scheduler_config(**lowerCamelCase_       )
         a		       =							scheduler_class(**lowerCamelCase_       )

        a		       =							10
        a		       =							self.dummy_model()
        a		       =							self.dummy_sample_deter
        scheduler.set_timesteps(lowerCamelCase_       )

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

        return sample
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        a		       =							dict(self.forward_default_kwargs       )

        a		       =							kwargs.pop("""num_inference_steps"""	,   lowerCamelCase_       )

        for scheduler_class in self.scheduler_classes:
         a		       =							self.get_scheduler_config()
         a		       =							scheduler_class(**lowerCamelCase_       )

         a		       =							self.dummy_sample
         a		       =							0.1 * sample

         if num_inference_steps is not None and hasattr(lowerCamelCase_	,   """set_timesteps"""       ):
          scheduler.set_timesteps(lowerCamelCase_       )
         elif num_inference_steps is not None and not hasattr(lowerCamelCase_	,   """set_timesteps"""       ):
          a		       =							num_inference_steps

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

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

         a		       =							scheduler.step(lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_	,   **lowerCamelCase_       ).prev_sample
         a		       =							scheduler.step(lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_	,   **lowerCamelCase_       ).prev_sample

         self.assertEqual(output_a.shape	,   sample.shape       )
         self.assertEqual(output_a.shape	,   output_a.shape       )
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase_       )
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        self.check_over_configs(thresholding=lowerCamelCase_       )
        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=lowerCamelCase_	,   prediction_type=lowerCamelCase_	,   sample_max_value=lowerCamelCase_	,   algorithm_type="""dpmsolver++"""	,   solver_order=lowerCamelCase_	,   solver_type=lowerCamelCase_	,   )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowerCamelCase_       )
       def    lowerCamelCase__							(		self		:Union[str, 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=lowerCamelCase_	,   solver_type=lowerCamelCase_	,   prediction_type=lowerCamelCase_	,   algorithm_type=lowerCamelCase_	,   )
            a		       =							self.full_loop(
                solver_order=lowerCamelCase_	,   solver_type=lowerCamelCase_	,   prediction_type=lowerCamelCase_	,   algorithm_type=lowerCamelCase_	,   )
            assert not torch.isnan(lowerCamelCase_       ).any(), "Samples have nan numbers"
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowerCamelCase_       )
        self.check_over_configs(lower_order_final=lowerCamelCase_       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float("""inf"""       )       )
        self.check_over_configs(lambda_min_clipped=-5.1       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        self.check_over_configs(variance_type=lowerCamelCase_       )
        self.check_over_configs(variance_type="""learned_range"""       )
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=lowerCamelCase_	,   time_step=0       )
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        a		       =							self.full_loop()
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 0.3301       ) < 1E-3
       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        a		       =							self.full_loop(thresholding=lowerCamelCase_	,   dynamic_thresholding_ratio=0.87	,   sample_max_value=0.5       )
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 1.1364       ) < 1E-3
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        a		       =							self.full_loop(prediction_type="""v_prediction"""       )
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 0.2251       ) < 1E-3
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        a		       =							self.full_loop(prediction_type="""v_prediction"""	,   use_karras_sigmas=lowerCamelCase_       )
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 0.2096       ) < 1E-3
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        a		       =							DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
        a		       =							self.full_loop(scheduler=lowerCamelCase_       )
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 0.3301       ) < 1E-3

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

        a		       =							self.full_loop(scheduler=lowerCamelCase_       )
        a		       =							torch.mean(torch.abs(lowerCamelCase_       )       )

        assert abs(result_mean.item() - 0.3301       ) < 1E-3
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        a		       =							self.scheduler_classes[0]
        a		       =							self.get_scheduler_config(thresholding=lowerCamelCase_	,   dynamic_thresholding_ratio=0       )
        a		       =							scheduler_class(**lowerCamelCase_       )

        a		       =							10
        a		       =							self.dummy_model()
        a		       =							self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCamelCase_       )

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

        assert sample.dtype == torch.floataa





       def    lowerCamelCase__							(		self		:Optional[Any]	,   **__magic_name__		:Tuple       ):







        '''simple docstring'''







        for scheduler_class in self.scheduler_classes:
         a		       =							self.get_scheduler_config(**lowerCamelCase_       )
         a		       =							scheduler_class(**lowerCamelCase_       )

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







import doctest

import projectq
from projectq.ops import H, Measure





def 	__A	(			__lowerCamelCase					)   -> int:
	a		       =							quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(__lowerCamelCase					)


if __name__ == "__main__":
		doctest.testmod()

		# initialises a new quantum backend
		__UpperCamelCase      : List[Any]					      =   projectq.MainEngine()

		# Generate a list of 10 random numbers
		__UpperCamelCase      : Optional[int]					      =   [get_random_number(quantum_engine) for _ in range(10)]

		# Flushes the quantum engine from memory
		quantum_engine.flush()

		print("Random numbers", random_numbers_list)

