
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__A							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			List[str]					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**__A   )
      return config

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]=0					, **SCREAMING_SNAKE_CASE__			:			Tuple   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str=None					, **SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=__A   )
      UpperCamelCase						    =					torch.mean(torch.abs(__A   )   )

      assert abs(result_mean.item() - 0.23916   ) < 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() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      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      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=__A   )
      self.check_over_configs(lower_order_final=__A   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """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=__A					, time_step=0   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(__A   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(__A   )   )

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






 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      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 collections.abc import Callable




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  a
   __a					=  b
   if function(lowerCAmelCase__   ) == 0:  # one of the a or b is a root for the function
      return a
   elif function(lowerCAmelCase__   ) == 0:
      return b
   elif (
       function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) > 0
   ):  # if none of these are root and they are both positive or negative,
      # then this algorithm can't find the root
      raise ValueError("""could not find root in given interval."""   )
   else:
      __a					=  start + (end - start) / 2.0
      while abs(start - mid   ) > 10**-7:  # until precisely equals to 10^-7
         if function(lowerCAmelCase__   ) == 0:
            return mid
         elif function(lowerCAmelCase__   ) * function(lowerCAmelCase__   ) < 0:
            __a					=  mid
         else:
            __a					=  mid
         __a					=  start + (end - start) / 2.0
      return mid




def  a   (lowerCAmelCase__   ):
   return x**3 - 2 * x - 5


if __name__ == "__main__":
  print(bisection(f, 1, 1_0_0_0))

  import doctest

  doctest.testmod()



