




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						(PNDMScheduler,)
       _a 					=						(('''num_inference_steps''', 50),)






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					**_lowerCAmelCase:					Tuple				):
         lowercase    :str      	=						{
             "num_train_timesteps": 10_00,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**_lowerCAmelCase				)
         return config






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					int=0       ,					**_lowerCAmelCase:					Dict				):
         lowercase    :Optional[Any]      	=						dict(self.forward_default_kwargs				)
         lowercase    :Optional[int]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)
         lowercase    :Optional[Any]      	=						self.dummy_sample
         lowercase    :Optional[Any]      	=						0.1 * sample
         lowercase    :List[str]      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           lowercase    :Any      	=						self.get_scheduler_config(**_lowerCAmelCase				)
           lowercase    :List[Any]      	=						scheduler_class(**_lowerCAmelCase				)
           scheduler.set_timesteps(_lowerCAmelCase				)
           # copy over dummy past residuals
           lowercase    :Optional[Any]      	=						dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase				)
             lowercase    :Tuple      	=						scheduler_class.from_pretrained(_lowerCAmelCase				)
             new_scheduler.set_timesteps(_lowerCAmelCase				)
             # copy over dummy past residuals
             lowercase    :Optional[Any]      	=						dummy_past_residuals[:]

           lowercase    :int      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :Optional[int]      	=						new_scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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

           lowercase    :Dict      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :Tuple      	=						new_scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					Optional[Any]=0       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :Union[str, Any]      	=						dict(self.forward_default_kwargs				)
         lowercase    :Union[str, Any]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)
         lowercase    :Union[str, Any]      	=						self.dummy_sample
         lowercase    :List[Any]      	=						0.1 * sample
         lowercase    :List[str]      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           lowercase    :List[str]      	=						self.get_scheduler_config()
           lowercase    :Any      	=						scheduler_class(**_lowerCAmelCase				)
           scheduler.set_timesteps(_lowerCAmelCase				)

           # copy over dummy past residuals (must be after setting timesteps)
           lowercase    :Any      	=						dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase				)
             lowercase    :Dict      	=						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)
             lowercase    :Union[str, Any]      	=						dummy_past_residuals[:]

           lowercase    :int      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :List[str]      	=						new_scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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

           lowercase    :str      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :List[str]      	=						new_scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						self.scheduler_classes[0]
         lowercase    :Optional[Any]      	=						self.get_scheduler_config(**_lowerCAmelCase				)
         lowercase    :str      	=						scheduler_class(**_lowerCAmelCase				)

         lowercase    :str      	=						10
         lowercase    :Any      	=						self.dummy_model()
         lowercase    :Tuple      	=						self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCAmelCase				)

         for i, t in enumerate(scheduler.prk_timesteps				):
           lowercase    :str      	=						model(_lowerCAmelCase       ,					_lowerCAmelCase				)
           lowercase    :Any      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps				):
           lowercase    :Any      	=						model(_lowerCAmelCase       ,					_lowerCAmelCase				)
           lowercase    :List[str]      	=						scheduler.step_plms(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample

         return sample






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Tuple      	=						dict(self.forward_default_kwargs				)

         lowercase    :List[str]      	=						kwargs.pop("num_inference_steps"       ,					_lowerCAmelCase				)

         for scheduler_class in self.scheduler_classes:
           lowercase    :List[str]      	=						self.get_scheduler_config()
           lowercase    :List[Any]      	=						scheduler_class(**_lowerCAmelCase				)

           lowercase    :List[str]      	=						self.dummy_sample
           lowercase    :Optional[Any]      	=						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"				):
             lowercase    :List[str]      	=						num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowercase    :Dict      	=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           lowercase    :List[str]      	=						dummy_past_residuals[:]

           lowercase    :Tuple      	=						scheduler.step_prk(_lowerCAmelCase       ,					0       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :Union[str, Any]      	=						scheduler.step_prk(_lowerCAmelCase       ,					1       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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

           lowercase    :Union[str, Any]      	=						scheduler.step_plms(_lowerCAmelCase       ,					0       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample
           lowercase    :int      	=						scheduler.step_plms(_lowerCAmelCase       ,					1       ,					_lowerCAmelCase       ,					**_lowerCAmelCase				).prev_sample

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         for timesteps in [1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=_lowerCAmelCase				)

         lowercase    :str      	=						self.scheduler_classes[0]
         lowercase    :Any      	=						self.get_scheduler_config(steps_offset=1				)
         lowercase    :Tuple      	=						scheduler_class(**_lowerCAmelCase				)
         scheduler.set_timesteps(10				)
         assert torch.equal(
             scheduler.timesteps       ,					torch.LongTensor(
                 [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]				)       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         for beta_start, beta_end in zip([0.00_01, 0.0_01]       ,					[0.0_02, 0.02]				):
           self.check_over_configs(beta_start=_lowerCAmelCase       ,					beta_end=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         for t in [1, 5, 10]:
           self.check_over_forward(time_step=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         for t, num_inference_steps in zip([1, 5, 10]       ,					[10, 50, 1_00]				):
           self.check_over_forward(num_inference_steps=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
         lowercase    :List[str]      	=						27

         for scheduler_class in self.scheduler_classes:
           lowercase    :Dict      	=						self.dummy_sample
           lowercase    :Tuple      	=						0.1 * sample

           lowercase    :int      	=						self.get_scheduler_config()
           lowercase    :Optional[Any]      	=						scheduler_class(**_lowerCAmelCase				)

           scheduler.set_timesteps(_lowerCAmelCase				)

           # before power of 3 fix, would error on first step, so we only need to do two
           for i, t in enumerate(scheduler.prk_timesteps[:2]				):
             lowercase    :Any      	=						scheduler.step_prk(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         with self.assertRaises(_lowerCAmelCase				):
           lowercase    :Optional[int]      	=						self.scheduler_classes[0]
           lowercase    :Union[str, Any]      	=						self.get_scheduler_config()
           lowercase    :Tuple      	=						scheduler_class(**_lowerCAmelCase				)

           scheduler.step_plms(self.dummy_sample       ,					1       ,					self.dummy_sample				).prev_sample






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Tuple      	=						self.full_loop()
         lowercase    :Optional[Any]      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Any      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 1_98.13_18				) < 1e-2
         assert abs(result_mean.item() - 0.25_80				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :str      	=						self.full_loop(prediction_type="v_prediction"				)
         lowercase    :Union[str, Any]      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Dict      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 67.39_86				) < 1e-2
         assert abs(result_mean.item() - 0.08_78				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         # We specify different beta, so that the first alpha is 0.99
         lowercase    :Union[str, Any]      	=						self.full_loop(set_alpha_to_one=_lowerCAmelCase       ,					beta_start=0.01				)
         lowercase    :str      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Union[str, Any]      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 2_30.03_99				) < 1e-2
         assert abs(result_mean.item() - 0.29_95				) < 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         # We specify different beta, so that the first alpha is 0.99
         lowercase    :Optional[int]      	=						self.full_loop(set_alpha_to_one=_lowerCAmelCase       ,					beta_start=0.01				)
         lowercase    :Any      	=						torch.sum(torch.abs(_lowerCAmelCase				)				)
         lowercase    :Tuple      	=						torch.mean(torch.abs(_lowerCAmelCase				)				)

         assert abs(result_sum.item() - 1_86.94_82				) < 1e-2
         assert abs(result_mean.item() - 0.24_34				) < 1e-3









def       UpperCAmelCase__  (     lowerCamelCase   ):
  return 10 - x * x



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  # Bolzano theory in order to find if there is a root between a and b
  if equation(lowerCamelCase   ) * equation(lowerCamelCase   ) >= 0:
    raise ValueError("Wrong space!"   )

  lowercase    :Optional[int]      	=						a
  while (b - a) >= 0.01:
    # Find middle point
    lowercase    :Optional[Any]      	=						(a + b) / 2
    # Check if middle point is root
    if equation(lowerCamelCase   ) == 0.0:
      break
    # Decide the side to repeat the steps
    if equation(lowerCamelCase   ) * equation(lowerCamelCase   ) < 0:
      lowercase    :Any      	=						c
    else:
      lowercase    :List[Any]      	=						c
  return c


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(bisection(-2, 5))
     print(bisection(0, 6))



