




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

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

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

           lowercase			:Optional[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			:Tuple							=  scheduler.step_plms(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    **_lowerCAmelCase ).prev_sample
           lowercase			:Optional[int]							=  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:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any				,    _lowerCAmelCase:    Union[str, Any]=0				,    **_lowerCAmelCase:    Any ):
         lowercase			:Any							=  dict(self.forward_default_kwargs )
         lowercase			:Union[str, Any]							=  kwargs.pop("num_inference_steps"				,    _lowerCAmelCase )
         lowercase			:Optional[int]							=  self.dummy_sample
         lowercase			:List[str]							=  0.1 * sample
         lowercase			:Optional[int]							=  [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()
           lowercase			:int							=  scheduler_class(**_lowerCAmelCase )
           scheduler.set_timesteps(_lowerCAmelCase )

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

           lowercase			:Tuple							=  scheduler.step_prk(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    **_lowerCAmelCase ).prev_sample
           lowercase			: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[Any]							=  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:    Union[str, Any]				,    **_lowerCAmelCase:    Optional[int] ):
         lowercase			:int							=  self.scheduler_classes[0]
         lowercase			:Any							=  self.get_scheduler_config(**_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  scheduler_class(**_lowerCAmelCase )

         lowercase			:str							=  10
         lowercase			:Any							=  self.dummy_model()
         lowercase			:List[Any]							=  self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCAmelCase )

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

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

         return sample






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Dict							=  dict(self.forward_default_kwargs )

         lowercase			:Tuple							=  kwargs.pop("num_inference_steps"				,    _lowerCAmelCase )

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

           lowercase			:Any							=  self.dummy_sample
           lowercase			:Union[str, 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			:Tuple							=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           lowercase			:Dict							=  dummy_past_residuals[:]

           lowercase			:Optional[int]							=  scheduler.step_prk(_lowerCAmelCase				,    0				,    _lowerCAmelCase				,    **_lowerCAmelCase ).prev_sample
           lowercase			:int							=  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			:Dict							=  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:    int ):
         for timesteps in [1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=_lowerCAmelCase )






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

         lowercase			:List[Any]							=  self.scheduler_classes[0]
         lowercase			:List[Any]							=  self.get_scheduler_config(steps_offset=1 )
         lowercase			:Optional[int]							=  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[Any] ):
         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:    Tuple ):
         for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_lowerCAmelCase )






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






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






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

         for scheduler_class in self.scheduler_classes:
           lowercase			:Any							=  self.dummy_sample
           lowercase			:str							=  0.1 * sample

           lowercase			:Union[str, Any]							=  self.get_scheduler_config()
           lowercase			:Optional[int]							=  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			:Tuple							=  scheduler.step_prk(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase ).prev_sample






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

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






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









import math
import sys



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  if number != int(lowerCamelCase      ):
    raise ValueError("the value of input must be a natural number"      )
  if number < 0:
    raise ValueError("the value of input must not be a negative number"      )
  if number == 0:
    return 1
  lowercase			:Dict							=  [-1] * (number + 1)
  lowercase			:Optional[Any]							=  0
  for i in range(1,	number + 1      ):
    lowercase			:Optional[int]							=  sys.maxsize
    lowercase			:Any							=  int(math.sqrt(lowerCamelCase      )      )
    for j in range(1,	root + 1      ):
      lowercase			:Any							=  1 + answers[i - (j**2)]
      lowercase			:Dict							=  min(lowerCamelCase,	lowerCamelCase      )
    lowercase			:Union[str, Any]							=  answer
  return answers[number]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



