






"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class    __lowerCamelCase    (  _a  ):
   a      :				str 						=(UnCLIPScheduler,)

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Tuple:
       UpperCamelCase__         =    {
           'num_train_timesteps': 1000,
           'variance_type': 'fixed_small_log',
           'clip_sample': True,
           'clip_sample_range': 1.0,
           'prediction_type': 'epsilon',
       }

       config.update(**snake_case_      )
       return config

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for clip_sample_range in [1, 5, 10, 20]:
           self.check_over_configs(clip_sample_range=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(prediction_type=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for time_step in [0, 500, 999]:
           for prev_timestep in [None, 5, 100, 250, 500, 750]:
               if prev_timestep is not None and prev_timestep >= time_step:
                   continue

               self.check_over_forward(time_step=snake_case_ ,				prev_timestep=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config(variance_type='fixed_small_log'      )
       UpperCamelCase__         =    scheduler_class(**snake_case_      )

       assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000E-10      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_549_625      )      ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.9_994_987      )      ) < 1E-5

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config(variance_type='learned_range'      )
       UpperCamelCase__         =    scheduler_class(**snake_case_      )

       UpperCamelCase__         =    0.5

       assert scheduler._get_variance(1 ,				predicted_variance=snake_case_      ) - -10.1_712_790 < 1E-5
       assert scheduler._get_variance(487 ,				predicted_variance=snake_case_      ) - -5.7_998_052 < 1E-5
       assert scheduler._get_variance(999 ,				predicted_variance=snake_case_      ) - -0.0_010_011 < 1E-5

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config()
       UpperCamelCase__         =    scheduler_class(**snake_case_      )

       UpperCamelCase__         =    scheduler.timesteps

       UpperCamelCase__         =    self.dummy_model()
       UpperCamelCase__         =    self.dummy_sample_deter
       UpperCamelCase__         =    torch.manual_seed(0      )

       for i, t in enumerate(snake_case_      ):
           # 1. predict noise residual
           UpperCamelCase__         =    model(snake_case_ ,				snake_case_      )

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase__         =    scheduler.step(snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=snake_case_      ).prev_sample

           UpperCamelCase__         =    pred_prev_sample

       UpperCamelCase__         =    torch.sum(torch.abs(snake_case_      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(snake_case_      )      )

       assert abs(result_sum.item() - 252.2_682_495      ) < 1E-2
       assert abs(result_mean.item() - 0.3_284_743      ) < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.scheduler_classes[0]
       UpperCamelCase__         =    self.get_scheduler_config()
       UpperCamelCase__         =    scheduler_class(**snake_case_      )

       scheduler.set_timesteps(25      )

       UpperCamelCase__         =    scheduler.timesteps

       UpperCamelCase__         =    self.dummy_model()
       UpperCamelCase__         =    self.dummy_sample_deter
       UpperCamelCase__         =    torch.manual_seed(0      )

       for i, t in enumerate(snake_case_      ):
           # 1. predict noise residual
           UpperCamelCase__         =    model(snake_case_ ,				snake_case_      )

           if i + 1 == timesteps.shape[0]:
               UpperCamelCase__         =    None
           else:
               UpperCamelCase__         =    timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase__         =    scheduler.step(
               snake_case_ ,				snake_case_ ,				snake_case_ ,				prev_timestep=snake_case_ ,				generator=snake_case_      ).prev_sample

           UpperCamelCase__         =    pred_prev_sample

       UpperCamelCase__         =    torch.sum(torch.abs(snake_case_      )      )
       UpperCamelCase__         =    torch.mean(torch.abs(snake_case_      )      )

       assert abs(result_sum.item() - 258.2_044_983      ) < 1E-2
       assert abs(result_mean.item() - 0.3_362_038      ) < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       pass







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       pass












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

       if arr is None and size is not None:
           UpperCamelCase__         =    size
           UpperCamelCase__         =    [0] * size
       elif arr is not None:
           self.init(snake_case_      )
       else:
           raise ValueError('Either arr or size must be specified'      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

       UpperCamelCase__         =    1  # Largest power of 2 <= size
       while j * 2 < self.size:
           j *= 2

       UpperCamelCase__         =    0

       while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
               value -= self.tree[i + j]
               i += j
           j //= 2
       return i


if __name__ == "__main__":
  import doctest

  doctest.testmod()




