

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class  __lowerCAmelCase     (					__snake_case							):
  _a									=  (DDPMScheduler,)




  def   A__     (				self							,   **lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        _lowercase	       ={
            'num_train_timesteps': 1_000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**__SCREAMING_SNAKE_CASE					)
        return config




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,   [0.002, 0.02, 0.2, 2]					):
              self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE							,   beta_end=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE					)
        for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__SCREAMING_SNAKE_CASE							,   prediction_type=__SCREAMING_SNAKE_CASE							,   sample_max_value=__SCREAMING_SNAKE_CASE							,   )




  def   A__     (				self					)  -> int:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        for t in [0, 500, 999]:
              self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00979					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =len(__SCREAMING_SNAKE_CASE					)

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

        for t in reversed(range(__SCREAMING_SNAKE_CASE					)					):
              # 1. predict noise residual
              _lowercase	       =model(__SCREAMING_SNAKE_CASE							,   __SCREAMING_SNAKE_CASE					)

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

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              _lowercase	       =pred_prev_sample

        _lowercase	       =torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
        _lowercase	       =torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 258.9606					) < 1e-2
        assert abs(result_mean.item() - 0.3372					) < 1e-3




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config(prediction_type='v_prediction'					)
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =len(__SCREAMING_SNAKE_CASE					)

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

        for t in reversed(range(__SCREAMING_SNAKE_CASE					)					):
              # 1. predict noise residual
              _lowercase	       =model(__SCREAMING_SNAKE_CASE							,   __SCREAMING_SNAKE_CASE					)

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

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              _lowercase	       =pred_prev_sample

        _lowercase	       =torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
        _lowercase	       =torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 202.0296					) < 1e-2
        assert abs(result_mean.item() - 0.2631					) < 1e-3




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)

        _lowercase	       =scheduler.timesteps

        for i, timestep in enumerate(__SCREAMING_SNAKE_CASE					):
              if i == len(__SCREAMING_SNAKE_CASE					) - 1:
                    _lowercase	       =-1
              else:
                    _lowercase	       =timesteps[i + 1]

              _lowercase	       =scheduler.previous_timestep(__SCREAMING_SNAKE_CASE					)
              _lowercase	       =prev_t.item()

              self.assertEqual(__SCREAMING_SNAKE_CASE							,   __SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =[100, 87, 50, 51, 0]

        with self.assertRaises(__SCREAMING_SNAKE_CASE							,   msg='`custom_timesteps` must be in descending order.'					):
              scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =[100, 87, 50, 1, 0]
        _lowercase	       =len(__SCREAMING_SNAKE_CASE					)

        with self.assertRaises(__SCREAMING_SNAKE_CASE							,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'					):
              scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE							,   timesteps=__SCREAMING_SNAKE_CASE					)




  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.scheduler_classes[0]
        _lowercase	       =self.get_scheduler_config()
        _lowercase	       =scheduler_class(**__SCREAMING_SNAKE_CASE					)

        _lowercase	       =[scheduler.config.num_train_timesteps]

        with self.assertRaises(
            __SCREAMING_SNAKE_CASE							,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'							,   ):
              scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE					)










"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




