"""simple docstring"""
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		(PNDMScheduler,)
      a__				=		(("""num_inference_steps""", 50),)



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      str:





        '''simple docstring'''





        a__:					List[Any]      							=     {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**lowercase)
        return config



      def   lowerCamelCase_       (						self   ,      lowercase=0   ,      **lowercase)     ->      Any:





        '''simple docstring'''





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

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

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

          a__:					Any      							=     scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample
          a__:					Tuple      							=     new_scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample

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

          a__:					List[Any]      							=     scheduler.step_plms(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample
          a__:					Optional[int]      							=     new_scheduler.step_plms(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self   ,      lowercase=0   ,      **lowercase)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     dict(self.forward_default_kwargs)
        a__:					Dict      							=     kwargs.pop('num_inference_steps'   ,      lowercase)
        a__:					str      							=     self.dummy_sample
        a__:					int      							=     0.1 * sample
        a__:					Any      							=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

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

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

          a__:					int      							=     scheduler.step_plms(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample
          a__:					Optional[int]      							=     new_scheduler.step_plms(lowercase   ,      lowercase   ,      lowercase   ,      **lowercase).prev_sample

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



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      Any:





        '''simple docstring'''





        a__:					str      							=     self.scheduler_classes[0]
        a__:					List[Any]      							=     self.get_scheduler_config(**lowercase)
        a__:					int      							=     scheduler_class(**lowercase)

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

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

        for i, t in enumerate(scheduler.plms_timesteps):
          a__:					Dict      							=     model(lowercase   ,      lowercase)
          a__:					int      							=     scheduler.step_plms(lowercase   ,      lowercase   ,      lowercase).prev_sample

        return sample



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[Any]      							=     dict(self.forward_default_kwargs)

        a__:					Union[str, Any]      							=     kwargs.pop('num_inference_steps'   ,      lowercase)

        for scheduler_class in self.scheduler_classes:
          a__:					Any      							=     self.get_scheduler_config()
          a__:					Any      							=     scheduler_class(**lowercase)

          a__:					Optional[Any]      							=     self.dummy_sample
          a__:					int      							=     0.1 * sample

          if num_inference_steps is not None and hasattr(lowercase   ,      'set_timesteps'):
            scheduler.set_timesteps(lowercase)
          elif num_inference_steps is not None and not hasattr(lowercase   ,      'set_timesteps'):
            a__:					int      							=     num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          a__:					Optional[int]      							=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          a__:					Any      							=     dummy_past_residuals[:]

          a__:					int      							=     scheduler.step_prk(lowercase   ,      0   ,      lowercase   ,      **lowercase).prev_sample
          a__:					List[str]      							=     scheduler.step_prk(lowercase   ,      1   ,      lowercase   ,      **lowercase).prev_sample

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

          a__:					Any      							=     scheduler.step_plms(lowercase   ,      0   ,      lowercase   ,      **lowercase).prev_sample
          a__:					List[str]      							=     scheduler.step_plms(lowercase   ,      1   ,      lowercase   ,      **lowercase).prev_sample

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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        for timesteps in [1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=lowercase)



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowercase)

        a__:					str      							=     self.scheduler_classes[0]
        a__:					Any      							=     self.get_scheduler_config(steps_offset=1)
        a__:					int      							=     scheduler_class(**lowercase)
        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   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for beta_start, beta_end in zip([0.0001, 0.001]   ,      [0.002, 0.02]):
          self.check_over_configs(beta_start=lowercase   ,      beta_end=lowercase)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        for t in [1, 5, 10]:
          self.check_over_forward(time_step=lowercase)



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        for t, num_inference_steps in zip([1, 5, 10]   ,      [10, 50, 1_00]):
          self.check_over_forward(num_inference_steps=lowercase)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     27

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

          a__:					str      							=     self.get_scheduler_config()
          a__:					Optional[Any]      							=     scheduler_class(**lowercase)

          scheduler.set_timesteps(lowercase)

          # 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]):
            a__:					int      							=     scheduler.step_prk(lowercase   ,      lowercase   ,      lowercase).prev_sample



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        with self.assertRaises(lowercase):
          a__:					Tuple      							=     self.scheduler_classes[0]
          a__:					Optional[Any]      							=     self.get_scheduler_config()
          a__:					List[Any]      							=     scheduler_class(**lowercase)

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.full_loop()
        a__:					Any      							=     torch.sum(torch.abs(lowercase))
        a__:					List[str]      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 198.1318) < 1e-2
        assert abs(result_mean.item() - 0.2580) < 1e-3



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[str]      							=     self.full_loop(prediction_type='v_prediction')
        a__:					int      							=     torch.sum(torch.abs(lowercase))
        a__:					Optional[Any]      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 67.3986) < 1e-2
        assert abs(result_mean.item() - 0.0878) < 1e-3



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					int      							=     self.full_loop(set_alpha_to_one=lowercase   ,      beta_start=0.01)
        a__:					Tuple      							=     torch.sum(torch.abs(lowercase))
        a__:					Dict      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 230.0399) < 1e-2
        assert abs(result_mean.item() - 0.2995) < 1e-3



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Tuple      							=     self.full_loop(set_alpha_to_one=lowercase   ,      beta_start=0.01)
        a__:					Optional[Any]      							=     torch.sum(torch.abs(lowercase))
        a__:					Tuple      							=     torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 186.9482) < 1e-2
        assert abs(result_mean.item() - 0.2434) < 1e-3



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


