


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		(PNDMScheduler,)
  _lowercase									=		(('num_inference_steps', 5_0),)

  def    _UpperCamelCase( self			:  int				,				**lowerCamelCase__			:  str       ):
        a__	:  Optional[int]     =   {
            "num_train_timesteps": 1_000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCamelCase__       )
        return config

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Any=0				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  List[str]     =   dict(self.forward_default_kwargs       )
        a__	:  Any     =   kwargs.pop("num_inference_steps"				,				lowerCamelCase__       )
        a__	:  Union[str, Any]     =   self.dummy_sample
        a__	:  Optional[int]     =   0.1 * sample
        a__	:  List[Any]     =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

              a__	:  Optional[Any]     =   scheduler.step_plms(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				**lowerCamelCase__       ).prev_sample
              a__	:  Dict     =   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    _UpperCamelCase( self			:  Tuple       ):
        pass

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  List[Any]=0				,				**lowerCamelCase__			:  List[Any]       ):
        a__	:  List[Any]     =   dict(self.forward_default_kwargs       )
        a__	:  List[Any]     =   kwargs.pop("num_inference_steps"				,				lowerCamelCase__       )
        a__	:  int     =   self.dummy_sample
        a__	:  List[str]     =   0.1 * sample
        a__	:  Union[str, Any]     =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase__       )
                    a__	:  Optional[Any]     =   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)
                    a__	:  Optional[Any]     =   dummy_past_residuals[:]

              a__	:  List[str]     =   scheduler.step_prk(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				**lowerCamelCase__       ).prev_sample
              a__	:  List[Any]     =   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"

              a__	:  Union[str, Any]     =   scheduler.step_plms(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				**lowerCamelCase__       ).prev_sample
              a__	:  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    _UpperCamelCase( self			:  int				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  Union[str, Any]     =   self.scheduler_classes[0]
        a__	:  Dict     =   self.get_scheduler_config(**lowerCamelCase__       )
        a__	:  Optional[Any]     =   scheduler_class(**lowerCamelCase__       )

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

        for i, t in enumerate(scheduler.prk_timesteps       ):
              a__	:  Tuple     =   model(lowerCamelCase__				,				lowerCamelCase__       )
              a__	:  str     =   scheduler.step_prk(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       ).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps       ):
              a__	:  str     =   model(lowerCamelCase__				,				lowerCamelCase__       )
              a__	:  Optional[int]     =   scheduler.step_plms(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       ).prev_sample

        return sample

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

        a__	:  Tuple     =   kwargs.pop("num_inference_steps"				,				lowerCamelCase__       )

        for scheduler_class in self.scheduler_classes:
              a__	:  int     =   self.get_scheduler_config()
              a__	:  List[Any]     =   scheduler_class(**lowerCamelCase__       )

              a__	:  Dict     =   self.dummy_sample
              a__	:  Tuple     =   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"       ):
                    a__	:  str     =   num_inference_steps

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

              a__	:  str     =   scheduler.step_prk(lowerCamelCase__				,				0				,				lowerCamelCase__				,				**lowerCamelCase__       ).prev_sample
              a__	:  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       )

              a__	:  Dict     =   scheduler.step_plms(lowerCamelCase__				,				0				,				lowerCamelCase__				,				**lowerCamelCase__       ).prev_sample
              a__	:  str     =   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    _UpperCamelCase( self			:  Optional[int]       ):
        for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=lowerCamelCase__       )

        a__	:  str     =   self.scheduler_classes[0]
        a__	:  str     =   self.get_scheduler_config(steps_offset=1       )
        a__	:  Optional[Any]     =   scheduler_class(**lowerCamelCase__       )
        scheduler.set_timesteps(10       )
        assert torch.equal(
            scheduler.timesteps				,				torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]       )				,				)

  def    _UpperCamelCase( self			:  List[str]       ):
        for beta_start, beta_end in zip([0.0001, 0.001]				,				[0.002, 0.02]       ):
              self.check_over_configs(beta_start=lowerCamelCase__				,				beta_end=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]       ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple       ):
        for t in [1, 5, 10]:
              self.check_over_forward(time_step=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        for t, num_inference_steps in zip([1, 5, 10]				,				[10, 50, 100]       ):
              self.check_over_forward(num_inference_steps=lowerCamelCase__       )

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

        for scheduler_class in self.scheduler_classes:
              a__	:  int     =   self.dummy_sample
              a__	:  Optional[int]     =   0.1 * sample

              a__	:  str     =   self.get_scheduler_config()
              a__	:  List[str]     =   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]       ):
                    a__	:  Dict     =   scheduler.step_prk(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       ).prev_sample

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        with self.assertRaises(lowerCamelCase__       ):
              a__	:  Union[str, Any]     =   self.scheduler_classes[0]
              a__	:  Optional[Any]     =   self.get_scheduler_config()
              a__	:  Union[str, Any]     =   scheduler_class(**lowerCamelCase__       )

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

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Union[str, Any]     =   self.full_loop()
        a__	:  str     =   torch.sum(torch.abs(lowerCamelCase__       )       )
        a__	:  Dict     =   torch.mean(torch.abs(lowerCamelCase__       )       )

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

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Dict     =   self.full_loop(prediction_type="v_prediction"       )
        a__	:  Optional[int]     =   torch.sum(torch.abs(lowerCamelCase__       )       )
        a__	:  int     =   torch.mean(torch.abs(lowerCamelCase__       )       )

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

  def    _UpperCamelCase( self			:  Tuple       ):
        # We specify different beta, so that the first alpha is 0.99
        a__	:  Tuple     =   self.full_loop(set_alpha_to_one=lowerCamelCase__				,				beta_start=0.01       )
        a__	:  Tuple     =   torch.sum(torch.abs(lowerCamelCase__       )       )
        a__	:  Tuple     =   torch.mean(torch.abs(lowerCamelCase__       )       )

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

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

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



from abc import ABC, abstractmethod
from argparse import ArgumentParser







class 							lowerCAmelCase__   ( __lowercase						):

						@staticmethod
						@abstractmethod
						def 			A_					(      a					)       ->   Union[str, Any]:


								'''simple docstring'''

								raise NotImplementedError()







						@abstractmethod
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								raise NotImplementedError()



