


'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(PNDMScheduler,)
     lowerCamelCase__       						=				(('num_inference_steps', 50),)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       str      =							{
              "num_train_timesteps": 1000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__  (   self			,     lowerCamelCase=0			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[Any]      =							dict(self.forward_default_kwargs		)
          __A	:       Union[str, Any]      =							kwargs.pop("num_inference_steps"			,     lowerCamelCase		)
          __A	:       List[str]      =							self.dummy_sample
          __A	:       Any      =							0.1 * sample
          __A	:       Dict      =							[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(**lowerCamelCase		)
               __A	:       Any      =							scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)
               # copy over dummy past residuals
               __A	:       Union[str, Any]      =							dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A	:       Dict      =							scheduler_class.from_pretrained(lowerCamelCase		)
                    new_scheduler.set_timesteps(lowerCamelCase		)
                    # copy over dummy past residuals
                    __A	:       List[Any]      =							dummy_past_residuals[:]

               __A	:       Tuple      =							scheduler.step_prk(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
               __A	:       Union[str, 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	:       Tuple      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self			,     lowerCamelCase=0			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       Tuple      =							dict(self.forward_default_kwargs		)
          __A	:       List[str]      =							kwargs.pop("num_inference_steps"			,     lowerCamelCase		)
          __A	:       int      =							self.dummy_sample
          __A	:       Tuple      =							0.1 * sample
          __A	:       Dict      =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
               __A	:       Any      =							self.get_scheduler_config()
               __A	:       List[Any]      =							scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)

               # copy over dummy past residuals (must be after setting timesteps)
               __A	:       Union[str, Any]      =							dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A	:       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	:       str      =							dummy_past_residuals[:]

               __A	:       int      =							scheduler.step_prk(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
               __A	:       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	:       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 lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.scheduler_classes[0]
          __A	:       str      =							self.get_scheduler_config(**lowerCamelCase		)
          __A	:       List[Any]      =							scheduler_class(**lowerCamelCase		)

          __A	:       List[Any]      =							10
          __A	:       List[str]      =							self.dummy_model()
          __A	:       Dict      =							self.dummy_sample_deter
          scheduler.set_timesteps(lowerCamelCase		)

          for i, t in enumerate(scheduler.prk_timesteps		):
               __A	:       Dict      =							model(lowerCamelCase			,     lowerCamelCase		)
               __A	:       List[Any]      =							scheduler.step_prk(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps		):
               __A	:       Optional[int]      =							model(lowerCamelCase			,     lowerCamelCase		)
               __A	:       Any      =							scheduler.step_plms(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		).prev_sample

          return sample


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							dict(self.forward_default_kwargs		)

          __A	:       Union[str, Any]      =							kwargs.pop("num_inference_steps"			,     lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A	:       Optional[int]      =							self.get_scheduler_config()
               __A	:       List[Any]      =							scheduler_class(**lowerCamelCase		)

               __A	:       Union[str, Any]      =							self.dummy_sample
               __A	:       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"		):
                    __A	:       Optional[Any]      =							num_inference_steps

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

               __A	:       List[Any]      =							scheduler.step_prk(lowerCamelCase			,     0			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
               __A	:       List[str]      =							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	:       str      =							scheduler.step_plms(lowerCamelCase			,     0			,     lowerCamelCase			,     **lowerCamelCase		).prev_sample
               __A	:       List[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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for timesteps in [100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          __A	:       int      =							self.scheduler_classes[0]
          __A	:       Union[str, Any]      =							self.get_scheduler_config(steps_offset=1		)
          __A	:       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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for t, num_inference_steps in zip([1, 5, 10]			,     [10, 50, 100]		):
               self.check_over_forward(num_inference_steps=lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							27

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

               __A	:       Optional[Any]      =							self.get_scheduler_config()
               __A	:       Dict      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          with self.assertRaises(lowerCamelCase		):
               __A	:       List[Any]      =							self.scheduler_classes[0]
               __A	:       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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.full_loop()
          __A	:       int      =							torch.sum(torch.abs(lowerCamelCase		)		)
          __A	:       Union[str, Any]      =							torch.mean(torch.abs(lowerCamelCase		)		)

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.full_loop(prediction_type="v_prediction"		)
          __A	:       List[Any]      =							torch.sum(torch.abs(lowerCamelCase		)		)
          __A	:       Dict      =							torch.mean(torch.abs(lowerCamelCase		)		)

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.full_loop(set_alpha_to_one=lowerCamelCase			,     beta_start=0.01		)
          __A	:       Union[str, Any]      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.full_loop(set_alpha_to_one=lowerCamelCase			,     beta_start=0.01		)
          __A	:       List[Any]      =							torch.sum(torch.abs(lowerCamelCase		)		)
          __A	:       Any      =							torch.mean(torch.abs(lowerCamelCase		)		)

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





'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = " "							):




     '''simple docstring'''





     __A	:       List[str]      =							[]

     __A	:       Tuple      =							0
     for index, char in enumerate(SCREAMING_SNAKE_CASE							):
          if char == separator:
               split_words.append(string[last_index:index]							)
               __A	:       Union[str, Any]      =							index + 1
          elif index + 1 == len(SCREAMING_SNAKE_CASE							):
               split_words.append(string[last_index : index + 1]							)
     return split_words


if __name__ == "__main__":
      from doctest import testmod

      testmod()

