






"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (IPNDMScheduler,)
      __UpperCamelCase	 =  (("num_inference_steps", 5_0),)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	**lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{'''num_train_timesteps''': 1000}
          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  Any=0						,	**lowercase_     :  Any):




          '''simple docstring'''


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

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

              if time_step is None:
                  SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.timesteps[len(scheduler.timesteps) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowercase_)
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler_class.from_pretrained(lowercase_)
                  new_scheduler.set_timesteps(lowercase_)
                  # copy over dummy past residuals
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  str    						=		new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  Optional[Any]=0						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


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

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)

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

              if time_step is None:
                  SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.timesteps[len(scheduler.timesteps) // 2]

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

              SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		new_scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	**lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		10
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_)

          for i, t in enumerate(scheduler.timesteps):
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          for i, t in enumerate(scheduler.timesteps):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		dict(self.forward_default_kwargs)

          SCREAMING_SNAKE_CASE_      :  int    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler_class(**lowercase_)

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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'''):
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_inference_steps

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

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.timesteps[5]
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler.timesteps[6]

              SCREAMING_SNAKE_CASE_      :  int    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler.step(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              self.assertEqual(output_a.shape						,	sample.shape)
              self.assertEqual(output_a.shape						,	output_a.shape)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_						,	time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.full_loop()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_mean.item() - 2540529) < 10








from ..utils import DummyObject, requires_backends







class     __lowerCAmelCase (       metaclass=__magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''onnx''']


			def __init__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							requires_backends(self			,						['onnx']							)


			@classmethod
			def       lowercase_    (      cls			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							requires_backends(cls			,						['onnx']							)






			@classmethod
			def       lowercase_    (      cls			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							requires_backends(cls			,						['onnx']							)


