





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Any							=     (PNDMScheduler,)
      _UpperCAmelCase			:List[str]							=     (('num_inference_steps', 5_0),)





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     {
           "num_train_timesteps": 1000,
           "beta_start": 0.00_01,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**A_     )
       return config





      def  __UpperCamelCase(				self ,							A_=0 ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     dict(self.forward_default_kwargs     )
       UpperCamelCase			:      Any 			=     kwargs.pop("num_inference_steps" ,							A_     )
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_sample
       UpperCamelCase			:      str 			=     0.1 * sample
       UpperCamelCase			:      str 			=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCamelCase			:      Tuple 			=     self.get_scheduler_config(**A_     )
        UpperCamelCase			:      Dict 			=     scheduler_class(**A_     )
        scheduler.set_timesteps(A_     )
        # copy over dummy past residuals
        UpperCamelCase			:      List[str] 			=     dummy_past_residuals[:]

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

        UpperCamelCase			:      int 			=     scheduler.step_prk(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      Tuple 			=     new_scheduler.step_prk(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample

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

        UpperCamelCase			:      str 			=     scheduler.step_plms(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      List[Any] 			=     new_scheduler.step_plms(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self ,							A_=0 ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     dict(self.forward_default_kwargs     )
       UpperCamelCase			:      Union[str, Any] 			=     kwargs.pop("num_inference_steps" ,							A_     )
       UpperCamelCase			:      str 			=     self.dummy_sample
       UpperCamelCase			:      Dict 			=     0.1 * sample
       UpperCamelCase			:      Union[str, Any] 			=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCamelCase			:      Dict 			=     self.get_scheduler_config()
        UpperCamelCase			:      Optional[int] 			=     scheduler_class(**A_     )
        scheduler.set_timesteps(A_     )

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

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(A_     )
         UpperCamelCase			:      Dict 			=     scheduler_class.from_pretrained(A_     )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(A_     )

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

        UpperCamelCase			:      Optional[Any] 			=     scheduler.step_prk(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      Union[str, Any] 			=     new_scheduler.step_prk(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample

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

        UpperCamelCase			:      Any 			=     scheduler.step_plms(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      int 			=     new_scheduler.step_plms(A_ ,							A_ ,							A_ ,							**A_     ).prev_sample

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





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.scheduler_classes[0]
       UpperCamelCase			:      str 			=     self.get_scheduler_config(**A_     )
       UpperCamelCase			:      Dict 			=     scheduler_class(**A_     )

       UpperCamelCase			:      Dict 			=     10
       UpperCamelCase			:      Any 			=     self.dummy_model()
       UpperCamelCase			:      int 			=     self.dummy_sample_deter
       scheduler.set_timesteps(A_     )

       for i, t in enumerate(scheduler.prk_timesteps     ):
        UpperCamelCase			:      Dict 			=     model(A_ ,							A_     )
        UpperCamelCase			:      Tuple 			=     scheduler.step_prk(A_ ,							A_ ,							A_     ).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps     ):
        UpperCamelCase			:      List[str] 			=     model(A_ ,							A_     )
        UpperCamelCase			:      Dict 			=     scheduler.step_plms(A_ ,							A_ ,							A_     ).prev_sample

       return sample





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     dict(self.forward_default_kwargs     )

       UpperCamelCase			:      Dict 			=     kwargs.pop("num_inference_steps" ,							A_     )

       for scheduler_class in self.scheduler_classes:
        UpperCamelCase			:      int 			=     self.get_scheduler_config()
        UpperCamelCase			:      Dict 			=     scheduler_class(**A_     )

        UpperCamelCase			:      Any 			=     self.dummy_sample
        UpperCamelCase			:      Any 			=     0.1 * sample

        if num_inference_steps is not None and hasattr(A_ ,							"set_timesteps"     ):
         scheduler.set_timesteps(A_     )
        elif num_inference_steps is not None and not hasattr(A_ ,							"set_timesteps"     ):
         UpperCamelCase			:      Optional[Any] 			=     num_inference_steps

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

        UpperCamelCase			:      Tuple 			=     scheduler.step_prk(A_ ,							0 ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      Any 			=     scheduler.step_prk(A_ ,							1 ,							A_ ,							**A_     ).prev_sample

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

        UpperCamelCase			:      Dict 			=     scheduler.step_plms(A_ ,							0 ,							A_ ,							**A_     ).prev_sample
        UpperCamelCase			:      Tuple 			=     scheduler.step_plms(A_ ,							1 ,							A_ ,							**A_     ).prev_sample

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

       UpperCamelCase			:      Any 			=     self.scheduler_classes[0]
       UpperCamelCase			:      Any 			=     self.get_scheduler_config(steps_offset=1     )
       UpperCamelCase			:      List[Any] 			=     scheduler_class(**A_     )
       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     ):
       '''simple docstring'''




       for beta_start, beta_end in zip([0.00_01, 0.0_01] ,							[0.0_02, 0.02]     ):
        self.check_over_configs(beta_start=A_ ,							beta_end=A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     27

       for scheduler_class in self.scheduler_classes:
        UpperCamelCase			:      str 			=     self.dummy_sample
        UpperCamelCase			:      List[Any] 			=     0.1 * sample

        UpperCamelCase			:      Optional[Any] 			=     self.get_scheduler_config()
        UpperCamelCase			:      List[str] 			=     scheduler_class(**A_     )

        scheduler.set_timesteps(A_     )

        # 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]     ):
         UpperCamelCase			:      Dict 			=     scheduler.step_prk(A_ ,							A_ ,							A_     ).prev_sample





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       with self.assertRaises(A_     ):
        UpperCamelCase			:      Tuple 			=     self.scheduler_classes[0]
        UpperCamelCase			:      Any 			=     self.get_scheduler_config()
        UpperCamelCase			:      int 			=     scheduler_class(**A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.full_loop()
       UpperCamelCase			:      Dict 			=     torch.sum(torch.abs(A_     )     )
       UpperCamelCase			:      str 			=     torch.mean(torch.abs(A_     )     )

       assert abs(result_sum.item() - 1_98.13_18     ) < 1e-2
       assert abs(result_mean.item() - 0.25_80     ) < 1e-3





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.full_loop(prediction_type="v_prediction"     )
       UpperCamelCase			:      Optional[int] 			=     torch.sum(torch.abs(A_     )     )
       UpperCamelCase			:      List[str] 			=     torch.mean(torch.abs(A_     )     )

       assert abs(result_sum.item() - 67.39_86     ) < 1e-2
       assert abs(result_mean.item() - 0.08_78     ) < 1e-3





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.full_loop(set_alpha_to_one=A_ ,							beta_start=0.01     )
       UpperCamelCase			:      Any 			=     torch.sum(torch.abs(A_     )     )
       UpperCamelCase			:      Tuple 			=     torch.mean(torch.abs(A_     )     )

       assert abs(result_sum.item() - 2_30.03_99     ) < 1e-2
       assert abs(result_mean.item() - 0.29_95     ) < 1e-3





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.full_loop(set_alpha_to_one=A_ ,							beta_start=0.01     )
       UpperCamelCase			:      Optional[Any] 			=     torch.sum(torch.abs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     torch.mean(torch.abs(A_     )     )

       assert abs(result_sum.item() - 1_86.94_82     ) < 1e-2
       assert abs(result_mean.item() - 0.24_34     ) < 1e-3







from ..utils import DummyObject, requires_backends



class       A__     ( metaclass=__snake_case    ):
      _UpperCAmelCase			:Tuple							=     ['note_seq']





      def __init__(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(self ,							["note_seq"]     )





      @classmethod
      def  __UpperCamelCase(				cls ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(cls ,							["note_seq"]     )





      @classmethod
      def  __UpperCamelCase(				cls ,							*A_ ,							**A_     ):
       '''simple docstring'''




       requires_backends(cls ,							["note_seq"]     )
