




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				(PNDMScheduler,)
    snake_case__ =				(("num_inference_steps", 5_0),)


    def        a				(	self					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Optional[int]:
         lowerCAmelCase__          =			{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**SCREAMING_SNAKE_CASE__       )
         return config


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Union[str, Any]:
         lowerCAmelCase__          =			dict(self.forward_default_kwargs       )
         lowerCAmelCase__          =			kwargs.pop("num_inference_steps"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.dummy_sample
         lowerCAmelCase__          =			0.1 * sample
         lowerCAmelCase__          =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__          =			self.get_scheduler_config(**SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       )
              # copy over dummy past residuals
              lowerCAmelCase__          =			dummy_past_residuals[:]

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

              lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			new_scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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

              lowerCAmelCase__          =			scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			new_scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         pass


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0       ,     **SCREAMING_SNAKE_CASE__					:					str       )				->	Union[str, Any]:
         lowerCAmelCase__          =			dict(self.forward_default_kwargs       )
         lowerCAmelCase__          =			kwargs.pop("num_inference_steps"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.dummy_sample
         lowerCAmelCase__          =			0.1 * sample
         lowerCAmelCase__          =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__          =			self.get_scheduler_config()
              lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )
              scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       )

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

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

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

              lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			new_scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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

              lowerCAmelCase__          =			scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			new_scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Dict       )				->	Dict:
         lowerCAmelCase__          =			self.scheduler_classes[0]
         lowerCAmelCase__          =			self.get_scheduler_config(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			10
         lowerCAmelCase__          =			self.dummy_model()
         lowerCAmelCase__          =			self.dummy_sample_deter
         scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       )

         for i, t in enumerate(scheduler.prk_timesteps       ):
              lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps       ):
              lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ).prev_sample

         return sample


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			dict(self.forward_default_kwargs       )

         lowerCAmelCase__          =			kwargs.pop("num_inference_steps"       ,     SCREAMING_SNAKE_CASE__       )

         for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__          =			self.get_scheduler_config()
              lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )

              lowerCAmelCase__          =			self.dummy_sample
              lowerCAmelCase__          =			0.1 * sample

              if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__       ,     "set_timesteps"       ):
                   scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       )
              elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__       ,     "set_timesteps"       ):
                   lowerCAmelCase__          =			num_inference_steps

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

              lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     0       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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

              lowerCAmelCase__          =			scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     0       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample
              lowerCAmelCase__          =			scheduler.step_plms(SCREAMING_SNAKE_CASE__       ,     1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ).prev_sample

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


    def        a				(	self					:					Optional[Any]       )				->	List[Any]:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Optional[int]:
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.scheduler_classes[0]
         lowerCAmelCase__          =			self.get_scheduler_config(steps_offset=1       )
         lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )
         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        a				(	self					:					Tuple       )				->	List[Any]:
         for beta_start, beta_end in zip([0.0_001, 0.001]       ,     [0.002, 0.02]       ):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__       ,     beta_end=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       )				->	Tuple:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       )				->	Dict:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Tuple:
         for t in [1, 5, 10]:
              self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Any       )				->	Any:
         for t, num_inference_steps in zip([1, 5, 10]       ,     [10, 50, 100]       ):
              self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       )				->	List[Any]:
         # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
         lowerCAmelCase__          =			27

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

              lowerCAmelCase__          =			self.get_scheduler_config()
              lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )

              scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       )

              # 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]       ):
                   lowerCAmelCase__          =			scheduler.step_prk(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ).prev_sample


    def        a				(	self					:					str       )				->	int:
         with self.assertRaises(SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			self.scheduler_classes[0]
              lowerCAmelCase__          =			self.get_scheduler_config()
              lowerCAmelCase__          =			scheduler_class(**SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					Tuple       )				->	Optional[int]:
         lowerCAmelCase__          =			self.full_loop()
         lowerCAmelCase__          =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__       )       )

         assert abs(result_sum.item() - 198.1_318       ) < 1e-2
         assert abs(result_mean.item() - 0.2_580       ) < 1e-3


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			self.full_loop(prediction_type="v_prediction"       )
         lowerCAmelCase__          =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__       )       )

         assert abs(result_sum.item() - 67.3_986       ) < 1e-2
         assert abs(result_mean.item() - 0.0_878       ) < 1e-3


    def        a				(	self					:					str       )				->	Dict:
         # We specify different beta, so that the first alpha is 0.99
         lowerCAmelCase__          =			self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__       ,     beta_start=0.01       )
         lowerCAmelCase__          =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__       )       )

         assert abs(result_sum.item() - 230.0_399       ) < 1e-2
         assert abs(result_mean.item() - 0.2_995       ) < 1e-3


    def        a				(	self					:					Tuple       )				->	Tuple:
         # We specify different beta, so that the first alpha is 0.99
         lowerCAmelCase__          =			self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__       ,     beta_start=0.01       )
         lowerCAmelCase__          =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__       )       )

         assert abs(result_sum.item() - 186.9_482       ) < 1e-2
         assert abs(result_mean.item() - 0.2_434       ) < 1e-3






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     if not isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			F'Input value of [number={number}] must be an integer'
          raise TypeError(lowerCAmelCase_      )
     if number < 1:
          raise ValueError("Input must be a positive integer"      )
     return -1 if len(prime_factors(lowerCAmelCase_      )      ) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()
