



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Tuple					=      (PNDMScheduler,)
    __lowerCamelCase			: Optional[Any]					=      (("num_inference_steps", 50),)






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Optional[int]          =   {
            """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__		):
        A :       Optional[Any]          =   dict(self.forward_default_kwargs		)
        A :       str          =   kwargs.pop("""num_inference_steps""",		lowerCamelCase__		)
        A :       int          =   self.dummy_sample
        A :       int          =   0.1 * sample
        A :       int          =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

            A :       Dict          =   scheduler.step_prk(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A :       Tuple          =   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 :       List[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		):
        pass






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

        for scheduler_class in self.scheduler_classes:
            A :       Optional[int]          =   self.get_scheduler_config()
            A :       Any          =   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 :       Dict          =   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 :       int          =   dummy_past_residuals[:]

            A :       Any          =   scheduler.step_prk(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A :       Dict          =   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[int]          =   scheduler.step_plms(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A :       Any          =   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__		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)

        A :       Optional[Any]          =   10
        A :       Union[str, Any]          =   self.dummy_model()
        A :       Any          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowerCamelCase__		)

        for i, t in enumerate(scheduler.prk_timesteps		):
            A :       str          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       Optional[int]          =   scheduler.step_prk(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps		):
            A :       str          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   scheduler.step_plms(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   dict(self.forward_default_kwargs		)

        A :       List[Any]          =   kwargs.pop("""num_inference_steps""",		lowerCamelCase__		)

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

            A :       Optional[int]          =   self.dummy_sample
            A :       List[str]          =   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[int]          =   num_inference_steps

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

            A :       Optional[Any]          =   scheduler.step_prk(lowerCamelCase__,		0,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A :       List[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 :       List[str]          =   scheduler.step_plms(lowerCamelCase__,		0,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A :       int          =   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		):
        for timesteps in [100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Optional[Any]          =   self.scheduler_classes[0]
        A :       str          =   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		):
        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		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase__		)






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






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






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






    def 			_lowerCAmelCase					(       self		):
        # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
        A :       Optional[Any]          =   27

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

            A :       List[str]          =   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 :       Optional[int]          =   scheduler.step_prk(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample






    def 			_lowerCAmelCase					(       self		):
        with self.assertRaises(lowerCamelCase__		):
            A :       Tuple          =   self.scheduler_classes[0]
            A :       Any          =   self.get_scheduler_config()
            A :       Optional[Any]          =   scheduler_class(**lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.full_loop()
        A :       Union[str, Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Tuple          =   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		):
        A :       Any          =   self.full_loop(prediction_type="""v_prediction"""		)
        A :       int          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Union[str, Any]          =   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		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Dict          =   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		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Union[str, Any]          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[str]          =   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







def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


if __name__ == "__main__":
     print(F"""{solution() = }""")


