



'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  (DDPMScheduler,)







 def _snake_case (							self  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						List[str]			     = {
            """num_train_timesteps""": 1_000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """variance_type""": """fixed_small""",
            """clip_sample""": True,
        }

        config.update(**a_							)
        return config







 def _snake_case (							self							)		->   Any:
        for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,[0.002, 0.02, 0.2, 2]							):
               self.check_over_configs(beta_start=a_  ,beta_end=a_							)







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







 def _snake_case (							self							)		->   Optional[Any]:
        for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=a_							)







 def _snake_case (							self							)		->   str:
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=a_							)







 def _snake_case (							self							)		->   int:
        self.check_over_configs(thresholding=a_							)
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=a_  ,prediction_type=a_  ,sample_max_value=a_  ,)







 def _snake_case (							self							)		->   Optional[Any]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        for t in [0, 500, 999]:
               self.check_over_forward(time_step=a_							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Any			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						int			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Optional[Any]			     = scheduler_class(**a_							)

        assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0979							)							) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1E-5







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Dict			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[Any]			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Tuple			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = len(a_							)

        _UpperCAmelCase  :						Optional[Any]			     = self.dummy_model()
        _UpperCAmelCase  :						Tuple			     = self.dummy_sample_deter
        _UpperCAmelCase  :						Optional[int]			     = torch.manual_seed(0							)

        for t in reversed(range(a_							)							):
               # 1. predict noise residual
               _UpperCAmelCase  :						List[Any]			     = model(a_  ,a_							)

               # 2. predict previous mean of sample x_t-1
               _UpperCAmelCase  :						Union[str, Any]			     = scheduler.step(a_  ,a_  ,a_  ,generator=a_							).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               _UpperCAmelCase  :						str			     = pred_prev_sample

        _UpperCAmelCase  :						List[str]			     = torch.sum(torch.abs(a_							)							)
        _UpperCAmelCase  :						Tuple			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_sum.item() - 258.9606							) < 1E-2
        assert abs(result_mean.item() - 0.3372							) < 1E-3







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Tuple			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Optional[int]			     = self.get_scheduler_config(prediction_type="""v_prediction"""							)
        _UpperCAmelCase  :						str			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						Tuple			     = len(a_							)

        _UpperCAmelCase  :						Any			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        _UpperCAmelCase  :						Optional[Any]			     = torch.manual_seed(0							)

        for t in reversed(range(a_							)							):
               # 1. predict noise residual
               _UpperCAmelCase  :						Dict			     = model(a_  ,a_							)

               # 2. predict previous mean of sample x_t-1
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,generator=a_							).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               _UpperCAmelCase  :						Any			     = pred_prev_sample

        _UpperCAmelCase  :						Optional[Any]			     = torch.sum(torch.abs(a_							)							)
        _UpperCAmelCase  :						List[str]			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_sum.item() - 202.0296							) < 1E-2
        assert abs(result_mean.item() - 0.2631							) < 1E-3







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Any			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Tuple			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						Tuple			     = [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=a_							)

        _UpperCAmelCase  :						str			     = scheduler.timesteps

        for i, timestep in enumerate(a_							):
               if i == len(a_							) - 1:
                      _UpperCAmelCase  :						Any			     = -1
               else:
                      _UpperCAmelCase  :						Union[str, Any]			     = timesteps[i + 1]

               _UpperCAmelCase  :						str			     = scheduler.previous_timestep(a_							)
               _UpperCAmelCase  :						List[str]			     = prev_t.item()

               self.assertEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Any			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[Any]			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Optional[int]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						int			     = [100, 87, 50, 51, 0]

        with self.assertRaises(a_  ,msg="""`custom_timesteps` must be in descending order."""							):
               scheduler.set_timesteps(timesteps=a_							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						List[Any]			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Optional[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						Any			     = [100, 87, 50, 1, 0]
        _UpperCAmelCase  :						Any			     = len(a_							)

        with self.assertRaises(a_  ,msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""							):
               scheduler.set_timesteps(num_inference_steps=a_  ,timesteps=a_							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						Tuple			     = self.get_scheduler_config()
        _UpperCAmelCase  :						Optional[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            a_  ,msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  ,):
               scheduler.set_timesteps(timesteps=a_							)








'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter




def     snake_case_					(     lowerCAmelCase_		)->		typing.Counter[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						typing.Counter[int]			     = Counter()
       for base in range(1       ,   max_perimeter + 1		):
              for perpendicular in range(lowerCAmelCase_       ,   max_perimeter + 1		):
                     _UpperCAmelCase  :						List[str]			     = (base * base + perpendicular * perpendicular) ** 0.5
                     if hypotenuse == int(lowerCAmelCase_		):
                            _UpperCAmelCase  :						Optional[Any]			     = int(base + perpendicular + hypotenuse		)
                            if perimeter > max_perimeter:
                                   continue
                            triplets[perimeter] += 1
       return triplets




def     snake_case_					(     lowerCAmelCase_ = 1000		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = pythagorean_triple(lowerCAmelCase_		)
       return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
   print(f"""Perimeter {solution()} has maximum solutions""")



