
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class    _a				(UpperCamelCase_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE      =  (('num_inference_steps', 25),)






       def        UpperCamelCase				(							self       ,							**A__      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				{
              'num_train_timesteps': 10_00,
              'beta_start': 0.0001,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'solver_order': 2,
          }

          config.update(**__a      )
          return config






       def        UpperCamelCase				(							self       ,							A__=0       ,							**A__      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				dict(self.forward_default_kwargs      )
          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""num_inference_steps"""       ,							__a      )
          _SCREAMING_SNAKE_CASE      =				self.dummy_sample
          _SCREAMING_SNAKE_CASE      =				0.1 * sample
          _SCREAMING_SNAKE_CASE      =				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
             _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config(**__a      )
             _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )
             scheduler.set_timesteps(__a      )
             # copy over dummy past residuals
             _SCREAMING_SNAKE_CASE      =				dummy_past_residuals[: scheduler.config.solver_order]

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

             _SCREAMING_SNAKE_CASE      =				sample, sample
             for t in range(__a       ,							time_step + scheduler.config.solver_order + 1      ):
                _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a       ,							**__a      ).prev_sample
                _SCREAMING_SNAKE_CASE      =				new_scheduler.step(__a       ,							__a       ,							__a       ,							**__a      ).prev_sample

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






       def        UpperCamelCase				(							self      )    ->			Tuple:
          pass






       def        UpperCamelCase				(							self       ,							A__=0       ,							**A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				dict(self.forward_default_kwargs      )
          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""num_inference_steps"""       ,							__a      )
          _SCREAMING_SNAKE_CASE      =				self.dummy_sample
          _SCREAMING_SNAKE_CASE      =				0.1 * sample
          _SCREAMING_SNAKE_CASE      =				[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
             _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config()
             _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )
             scheduler.set_timesteps(__a      )

             # copy over dummy past residuals (must be after setting timesteps)
             _SCREAMING_SNAKE_CASE      =				dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__a      )
                _SCREAMING_SNAKE_CASE      =				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)
                _SCREAMING_SNAKE_CASE      =				dummy_past_residuals[: new_scheduler.config.solver_order]

             _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a       ,							**__a      ).prev_sample
             _SCREAMING_SNAKE_CASE      =				new_scheduler.step(__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__=None       ,							**A__      )    ->			Optional[Any]:






          if scheduler is None:
             _SCREAMING_SNAKE_CASE      =				self.scheduler_classes[0]
             _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config(**__a      )
             _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )

          _SCREAMING_SNAKE_CASE      =				self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config(**__a      )
          _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )

          _SCREAMING_SNAKE_CASE      =				10
          _SCREAMING_SNAKE_CASE      =				self.dummy_model()
          _SCREAMING_SNAKE_CASE      =				self.dummy_sample_deter
          scheduler.set_timesteps(__a      )

          for i, t in enumerate(scheduler.timesteps      ):
             _SCREAMING_SNAKE_CASE      =				model(__a       ,							__a      )
             _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a      ).prev_sample

          return sample






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				dict(self.forward_default_kwargs      )

          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""num_inference_steps"""       ,							__a      )

          for scheduler_class in self.scheduler_classes:
             _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config()
             _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )

             _SCREAMING_SNAKE_CASE      =				self.dummy_sample
             _SCREAMING_SNAKE_CASE      =				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"""      ):
                _SCREAMING_SNAKE_CASE      =				num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _SCREAMING_SNAKE_CASE      =				[residual + 0.2, residual + 0.15, residual + 0.10]
             _SCREAMING_SNAKE_CASE      =				dummy_past_residuals[: scheduler.config.solver_order]

             _SCREAMING_SNAKE_CASE      =				scheduler.timesteps[5]
             _SCREAMING_SNAKE_CASE      =				scheduler.timesteps[6]

             _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a       ,							**__a      ).prev_sample
             _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a       ,							**__a      ).prev_sample

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






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				DEISMultistepScheduler(**self.get_scheduler_config()      )
          _SCREAMING_SNAKE_CASE      =				self.full_loop(scheduler=__a      )
          _SCREAMING_SNAKE_CASE      =				torch.mean(torch.abs(__a      )      )

          assert abs(result_mean.item() - 0.2_3916      ) < 1E-3

          _SCREAMING_SNAKE_CASE      =				DPMSolverSinglestepScheduler.from_config(scheduler.config      )
          _SCREAMING_SNAKE_CASE      =				DPMSolverMultistepScheduler.from_config(scheduler.config      )
          _SCREAMING_SNAKE_CASE      =				UniPCMultistepScheduler.from_config(scheduler.config      )
          _SCREAMING_SNAKE_CASE      =				DEISMultistepScheduler.from_config(scheduler.config      )

          _SCREAMING_SNAKE_CASE      =				self.full_loop(scheduler=__a      )
          _SCREAMING_SNAKE_CASE      =				torch.mean(torch.abs(__a      )      )

          assert abs(result_mean.item() - 0.2_3916      ) < 1E-3






       def        UpperCamelCase				(							self      )    ->			int:






          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
             self.check_over_configs(num_train_timesteps=__a      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          self.check_over_configs(thresholding=__a      )
          for order in [1, 2, 3]:
             for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=__a       ,							prediction_type=__a       ,							sample_max_value=__a       ,							algorithm_type="""deis"""       ,							solver_order=__a       ,							solver_type=__a       ,							)






       def        UpperCamelCase				(							self      )    ->			int:






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






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:






          for algorithm_type in ["deis"]:
             for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          solver_order=__a       ,							solver_type=__a       ,							prediction_type=__a       ,							algorithm_type=__a       ,							)
                      _SCREAMING_SNAKE_CASE      =				self.full_loop(
                          solver_order=__a       ,							solver_type=__a       ,							prediction_type=__a       ,							algorithm_type=__a       ,							)
                      assert not torch.isnan(__a      ).any(), "Samples have nan numbers"






       def        UpperCamelCase				(							self      )    ->			Any:
          self.check_over_configs(lower_order_final=__a      )
          self.check_over_configs(lower_order_final=__a      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:






          for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
             self.check_over_forward(num_inference_steps=__a       ,							time_step=0      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.full_loop()
          _SCREAMING_SNAKE_CASE      =				torch.mean(torch.abs(__a      )      )

          assert abs(result_mean.item() - 0.2_3916      ) < 1E-3






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				self.full_loop(prediction_type="""v_prediction"""      )
          _SCREAMING_SNAKE_CASE      =				torch.mean(torch.abs(__a      )      )

          assert abs(result_mean.item() - 0.091      ) < 1E-3






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE      =				self.get_scheduler_config(thresholding=__a       ,							dynamic_thresholding_ratio=0      )
          _SCREAMING_SNAKE_CASE      =				scheduler_class(**__a      )

          _SCREAMING_SNAKE_CASE      =				10
          _SCREAMING_SNAKE_CASE      =				self.dummy_model()
          _SCREAMING_SNAKE_CASE      =				self.dummy_sample_deter.half()
          scheduler.set_timesteps(__a      )

          for i, t in enumerate(scheduler.timesteps      ):
             _SCREAMING_SNAKE_CASE      =				model(__a       ,							__a      )
             _SCREAMING_SNAKE_CASE      =				scheduler.step(__a       ,							__a       ,							__a      ).prev_sample

          assert sample.dtype == torch.floataa








'''simple docstring'''



import math
from collections.abc import Iterator
from itertools import takewhile
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bool:




   """simple docstring"""

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5    ,      int(math.sqrt(SCREAMING_SNAKE_CASE_						) + 1						)    ,      6						):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def    lowerCAmelCase_	(				)     ->     Iterator[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				2
   while True:
      if is_prime(SCREAMING_SNAKE_CASE_						):
         yield num
      num += 1
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = 2_00_00_00						)     ->     int:




   """simple docstring"""
   return sum(takewhile(lambda SCREAMING_SNAKE_CASE_						: x < n    ,      prime_generator()						)						)


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






