





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (UniPCMultistepScheduler,)
      A_						:    Dict							=   (('num_inference_steps', 25),)


      def        a			(self	:				Optional[int]    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''solver_type''': '''bh2''',
         }

         config.update(**a__				)
         return config


      def        a			(self	:				int    ,      a__	:				int=0    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __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        a			(self	:				Optional[int]    ,      a__	:				str=0    ,      **a__	:				Dict				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __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)
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __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        a			(self	:				Optional[int]    ,      a__	:				List[Any]=None    ,      **a__	:				Dict				):





         """simple docstring"""



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

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

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

         return sample


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)

         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)

            __snake_case								=       self.dummy_sample
            __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'''				):
               __snake_case								=       num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

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

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __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        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       UniPCMultistepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3

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

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a__				)


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         for order in [1, 2, 3]:
            for solver_type in ["bh1", "bh2"]:
               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__    ,      solver_order=a__    ,      solver_type=a__    ,      )


      def        a			(self	:				Tuple				):





         """simple docstring"""



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


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         for solver_type in ["bh1", "bh2"]:
            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__    ,      )
                  __snake_case								=       self.full_loop(
                      solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      )
                  assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				str				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Dict				):





         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_0_1_4				) < 1E-3


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

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

         assert sample.dtype == torch.floataa


      def        a			(self	:				Any    ,      **a__	:				Optional[int]				):





         """simple docstring"""



         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

            scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
            assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps








import math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

