


"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				List[Any]             =    (DPMSolverSinglestepScheduler,)
    a_    :				Optional[Any]             =    (('num_inference_steps', 25),)
    def      _UpperCamelCase       ( self							:					Tuple							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		{
              'num_train_timesteps': 10_00,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'solver_order': 2,
              'prediction_type': 'epsilon',
              'thresholding': False,
              'sample_max_value': 1.0,
              'algorithm_type': 'dpmsolver++',
              'solver_type': 'midpoint',
              'lambda_min_clipped': -float('inf'				),
              'variance_type': None,
          }

          config.update(**SCREAMING_SNAKE_CASE__				)
          return config
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Any=0							,					**SCREAMING_SNAKE_CASE__							:					str				):
          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 + 0.10]

          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[: scheduler.config.solver_order]

                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[: new_scheduler.config.solver_order]

                lowerCamelCase__    ,  lowerCamelCase__           =		sample, sample
                for t in range(SCREAMING_SNAKE_CASE__							,					time_step + scheduler.config.solver_order + 1				):
                      lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				).prev_sample
                      lowerCamelCase__           =		new_scheduler.step(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      _UpperCamelCase       ( self							:					List[Any]				):
          pass
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0							,					**SCREAMING_SNAKE_CASE__							:					Tuple				):
          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 + 0.10]

          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[: scheduler.config.solver_order]

                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[: new_scheduler.config.solver_order]

                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				).prev_sample
                lowerCamelCase__           =		new_scheduler.step(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      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					**SCREAMING_SNAKE_CASE__							:					Tuple				):
          if scheduler is None:
                lowerCamelCase__           =		self.scheduler_classes[0]
                lowerCamelCase__           =		self.get_scheduler_config(**SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__				)

          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.timesteps				):
                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				).prev_sample

          return sample
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
          lowerCamelCase__           =		50
          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter
          scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)

          # make sure that the first t is uneven
          for i, t in enumerate(scheduler.timesteps[3:]				):
                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				).prev_sample

          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.25_74				) < 1e-3
    def      _UpperCamelCase       ( self							:					Tuple				):
          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          lowerCamelCase__           =		DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
          lowerCamelCase__           =		self.full_loop(scheduler=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.27_91				) < 1e-3

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

          lowerCamelCase__           =		self.full_loop(scheduler=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.27_91				) < 1e-3
    def      _UpperCamelCase       ( self							:					Any				):
          self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__				)
          for order in [1, 2, 3]:
                for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=SCREAMING_SNAKE_CASE__							,					prediction_type=SCREAMING_SNAKE_CASE__							,					sample_max_value=SCREAMING_SNAKE_CASE__							,					algorithm_type='dpmsolver++'							,					solver_order=SCREAMING_SNAKE_CASE__							,					solver_type=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      solver_order=SCREAMING_SNAKE_CASE__							,					solver_type=SCREAMING_SNAKE_CASE__							,					prediction_type=SCREAMING_SNAKE_CASE__							,					algorithm_type=SCREAMING_SNAKE_CASE__							,					)
                                  lowerCamelCase__           =		self.full_loop(
                                      solver_order=SCREAMING_SNAKE_CASE__							,					solver_type=SCREAMING_SNAKE_CASE__							,					prediction_type=SCREAMING_SNAKE_CASE__							,					algorithm_type=SCREAMING_SNAKE_CASE__							,					)
                                  assert not torch.isnan(SCREAMING_SNAKE_CASE__				).any(), "Samples have nan numbers"
    def      _UpperCamelCase       ( self							:					List[Any]				):
          self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__				)
          self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Any				):
          self.check_over_configs(lambda_min_clipped=-float('inf'				)				)
          self.check_over_configs(lambda_min_clipped=-5.1				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__				)
          self.check_over_configs(variance_type='learned_range'				)
    def      _UpperCamelCase       ( self							:					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=SCREAMING_SNAKE_CASE__							,					time_step=0				)
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		self.full_loop()
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.27_91				) < 1e-3
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		self.full_loop(use_karras_sigmas=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.22_48				) < 1e-3
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		self.full_loop(prediction_type='v_prediction'				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.14_53				) < 1e-3
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.full_loop(prediction_type='v_prediction'							,					use_karras_sigmas=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          assert abs(result_mean.item() - 0.06_49				) < 1e-3


    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__							,					dynamic_thresholding_ratio=0				)
          lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		10
          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter.half()
          scheduler.set_timesteps(SCREAMING_SNAKE_CASE__				)

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

          assert sample.dtype == torch.floataa







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


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



