


'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   (PNDMScheduler,)
    a													=   (("num_inference_steps", 50),)


    def        A   (  self      :							int				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Optional[int]     = {
               'num_train_timesteps': 1000,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]=0				,							**SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Tuple     = dict(self.forward_default_kwargs)
           _A					:     str     = kwargs.pop('num_inference_steps'				,							SCREAMING_SNAKE_CASE)
           _A					:     Any     = self.dummy_sample
           _A					:     Dict     = 0.1 * sample
           _A					:     Optional[int]     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  _A					:     Union[str, Any]     = self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
                  _A					:     List[str]     = scheduler_class(**SCREAMING_SNAKE_CASE)
                  scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
                  # copy over dummy past residuals
                  _A					:     Dict     = dummy_past_residuals[:]

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

                  _A					:     Tuple     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     str     = new_scheduler.step_prk(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"

                  _A					:     Dict     = scheduler.step_plms(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     Union[str, Any]     = new_scheduler.step_plms(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        A   (  self      :							Optional[int]):
           pass


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     str     = dict(self.forward_default_kwargs)
           _A					:     Union[str, Any]     = kwargs.pop('num_inference_steps'				,							SCREAMING_SNAKE_CASE)
           _A					:     int     = self.dummy_sample
           _A					:     Dict     = 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					:     int     = self.get_scheduler_config()
                  _A					:     List[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)
                  scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

                  # copy over dummy past residuals (must be after setting timesteps)
                  _A					:     Union[str, Any]     = dummy_past_residuals[:]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(SCREAMING_SNAKE_CASE)
                         _A					:     Dict     = 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)
                         _A					:     Optional[Any]     = dummy_past_residuals[:]

                  _A					:     List[Any]     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     str     = new_scheduler.step_prk(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"

                  _A					:     List[Any]     = scheduler.step_plms(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     int     = new_scheduler.step_plms(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        A   (  self      :							Optional[Any]				,							**SCREAMING_SNAKE_CASE      :							int):
           _A					:     Optional[int]     = self.scheduler_classes[0]
           _A					:     int     = self.get_scheduler_config(**SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     Any     = 10
           _A					:     Optional[Any]     = self.dummy_model()
           _A					:     List[str]     = self.dummy_sample_deter
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.prk_timesteps):
                  _A					:     List[Any]     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     List[str]     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps):
                  _A					:     Dict     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     List[str]     = scheduler.step_plms(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE).prev_sample

           return sample


    def        A   (  self      :							List[str]):
           _A					:     Optional[Any]     = dict(self.forward_default_kwargs)

           _A					:     List[Any]     = kwargs.pop('num_inference_steps'				,							SCREAMING_SNAKE_CASE)

           for scheduler_class in self.scheduler_classes:
                  _A					:     List[Any]     = self.get_scheduler_config()
                  _A					:     Optional[int]     = scheduler_class(**SCREAMING_SNAKE_CASE)

                  _A					:     List[str]     = self.dummy_sample
                  _A					:     Dict     = 0.1 * sample

                  if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE				,							'set_timesteps'):
                         scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
                  elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE				,							'set_timesteps'):
                         _A					:     int     = num_inference_steps

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

                  _A					:     Optional[Any]     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							0				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     str     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							1				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample

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

                  _A					:     List[Any]     = scheduler.step_plms(SCREAMING_SNAKE_CASE				,							0				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample
                  _A					:     int     = scheduler.step_plms(SCREAMING_SNAKE_CASE				,							1				,							SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE).prev_sample

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


    def        A   (  self      :							str):
           for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Dict):
           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE)

           _A					:     Optional[Any]     = self.scheduler_classes[0]
           _A					:     Optional[int]     = self.get_scheduler_config(steps_offset=1)
           _A					:     Dict     = scheduler_class(**SCREAMING_SNAKE_CASE)
           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        A   (  self      :							Optional[int]):
           for beta_start, beta_end in zip([0.0001, 0.001]				,							[0.002, 0.02]):
                  self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE				,							beta_end=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           for t in [1, 5, 10]:
                  self.check_over_forward(time_step=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           for t, num_inference_steps in zip([1, 5, 10]				,							[10, 50, 100]):
                  self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           # 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					:     List[Any]     = self.dummy_sample
                  _A					:     Dict     = 0.1 * sample

                  _A					:     Tuple     = self.get_scheduler_config()
                  _A					:     str     = scheduler_class(**SCREAMING_SNAKE_CASE)

                  scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

                  # 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					:     List[Any]     = scheduler.step_prk(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE).prev_sample


    def        A   (  self      :							List[Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  _A					:     Dict     = self.scheduler_classes[0]
                  _A					:     List[str]     = self.get_scheduler_config()
                  _A					:     List[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

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


    def        A   (  self      :							Tuple):
           _A					:     str     = self.full_loop()
           _A					:     List[str]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Optional[int]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 198.1318) < 1e-2
           assert abs(result_mean.item() - 0.2580) < 1e-3


    def        A   (  self      :							List[str]):
           _A					:     str     = self.full_loop(prediction_type='v_prediction')
           _A					:     Union[str, Any]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Optional[int]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 67.3986) < 1e-2
           assert abs(result_mean.item() - 0.0878) < 1e-3


    def        A   (  self      :							List[Any]):
           # We specify different beta, so that the first alpha is 0.99
           _A					:     str     = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE				,							beta_start=0.01)
           _A					:     List[Any]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Optional[Any]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 230.0399) < 1e-2
           assert abs(result_mean.item() - 0.2995) < 1e-3


    def        A   (  self      :							Union[str, Any]):
           # We specify different beta, so that the first alpha is 0.99
           _A					:     Dict     = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE				,							beta_start=0.01)
           _A					:     List[Any]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     List[str]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 186.9482) < 1e-2
           assert abs(result_mean.item() - 0.2434) < 1e-3










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






