'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=(DDIMParallelScheduler,)
   lowerCamelCase_    					=(('eta', 0.0), ('num_inference_steps', 5_0))


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Optional[int])      ->					str:
         lowercase_     =      {
             "num_train_timesteps": 1000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**__lowerCAmelCase)
         return config


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		**__lowerCAmelCase				:	int)      ->					Any:
         lowercase_     =      self.scheduler_classes[0]
         lowercase_     =      self.get_scheduler_config(**__lowerCAmelCase)
         lowercase_     =      scheduler_class(**__lowerCAmelCase)

         lowercase_ ,							lowercase_     =      10, 0.0

         lowercase_     =      self.dummy_model()
         lowercase_     =      self.dummy_sample_deter

         scheduler.set_timesteps(__lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase_     =      model(__lowerCAmelCase   ,		__lowerCAmelCase)
               lowercase_     =      scheduler.step(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase).prev_sample

         return sample


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[str]:
         for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any)      ->					Any:
         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCAmelCase)

         lowercase_     =      self.scheduler_classes[0]
         lowercase_     =      self.get_scheduler_config(steps_offset=1)
         lowercase_     =      scheduler_class(**__lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps   ,		torch.LongTensor([801, 601, 401, 201, 1]))


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Optional[int]:
         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=__lowerCAmelCase   ,		beta_end=__lowerCAmelCase)


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


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int)      ->					Dict:
         for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Any:
         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[int]:
         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         self.check_over_configs(thresholding=__lowerCAmelCase)
         for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=__lowerCAmelCase   ,		prediction_type=__lowerCAmelCase   ,		sample_max_value=__lowerCAmelCase   ,		)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					str:
         for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Union[str, Any]:
         for t, num_inference_steps in zip([1, 10, 50]   ,		[10, 50, 500]):
               self.check_over_forward(time_step=__lowerCAmelCase   ,		num_inference_steps=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         for t, eta in zip([1, 10, 49]   ,		[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=__lowerCAmelCase   ,		eta=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str])      ->					List[Any]:
         lowercase_     =      self.scheduler_classes[0]
         lowercase_     =      self.get_scheduler_config()
         lowercase_     =      scheduler_class(**__lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0   ,		0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420   ,		400) - 0.1_4771)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980   ,		960) - 0.3_2460)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0   ,		0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487   ,		486) - 0.0_0979)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999   ,		998) - 0.02)) < 1e-5


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         lowercase_     =      self.scheduler_classes[0]
         lowercase_     =      self.get_scheduler_config()
         lowercase_     =      scheduler_class(**__lowerCAmelCase)

         lowercase_ ,							lowercase_     =      10, 0.0
         scheduler.set_timesteps(__lowerCAmelCase)

         lowercase_     =      self.dummy_model()
         lowercase_     =      self.dummy_sample_deter
         lowercase_     =      self.dummy_sample_deter + 0.1
         lowercase_     =      self.dummy_sample_deter - 0.1

         lowercase_     =      samplea.shape[0]
         lowercase_     =      torch.stack([samplea, samplea, samplea]   ,		dim=0)
         lowercase_     =      torch.arange(__lowerCAmelCase)[0:3, None].repeat(1   ,		__lowerCAmelCase)

         lowercase_     =      model(samples.flatten(0   ,		1)   ,		timesteps.flatten(0   ,		1))
         lowercase_     =      scheduler.batch_step_no_noise(__lowerCAmelCase   ,		timesteps.flatten(0   ,		1)   ,		samples.flatten(0   ,		1)   ,		__lowerCAmelCase)

         lowercase_     =      torch.sum(torch.abs(__lowerCAmelCase))
         lowercase_     =      torch.mean(torch.abs(__lowerCAmelCase))

         assert abs(result_sum.item() - 1147.7904) < 1e-2
         assert abs(result_mean.item() - 0.4982) < 1e-3


   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         lowercase_     =      self.full_loop()

         lowercase_     =      torch.sum(torch.abs(__lowerCAmelCase))
         lowercase_     =      torch.mean(torch.abs(__lowerCAmelCase))

         assert abs(result_sum.item() - 172.0067) < 1e-2
         assert abs(result_mean.item() - 0.22_3967) < 1e-3


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Any:
         lowercase_     =      self.full_loop(prediction_type="v_prediction")

         lowercase_     =      torch.sum(torch.abs(__lowerCAmelCase))
         lowercase_     =      torch.mean(torch.abs(__lowerCAmelCase))

         assert abs(result_sum.item() - 52.5302) < 1e-2
         assert abs(result_mean.item() - 0.0684) < 1e-3


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         # We specify different beta, so that the first alpha is 0.99
         lowercase_     =      self.full_loop(set_alpha_to_one=__lowerCAmelCase   ,		beta_start=0.01)
         lowercase_     =      torch.sum(torch.abs(__lowerCAmelCase))
         lowercase_     =      torch.mean(torch.abs(__lowerCAmelCase))

         assert abs(result_sum.item() - 149.8295) < 1e-2
         assert abs(result_mean.item() - 0.1951) < 1e-3


   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         # We specify different beta, so that the first alpha is 0.99
         lowercase_     =      self.full_loop(set_alpha_to_one=__lowerCAmelCase   ,		beta_start=0.01)
         lowercase_     =      torch.sum(torch.abs(__lowerCAmelCase))
         lowercase_     =      torch.mean(torch.abs(__lowerCAmelCase))

         assert abs(result_sum.item() - 149.0784) < 1e-2
         assert abs(result_mean.item() - 0.1941) < 1e-3







'''simple docstring'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






