import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class      _A (     a_				):
  SCREAMING_SNAKE_CASE_						:						str    =(PNDMScheduler,)
  SCREAMING_SNAKE_CASE_						:						str    =(("num_inference_steps", 50),)






  def 		_a       (self ,					**SCREAMING_SNAKE_CASE_						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   {
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__UpperCamelCase						)
       return config






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0 ,					**SCREAMING_SNAKE_CASE_						)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__					    =   dict(self.forward_default_kwargs						)
       UpperCamelCase__					    =   kwargs.pop('''num_inference_steps''' ,					__UpperCamelCase						)
       UpperCamelCase__					    =   self.dummy_sample
       UpperCamelCase__					    =   0.1 * sample
       UpperCamelCase__					    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__					    =   self.get_scheduler_config(**__UpperCamelCase						)
            UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)
            scheduler.set_timesteps(__UpperCamelCase						)
            # copy over dummy past residuals
            UpperCamelCase__					    =   dummy_past_residuals[:]

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

            UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   new_scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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

            UpperCamelCase__					    =   scheduler.step_plms(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   new_scheduler.step_plms(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       pass






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0 ,					**SCREAMING_SNAKE_CASE_						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__					    =   dict(self.forward_default_kwargs						)
       UpperCamelCase__					    =   kwargs.pop('''num_inference_steps''' ,					__UpperCamelCase						)
       UpperCamelCase__					    =   self.dummy_sample
       UpperCamelCase__					    =   0.1 * sample
       UpperCamelCase__					    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__					    =   self.get_scheduler_config()
            UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)
            scheduler.set_timesteps(__UpperCamelCase						)

            # copy over dummy past residuals (must be after setting timesteps)
            UpperCamelCase__					    =   dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(__UpperCamelCase						)
                 UpperCamelCase__					    =   scheduler_class.from_pretrained(__UpperCamelCase						)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(__UpperCamelCase						)

                 # copy over dummy past residual (must be after setting timesteps)
                 UpperCamelCase__					    =   dummy_past_residuals[:]

            UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   new_scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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

            UpperCamelCase__					    =   scheduler.step_plms(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   new_scheduler.step_plms(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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






  def 		_a       (self ,					**SCREAMING_SNAKE_CASE_						)		->   Tuple:





       '''simple docstring'''





       UpperCamelCase__					    =   self.scheduler_classes[0]
       UpperCamelCase__					    =   self.get_scheduler_config(**__UpperCamelCase						)
       UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)

       UpperCamelCase__					    =   10
       UpperCamelCase__					    =   self.dummy_model()
       UpperCamelCase__					    =   self.dummy_sample_deter
       scheduler.set_timesteps(__UpperCamelCase						)

       for i, t in enumerate(scheduler.prk_timesteps						):
            UpperCamelCase__					    =   model(__UpperCamelCase ,					__UpperCamelCase						)
            UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps						):
            UpperCamelCase__					    =   model(__UpperCamelCase ,					__UpperCamelCase						)
            UpperCamelCase__					    =   scheduler.step_plms(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						).prev_sample

       return sample






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   dict(self.forward_default_kwargs						)

       UpperCamelCase__					    =   kwargs.pop('''num_inference_steps''' ,					__UpperCamelCase						)

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__					    =   self.get_scheduler_config()
            UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)

            UpperCamelCase__					    =   self.dummy_sample
            UpperCamelCase__					    =   0.1 * sample

            if num_inference_steps is not None and hasattr(__UpperCamelCase ,					'''set_timesteps'''						):
                 scheduler.set_timesteps(__UpperCamelCase						)
            elif num_inference_steps is not None and not hasattr(__UpperCamelCase ,					'''set_timesteps'''						):
                 UpperCamelCase__					    =   num_inference_steps

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

            UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					0 ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					1 ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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

            UpperCamelCase__					    =   scheduler.step_plms(__UpperCamelCase ,					0 ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample
            UpperCamelCase__					    =   scheduler.step_plms(__UpperCamelCase ,					1 ,					__UpperCamelCase ,					**__UpperCamelCase						).prev_sample

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






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       for timesteps in [100, 1000]:
            self.check_over_configs(num_train_timesteps=__UpperCamelCase						)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=__UpperCamelCase						)

       UpperCamelCase__					    =   self.scheduler_classes[0]
       UpperCamelCase__					    =   self.get_scheduler_config(steps_offset=1						)
       UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)
       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						)		->   int:





       '''simple docstring'''





       for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]						):
            self.check_over_configs(beta_start=__UpperCamelCase ,					beta_end=__UpperCamelCase						)






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





       '''simple docstring'''





       for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__UpperCamelCase						)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





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






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       for t in [1, 5, 10]:
            self.check_over_forward(time_step=__UpperCamelCase						)






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





       '''simple docstring'''





       for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]						):
            self.check_over_forward(num_inference_steps=__UpperCamelCase						)






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   27

       for scheduler_class in self.scheduler_classes:
            UpperCamelCase__					    =   self.dummy_sample
            UpperCamelCase__					    =   0.1 * sample

            UpperCamelCase__					    =   self.get_scheduler_config()
            UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)

            scheduler.set_timesteps(__UpperCamelCase						)

            # 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]						):
                 UpperCamelCase__					    =   scheduler.step_prk(__UpperCamelCase ,					__UpperCamelCase ,					__UpperCamelCase						).prev_sample






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       with self.assertRaises(__UpperCamelCase						):
            UpperCamelCase__					    =   self.scheduler_classes[0]
            UpperCamelCase__					    =   self.get_scheduler_config()
            UpperCamelCase__					    =   scheduler_class(**__UpperCamelCase						)

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






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   self.full_loop()
       UpperCamelCase__					    =   torch.sum(torch.abs(__UpperCamelCase						)						)
       UpperCamelCase__					    =   torch.mean(torch.abs(__UpperCamelCase						)						)

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






  def 		_a       (self						)		->   str:





       '''simple docstring'''





       UpperCamelCase__					    =   self.full_loop(prediction_type='''v_prediction'''						)
       UpperCamelCase__					    =   torch.sum(torch.abs(__UpperCamelCase						)						)
       UpperCamelCase__					    =   torch.mean(torch.abs(__UpperCamelCase						)						)

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






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   self.full_loop(set_alpha_to_one=__UpperCamelCase ,					beta_start=0.01						)
       UpperCamelCase__					    =   torch.sum(torch.abs(__UpperCamelCase						)						)
       UpperCamelCase__					    =   torch.mean(torch.abs(__UpperCamelCase						)						)

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






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





       '''simple docstring'''





       UpperCamelCase__					    =   self.full_loop(set_alpha_to_one=__UpperCamelCase ,					beta_start=0.01						)
       UpperCamelCase__					    =   torch.sum(torch.abs(__UpperCamelCase						)						)
       UpperCamelCase__					    =   torch.mean(torch.abs(__UpperCamelCase						)						)

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











import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


if __name__ == "__main__":
 print(solution())






