




"""simple docstring"""

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      (PNDMScheduler,)
 UpperCAmelCase       :	Any     =      (('''num_inference_steps''', 50),)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

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

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

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

             _A     =  scheduler.step_prk(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  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"

             _A     =  scheduler.step_plms(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  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      lowerCAmelCase_				(					self			:			List[Any]						):
       pass

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]=0       ,				**_UpperCAmelCase			:			str						):
       _A     =  dict(self.forward_default_kwargs						)
       _A     =  kwargs.pop('num_inference_steps'       ,				_UpperCAmelCase						)
       _A     =  self.dummy_sample
       _A     =  0.1 * sample
       _A     =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A     =  self.get_scheduler_config()
             _A     =  scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(_UpperCAmelCase						)

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase						)
                   _A     =  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)
                   _A     =  dummy_past_residuals[:]

             _A     =  scheduler.step_prk(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  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"

             _A     =  scheduler.step_plms(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  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      lowerCAmelCase_				(					self			:			Tuple       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(**_UpperCAmelCase						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       _A     =  10
       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase						)

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

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

       return sample

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  dict(self.forward_default_kwargs						)

       _A     =  kwargs.pop('num_inference_steps'       ,				_UpperCAmelCase						)

       for scheduler_class in self.scheduler_classes:
             _A     =  self.get_scheduler_config()
             _A     =  scheduler_class(**_UpperCAmelCase						)

             _A     =  self.dummy_sample
             _A     =  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'						):
                   _A     =  num_inference_steps

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

             _A     =  scheduler.step_prk(_UpperCAmelCase       ,				0       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  scheduler.step_prk(_UpperCAmelCase       ,				1       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample

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

             _A     =  scheduler.step_plms(_UpperCAmelCase       ,				0       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  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      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str						):
       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_UpperCAmelCase						)

       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(steps_offset=1						)
       _A     =  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      lowerCAmelCase_				(					self			:			Tuple						):
       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      lowerCAmelCase_				(					self			:			Dict						):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       for t in [1, 5, 10]:
             self.check_over_forward(time_step=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       for t, num_inference_steps in zip([1, 5, 10]       ,				[10, 50, 100]						):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str						):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _A     =  27

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

             _A     =  self.get_scheduler_config()
             _A     =  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]						):
                   _A     =  scheduler.step_prk(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						).prev_sample

 def      lowerCAmelCase_				(					self			:			Any						):
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  self.scheduler_classes[0]
             _A     =  self.get_scheduler_config()
             _A     =  scheduler_class(**_UpperCAmelCase						)

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

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.full_loop()
       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  self.full_loop(prediction_type='v_prediction'						)
       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

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

 def      lowerCAmelCase_				(					self			:			str						):
       # We specify different beta, so that the first alpha is 0.99
       _A     =  self.full_loop(set_alpha_to_one=_UpperCAmelCase       ,				beta_start=0.01						)
       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

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



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

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







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      return " ".join(
          ''.join(word[::-1]				) if len(_snake_case				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('''Hey wollef sroirraw'''))

