





'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class    lowerCAmelCase_		(		_snake_case		):
 __lowerCamelCase						:							int         =					(PNDMScheduler,)
 __lowerCamelCase						:							List[Any]         =					(("""num_inference_steps""", 50),)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							List[Any]:
     _lowerCAmelCase				=      {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**snake_case_		)
     return config




 def 							_snake_case   (	self			,     _lowerCAmelCase=0			,     **_lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      dict(self.forward_default_kwargs		)
     _lowerCAmelCase				=      kwargs.pop("num_inference_steps"			,     snake_case_		)
     _lowerCAmelCase				=      self.dummy_sample
     _lowerCAmelCase				=      0.1 * sample
     _lowerCAmelCase				=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

         _lowerCAmelCase				=      scheduler.step_prk(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      new_scheduler.step_prk(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample

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

         _lowerCAmelCase				=      scheduler.step_plms(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      new_scheduler.step_plms(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample

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




 def 							_snake_case   (	self		)    ->							List[Any]:
     pass




 def 							_snake_case   (	self			,     _lowerCAmelCase=0			,     **_lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      dict(self.forward_default_kwargs		)
     _lowerCAmelCase				=      kwargs.pop("num_inference_steps"			,     snake_case_		)
     _lowerCAmelCase				=      self.dummy_sample
     _lowerCAmelCase				=      0.1 * sample
     _lowerCAmelCase				=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase				=      self.get_scheduler_config()
         _lowerCAmelCase				=      scheduler_class(**snake_case_		)
         scheduler.set_timesteps(snake_case_		)

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

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

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

         _lowerCAmelCase				=      scheduler.step_prk(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      new_scheduler.step_prk(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample

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

         _lowerCAmelCase				=      scheduler.step_plms(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      new_scheduler.step_plms(snake_case_			,     snake_case_			,     snake_case_			,     **snake_case_		).prev_sample

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




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config(**snake_case_		)
     _lowerCAmelCase				=      scheduler_class(**snake_case_		)

     _lowerCAmelCase				=      10
     _lowerCAmelCase				=      self.dummy_model()
     _lowerCAmelCase				=      self.dummy_sample_deter
     scheduler.set_timesteps(snake_case_		)

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

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

     return sample




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      dict(self.forward_default_kwargs		)

     _lowerCAmelCase				=      kwargs.pop("num_inference_steps"			,     snake_case_		)

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase				=      self.get_scheduler_config()
         _lowerCAmelCase				=      scheduler_class(**snake_case_		)

         _lowerCAmelCase				=      self.dummy_sample
         _lowerCAmelCase				=      0.1 * sample

         if num_inference_steps is not None and hasattr(snake_case_			,     "set_timesteps"		):
             scheduler.set_timesteps(snake_case_		)
         elif num_inference_steps is not None and not hasattr(snake_case_			,     "set_timesteps"		):
             _lowerCAmelCase				=      num_inference_steps

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

         _lowerCAmelCase				=      scheduler.step_prk(snake_case_			,     0			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      scheduler.step_prk(snake_case_			,     1			,     snake_case_			,     **snake_case_		).prev_sample

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

         _lowerCAmelCase				=      scheduler.step_plms(snake_case_			,     0			,     snake_case_			,     **snake_case_		).prev_sample
         _lowerCAmelCase				=      scheduler.step_plms(snake_case_			,     1			,     snake_case_			,     **snake_case_		).prev_sample

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




 def 							_snake_case   (	self		)    ->							List[str]:
     for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=snake_case_		)




 def 							_snake_case   (	self		)    ->							Tuple:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=snake_case_		)

     _lowerCAmelCase				=      self.scheduler_classes[0]
     _lowerCAmelCase				=      self.get_scheduler_config(steps_offset=1		)
     _lowerCAmelCase				=      scheduler_class(**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 							_snake_case   (	self		)    ->							Optional[int]:
     for beta_start, beta_end in zip([0.0001, 0.001]			,     [0.002, 0.02]		):
         self.check_over_configs(beta_start=snake_case_			,     beta_end=snake_case_		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=snake_case_		)




 def 							_snake_case   (	self		)    ->							List[str]:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=snake_case_		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=snake_case_		)




 def 							_snake_case   (	self		)    ->							Tuple:
     for t, num_inference_steps in zip([1, 5, 10]			,     [10, 50, 100]		):
         self.check_over_forward(num_inference_steps=snake_case_		)




 def 							_snake_case   (	self		)    ->							Dict:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     _lowerCAmelCase				=      27

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

         _lowerCAmelCase				=      self.get_scheduler_config()
         _lowerCAmelCase				=      scheduler_class(**snake_case_		)

         scheduler.set_timesteps(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]		):
             _lowerCAmelCase				=      scheduler.step_prk(snake_case_			,     snake_case_			,     snake_case_		).prev_sample




 def 							_snake_case   (	self		)    ->							List[str]:
     with self.assertRaises(snake_case_		):
         _lowerCAmelCase				=      self.scheduler_classes[0]
         _lowerCAmelCase				=      self.get_scheduler_config()
         _lowerCAmelCase				=      scheduler_class(**snake_case_		)

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




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.full_loop()
     _lowerCAmelCase				=      torch.sum(torch.abs(snake_case_		)		)
     _lowerCAmelCase				=      torch.mean(torch.abs(snake_case_		)		)

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




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase				=      torch.sum(torch.abs(snake_case_		)		)
     _lowerCAmelCase				=      torch.mean(torch.abs(snake_case_		)		)

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




 def 							_snake_case   (	self		)    ->							Any:
     # We specify different beta, so that the first alpha is 0.99
     _lowerCAmelCase				=      self.full_loop(set_alpha_to_one=snake_case_			,     beta_start=0.01		)
     _lowerCAmelCase				=      torch.sum(torch.abs(snake_case_		)		)
     _lowerCAmelCase				=      torch.mean(torch.abs(snake_case_		)		)

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





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

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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


