'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(IPNDMScheduler,)
     SCREAMING_SNAKE_CASE__		   =						(('''num_inference_steps''', 50),)







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  {'num_train_timesteps': 1_0_0_0}
         config.update(**_lowerCamelCase    )
         return config







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  dict(self.forward_default_kwargs    )
         __UpperCamelCase	:	str		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.dummy_sample
         __UpperCamelCase	:	List[str]		    =  0.1 * sample
         __UpperCamelCase	:	Optional[int]		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

             if time_step is None:
                 __UpperCamelCase	:	str		    =  scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

             __UpperCamelCase	:	List[str]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	List[Any]		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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

             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	str		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  dict(self.forward_default_kwargs    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.dummy_sample
         __UpperCamelCase	:	Tuple		    =  0.1 * sample
         __UpperCamelCase	:	int		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

         for scheduler_class in self.scheduler_classes:
             __UpperCamelCase	:	Tuple		    =  self.get_scheduler_config()
             __UpperCamelCase	:	str		    =  scheduler_class(**_lowerCamelCase    )
             scheduler.set_timesteps(_lowerCamelCase    )

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

             if time_step is None:
                 __UpperCamelCase	:	Tuple		    =  scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

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

             __UpperCamelCase	:	Optional[int]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Tuple		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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

             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Dict		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  self.scheduler_classes[0]
         __UpperCamelCase	:	str		    =  self.get_scheduler_config(**_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  scheduler_class(**_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  1_0
         __UpperCamelCase	:	Any		    =  self.dummy_model()
         __UpperCamelCase	:	Tuple		    =  self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCamelCase    )

         for i, t in enumerate(scheduler.timesteps    ):
             __UpperCamelCase	:	List[Any]		    =  model(_lowerCamelCase     ,			_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ).prev_sample

         for i, t in enumerate(scheduler.timesteps    ):
             __UpperCamelCase	:	Dict		    =  model(_lowerCamelCase     ,			_lowerCamelCase    )
             __UpperCamelCase	:	int		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ).prev_sample

         return sample







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  dict(self.forward_default_kwargs    )

         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )

         for scheduler_class in self.scheduler_classes:
             __UpperCamelCase	:	List[Any]		    =  self.get_scheduler_config()
             __UpperCamelCase	:	Union[str, Any]		    =  scheduler_class(**_lowerCamelCase    )

             __UpperCamelCase	:	Optional[Any]		    =  self.dummy_sample
             __UpperCamelCase	:	Tuple		    =  0.1 * sample

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

             # copy over dummy past residuals (must be done after set_timesteps)
             __UpperCamelCase	:	Union[str, Any]		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
             __UpperCamelCase	:	int		    =  dummy_past_residuals[:]

             __UpperCamelCase	:	Dict		    =  scheduler.timesteps[5]
             __UpperCamelCase	:	List[str]		    =  scheduler.timesteps[6]

             __UpperCamelCase	:	Optional[int]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Tuple		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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

             __UpperCamelCase	:	int		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self    ):
         for timesteps in [1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase     ,			time_step=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         for t, num_inference_steps in zip([1, 5, 1_0]     ,			[1_0, 5_0, 1_0_0]    ):
             self.check_over_forward(num_inference_steps=_lowerCamelCase     ,			time_step=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  self.full_loop()
         __UpperCamelCase	:	Any		    =  torch.mean(torch.abs(_lowerCamelCase    )    )

         assert abs(result_mean.item() - 2_5_4_0_5_2_9    ) < 1_0




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



