'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     A__							(       _lowerCAmelCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Dict    				=     (IPNDMScheduler,)
   UpperCamelCase_       :						Any    				=     (("""num_inference_steps""", 50),)




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     **lowerCAmelCase__     :		str			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   {"""num_train_timesteps""": 1_0_0_0}
      config.update(**lowerCAmelCase__			)
      return config




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		int=0						,     **lowerCAmelCase__     :		str			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   dict(self.forward_default_kwargs			)
      _UpperCAmelCase							:	str							=   kwargs.pop("num_inference_steps"						,     lowerCAmelCase__			)
      _UpperCAmelCase							:	Optional[int]							=   self.dummy_sample
      _UpperCAmelCase							:	Tuple							=   0.1 * sample
      _UpperCAmelCase							:	str							=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

         if time_step is None:
            _UpperCAmelCase							:	Optional[Any]							=   scheduler.timesteps[len(scheduler.timesteps			) // 2]

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

         _UpperCAmelCase							:	int							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample
         _UpperCAmelCase							:	Optional[int]							=   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							:	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"




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







      """simple docstring"""




      pass




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		str=0						,     **lowerCAmelCase__     :		Dict			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   dict(self.forward_default_kwargs			)
      _UpperCAmelCase							:	Optional[int]							=   kwargs.pop("num_inference_steps"						,     lowerCAmelCase__			)
      _UpperCAmelCase							:	Dict							=   self.dummy_sample
      _UpperCAmelCase							:	Optional[Any]							=   0.1 * sample
      _UpperCAmelCase							:	Dict							=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase							:	Tuple							=   self.get_scheduler_config()
         _UpperCAmelCase							:	List[str]							=   scheduler_class(**lowerCAmelCase__			)
         scheduler.set_timesteps(lowerCAmelCase__			)

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

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

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__			)
            _UpperCAmelCase							:	int							=   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							:	Union[str, Any]							=   dummy_past_residuals[:]

         _UpperCAmelCase							:	List[Any]							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample
         _UpperCAmelCase							:	Union[str, 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							:	Tuple							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample
         _UpperCAmelCase							:	List[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     :		Optional[int]						,     **lowerCAmelCase__     :		Tuple			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   self.scheduler_classes[0]
      _UpperCAmelCase							:	List[Any]							=   self.get_scheduler_config(**lowerCAmelCase__			)
      _UpperCAmelCase							:	int							=   scheduler_class(**lowerCAmelCase__			)

      _UpperCAmelCase							:	str							=   1_0
      _UpperCAmelCase							:	Optional[Any]							=   self.dummy_model()
      _UpperCAmelCase							:	int							=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCAmelCase__			)

      for i, t in enumerate(scheduler.timesteps			):
         _UpperCAmelCase							:	Tuple							=   model(lowerCAmelCase__						,     lowerCAmelCase__			)
         _UpperCAmelCase							:	Dict							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			).prev_sample

      for i, t in enumerate(scheduler.timesteps			):
         _UpperCAmelCase							:	Optional[int]							=   model(lowerCAmelCase__						,     lowerCAmelCase__			)
         _UpperCAmelCase							:	Any							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			).prev_sample

      return sample




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







      """simple docstring"""




      _UpperCAmelCase							:	str							=   dict(self.forward_default_kwargs			)

      _UpperCAmelCase							:	int							=   kwargs.pop("num_inference_steps"						,     lowerCAmelCase__			)

      for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase							:	List[str]							=   self.get_scheduler_config()
         _UpperCAmelCase							:	Any							=   scheduler_class(**lowerCAmelCase__			)

         _UpperCAmelCase							:	List[str]							=   self.dummy_sample
         _UpperCAmelCase							:	Dict							=   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 + 0.15, residual + 0.1, residual + 0.05]
         _UpperCAmelCase							:	Optional[int]							=   dummy_past_residuals[:]

         _UpperCAmelCase							:	Optional[int]							=   scheduler.timesteps[5]
         _UpperCAmelCase							:	str							=   scheduler.timesteps[6]

         _UpperCAmelCase							:	int							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample
         _UpperCAmelCase							:	List[str]							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample

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

         _UpperCAmelCase							:	str							=   scheduler.step(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     **lowerCAmelCase__			).prev_sample
         _UpperCAmelCase							:	Optional[int]							=   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     :		Tuple			)       ->    Dict:







      """simple docstring"""




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




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







      """simple docstring"""




      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     :		Optional[Any]			)       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   self.full_loop()
      _UpperCAmelCase							:	str							=   torch.mean(torch.abs(lowerCAmelCase__			)			)

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




"""simple docstring"""
import re
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			re.compile(
						    r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$"""	)

						return bool(re.search(_lowercase,					_lowercase	)	)


if __name__ == "__main__":
						__a      =				"0094702343221"

						print(is_sri_lankan_phone_number(phone))
