


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			(PNDMScheduler,)
 __lowercase			:							Optional[Any]       					=			(('num_inference_steps', 50),)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       {
            'num_train_timesteps': 1_000,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**lowerCAmelCase__			)
        return config
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=0    ,			**lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        lowercase__:       Dict        =       dict(self.forward_default_kwargs			)
        lowercase__:       int        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       self.dummy_sample
        lowercase__:       str        =       0.1 * sample
        lowercase__:       List[str]        =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

        for scheduler_class in self.scheduler_classes:
               lowercase__:       Optional[Any]        =       self.get_scheduler_config(**lowerCAmelCase__			)
               lowercase__:       Tuple        =       scheduler_class(**lowerCAmelCase__			)
               scheduler.set_timesteps(lowerCAmelCase__			)
               # copy over dummy past residuals
               lowercase__:       Any        =       dummy_past_residuals[:]

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

               lowercase__:       List[str]        =       scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       str        =       new_scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

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

               lowercase__:       str        =       scheduler.step_plms(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       List[Any]        =       new_scheduler.step_plms(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

               assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=0    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       dict(self.forward_default_kwargs			)
        lowercase__:       Optional[Any]        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       self.dummy_sample
        lowercase__:       str        =       0.1 * sample
        lowercase__:       Union[str, Any]        =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__			)
                      lowercase__:       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)
                      lowercase__:       str        =       dummy_past_residuals[:]

               lowercase__:       int        =       scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Union[str, Any]        =       new_scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

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

               lowercase__:       Union[str, Any]        =       scheduler.step_plms(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Optional[int]        =       new_scheduler.step_plms(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

               assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       self.scheduler_classes[0]
        lowercase__:       Any        =       self.get_scheduler_config(**lowerCAmelCase__			)
        lowercase__:       int        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       10
        lowercase__:       Dict        =       self.dummy_model()
        lowercase__:       Dict        =       self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__			)

        for i, t in enumerate(scheduler.prk_timesteps			):
               lowercase__:       Dict        =       model(lowerCAmelCase__    ,			lowerCAmelCase__			)
               lowercase__:       Optional[Any]        =       scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps			):
               lowercase__:       int        =       model(lowerCAmelCase__    ,			lowerCAmelCase__			)
               lowercase__:       List[str]        =       scheduler.step_plms(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample

        return sample
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       Dict        =       dict(self.forward_default_kwargs			)

        lowercase__:       List[Any]        =       kwargs.pop('num_inference_steps'    ,			lowerCAmelCase__			)

        for scheduler_class in self.scheduler_classes:
               lowercase__:       Any        =       self.get_scheduler_config()
               lowercase__:       Optional[int]        =       scheduler_class(**lowerCAmelCase__			)

               lowercase__:       Optional[Any]        =       self.dummy_sample
               lowercase__:       Optional[int]        =       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'			):
                      lowercase__:       Dict        =       num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               lowercase__:       Optional[int]        =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
               lowercase__:       Dict        =       dummy_past_residuals[:]

               lowercase__:       int        =       scheduler.step_prk(lowerCAmelCase__    ,			0    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Tuple        =       scheduler.step_prk(lowerCAmelCase__    ,			1    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

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

               lowercase__:       Dict        =       scheduler.step_plms(lowerCAmelCase__    ,			0    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample
               lowercase__:       Any        =       scheduler.step_plms(lowerCAmelCase__    ,			1    ,			lowerCAmelCase__    ,			**lowerCAmelCase__			).prev_sample

               self.assertEqual(output_a.shape    ,			sample.shape			)
               self.assertEqual(output_a.shape    ,			output_a.shape			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       self.scheduler_classes[0]
        lowercase__:       List[str]        =       self.get_scheduler_config(steps_offset=1			)
        lowercase__:       Dict        =       scheduler_class(**lowerCAmelCase__			)
        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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,			[0.0_0_2, 0.0_2]			):
               self.check_over_configs(beta_start=lowerCAmelCase__    ,			beta_end=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        for t in [1, 5, 10]:
               self.check_over_forward(time_step=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        for t, num_inference_steps in zip([1, 5, 10]    ,			[10, 50, 100]			):
               self.check_over_forward(num_inference_steps=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       27

        for scheduler_class in self.scheduler_classes:
               lowercase__:       List[Any]        =       self.dummy_sample
               lowercase__:       Any        =       0.1 * sample

               lowercase__:       Optional[Any]        =       self.get_scheduler_config()
               lowercase__:       Dict        =       scheduler_class(**lowerCAmelCase__			)

               scheduler.set_timesteps(lowerCAmelCase__			)

               # 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]			):
                      lowercase__:       str        =       scheduler.step_prk(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        with self.assertRaises(lowerCAmelCase__			):
               lowercase__:       Any        =       self.scheduler_classes[0]
               lowercase__:       Optional[int]        =       self.get_scheduler_config()
               lowercase__:       str        =       scheduler_class(**lowerCAmelCase__			)

               scheduler.step_plms(self.dummy_sample    ,			1    ,			self.dummy_sample			).prev_sample
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       self.full_loop()
        lowercase__:       int        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       int        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 198.1_318			) < 1E-2
        assert abs(result_mean.item() - 0.2_5_8_0			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.full_loop(prediction_type='v_prediction'			)
        lowercase__:       List[Any]        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Union[str, Any]        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 67.3_986			) < 1E-2
        assert abs(result_mean.item() - 0.0_8_7_8			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       str        =       self.full_loop(set_alpha_to_one=lowerCAmelCase__    ,			beta_start=0.0_1			)
        lowercase__:       str        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       int        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 230.0_399			) < 1E-2
        assert abs(result_mean.item() - 0.2_9_9_5			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       str        =       self.full_loop(set_alpha_to_one=lowerCAmelCase__    ,			beta_start=0.0_1			)
        lowercase__:       Optional[int]        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Any        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 186.9_482			) < 1E-2
        assert abs(result_mean.item() - 0.2_4_3_4			) < 1E-3






from __future__ import annotations




def        snake_case_					(  snake_case      ,							snake_case					)  ->    list[int]:
       lowercase__:       Tuple        =       0
       lowercase__:       str        =       len(snake_case					) - 1

       while i < j:
              if nums[i] + nums[j] == target:
                     return [i, j]
              elif nums[i] + nums[j] < target:
                     lowercase__:       str        =       i + 1
              else:
                     lowercase__:       Dict        =       j - 1

       return []


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')


