




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class        lowerCamelCase_							(      __UpperCamelCase					):


 '''simple docstring'''







 lowercase_				    =    (UniPCMultistepScheduler,)
 lowercase_				    =    (('num_inference_steps', 25),)



 def       lowerCAmelCase_  (							self  :		List[str]				,				**_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			{
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
           'solver_type': 'bh2',
       }

       config.update(**lowerCAmelCase__						)
       return config



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Any=0				,				**_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)
       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample
       SCREAMING_SNAKE_CASE_							=			0.1 * sample
       SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

             SCREAMING_SNAKE_CASE_							=			sample, sample
             for t in range(lowerCAmelCase__				,				time_step + scheduler.config.solver_order + 1						):
                   SCREAMING_SNAKE_CASE_							=			scheduler.step(lowerCAmelCase__				,				lowerCAmelCase__				,				lowerCAmelCase__				,				**lowerCAmelCase__						).prev_sample
                   SCREAMING_SNAKE_CASE_							=			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  :		List[str]				,				_lowerCAmelCase  :		List[str]=0				,				**_lowerCAmelCase  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)
       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample
       SCREAMING_SNAKE_CASE_							=			0.1 * sample
       SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config()
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)
             scheduler.set_timesteps(lowerCAmelCase__						)

             # copy over dummy past residuals (must be after setting timesteps)
             SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCAmelCase__						)
                   SCREAMING_SNAKE_CASE_							=			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)
                   SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: new_scheduler.config.solver_order]

             SCREAMING_SNAKE_CASE_							=			scheduler.step(lowerCAmelCase__				,				lowerCAmelCase__				,				lowerCAmelCase__				,				**lowerCAmelCase__						).prev_sample
             SCREAMING_SNAKE_CASE_							=			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[Any]				,				_lowerCAmelCase  :		Union[str, Any]=None				,				**_lowerCAmelCase  :		Tuple						):
       if scheduler is None:
             SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**lowerCAmelCase__						)
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)

       SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)

       SCREAMING_SNAKE_CASE_							=			10
       SCREAMING_SNAKE_CASE_							=			self.dummy_model()
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample_deter
       scheduler.set_timesteps(lowerCAmelCase__						)

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

       return sample



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			dict(self.forward_default_kwargs						)

       SCREAMING_SNAKE_CASE_							=			kwargs.pop('num_inference_steps'				,				lowerCAmelCase__						)

       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config()
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)

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

             # copy over dummy past residuals (must be done after set_timesteps)
             SCREAMING_SNAKE_CASE_							=			[residual + 0.2, residual + 0.15, residual + 0.10]
             SCREAMING_SNAKE_CASE_							=			dummy_past_residuals[: scheduler.config.solver_order]

             SCREAMING_SNAKE_CASE_							=			scheduler.timesteps[5]
             SCREAMING_SNAKE_CASE_							=			scheduler.timesteps[6]

             SCREAMING_SNAKE_CASE_							=			scheduler.step(lowerCAmelCase__				,				lowerCAmelCase__				,				lowerCAmelCase__				,				**lowerCAmelCase__						).prev_sample
             SCREAMING_SNAKE_CASE_							=			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  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			UniPCMultistepScheduler(**self.get_scheduler_config()						)
       SCREAMING_SNAKE_CASE_							=			self.full_loop(scheduler=lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(lowerCAmelCase__						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3

       SCREAMING_SNAKE_CASE_							=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			DEISMultistepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
       SCREAMING_SNAKE_CASE_							=			UniPCMultistepScheduler.from_config(scheduler.config						)

       SCREAMING_SNAKE_CASE_							=			self.full_loop(scheduler=lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(lowerCAmelCase__						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3



 def       lowerCAmelCase_  (							self  :		int						):
       for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)



 def       lowerCAmelCase_  (							self  :		int						):
       self.check_over_configs(thresholding=lowerCAmelCase__						)
       for order in [1, 2, 3]:
             for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   thresholding=lowerCAmelCase__				,				prediction_type=lowerCAmelCase__				,				sample_max_value=lowerCAmelCase__				,				solver_order=lowerCAmelCase__				,				solver_type=lowerCAmelCase__				,				)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=lowerCAmelCase__						)



 def       lowerCAmelCase_  (							self  :		Any						):
       for solver_type in ["bh1", "bh2"]:
             for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             solver_order=lowerCAmelCase__				,				solver_type=lowerCAmelCase__				,				prediction_type=lowerCAmelCase__				,				)
                         SCREAMING_SNAKE_CASE_							=			self.full_loop(
                             solver_order=lowerCAmelCase__				,				solver_type=lowerCAmelCase__				,				prediction_type=lowerCAmelCase__				,				)
                         assert not torch.isnan(lowerCAmelCase__						).any(), "Samples have nan numbers"



 def       lowerCAmelCase_  (							self  :		str						):
       self.check_over_configs(lower_order_final=lowerCAmelCase__						)
       self.check_over_configs(lower_order_final=lowerCAmelCase__						)



 def       lowerCAmelCase_  (							self  :		Dict						):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=lowerCAmelCase__				,				time_step=0						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.full_loop()
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(lowerCAmelCase__						)						)

       assert abs(result_mean.item() - 0.2464						) < 1E-3



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			self.full_loop(prediction_type='v_prediction'						)
       SCREAMING_SNAKE_CASE_							=			torch.mean(torch.abs(lowerCAmelCase__						)						)

       assert abs(result_mean.item() - 0.1014						) < 1E-3



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(thresholding=lowerCAmelCase__				,				dynamic_thresholding_ratio=0						)
       SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)

       SCREAMING_SNAKE_CASE_							=			10
       SCREAMING_SNAKE_CASE_							=			self.dummy_model()
       SCREAMING_SNAKE_CASE_							=			self.dummy_sample_deter.half()
       scheduler.set_timesteps(lowerCAmelCase__						)

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

       assert sample.dtype == torch.floataa




 def       lowerCAmelCase_  (							self  :		int				,				**_lowerCAmelCase  :		List[str]						):
       for scheduler_class in self.scheduler_classes:
             SCREAMING_SNAKE_CASE_							=			self.get_scheduler_config(**lowerCAmelCase__						)
             SCREAMING_SNAKE_CASE_							=			scheduler_class(**lowerCAmelCase__						)

             scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
             assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps



from __future__ import annotations




def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       if len(snake_case     ) == 0:
              return False
       lowercase__:   Any  			=   len(snake_case     ) // 2
       if a_list[midpoint] == item:
              return True
       if item < a_list[midpoint]:
              return binary_search(a_list[:midpoint]			,							snake_case     )
       else:
              return binary_search(a_list[midpoint + 1 :]			,							snake_case     )


if __name__ == "__main__":
 __lowerCAmelCase		       =							input('''Enter numbers separated by comma:\n''').strip()
 __lowerCAmelCase		       =							[int(item.strip()) for item in user_input.split(''',''')]
 __lowerCAmelCase		       =							int(input('''Enter the number to be found in the list:\n''').strip())
 __lowerCAmelCase		       =							'''''' if binary_search(sequence, target) else '''not '''
 print(F'''{target} was {not_str}found in {sequence}''')


