'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class       __UpperCAmelCase							(							lowerCamelCase__	):





       '''simple docstring'''



       __lowercase   :   Tuple  					=							(DPMSolverMultistepScheduler,)
       __lowercase   :   List[str]  					=							(("""num_inference_steps""", 25),)

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	str:
           A_      =  {
               '''num_train_timesteps''': 1000,
               '''beta_start''': 0.0_001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
               '''sample_max_value''': 1.0,
               '''algorithm_type''': '''dpmsolver++''',
               '''solver_type''': '''midpoint''',
               '''lower_order_final''': False,
               '''lambda_min_clipped''': -float('''inf'''					),
               '''variance_type''': None,
           }

           config.update(**snake_case__					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	str:
           A_      =  dict(self.forward_default_kwargs					)
           A_      =  kwargs.pop('''num_inference_steps'''       ,	snake_case__					)
           A_      =  self.dummy_sample
           A_      =  0.1 * sample
           A_      =  [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               A_      =  sample, sample
               for t in range(snake_case__       ,	time_step + scheduler.config.solver_order + 1					):
                   A_      =  scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	**snake_case__					).prev_sample
                   A_      =  new_scheduler.step(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    __A			( self					)						->	str:
           pass

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_      =  dict(self.forward_default_kwargs					)
           A_      =  kwargs.pop('''num_inference_steps'''       ,	snake_case__					)
           A_      =  self.dummy_sample
           A_      =  0.1 * sample
           A_      =  [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**snake_case__					)
               scheduler.set_timesteps(snake_case__					)

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

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

               A_      =  scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	**snake_case__					).prev_sample
               A_      =  new_scheduler.step(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    __A			( self       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	Dict:





           if scheduler is None:
               A_      =  self.scheduler_classes[0]
               A_      =  self.get_scheduler_config(**snake_case__					)
               A_      =  scheduler_class(**snake_case__					)

           A_      =  10
           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter
           scheduler.set_timesteps(snake_case__					)

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

           return sample

       def    __A			( self					)						->	Dict:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	snake_case__					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**snake_case__					)

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

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

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

               A_      =  scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	**snake_case__					).prev_sample
               A_      =  scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__       ,	**snake_case__					).prev_sample

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

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





           for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=snake_case__					)

       def    __A			( self					)						->	int:
           self.check_over_configs(thresholding=snake_case__					)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=snake_case__       ,	prediction_type=snake_case__       ,	sample_max_value=snake_case__       ,	algorithm_type='''dpmsolver++'''       ,	solver_order=snake_case__       ,	solver_type=snake_case__       ,	)

       def    __A			( self					)						->	List[Any]:





           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=snake_case__					)

       def    __A			( self					)						->	Union[str, Any]:





           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                               if order == 3:
                                   continue
                           else:
                               self.check_over_configs(
                                   solver_order=snake_case__       ,	solver_type=snake_case__       ,	prediction_type=snake_case__       ,	algorithm_type=snake_case__       ,	)
                           A_      =  self.full_loop(
                               solver_order=snake_case__       ,	solver_type=snake_case__       ,	prediction_type=snake_case__       ,	algorithm_type=snake_case__       ,	)
                           assert not torch.isnan(snake_case__					).any(), "Samples have nan numbers"

       def    __A			( self					)						->	Optional[Any]:
           self.check_over_configs(lower_order_final=snake_case__					)
           self.check_over_configs(lower_order_final=snake_case__					)

       def    __A			( self					)						->	int:
           self.check_over_configs(lambda_min_clipped=-float('''inf'''					)					)
           self.check_over_configs(lambda_min_clipped=-5.1					)

       def    __A			( self					)						->	Optional[Any]:
           self.check_over_configs(variance_type=snake_case__					)
           self.check_over_configs(variance_type='''learned_range'''					)

       def    __A			( self					)						->	int:





           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=snake_case__       ,	time_step=0					)

       def    __A			( self					)						->	List[str]:
           A_      =  self.full_loop()
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 0.3_301					) < 1E-3

       def    __A			( self					)						->	Optional[Any]:
           A_      =  self.full_loop(thresholding=snake_case__       ,	dynamic_thresholding_ratio=0.87       ,	sample_max_value=0.5					)
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 1.1_364					) < 1E-3

       def    __A			( self					)						->	Optional[Any]:
           A_      =  self.full_loop(prediction_type='''v_prediction'''					)
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 0.2_251					) < 1E-3

       def    __A			( self					)						->	Dict:
           A_      =  self.full_loop(prediction_type='''v_prediction'''       ,	use_karras_sigmas=snake_case__					)
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 0.2_096					) < 1E-3

       def    __A			( self					)						->	Optional[int]:
           A_      =  DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
           A_      =  self.full_loop(scheduler=snake_case__					)
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 0.3_301					) < 1E-3

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

           A_      =  self.full_loop(scheduler=snake_case__					)
           A_      =  torch.mean(torch.abs(snake_case__					)					)

           assert abs(result_mean.item() - 0.3_301					) < 1E-3

       def    __A			( self					)						->	str:
           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config(thresholding=snake_case__       ,	dynamic_thresholding_ratio=0					)
           A_      =  scheduler_class(**snake_case__					)

           A_      =  10
           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter.half()
           scheduler.set_timesteps(snake_case__					)

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

           assert sample.dtype == torch.floataa

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:





           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config(**snake_case__					)
               A_      =  scheduler_class(**snake_case__					)

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







from hashlib import mda, shaaaa

__snake_case         =     (shaaaa, mda)







class       __snake_case :
 def __init__(	self		, snake_case__ = 8    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=0b0
       UpperCAmelCase     :			str     	=size
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.hash_(snake_case__    )
       self.bitarray |= h
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.hash_(snake_case__    )
       return (h & self.bitarray) == h
 def __contains__(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       return self.exists(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=bin(snake_case__    )[2:]
       return res.zfill(self.size    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.bitarray    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=0b0
       for func in HASH_FUNCTIONS:
             UpperCAmelCase     :			Optional[Any]     	=(
                 int.from_bytes(func(value.encode()    ).digest()		, '''little'''    ) % self.size
             )
             res |= 2**position
       return res
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.hash_(snake_case__    )    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=bin(self.bitarray    ).count('''1'''    )
       return (n_ones / self.size) ** len(snake_case__    )
