




def    snake_case_	(						SCREAMING_SNAKE_CASE_ = "The quick brown fox jumps over the lazy dog"							,)     ->					str:
							lowercase__    :			Optional[Any]     =				set()

							# Replace all the whitespace in our sentence
							lowercase__    :			Optional[Any]     =				input_str.replace(" "							,""							)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()							)
							return len(UpperCamelCase__							) == 26
def    snake_case_	(						SCREAMING_SNAKE_CASE_ = "The quick brown fox jumps over the lazy dog"							,)     ->					Dict:
							lowercase__    :			Tuple     =				[False] * 26
							for char in input_str:
														if char.islower():
																					lowercase__    :			str     =				True
														elif char.isupper():
																					lowercase__    :			List[str]     =				True
							return all(UpperCamelCase__							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_ = "The quick brown fox jumps over the lazy dog"							,)     ->					List[str]:
							return len({char for char in input_str.lower() if char.isalpha()}							) == 26
def    snake_case_	(						)     ->					Optional[int]:
							from timeit import timeit

							lowercase__    :			Optional[Any]     =				'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
							print(timeit("is_pangram()"							,setup=UpperCamelCase__							)							)
							print(timeit("is_pangram_faster()"							,setup=UpperCamelCase__							)							)
							print(timeit("is_pangram_fastest()"							,setup=UpperCamelCase__							)							)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE_		 =	(('''num_inference_steps''', 25),)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**__SCREAMING_SNAKE_CASE	)
           return config






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Dict											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.dummy_sample
           UpperCamelCase__     : Optional[int]											=					0.1 * sample
           UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Union[str, Any]											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
                 # copy over dummy past residuals
                 UpperCamelCase__     : Optional[Any]											=					dummy_past_residuals[: scheduler.config.solver_order]

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

                 UpperCamelCase__					,UpperCamelCase__     : Any											=					sample, sample
                 for t in range(__SCREAMING_SNAKE_CASE    ,    time_step + scheduler.config.solver_order + 1	):
                       UpperCamelCase__     : Union[str, Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                       UpperCamelCase__     : int											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Tuple											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					self.dummy_sample
           UpperCamelCase__     : List[Any]											=					0.1 * sample
           UpperCamelCase__     : Dict											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Union[str, Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : str											=					scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE	)
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

                       # copy over dummy past residual (must be after setting timesteps)
                       UpperCamelCase__     : Optional[int]											=					dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Dict											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if scheduler is None:
                 UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
                 UpperCamelCase__     : str											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Dict											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Tuple											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Tuple											=					self.dummy_model()
           UpperCamelCase__     : Dict											=					self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

           return sample






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					dict(self.forward_default_kwargs	)

           UpperCamelCase__     : List[Any]											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)

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

                 UpperCamelCase__     : int											=					self.dummy_sample
                 UpperCamelCase__     : int											=					0.1 * sample

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

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

                 UpperCamelCase__     : int											=					scheduler.timesteps[5]
                 UpperCamelCase__     : Optional[int]											=					scheduler.timesteps[6]

                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler(**self.get_scheduler_config()	)
           UpperCamelCase__     : Tuple											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3

           UpperCamelCase__     : List[Any]											=					DPMSolverSinglestepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : Union[str, Any]											=					DPMSolverMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : int											=					UniPCMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler.from_config(scheduler.config	)

           UpperCamelCase__     : Dict											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






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




           """simple docstring"""







           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    sample_max_value=__SCREAMING_SNAKE_CASE    ,    algorithm_type='''deis'''    ,    solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    )






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




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   UpperCamelCase__     : Any											=					self.full_loop(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   assert not torch.isnan(__SCREAMING_SNAKE_CASE	).any(), "Samples have nan numbers"






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,    time_step=0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.full_loop()
           UpperCamelCase__     : Optional[int]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.full_loop(prediction_type='''v_prediction'''	)
           UpperCamelCase__     : List[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.091	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Optional[Any]											=					self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,    dynamic_thresholding_ratio=0	)
           UpperCamelCase__     : Tuple											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Dict											=					self.dummy_model()
           UpperCamelCase__     : Tuple											=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Tuple											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           assert sample.dtype == torch.floataa


