

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     (DEISMultistepScheduler,)
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     (('''num_inference_steps''', 25),)




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       {
											    'num_train_timesteps': 1000,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'solver_order': 2,
											}

											config.update(**snake_case			)
											return config




				def        a				(   self ,  snake_case=0 ,  **snake_case			):
											snake_case_     							=       dict(self.forward_default_kwargs			)
											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)
											snake_case_     							=       self.dummy_sample
											snake_case_     							=       0.1 * sample
											snake_case_     							=       [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

																		snake_case_							,       snake_case_     							=       sample, sample
																		for t in range(snake_case ,  time_step + scheduler.config.solver_order + 1			):
																									snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																									snake_case_     							=       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			):
											pass




				def        a				(   self ,  snake_case=0 ,  **snake_case			):
											snake_case_     							=       dict(self.forward_default_kwargs			)
											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)
											snake_case_     							=       self.dummy_sample
											snake_case_     							=       0.1 * sample
											snake_case_     							=       [residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		snake_case_     							=       self.get_scheduler_config()
																		snake_case_     							=       scheduler_class(**snake_case			)
																		scheduler.set_timesteps(snake_case			)

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

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       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 ,  snake_case=None ,  **snake_case			):
											if scheduler is None:
																		snake_case_     							=       self.scheduler_classes[0]
																		snake_case_     							=       self.get_scheduler_config(**snake_case			)
																		snake_case_     							=       scheduler_class(**snake_case			)

											snake_case_     							=       self.scheduler_classes[0]
											snake_case_     							=       self.get_scheduler_config(**snake_case			)
											snake_case_     							=       scheduler_class(**snake_case			)

											snake_case_     							=       10
											snake_case_     							=       self.dummy_model()
											snake_case_     							=       self.dummy_sample_deter
											scheduler.set_timesteps(snake_case			)

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

											return sample




				def        a				(   self			):
											snake_case_     							=       dict(self.forward_default_kwargs			)

											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)

											for scheduler_class in self.scheduler_classes:
																		snake_case_     							=       self.get_scheduler_config()
																		snake_case_     							=       scheduler_class(**snake_case			)

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

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

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       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			):
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											snake_case_     							=       DEISMultistepScheduler(**self.get_scheduler_config()			)
											snake_case_     							=       self.full_loop(scheduler=snake_case			)
											snake_case_     							=       torch.mean(torch.abs(snake_case			)			)

											assert abs(result_mean.item() - 0.2_39_16			) < 1e-3

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

											snake_case_     							=       self.full_loop(scheduler=snake_case			)
											snake_case_     							=       torch.mean(torch.abs(snake_case			)			)

											assert abs(result_mean.item() - 0.2_39_16			) < 1e-3




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




				def        a				(   self			):
											self.check_over_configs(thresholding=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=snake_case ,  prediction_type=snake_case ,  sample_max_value=snake_case ,  algorithm_type='deis' ,  solver_order=snake_case ,  solver_type=snake_case ,  )




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




				def        a				(   self			):
											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=snake_case ,  solver_type=snake_case ,  prediction_type=snake_case ,  algorithm_type=snake_case ,  )
																																							snake_case_     							=       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			):
											self.check_over_configs(lower_order_final=snake_case			)
											self.check_over_configs(lower_order_final=snake_case			)




				def        a				(   self			):
											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			):
											snake_case_     							=       self.full_loop()
											snake_case_     							=       torch.mean(torch.abs(snake_case			)			)

											assert abs(result_mean.item() - 0.2_39_16			) < 1e-3




				def        a				(   self			):
											snake_case_     							=       self.full_loop(prediction_type='v_prediction'			)
											snake_case_     							=       torch.mean(torch.abs(snake_case			)			)

											assert abs(result_mean.item() - 0.0_91			) < 1e-3




				def        a				(   self			):
											snake_case_     							=       self.scheduler_classes[0]
											snake_case_     							=       self.get_scheduler_config(thresholding=snake_case ,  dynamic_thresholding_ratio=0			)
											snake_case_     							=       scheduler_class(**snake_case			)

											snake_case_     							=       10
											snake_case_     							=       self.dummy_model()
											snake_case_     							=       self.dummy_sample_deter.half()
											scheduler.set_timesteps(snake_case			)

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

											assert sample.dtype == torch.floataa







import string





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       ''
							for i in sequence:
														snake_case_     							=       ord(UpperCamelCase__ )
														if 65 <= extract <= 90:
																					output += chr(155 - extract )
														elif 97 <= extract <= 122:
																					output += chr(219 - extract )
														else:
																					output += i
							return output





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       string.ascii_letters
							snake_case_     							=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(UpperCamelCase__ )] if c in letters else c for c in sequence )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							from timeit import timeit

							print('Running performance benchmarks...' )
							snake_case_     							=       'from string import printable ; from __main__ import atbash, atbash_slow'
							print(F'''> atbash_slow(): {timeit("atbash_slow(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )
							print(F'''>      atbash(): {timeit("atbash(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )


if __name__ == "__main__":
					for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
										print(F'''{example} encrypted in atbash: {atbash(example)}''')
					benchmark()




