



'''simple docstring'''

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[str]									=		(EulerDiscreteScheduler,)
						UpperCAmelCase							:   Optional[Any]									=		10


						def     __snake_case   ( self     ,	**A_				)     ->     Any:
							lowerCAmelCase   =     {
							    """num_train_timesteps""": 1100,
							    """beta_start""": 0.0_0_0_1,
							    """beta_end""": 0.0_2,
							    """beta_schedule""": """linear""",
							}

							config.update(**A_				)
							return config


						def     __snake_case   ( self				)     ->     Optional[int]:
							for timesteps in [10, 50, 100, 1000]:
								self.check_over_configs(num_train_timesteps=A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]				):
								self.check_over_configs(beta_start=A_     ,	beta_end=A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config()
							lowerCAmelCase   =     scheduler_class(**A_				)

							scheduler.set_timesteps(self.num_inference_steps				)

							lowerCAmelCase   =     torch.manual_seed(0				)

							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCAmelCase   =     sample.to(A_				)

							for i, t in enumerate(scheduler.timesteps				):
								lowerCAmelCase   =     scheduler.scale_model_input(A_     ,	A_				)

								lowerCAmelCase   =     model(A_     ,	A_				)

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	generator=A_				)
								lowerCAmelCase   =     output.prev_sample

							lowerCAmelCase   =     torch.sum(torch.abs(A_				)				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_sum.item() - 10.0807				) < 1e-2
							assert abs(result_mean.item() - 0.0_1_3_1				) < 1e-3


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config(prediction_type="""v_prediction"""				)
							lowerCAmelCase   =     scheduler_class(**A_				)

							scheduler.set_timesteps(self.num_inference_steps				)

							lowerCAmelCase   =     torch.manual_seed(0				)

							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCAmelCase   =     sample.to(A_				)

							for i, t in enumerate(scheduler.timesteps				):
								lowerCAmelCase   =     scheduler.scale_model_input(A_     ,	A_				)

								lowerCAmelCase   =     model(A_     ,	A_				)

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	generator=A_				)
								lowerCAmelCase   =     output.prev_sample

							lowerCAmelCase   =     torch.sum(torch.abs(A_				)				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_sum.item() - 0.0_0_0_2				) < 1e-2
							assert abs(result_mean.item() - 2.26_76e-06				) < 1e-3


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config()
							lowerCAmelCase   =     scheduler_class(**A_				)

							scheduler.set_timesteps(self.num_inference_steps     ,	device=A_				)

							lowerCAmelCase   =     torch.manual_seed(0				)

							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowerCAmelCase   =     sample.to(A_				)

							for t in scheduler.timesteps:
								lowerCAmelCase   =     scheduler.scale_model_input(A_     ,	A_				)

								lowerCAmelCase   =     model(A_     ,	A_				)

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	generator=A_				)
								lowerCAmelCase   =     output.prev_sample

							lowerCAmelCase   =     torch.sum(torch.abs(A_				)				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_sum.item() - 10.0807				) < 1e-2
							assert abs(result_mean.item() - 0.0_1_3_1				) < 1e-3




						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config()
							lowerCAmelCase   =     scheduler_class(**A_     ,	use_karras_sigmas=A_				)

							scheduler.set_timesteps(self.num_inference_steps     ,	device=A_				)

							lowerCAmelCase   =     torch.manual_seed(0				)

							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							lowerCAmelCase   =     sample.to(A_				)

							for t in scheduler.timesteps:
								lowerCAmelCase   =     scheduler.scale_model_input(A_     ,	A_				)

								lowerCAmelCase   =     model(A_     ,	A_				)

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	generator=A_				)
								lowerCAmelCase   =     output.prev_sample

							lowerCAmelCase   =     torch.sum(torch.abs(A_				)				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_sum.item() - 124.52_2994_9951_1719				) < 1e-2
							assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3				) < 1e-3




'''simple docstring'''

from __future__ import annotations

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_ = 0				)     ->     Dict:
							lowerCAmelCase   =     key


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     list[str]:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							lowerCAmelCase   =     key or self.__key or 1

							# make sure key is an appropriate size
							key %= 255

							return [chr(ord(A_				) ^ key				) for ch in content]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     list[str]:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							lowerCAmelCase   =     key or self.__key or 1

							# make sure key is an appropriate size
							key %= 255

							return [chr(ord(A_				) ^ key				) for ch in content]


						def     __snake_case   ( self     ,	A_     ,	A_ = 0				)     ->     str:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							lowerCAmelCase   =     key or self.__key or 1

							# make sure key can be any size
							while key > 255:
								key -= 255

							# This will be returned
							lowerCAmelCase   =     """"""

							for ch in content:
								ans += chr(ord(A_				) ^ key				)

							return ans


						def     __snake_case   ( self     ,	A_     ,	A_ = 0				)     ->     str:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							lowerCAmelCase   =     key or self.__key or 1

							# make sure key can be any size
							while key > 255:
								key -= 255

							# This will be returned
							lowerCAmelCase   =     """"""

							for ch in content:
								ans += chr(ord(A_				) ^ key				)

							return ans


						def     __snake_case   ( self     ,	A_     ,	A_ = 0				)     ->     bool:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							try:
								with open(A_				) as fin, open("""encrypt.out"""     ,	"""w+"""				) as fout:
									# actual encrypt-process
									for line in fin:
										fout.write(self.encrypt_string(A_     ,	A_				)				)

							except OSError:
								return False

							return True




						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     bool:
							assert isinstance(A_     ,	A_				) and isinstance(A_     ,	A_				)

							try:
								with open(A_				) as fin, open("""decrypt.out"""     ,	"""w+"""				) as fout:
									# actual encrypt-process
									for line in fin:
										fout.write(self.decrypt_string(A_     ,	A_				)				)

							except OSError:
								return False

							return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")