



'''simple docstring'''


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class      __UpperCamelCase       (UpperCAmelCase_			):
	__A			  =					(DDPMScheduler,)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							{
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """variance_type""": """fixed_small""",
							    """clip_sample""": True,
							}

							config.update(**_snake_case						)
							return config







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=_snake_case						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,				[0.002, 0.02, 0.2, 2]						):
													self.check_over_configs(beta_start=_snake_case ,				beta_end=_snake_case						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_snake_case						)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=_snake_case						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_snake_case						)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							self.check_over_configs(thresholding=_snake_case						)
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=_snake_case ,				prediction_type=_snake_case ,				sample_max_value=_snake_case ,				)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							for t in [0, 500, 999]:
													self.check_over_forward(time_step=_snake_case						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.0_0979						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.02						)						) < 1E-5







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							len(_snake_case						)

							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter
							lowercase        =							torch.manual_seed(0						)

							for t in reversed(range(_snake_case						)						):
													# 1. predict noise residual
													lowercase        =							model(_snake_case ,				_snake_case						)

													# 2. predict previous mean of sample x_t-1
													lowercase        =							scheduler.step(_snake_case ,				_snake_case ,				_snake_case ,				generator=_snake_case						).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowercase        =							pred_prev_sample

							lowercase        =							torch.sum(torch.abs(_snake_case						)						)
							lowercase        =							torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 258.9606						) < 1E-2
							assert abs(result_mean.item() - 0.3372						) < 1E-3







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config(prediction_type="""v_prediction"""						)
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							len(_snake_case						)

							lowercase        =							self.dummy_model()
							lowercase        =							self.dummy_sample_deter
							lowercase        =							torch.manual_seed(0						)

							for t in reversed(range(_snake_case						)						):
													# 1. predict noise residual
													lowercase        =							model(_snake_case ,				_snake_case						)

													# 2. predict previous mean of sample x_t-1
													lowercase        =							scheduler.step(_snake_case ,				_snake_case ,				_snake_case ,				generator=_snake_case						).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowercase        =							pred_prev_sample

							lowercase        =							torch.sum(torch.abs(_snake_case						)						)
							lowercase        =							torch.mean(torch.abs(_snake_case						)						)

							assert abs(result_sum.item() - 202.0296						) < 1E-2
							assert abs(result_mean.item() - 0.2631						) < 1E-3







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							[100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=_snake_case						)

							lowercase        =							scheduler.timesteps

							for i, timestep in enumerate(_snake_case						):
													if i == len(_snake_case						) - 1:
																			lowercase        =							-1
													else:
																			lowercase        =							timesteps[i + 1]

													lowercase        =							scheduler.previous_timestep(_snake_case						)
													lowercase        =							prev_t.item()

													self.assertEqual(_snake_case ,				_snake_case						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							[100, 87, 50, 51, 0]

							with self.assertRaises(_snake_case ,				msg="""`custom_timesteps` must be in descending order."""						):
													scheduler.set_timesteps(timesteps=_snake_case						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							[100, 87, 50, 1, 0]
							lowercase        =							len(_snake_case						)

							with self.assertRaises(_snake_case ,				msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""						):
													scheduler.set_timesteps(num_inference_steps=_snake_case ,				timesteps=_snake_case						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.scheduler_classes[0]
							lowercase        =							self.get_scheduler_config()
							lowercase        =							scheduler_class(**_snake_case						)

							lowercase        =							[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _snake_case ,				msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" ,				):
													scheduler.set_timesteps(timesteps=_snake_case						)







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      str						):
						lowercase        =							""""""
						for i in table:
												res += inp[i - 1]
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):
						return data[1:] + data[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict						):
						lowercase        =							""""""
						for i in range(len(lowercase_						)						):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							int("""0b""" + data[0] + data[-1] ,  2						)
						lowercase        =							int("""0b""" + data[1:3] ,  2						)
						return bin(s[row][col]						)[2:]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      Tuple ,  lowercase_    :      Tuple ,  lowercase_    :      str ,  lowercase_    :      Any						):
						lowercase        =							message[:4]
						lowercase        =							message[4:]
						lowercase        =							apply_table(lowercase_ ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						lowercase        =							apply_sbox(lowercase_ ,  temp[:4]						)  # noqa: E741
						lowercase        =							apply_sbox(lowercase_ ,  temp[4:]						)
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + l  # noqa: E741
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + r
						lowercase        =							apply_table(l + r ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						return temp + right


if __name__ == "__main__":
						lowercase_      :				Tuple   					=    input('''Enter 10 bit key: ''')
						lowercase_      :				Any   					=    input('''Enter 8 bit message: ''')

						lowercase_      :				Dict   					=    [6, 3, 7, 4, 8, 5, 10, 9]
						lowercase_      :				str   					=    [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
						lowercase_      :				List[Any]   					=    [2, 4, 3, 1]
						lowercase_      :				List[str]   					=    [2, 6, 3, 1, 4, 8, 5, 7]
						lowercase_      :				Tuple   					=    [4, 1, 3, 5, 7, 2, 8, 6]
						lowercase_      :				Optional[Any]   					=    [4, 1, 2, 3, 2, 3, 4, 1]
						lowercase_      :				List[str]   					=    [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
						lowercase_      :				List[Any]   					=    [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

						# key generation
						lowercase_      :				Union[str, Any]   					=    apply_table(key, paa_table)
						lowercase_      :				Optional[Any]   					=    temp[:5]
						lowercase_      :				int   					=    temp[5:]
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				int   					=    left_shift(right)
						lowercase_      :				Tuple   					=    apply_table(left + right, pa_table)
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				Optional[Any]   					=    left_shift(right)
						lowercase_      :				Union[str, Any]   					=    left_shift(left)
						lowercase_      :				Union[str, Any]   					=    left_shift(right)
						lowercase_      :				Optional[int]   					=    apply_table(left + right, pa_table)

						# encryption
						lowercase_      :				int   					=    apply_table(message, IP)
						lowercase_      :				Dict   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Any   					=    temp[4:] + temp[:4]
						lowercase_      :				List[Any]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Tuple   					=    apply_table(temp, IP_inv)
						print('''Cipher text is:''', CT)

						# decryption
						lowercase_      :				List[str]   					=    apply_table(CT, IP)
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    temp[4:] + temp[:4]
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    apply_table(temp, IP_inv)
						print('''Plain text after decypting is:''', PT)


