

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=(UnCLIPScheduler,)
			def 							__UpperCamelCase       (	self							:       List[str]					,					**a							:       Tuple		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				{
				    "num_train_timesteps": 1000,
				    "variance_type": "fixed_small_log",
				    "clip_sample": True,
				    "clip_sample_range": 1.0,
				    "prediction_type": "epsilon",
				}

				config.update(**a		)
				return config
			def 							__UpperCamelCase       (	self							:       Any		)							->				str:

				"""simple docstring"""

				for timesteps in [1, 5, 100, 1000]:
					self.check_over_configs(num_train_timesteps=a		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=a		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[Any]:

				"""simple docstring"""

				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=a		)
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[int]:

				"""simple docstring"""

				for clip_sample_range in [1, 5, 10, 20]:
					self.check_over_configs(clip_sample_range=a		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=a		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				for time_step in [0, 500, 999]:
					for prev_timestep in [None, 5, 100, 250, 500, 750]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=a					,					prev_timestep=a		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					int    =				self.get_scheduler_config(variance_type="fixed_small_log"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				scheduler_class(**a		)

				assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.00_00e-10		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.054_9625		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.999_4987		)		) < 1e-5
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					Any    =				self.get_scheduler_config(variance_type="learned_range"		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				scheduler_class(**a		)

				SCREAMING_SNAKE_CASE      :					int    =				0.5

				assert scheduler._get_variance(1					,					predicted_variance=a		) - -10.171_2790 < 1e-5
				assert scheduler._get_variance(487					,					predicted_variance=a		) - -5.799_8052 < 1e-5
				assert scheduler._get_variance(999					,					predicted_variance=a		) - -0.001_0011 < 1e-5
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.get_scheduler_config()
				SCREAMING_SNAKE_CASE      :					Dict    =				scheduler_class(**a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				scheduler.timesteps

				SCREAMING_SNAKE_CASE      :					str    =				self.dummy_model()
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.dummy_sample_deter
				SCREAMING_SNAKE_CASE      :					str    =				torch.manual_seed(0		)

				for i, t in enumerate(a		):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE      :					Tuple    =				model(a					,					a		)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				scheduler.step(a					,					a					,					a					,					generator=a		).prev_sample

					SCREAMING_SNAKE_CASE      :					Optional[int]    =				pred_prev_sample

				SCREAMING_SNAKE_CASE      :					str    =				torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.mean(torch.abs(a		)		)

				assert abs(result_sum.item() - 252.268_2495		) < 1e-2
				assert abs(result_mean.item() - 0.328_4743		) < 1e-3
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.get_scheduler_config()
				SCREAMING_SNAKE_CASE      :					Tuple    =				scheduler_class(**a		)

				scheduler.set_timesteps(25		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				scheduler.timesteps

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.dummy_model()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.dummy_sample_deter
				SCREAMING_SNAKE_CASE      :					str    =				torch.manual_seed(0		)

				for i, t in enumerate(a		):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(a					,					a		)

					if i + 1 == timesteps.shape[0]:
						SCREAMING_SNAKE_CASE      :					List[str]    =				None
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				scheduler.step(
					    a					,					a					,					a					,					prev_timestep=a					,					generator=a		).prev_sample

					SCREAMING_SNAKE_CASE      :					Tuple    =				pred_prev_sample

				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.sum(torch.abs(a		)		)
				SCREAMING_SNAKE_CASE      :					Any    =				torch.mean(torch.abs(a		)		)

				assert abs(result_sum.item() - 258.204_4983		) < 1e-2
				assert abs(result_mean.item() - 0.336_2038		) < 1e-3
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[str]:

				"""simple docstring"""

				pass






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

				"""simple docstring"""

				pass



def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      credit_card_number
		lowercase__            =      0
		lowercase__            =      len(SCREAMING_SNAKE_CASE     ) - 2
		for i in range(SCREAMING_SNAKE_CASE						,				-1						,				-2     ):
				#  double the value of every second digit
				lowercase__            =      int(cc_number[i]     )
				digit *= 2
				# If doubling of a number results in a two digit number
				# i.e greater than 9(e.g., 6 × 2 = 12),
				# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
				# to get a single digit number.
				if digit > 9:
						digit %= 10
						digit += 1
				lowercase__            =      cc_number[:i] + str(SCREAMING_SNAKE_CASE     ) + cc_number[i + 1 :]
				total += digit

		# Sum up the remaining digits
		for i in range(len(SCREAMING_SNAKE_CASE     ) - 1						,				-1						,				-2     ):
				total += int(cc_number[i]     )

		return total % 10 == 0
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      f'{credit_card_number} is an invalid credit card number because'
		if not credit_card_number.isdigit():
				print(f'{error_message} it has nonnumerical characters.'     )
				return False

		if not 13 <= len(SCREAMING_SNAKE_CASE     ) <= 16:
				print(f'{error_message} of its length.'     )
				return False

		if not validate_initial_digits(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} of its first two digits.'     )
				return False

		if not luhn_validation(SCREAMING_SNAKE_CASE     ):
				print(f'{error_message} it fails the Luhn check.'     )
				return False

		print(f'{credit_card_number} is a valid credit card number.'     )
		return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			validate_credit_card_number('4111111111111111')
			validate_credit_card_number('32323')


