

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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	credit_card_number
						snake_case_   =	0
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       ) - 2
						for i in range(SCREAMING_SNAKE_CASE__  ,		-1  ,		-2       ):
												#  double the value of every second digit
												snake_case_   =	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
												snake_case_   =	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	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''')




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						self.register_modules(unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		)




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

						a					     =   (image / 2 + 0.5).clamp(0		,       1		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		).numpy()
						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
