


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int							)			->   Optional[int]:





									"""simple docstring"""







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									lowercase__            =      DDIMScheduler.from_config(scheduler.config							)

									self.register_modules(unet=UpperCamelCase_    ,						scheduler=UpperCamelCase_							)


							@torch.no_grad()
							def __call__(							self:	int    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase_:	float = 0.0    ,						UpperCamelCase_:	int = 50    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[str] = "pil"    ,						UpperCamelCase_:	bool = True    ,						)			->   Union[ImagePipelineOutput, Tuple]:





									"""simple docstring"""







									if isinstance(self.unet.config.sample_size    ,						UpperCamelCase_							):
											lowercase__            =      (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
									else:
											lowercase__            =      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(UpperCamelCase_    ,						UpperCamelCase_							) and len(UpperCamelCase_							) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(UpperCamelCase_							)}, but requested an effective batch'
											    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'							)

									lowercase__            =      randn_tensor(UpperCamelCase_    ,						generator=UpperCamelCase_    ,						device=self.device    ,						dtype=self.unet.dtype							)

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

									for t in self.progress_bar(self.scheduler.timesteps							):
											# 1. predict noise model_output
											lowercase__            =      self.unet(UpperCamelCase_    ,						UpperCamelCase_							).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
											lowercase__            =      self.scheduler.step(
											    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						eta=UpperCamelCase_    ,						use_clipped_model_output=UpperCamelCase_    ,						generator=UpperCamelCase_							).prev_sample

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_							)






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')


