






"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     bool:
							return credit_card_number.startswith(("34", "35", "37", "4", "5", "6")  )

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     bool:
							lowerCamelCase_	:							Union[str, Any]									=credit_card_number
							lowerCamelCase_	:							List[Any]									=0
							lowerCamelCase_	:							Optional[int]									=len(lowerCamelCase__  ) - 2
							for i in range(lowerCamelCase__    ,						-1    ,						-2  ):
														#  double the value of every second digit
														lowerCamelCase_	:							List[Any]									=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
														lowerCamelCase_	:							Optional[Any]									=cc_number[:i] + str(lowerCamelCase__  ) + cc_number[i + 1 :]
														total += digit

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

							return total % 10 == 0

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     bool:
							lowerCamelCase_	:							Any									=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(lowerCamelCase__  ) <= 16:
														print(F"""{error_message} of its length."""  )
														return False

							if not validate_initial_digits(lowerCamelCase__  ):
														print(F"""{error_message} of its first two digits."""  )
														return False

							if not luhn_validation(lowerCamelCase__  ):
														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')











"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        lowercase__      (							unittest.TestCase       ):


			def      UpperCAmelCase__						(				self					:  Any ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=1
										lowerCamelCase_	:							Union[str, Any]									=3
										lowerCamelCase_	:							Dict									=(32, 32)

										lowerCamelCase_	:							List[Any]									=floats_tensor((batch_size, num_channels) + sizes       ,	rng=random.Random(0 ) ).to(snake_case__ )
										return image


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Dict									=UNetaDConditionModel(
										    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,	cross_attention_dim=32       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Union[str, Any]									=AutoencoderKL(
										    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,	latent_channels=4       ,	)
										return model


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										torch.manual_seed(0 )
										lowerCamelCase_	:							Any									=RobertaSeriesConfig(
										    hidden_size=32       ,	project_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=5006       ,	)
										return RobertaSeriesModelWithTransformation(snake_case__ )


			@property
			def      UpperCAmelCase__						(				self					:  int ):
										def extract(*snake_case__					:  Dict       ,	**snake_case__					:  int ):







																	class        lowercase__      :


																				def __init__(				self					:  Optional[Any] ):
																											lowerCamelCase_	:							Union[str, Any]									=torch.ones([0] )


																				def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Any ):
																											self.pixel_values.to(snake_case__ )
																											return self


																	return Out()

										return extract


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Dict									="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							List[str]									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							List[Any]									=self.dummy_vae
										lowerCamelCase_	:							Any									=self.dummy_text_encoder
										lowerCamelCase_	:							List[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							List[str]									=77

										lowerCamelCase_	:							Optional[int]									=self.dummy_image.to(snake_case__ )
										lowerCamelCase_	:							Any									=init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							List[Any]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							int									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							Optional[int]									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Any									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Union[str, Any]									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							str									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	)

										lowerCamelCase_	:							List[Any]									=output.images

										lowerCamelCase_	:							str									=torch.Generator(device=snake_case__ ).manual_seed(0 )
										lowerCamelCase_	:							Optional[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	return_dict=snake_case__       ,	)[0]

										lowerCamelCase_	:							Tuple									=image[0, -3:, -3:, -1]
										lowerCamelCase_	:							Union[str, Any]									=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_	:							List[str]									=np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Dict									=self.dummy_cond_unet
										lowerCamelCase_	:							Any									=PNDMScheduler(skip_prk_steps=snake_case__ )
										lowerCamelCase_	:							Tuple									=self.dummy_vae
										lowerCamelCase_	:							Union[str, Any]									=self.dummy_text_encoder
										lowerCamelCase_	:							Optional[Any]									=XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase_	:							Tuple									=77

										lowerCamelCase_	:							str									=self.dummy_image.to(snake_case__ )

										# put models in fp16
										lowerCamelCase_	:							Optional[Any]									=unet.half()
										lowerCamelCase_	:							Dict									=vae.half()
										lowerCamelCase_	:							str									=bert.half()

										# make sure here that pndm scheduler skips prk
										lowerCamelCase_	:							Optional[int]									=AltDiffusionImgaImgPipeline(
										    unet=snake_case__       ,	scheduler=snake_case__       ,	vae=snake_case__       ,	text_encoder=snake_case__       ,	tokenizer=snake_case__       ,	safety_checker=snake_case__       ,	feature_extractor=self.dummy_extractor       ,	)
										lowerCamelCase_	:							List[Any]									=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,	do_normalize=snake_case__ )
										lowerCamelCase_	:							int									=alt_pipe.to(snake_case__ )
										alt_pipe.set_progress_bar_config(disable=snake_case__ )

										lowerCamelCase_	:							Tuple									="A painting of a squirrel eating a burger"
										lowerCamelCase_	:							Tuple									=torch.manual_seed(0 )
										lowerCamelCase_	:							List[Any]									=alt_pipe(
										    [prompt]       ,	generator=snake_case__       ,	num_inference_steps=2       ,	output_type="np"       ,	image=snake_case__       ,	).images

										assert image.shape == (1, 32, 32, 3)


			@unittest.skipIf(torch_device != "cuda"       ,	"This test requires a GPU" )
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							int									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowerCamelCase_	:							Any									=init_image.resize((760, 504) )

										lowerCamelCase_	:							List[str]									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[int]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							List[Any]									=torch.manual_seed(0 )
										lowerCamelCase_	:							Optional[int]									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							Optional[int]									=output.images[0]

										lowerCamelCase_	:							Tuple									=image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										lowerCamelCase_	:							Optional[int]									=np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2







@slow
@require_torch_gpu
class        lowercase__      (							unittest.TestCase       ):


			def      UpperCAmelCase__						(				self					:  str ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										lowerCamelCase_	:							Any									=init_image.resize((768, 512) )
										lowerCamelCase_	:							Optional[int]									=load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )

										lowerCamelCase_	:							Dict									="BAAI/AltDiffusion"
										lowerCamelCase_	:							List[str]									=AltDiffusionImgaImgPipeline.from_pretrained(
										    snake_case__       ,	safety_checker=snake_case__       ,	)
										pipe.to(snake_case__ )
										pipe.set_progress_bar_config(disable=snake_case__ )
										pipe.enable_attention_slicing()

										lowerCamelCase_	:							Optional[Any]									="A fantasy landscape, trending on artstation"

										lowerCamelCase_	:							Dict									=torch.manual_seed(0 )
										lowerCamelCase_	:							int									=pipe(
										    prompt=snake_case__       ,	image=snake_case__       ,	strength=0.75       ,	guidance_scale=7.5       ,	generator=snake_case__       ,	output_type="np"       ,	)
										lowerCamelCase_	:							int									=output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image ).max() < 1E-2



