





"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class        _lowerCamelCase      (       _snake_case  ,			unittest.TestCase ):
						UpperCAmelCase_ =			DiTPipeline
						UpperCAmelCase_ =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
						UpperCAmelCase_ =			PipelineTesterMixin.required_optional_params - {
						    "latents",
						    "num_images_per_prompt",
						    "callback",
						    "callback_steps",
						}
						UpperCAmelCase_ =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   Any:
								torch.manual_seed(0		)
								UpperCamelCase   =	TransformeraDModel(
								    sample_size=16   ,							num_layers=2   ,							patch_size=4   ,							attention_head_dim=8   ,							num_attention_heads=2   ,							in_channels=4   ,							out_channels=8   ,							attention_bias=lowerCAmelCase__   ,							activation_fn="gelu-approximate"   ,							num_embeds_ada_norm=10_00   ,							norm_type="ada_norm_zero"   ,							norm_elementwise_affine=lowerCAmelCase__   ,							)
								UpperCamelCase   =	AutoencoderKL()
								UpperCamelCase   =	DDIMScheduler()
								UpperCamelCase   =	{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   Tuple:
								if str(lowerCAmelCase__		).startswith("mps"		):
										UpperCamelCase   =	torch.manual_seed(lowerCAmelCase__		)
								else:
										UpperCamelCase   =	torch.Generator(device=lowerCAmelCase__		).manual_seed(lowerCAmelCase__		)
								UpperCamelCase   =	{
								    "class_labels": [1],
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	"cpu"

								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	self.pipeline_class(**lowerCAmelCase__		)
								pipe.to(lowerCAmelCase__		)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

								UpperCamelCase   =	self.get_dummy_inputs(lowerCAmelCase__		)
								UpperCamelCase   =	pipe(**lowerCAmelCase__		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

								self.assertEqual(image.shape   ,							(1, 16, 16, 3)		)
								UpperCamelCase   =	np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]		)
								UpperCamelCase   =	np.abs(image_slice.flatten() - expected_slice		).max()
								self.assertLessEqual(lowerCAmelCase__   ,							1e-3		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase__   ,							expected_max_diff=1e-3		)




						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()   ,							reason="XFormers attention is only available with CUDA and `xformers` installed"   ,							)
						def     snake_case_   (self		) ->   str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)





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


						def     snake_case_   (self		) ->   Dict:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	torch.manual_seed(0		)

								UpperCamelCase   =	DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"		)
								pipe.to("cuda"		)

								UpperCamelCase   =	["vase", "umbrella", "white shark", "white wolf"]
								UpperCamelCase   =	pipe.get_label_ids(lowerCAmelCase__		)

								UpperCamelCase   =	pipe(lowerCAmelCase__   ,							generator=lowerCAmelCase__   ,							num_inference_steps=40   ,							output_type="np"		).images

								for word, image in zip(lowerCAmelCase__   ,							lowerCAmelCase__		):
										UpperCamelCase   =	load_numpy(
										    F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"		)
										assert np.abs((expected_image - image).max()		) < 1e-2




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"		)
								UpperCamelCase   =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
								pipe.to("cuda"		)

								UpperCamelCase   =	["vase", "umbrella"]
								UpperCamelCase   =	pipe.get_label_ids(lowerCAmelCase__		)

								UpperCamelCase   =	torch.manual_seed(0		)
								UpperCamelCase   =	pipe(lowerCAmelCase__   ,							generator=lowerCAmelCase__   ,							num_inference_steps=25   ,							output_type="np"		).images

								for word, image in zip(lowerCAmelCase__   ,							lowerCAmelCase__		):
										UpperCamelCase   =	load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    F"/dit/{word}_512.npy"		)

										assert np.abs((expected_image - image).max()		) < 1e-1













"""simple docstring"""





import string


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in range(len(string.ascii_uppercase    )    ):
				UpperCamelCase   =	""
				for symbol in message:
						if symbol in string.ascii_uppercase:
								UpperCamelCase   =	string.ascii_uppercase.find(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	num - key
								if num < 0:
										UpperCamelCase   =	num + len(string.ascii_uppercase    )
								UpperCamelCase   =	translated + string.ascii_uppercase[num]
						else:
								UpperCamelCase   =	translated + symbol
				print(F"Decryption using Key #{key}: {translated}"    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	input("Encrypted message: "    )
		UpperCamelCase   =	message.upper()
		decrypt(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






