





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class        _lowerCamelCase      (       _lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			ShapEImgaImgPipeline
						UpperCAmelCase_ =			["image"]
						UpperCAmelCase_ =			["image"]
						UpperCAmelCase_ =			[
						    "num_images_per_prompt",
						    "num_inference_steps",
						    "generator",
						    "latents",
						    "guidance_scale",
						    "frame_size",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase_ =			False


						@property
						def     snake_case_   (self		) ->   Any:
								return 32


						@property
						def     snake_case_   (self		) ->   List[str]:
								return 32


						@property
						def     snake_case_   (self		) ->   str:
								return self.time_input_dim * 4


						@property
						def     snake_case_   (self		) ->   Optional[Any]:
								return 8


						@property
						def     snake_case_   (self		) ->   Union[str, Any]:
								torch.manual_seed(0		)
								UpperCamelCase   =	CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size   ,							image_size=64   ,							projection_dim=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							num_attention_heads=4   ,							num_channels=3   ,							num_hidden_layers=5   ,							patch_size=1   ,							)

								UpperCamelCase   =	CLIPVisionModel(__a		)
								return model


						@property
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	CLIPImageProcessor(
								    crop_size=2_24   ,							do_center_crop=__a   ,							do_normalize=__a   ,							do_resize=__a   ,							image_mean=[0.48145466, 0.4578275, 0.40821073]   ,							image_std=[0.26862954, 0.26130258, 0.27577711]   ,							resample=3   ,							size=2_24   ,							)

								return image_processor


						@property
						def     snake_case_   (self		) ->   List[str]:
								torch.manual_seed(0		)

								UpperCamelCase   =	{
								    "num_attention_heads": 2,
								    "attention_head_dim": 16,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 32,
								    "embedding_proj_dim": self.text_embedder_hidden_size,
								    "time_embed_dim": self.time_embed_dim,
								    "num_layers": 1,
								    "clip_embed_dim": self.time_input_dim * 2,
								    "additional_embeddings": 0,
								    "time_embed_act_fn": "gelu",
								    "norm_in_type": "layer",
								    "embedding_proj_norm_type": "layer",
								    "encoder_hid_proj_type": None,
								    "added_emb_type": None,
								}

								UpperCamelCase   =	PriorTransformer(**__a		)
								return model


						@property
						def     snake_case_   (self		) ->   Tuple:
								torch.manual_seed(0		)

								UpperCamelCase   =	{
								    "param_shapes": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    "d_latent": self.time_input_dim,
								    "d_hidden": self.renderer_dim,
								    "n_output": 12,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								UpperCamelCase   =	ShapERenderer(**__a		)
								return model


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.dummy_prior
								UpperCamelCase   =	self.dummy_image_encoder
								UpperCamelCase   =	self.dummy_image_processor
								UpperCamelCase   =	self.dummy_renderer

								UpperCamelCase   =	HeunDiscreteScheduler(
								    beta_schedule="exp"   ,							num_train_timesteps=10_24   ,							prediction_type="sample"   ,							use_karras_sigmas=__a   ,							clip_sample=__a   ,							clip_sample_range=1.0   ,							)
								UpperCamelCase   =	{
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "image_processor": image_processor,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   Any:
								UpperCamelCase   =	floats_tensor((1, 3, 64, 64)   ,							rng=random.Random(__a		)		).to(__a		)

								if str(__a		).startswith("mps"		):
										UpperCamelCase   =	torch.manual_seed(__a		)
								else:
										UpperCamelCase   =	torch.Generator(device=__a		).manual_seed(__a		)
								UpperCamelCase   =	{
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 32,
								    "output_type": "np",
								}
								return inputs


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

								UpperCamelCase   =	self.get_dummy_components()

								UpperCamelCase   =	self.pipeline_class(**__a		)
								UpperCamelCase   =	pipe.to(__a		)

								pipe.set_progress_bar_config(disable=__a		)

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

								assert image.shape == (20, 32, 32, 3)

								UpperCamelCase   =	np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]		)

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


						def     snake_case_   (self		) ->   Union[str, Any]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	torch_device == "cpu"
								UpperCamelCase   =	True
								self._test_inference_batch_single_identical(
								    batch_size=2   ,							test_max_difference=__a   ,							relax_max_difference=__a   ,							)




						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	self.pipeline_class(**__a		)
								UpperCamelCase   =	pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	1
								UpperCamelCase   =	2

								UpperCamelCase   =	self.get_dummy_inputs(__a		)

								for key in inputs.keys():
										if key in self.batch_params:
												UpperCamelCase   =	batch_size * [inputs[key]]

								UpperCamelCase   =	pipe(**__a   ,							num_images_per_prompt=__a		)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt





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


						def     snake_case_   (self		) ->   List[str]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"		)
								UpperCamelCase   =	load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/shap_e/test_shap_e_img2img_out.npy"		)
								UpperCamelCase   =	ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"		)
								UpperCamelCase   =	pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	torch.Generator(device=__a		).manual_seed(0		)

								UpperCamelCase   =	pipe(
								    __a   ,							generator=__a   ,							guidance_scale=3.0   ,							num_inference_steps=64   ,							frame_size=64   ,							output_type="np"   ,							).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__a   ,							__a		)













"""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()






