






"""simple docstring"""





from string import ascii_uppercase

__lowerCamelCase							=  {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase							=  dict(enumerate(ascii_uppercase))



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							str:
						__magic_name__:	Union[str, Any]							=       len(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       0
						while True:
												if x == i:
																		__magic_name__:	List[Any]							=       0
												if len(__UpperCAmelCase  ) == len(__UpperCAmelCase  ):
																		break
												key += key[i]
												i += 1
						return key



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							str:
						__magic_name__:	int							=       """"""
						__magic_name__:	int							=       0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__magic_name__:	Optional[Any]							=       (dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							str:
						__magic_name__:	Dict							=       """"""
						__magic_name__:	int							=       0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__magic_name__:	str							=       (dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt



def   a    (     )    ->							None:
						__magic_name__:	Optional[Any]							=       """THE GERMAN ATTACK"""
						__magic_name__:	str							=       """SECRET"""
						__magic_name__:	Any							=       generate_key(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       cipher_text(__UpperCAmelCase      ,   __UpperCAmelCase  )
						print(f'Encrypted Text = {s}'  )
						print(f'Original Text = {original_text(__UpperCAmelCase      ,   __UpperCAmelCase  )}'  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				StableDiffusionInstructPixaPixPipeline
					UpperCAmelCase__								=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					UpperCAmelCase__								=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__								=				IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__								=				IMAGE_TO_IMAGE_IMAGE_PARAMS

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											torch.manual_seed(0					)
											__magic_name__:	str							=       UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=8     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		cross_attention_dim=3_2     ,		)
											__magic_name__:	Union[str, Any]							=       PNDMScheduler(skip_prk_steps=__snake_case					)
											torch.manual_seed(0					)
											__magic_name__:	Union[str, Any]							=       AutoencoderKL(
											    block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,		latent_channels=4     ,		)
											torch.manual_seed(0					)
											__magic_name__:	int							=       CLIPTextConfig(
											    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1E-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
											__magic_name__:	Optional[int]							=       CLIPTextModel(__snake_case					)
											__magic_name__:	Union[str, Any]							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

											__magic_name__:	Tuple							=       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      List[Any]=0					)			->							Optional[Any]:
											__magic_name__:	Optional[int]							=       floats_tensor((1, 3, 3_2, 3_2)     ,		rng=random.Random(__snake_case					)					).to(__snake_case					)
											__magic_name__:	str							=       image.cpu().permute(0     ,		2     ,		3     ,		1					)[0]
											__magic_name__:	Any							=       Image.fromarray(np.uinta(__snake_case					)					).convert("""RGB"""					)
											if str(__snake_case					).startswith("""mps"""					):
																	__magic_name__:	Optional[Any]							=       torch.manual_seed(__snake_case					)
											else:
																	__magic_name__:	str							=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
											__magic_name__:	Optional[int]							=       {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 6.0,
											    """image_guidance_scale""": 1,
											    """output_type""": """numpy""",
											}
											return inputs

					def   lowerCamelCase__   (			self  :      Any					)			->							Tuple:
											__magic_name__:	int							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	List[Any]							=       self.get_dummy_components()
											__magic_name__:	int							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	List[Any]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	Tuple							=       sd_pipe(**__snake_case					).images
											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1]
											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	List[Any]							=       np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]					)

											assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											__magic_name__:	Any							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	str							=       self.get_dummy_components()
											__magic_name__:	Dict							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	str							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Optional[Any]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	List[Any]							=       """french fries"""
											__magic_name__:	int							=       sd_pipe(**__snake_case     ,		negative_prompt=__snake_case					)
											__magic_name__:	Dict							=       output.images
											__magic_name__:	Any							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	str							=       np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]					)

											assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Tuple:
											__magic_name__:	Union[str, Any]							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	int							=       self.get_dummy_components()
											__magic_name__:	Dict							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Dict							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	str							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	List[str]							=       [inputs["""prompt"""]] * 2

											__magic_name__:	List[str]							=       np.array(inputs["""image"""]					).astype(np.floataa					) / 255.0
											__magic_name__:	Optional[int]							=       torch.from_numpy(__snake_case					).unsqueeze(0					).to(__snake_case					)
											__magic_name__:	Tuple							=       image / 2 + 0.5
											__magic_name__:	Dict							=       image.permute(0     ,		3     ,		1     ,		2					)
											__magic_name__:	List[str]							=       image.repeat(2     ,		1     ,		1     ,		1					)

											__magic_name__:	str							=       sd_pipe(**__snake_case					).images
											__magic_name__:	List[str]							=       image[-1, -3:, -3:, -1]

											assert image.shape == (2, 3_2, 3_2, 3)
											__magic_name__:	Optional[int]							=       np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]					)

											assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[int]:
											__magic_name__:	Union[str, Any]							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	Union[str, Any]							=       self.get_dummy_components()
											__magic_name__:	Union[str, Any]							=       EulerAncestralDiscreteScheduler(
											    beta_start=0.00085     ,		beta_end=0.012     ,		beta_schedule="""scaled_linear"""					)
											__magic_name__:	Union[str, Any]							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Optional[int]							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	List[str]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	Tuple							=       sd_pipe(**__snake_case					).images
											__magic_name__:	Any							=       image[0, -3:, -3:, -1]

											__magic_name__:	str							=       [round(__snake_case     ,		4					) for x in image_slice.flatten().tolist()]
											print(""",""".join([str(__snake_case					) for x in slice]					)					)

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]					)

											assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											__magic_name__:	Tuple							=       self.get_dummy_components()
											__magic_name__:	Tuple							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	str							=       VaeImageProcessor(do_resize=__snake_case     ,		do_normalize=__snake_case					)
											__magic_name__:	Union[str, Any]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Union[str, Any]							=       pipe(**self.get_dummy_inputs_by_type(__snake_case     ,		input_image_type="""pt"""					)					)[0]

											__magic_name__:	Union[str, Any]							=       components["""vae"""]
											__magic_name__:	str							=       self.get_dummy_inputs_by_type(__snake_case     ,		input_image_type="""pt"""					)

											for image_param in self.image_latents_params:
																	if image_param in inputs.keys():
																							__magic_name__:	int							=       vae.encode(inputs[image_param]					).latent_dist.mode()

											__magic_name__:	Dict							=       pipe(**__snake_case					)[0]

											__magic_name__:	Optional[Any]							=       np.abs(out - out_latents_inputs					).max()
											self.assertLess(__snake_case     ,		1E-4     ,		"""passing latents as image input generate different result from passing image"""					)



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

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Any:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[str]=0					)			->							Dict:
											__magic_name__:	Union[str, Any]							=       torch.manual_seed(__snake_case					)
											__magic_name__:	Optional[int]							=       load_image(
											    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""					)
											__magic_name__:	Optional[Any]							=       {
											    """prompt""": """turn him into a cyborg""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """image_guidance_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs

					def   lowerCamelCase__   (			self  :      Any					)			->							Any:
											__magic_name__:	str							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Optional[Any]							=       self.get_inputs()
											__magic_name__:	str							=       pipe(**__snake_case					).images
											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Any							=       np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]					)

											assert np.abs(expected_slice - image_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											__magic_name__:	Tuple							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											__magic_name__:	List[str]							=       LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	List[str]							=       self.get_inputs()
											__magic_name__:	Dict							=       pipe(**__snake_case					).images
											__magic_name__:	str							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]					)

											assert np.abs(expected_slice - image_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	str							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											__magic_name__:	int							=       DDIMScheduler.from_config(pipe.scheduler.config					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Union[str, Any]							=       self.get_inputs()
											__magic_name__:	Any							=       pipe(**__snake_case					).images
											__magic_name__:	int							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]					)

											assert np.abs(expected_slice - image_slice					).max() < 1E-3

					def   lowerCamelCase__   (			self  :      int					)			->							Dict:
											__magic_name__:	Tuple							=       0

											def callback_fn(__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      torch.FloatTensor					) -> None:
																	__magic_name__:	Union[str, Any]							=       True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																							__magic_name__:	List[str]							=       latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__:	int							=       latents[0, -3:, -3:, -1]
																							__magic_name__:	Union[str, Any]							=       np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]					)

																							assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
																	elif step == 2:
																							__magic_name__:	Union[str, Any]							=       latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__:	str							=       latents[0, -3:, -3:, -1]
																							__magic_name__:	Optional[Any]							=       np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]					)

																							assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

											__magic_name__:	Tuple							=       False

											__magic_name__:	List[Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case     ,		torch_dtype=torch.floataa					)
											__magic_name__:	Union[str, Any]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Dict							=       self.get_inputs()
											pipe(**__snake_case     ,		callback=__snake_case     ,		callback_steps=1					)
											assert callback_fn.has_been_called
											assert number_of_steps == 3

					def   lowerCamelCase__   (			self  :      Tuple					)			->							int:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__magic_name__:	List[Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case     ,		torch_dtype=torch.floataa					)
											__magic_name__:	int							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing(1					)
											pipe.enable_sequential_cpu_offload()

											__magic_name__:	Optional[int]							=       self.get_inputs()
											__magic_name__:	Any							=       pipe(**__snake_case					)

											__magic_name__:	List[str]							=       torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 1_0**9






					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											__magic_name__:	Optional[int]							=       self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											__magic_name__:	Any							=       inputs["""image"""].resize((5_0_4, 5_0_4)					)

											__magic_name__:	List[str]							=       """timbrooks/instruct-pix2pix"""
											__magic_name__:	Union[str, Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    __snake_case     ,		safety_checker=__snake_case     ,		)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	str							=       pipe(**__snake_case					)
											__magic_name__:	Optional[int]							=       output.images[0]

											__magic_name__:	Union[str, Any]							=       image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

											assert image.shape == (5_0_4, 5_0_4, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]					)

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



