






"""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







"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





