
"""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 							__magic_name__     (		lowercase__      ,		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   StableDiffusionInstructPixaPixPipeline
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   IMAGE_TO_IMAGE_IMAGE_PARAMS





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												torch.manual_seed(0    )
												__snake_case     			=       UNetaDConditionModel(
												    block_out_channels=(32, 64)							,						layers_per_block=2							,						sample_size=32							,						in_channels=8							,						out_channels=4							,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,						cross_attention_dim=32							,						)
												__snake_case     			=       PNDMScheduler(skip_prk_steps=snake_case_    )
												torch.manual_seed(0    )
												__snake_case     			=       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							,						)
												torch.manual_seed(0    )
												__snake_case     			=       CLIPTextConfig(
												    bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1e-05							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=1000							,						)
												__snake_case     			=       CLIPTextModel(snake_case_    )
												__snake_case     			=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

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





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]=0    ):
												__snake_case     			=       floats_tensor((1, 3, 32, 32)							,						rng=random.Random(snake_case_    )    ).to(snake_case_    )
												__snake_case     			=       image.cpu().permute(0							,						2							,						3							,						1    )[0]
												__snake_case     			=       Image.fromarray(np.uinta(snake_case_    )    ).convert("RGB"    )
												if str(snake_case_    ).startswith("mps"    ):
																			__snake_case     			=       torch.manual_seed(snake_case_    )
												else:
																			__snake_case     			=       torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
												__snake_case     			=       {
												    "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   :							int    ):
												__snake_case     			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												__snake_case     			=       self.get_dummy_components()
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline(**snake_case_    )
												__snake_case     			=       sd_pipe.to(snake_case_    )
												sd_pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       self.get_dummy_inputs(snake_case_    )
												__snake_case     			=       sd_pipe(**snake_case_    ).images
												__snake_case     			=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 32, 32, 3)
												__snake_case     			=       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[Any]    ):
												__snake_case     			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												__snake_case     			=       self.get_dummy_components()
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline(**snake_case_    )
												__snake_case     			=       sd_pipe.to(snake_case_    )
												sd_pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       self.get_dummy_inputs(snake_case_    )
												__snake_case     			=       "french fries"
												__snake_case     			=       sd_pipe(**snake_case_							,						negative_prompt=snake_case_    )
												__snake_case     			=       output.images
												__snake_case     			=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												__snake_case     			=       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   :							List[Any]    ):
												__snake_case     			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												__snake_case     			=       self.get_dummy_components()
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline(**snake_case_    )
												__snake_case     			=       sd_pipe.to(snake_case_    )
												sd_pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       self.get_dummy_inputs(snake_case_    )
												__snake_case     			=       [inputs["prompt"]] * 2

												__snake_case     			=       np.array(inputs["image"]    ).astype(np.floataa    ) / 255.0
												__snake_case     			=       torch.from_numpy(snake_case_    ).unsqueeze(0    ).to(snake_case_    )
												__snake_case     			=       image / 2 + 0.5
												__snake_case     			=       image.permute(0							,						3							,						1							,						2    )
												__snake_case     			=       image.repeat(2							,						1							,						1							,						1    )

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

												assert image.shape == (2, 32, 32, 3)
												__snake_case     			=       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   :							List[Any]    ):
												__snake_case     			=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												__snake_case     			=       self.get_dummy_components()
												__snake_case     			=       EulerAncestralDiscreteScheduler(
												    beta_start=0.00085							,						beta_end=0.012							,						beta_schedule="scaled_linear"    )
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline(**snake_case_    )
												__snake_case     			=       sd_pipe.to(snake_case_    )
												sd_pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       self.get_dummy_inputs(snake_case_    )
												__snake_case     			=       sd_pipe(**snake_case_    ).images
												__snake_case     			=       image[0, -3:, -3:, -1]

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

												assert image.shape == (1, 32, 32, 3)
												__snake_case     			=       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   :							str    ):
												super().test_inference_batch_single_identical(expected_max_diff=3e-3    )





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.get_dummy_components()
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline(**snake_case_    )
												__snake_case     			=       VaeImageProcessor(do_resize=snake_case_							,						do_normalize=snake_case_    )
												__snake_case     			=       pipe.to(snake_case_    )
												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       pipe(**self.get_dummy_inputs_by_type(snake_case_							,						input_image_type="pt"    )    )[0]

												__snake_case     			=       components["vae"]
												__snake_case     			=       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():
																										__snake_case     			=       vae.encode(inputs[image_param]    ).latent_dist.mode()

												__snake_case     			=       pipe(**snake_case_    )[0]

												__snake_case     			=       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 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Any=0    ):
												__snake_case     			=       torch.manual_seed(snake_case_    )
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"    )
												__snake_case     			=       {
												    "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   :							Optional[int]    ):
												__snake_case     			=       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()

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

												assert image.shape == (1, 512, 512, 3)
												__snake_case     			=       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   :							Dict    ):
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    "timbrooks/instruct-pix2pix"							,						safety_checker=snake_case_    )
												__snake_case     			=       LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
												pipe.to(snake_case_    )
												pipe.set_progress_bar_config(disable=snake_case_    )
												pipe.enable_attention_slicing()

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

												assert image.shape == (1, 512, 512, 3)
												__snake_case     			=       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   :							Optional[Any]    ):
												__snake_case     			=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    "timbrooks/instruct-pix2pix"							,						safety_checker=snake_case_    )
												__snake_case     			=       DDIMScheduler.from_config(pipe.scheduler.config    )
												pipe.to(snake_case_    )
												pipe.set_progress_bar_config(disable=snake_case_    )
												pipe.enable_attention_slicing()

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

												assert image.shape == (1, 512, 512, 3)
												__snake_case     			=       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   :							Union[str, Any]    ):
												__snake_case     			=       0

												def callback_fn(snake_case_   :							int							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor    ) -> None:
																			__snake_case     			=       True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 1:
																										__snake_case     			=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 64, 64)
																										__snake_case     			=       latents[0, -3:, -3:, -1]
																										__snake_case     			=       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:
																										__snake_case     			=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 64, 64)
																										__snake_case     			=       latents[0, -3:, -3:, -1]
																										__snake_case     			=       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

												__snake_case     			=       False

												__snake_case     			=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    "timbrooks/instruct-pix2pix"							,						safety_checker=snake_case_							,						torch_dtype=torch.floataa    )
												__snake_case     			=       pipe.to(snake_case_    )
												pipe.set_progress_bar_config(disable=snake_case_    )
												pipe.enable_attention_slicing()

												__snake_case     			=       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   :							str    ):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												__snake_case     			=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    "timbrooks/instruct-pix2pix"							,						safety_checker=snake_case_							,						torch_dtype=torch.floataa    )
												__snake_case     			=       pipe.to(snake_case_    )
												pipe.set_progress_bar_config(disable=snake_case_    )
												pipe.enable_attention_slicing(1    )
												pipe.enable_sequential_cpu_offload()

												__snake_case     			=       self.get_inputs()
												__snake_case     			=       pipe(**snake_case_    )

												__snake_case     			=       torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 10**9





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												__snake_case     			=       inputs["image"].resize((504, 504)    )

												__snake_case     			=       "timbrooks/instruct-pix2pix"
												__snake_case     			=       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()

												__snake_case     			=       pipe(**snake_case_    )
												__snake_case     			=       output.images[0]

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

												assert image.shape == (504, 504, 3)
												__snake_case     			=       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 baseaa
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bytes:







							"""simple docstring"""


							return baseaa.baaencode(string.encode("utf-8" ) )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							return baseaa.baadecode(SCREAMING_SNAKE_CASE ).decode("utf-8" )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      """Hello World!"""
		_SCREAMING_SNAKE_CASE	       =      baseaa_encode(test)
		print(encoded)

		_SCREAMING_SNAKE_CASE	       =      baseaa_decode(encoded)
		print(decoded)
