


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 	lowerCamelCase_       (				_A     ,     _A     ,     _A     ,     unittest.TestCase				):



					'''simple docstring'''







					__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 A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								torch.manual_seed(0						)
								__lowercase 					=   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 ,       )
								__lowercase 					=   PNDMScheduler(skip_prk_steps=__lowerCamelCase						)
								torch.manual_seed(0						)
								__lowercase 					=   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						)
								__lowercase 					=   CLIPTextConfig(
								    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       intermediate_size=3_7 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       )
								__lowercase 					=   CLIPTextModel(__lowerCamelCase						)
								__lowercase 					=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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






					def A      (				self ,       snake_case_ ,       snake_case_=0						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(__lowerCamelCase						)						).to(__lowerCamelCase						)
								__lowercase 					=   image.cpu().permute(0 ,       2 ,       3 ,       1						)[0]
								__lowercase 					=   Image.fromarray(np.uinta(__lowerCamelCase						)						).convert('''RGB'''						)
								if str(__lowerCamelCase						).startswith('''mps'''						):
											__lowercase 					=   torch.manual_seed(__lowerCamelCase						)
								else:
											__lowercase 					=   torch.Generator(device=__lowerCamelCase						).manual_seed(__lowerCamelCase						)
								__lowercase 					=   {
								    "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 A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase						)
								__lowercase 					=   sd_pipe.to(__lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

								__lowercase 					=   self.get_dummy_inputs(__lowerCamelCase						)
								__lowercase 					=   sd_pipe(**__lowerCamelCase						).images
								__lowercase 					=   image[0, -3:, -3:, -1]
								assert image.shape == (1, 3_2, 3_2, 3)
								__lowercase 					=   np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]						)

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






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase						)
								__lowercase 					=   sd_pipe.to(__lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

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

								assert image.shape == (1, 3_2, 3_2, 3)
								__lowercase 					=   np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]						)

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






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase						)
								__lowercase 					=   sd_pipe.to(__lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

								__lowercase 					=   self.get_dummy_inputs(__lowerCamelCase						)
								__lowercase 					=   [inputs["prompt"]] * 2

								__lowercase 					=   np.array(inputs['''image''']						).astype(np.floataa						) / 2_5_5.0
								__lowercase 					=   torch.from_numpy(__lowerCamelCase						).unsqueeze(0						).to(__lowerCamelCase						)
								__lowercase 					=   image / 2 + 0.5
								__lowercase 					=   image.permute(0 ,       3 ,       1 ,       2						)
								__lowercase 					=   image.repeat(2 ,       1 ,       1 ,       1						)

								__lowercase 					=   sd_pipe(**__lowerCamelCase						).images
								__lowercase 					=   image[-1, -3:, -3:, -1]

								assert image.shape == (2, 3_2, 3_2, 3)
								__lowercase 					=   np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]						)

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






					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5 ,       beta_end=0.0_1_2 ,       beta_schedule='''scaled_linear'''						)
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase						)
								__lowercase 					=   sd_pipe.to(__lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

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

								__lowercase 					=   [round(__lowerCamelCase ,       4						) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(__lowerCamelCase						) for x in slice]						)						)

								assert image.shape == (1, 3_2, 3_2, 3)
								__lowercase 					=   np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]						)

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase						)
								__lowercase 					=   VaeImageProcessor(do_resize=__lowerCamelCase ,       do_normalize=__lowerCamelCase						)
								__lowercase 					=   pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)

								__lowercase 					=   pipe(**self.get_dummy_inputs_by_type(__lowerCamelCase ,       input_image_type='''pt'''						)						)[0]

								__lowercase 					=   components["vae"]
								__lowercase 					=   self.get_dummy_inputs_by_type(__lowerCamelCase ,       input_image_type='''pt'''						)

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

								__lowercase 					=   pipe(**__lowerCamelCase						)[0]

								__lowercase 					=   np.abs(out - out_latents_inputs						).max()
								self.assertLess(__lowerCamelCase ,       1e-4 ,       '''passing latents as image input generate different result from passing image'''						)




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



					'''simple docstring'''







					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






					def A      (				self ,       snake_case_=0						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   torch.manual_seed(__lowerCamelCase						)
								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''						)
								__lowercase 					=   {
								    "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 A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''' ,       safety_checker=__lowerCamelCase						)
								pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowercase 					=   np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]						)

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''' ,       safety_checker=__lowerCamelCase						)
								__lowercase 					=   LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
								pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowercase 					=   np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]						)

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






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''' ,       safety_checker=__lowerCamelCase						)
								__lowercase 					=   DDIMScheduler.from_config(pipe.scheduler.config						)
								pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowercase 					=   np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]						)

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






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   0

								def callback_fn(snake_case_ ,       snake_case_ ,       snake_case_						) -> None:
											__lowercase 					=   True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
														__lowercase 					=   latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 6_4, 6_4)
														__lowercase 					=   latents[0, -3:, -3:, -1]
														__lowercase 					=   np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]						)

														assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2
											elif step == 2:
														__lowercase 					=   latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 6_4, 6_4)
														__lowercase 					=   latents[0, -3:, -3:, -1]
														__lowercase 					=   np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]						)

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

								__lowercase 					=   False

								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''' ,       safety_checker=__lowerCamelCase ,       torch_dtype=torch.floataa						)
								__lowercase 					=   pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing()

								__lowercase 					=   self.get_inputs()
								pipe(**__lowerCamelCase ,       callback=__lowerCamelCase ,       callback_steps=1						)
								assert callback_fn.has_been_called
								assert number_of_steps == 3






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''' ,       safety_checker=__lowerCamelCase ,       torch_dtype=torch.floataa						)
								__lowercase 					=   pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing(1						)
								pipe.enable_sequential_cpu_offload()

								__lowercase 					=   self.get_inputs()
								__lowercase 					=   pipe(**__lowerCamelCase						)

								__lowercase 					=   torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 1_0**9






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								__lowercase 					=   inputs["image"].resize((5_0_4, 5_0_4)						)

								__lowercase 					=   "timbrooks/instruct-pix2pix"
								__lowercase 					=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    __lowerCamelCase ,       safety_checker=__lowerCamelCase ,       )
								pipe.to(__lowerCamelCase						)
								pipe.set_progress_bar_config(disable=__lowerCamelCase						)
								pipe.enable_attention_slicing()

								__lowercase 					=   pipe(**__lowerCamelCase						)
								__lowercase 					=   output.images[0]

								__lowercase 					=   image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 5_0_4, 3)
								__lowercase 					=   np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]						)

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






a : Any		=				'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a : Union[str, Any]		=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a : Union[str, Any]		=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


