


'''simple docstring'''



# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


A__		:					Any												=  open  # noqa: we just need to have a builtin inside this module to test it properly










'''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    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			StableDiffusionInstructPixaPixPipeline
    lowerCamelCase  :					List[Any]							     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    lowerCamelCase  :					List[str]							     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase  :					Optional[int]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase  :					List[str]							     =			IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_				(							self	)				->     int:
         torch.manual_seed(0	)
         __lowerCamelCase   :	Any								=		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			,     )
         __lowerCamelCase   :	Union[str, Any]								=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_	)
         torch.manual_seed(0	)
         __lowerCamelCase   :	Optional[Any]								=		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	)
         __lowerCamelCase   :	Optional[int]								=		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=10_00			,     )
         __lowerCamelCase   :	int								=		CLIPTextModel(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         __lowerCamelCase   :	Optional[Any]								=		{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







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

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	List[str]								=		image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[int]								=		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       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	List[Any]								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Union[str, Any]								=		'french fries'
         __lowerCamelCase   :	List[Any]								=		sd_pipe(**SCREAMING_SNAKE_CASE_			,     negative_prompt=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		output.images
         __lowerCamelCase   :	str								=		image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	Optional[Any]								=		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       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	List[str]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Any								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		[inputs['prompt']] * 2

         __lowerCamelCase   :	Tuple								=		np.array(inputs['image']	).astype(np.floataa	) / 2_5_5.0
         __lowerCamelCase   :	List[str]								=		torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		image / 2 + 0.5
         __lowerCamelCase   :	Optional[Any]								=		image.permute(0			,     3			,     1			,     2	)
         __lowerCamelCase   :	Dict								=		image.repeat(2			,     1			,     1			,     1	)

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

         assert image.shape == (2, 32, 32, 3)
         __lowerCamelCase   :	Union[str, Any]								=		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       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase   :	int								=		self.get_dummy_components()
         __lowerCamelCase   :	Union[str, Any]								=		EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5			,     beta_end=0.0_1_2			,     beta_schedule='scaled_linear'	)
         __lowerCamelCase   :	str								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe.to(SCREAMING_SNAKE_CASE_	)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		sd_pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Optional[Any]								=		image[0, -3:, -3:, -1]

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

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase   :	List[str]								=		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       lowercase_				(							self	)				->     List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_components()
         __lowerCamelCase   :	Tuple								=		StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_			,     do_normalize=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	str								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

         __lowerCamelCase   :	Optional[Any]								=		components['vae']
         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_			,     input_image_type='pt'	)

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

         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	)[0]

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







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





    """simple docstring"""






    def       lowercase_				(							self	)				->     int:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0	)				->     str:
         __lowerCamelCase   :	str								=		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'	)
         __lowerCamelCase   :	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       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Any								=		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       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Optional[Any]								=		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       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Dict								=		DDIMScheduler.from_config(pipe.scheduler.config	)
         pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Union[str, Any]								=		self.get_inputs()
         __lowerCamelCase   :	str								=		pipe(**SCREAMING_SNAKE_CASE_	).images
         __lowerCamelCase   :	Dict								=		image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		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       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Union[str, Any]								=		0

         def callback_fn(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) -> None:
              __lowerCamelCase   :	List[Any]								=		True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   __lowerCamelCase   :	Any								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	Union[str, Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	str								=		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:
                   __lowerCamelCase   :	Optional[int]								=		latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase   :	List[Any]								=		latents[0, -3:, -3:, -1]
                   __lowerCamelCase   :	Any								=		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

         __lowerCamelCase   :	int								=		False

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing()

         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         pipe(**SCREAMING_SNAKE_CASE_			,     callback=SCREAMING_SNAKE_CASE_			,     callback_steps=1	)
         assert callback_fn.has_been_called
         assert number_of_steps == 3







    def       lowercase_				(							self	)				->     Union[str, Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase   :	Dict								=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	List[Any]								=		pipe.to(SCREAMING_SNAKE_CASE_	)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)
         pipe.enable_attention_slicing(1	)
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase   :	List[str]								=		self.get_inputs()
         __lowerCamelCase   :	Tuple								=		pipe(**SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[int]								=		self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase   :	Union[str, Any]								=		inputs['image'].resize((5_04, 5_04)	)

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

         __lowerCamelCase   :	Dict								=		pipe(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		output.images[0]

         __lowerCamelCase   :	Optional[int]								=		image[2_55:2_58, 3_83:3_86, -1]

         assert image.shape == (5_04, 5_04, 3)
         __lowerCamelCase   :	List[str]								=		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






