


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      (				lowerCAmelCase__    ,      lowerCAmelCase__    ,      lowerCAmelCase__    ,      unittest.TestCase			):
		_a	:  List[Any]				=	StableDiffusionInstructPixaPixPipeline
		_a	:  Optional[int]				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
		_a	:  Optional[int]				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a	:  Optional[Any]				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a	:  List[str]				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase 		=			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			,    )
									_UpperCAmelCase 		=			PNDMScheduler(skip_prk_steps=__lowerCAmelCase						)
									torch.manual_seed(0						)
									_UpperCAmelCase 		=			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						)
									_UpperCAmelCase 		=			CLIPTextConfig(
									    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-0_5			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    )
									_UpperCAmelCase 		=			CLIPTextModel(__lowerCAmelCase						)
									_UpperCAmelCase 		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_UpperCAmelCase 		=			{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=0						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			floats_tensor((1, 3, 32, 32)			,    rng=random.Random(__lowerCAmelCase						)						).to(__lowerCAmelCase						)
									_UpperCAmelCase 		=			image.cpu().permute(0			,    2			,    3			,    1						)[0]
									_UpperCAmelCase 		=			Image.fromarray(np.uinta(__lowerCAmelCase						)						).convert('RGB'						)
									if str(__lowerCAmelCase						).startswith('mps'						):
																_UpperCAmelCase 		=			torch.manual_seed(__lowerCAmelCase						)
									else:
																_UpperCAmelCase 		=			torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
									_UpperCAmelCase 		=			{
									    '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 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase 		=			self.get_dummy_components()
									_UpperCAmelCase 		=			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase						)
									_UpperCAmelCase 		=			sd_pipe(**__lowerCAmelCase						).images
									_UpperCAmelCase 		=			image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase 		=			self.get_dummy_components()
									_UpperCAmelCase 		=			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase						)
									_UpperCAmelCase 		=			'french fries'
									_UpperCAmelCase 		=			sd_pipe(**__lowerCAmelCase			,    negative_prompt=__lowerCAmelCase						)
									_UpperCAmelCase 		=			output.images
									_UpperCAmelCase 		=			image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase 		=			self.get_dummy_components()
									_UpperCAmelCase 		=			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase						)
									_UpperCAmelCase 		=			[inputs['prompt']] * 2

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

									_UpperCAmelCase 		=			sd_pipe(**__lowerCAmelCase						).images
									_UpperCAmelCase 		=			image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase 		=			self.get_dummy_components()
									_UpperCAmelCase 		=			EulerAncestralDiscreteScheduler(
									    beta_start=0.00085			,    beta_end=0.012			,    beta_schedule='scaled_linear'						)
									_UpperCAmelCase 		=			StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			sd_pipe.to(__lowerCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

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

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

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Optional[int]:




									"""simple docstring"""





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






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




									"""simple docstring"""





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

									_UpperCAmelCase 		=			pipe(**self.get_dummy_inputs_by_type(__lowerCAmelCase			,    input_image_type='pt'						)						)[0]

									_UpperCAmelCase 		=			components['vae']
									_UpperCAmelCase 		=			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():
																							_UpperCAmelCase 		=			vae.encode(inputs[image_param]						).latent_dist.mode()

									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)[0]

									_UpperCAmelCase 		=			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 		__a      (				unittest.TestCase			):
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE=0						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			torch.manual_seed(__lowerCAmelCase						)
									_UpperCAmelCase 		=			load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'						)
									_UpperCAmelCase 		=			{
									    '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 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





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

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





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

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Optional[int]:




									"""simple docstring"""





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

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			0

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

									_UpperCAmelCase 		=			False

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

									_UpperCAmelCase 		=			self.get_inputs()
									pipe(**__lowerCAmelCase			,    callback=__lowerCAmelCase			,    callback_steps=1						)
									assert callback_fn.has_been_called
									assert number_of_steps == 3
		def 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





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

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

									_UpperCAmelCase 		=			self.get_inputs()
									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)

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






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




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									_UpperCAmelCase 		=			inputs['image'].resize((504, 504)						)

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

									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			output.images[0]

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

									assert image.shape == (504, 504, 3)
									_UpperCAmelCase 		=			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 warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




