

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__  (    snake_case							,		snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				StableDiffusionInstructPixaPixPipeline
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__lowerCamelCase       				=				IMAGE_TO_IMAGE_IMAGE_PARAMS
			__lowerCamelCase       				=				IMAGE_TO_IMAGE_IMAGE_PARAMS


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							torch.manual_seed(0				)
							A__						=	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					,					)
							A__						=	PNDMScheduler(skip_prk_steps=lowercase				)
							torch.manual_seed(0				)
							A__						=	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				)
							A__						=	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					,					)
							A__						=	CLIPTextModel(lowercase				)
							A__						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=0				)     ->  int:
							'''simple docstring'''






							A__						=	floats_tensor((1, 3, 32, 32)					,					rng=random.Random(lowercase				)				).to(lowercase				)
							A__						=	image.cpu().permute(0					,					2					,					3					,					1				)[0]
							A__						=	Image.fromarray(np.uinta(lowercase				)				).convert("RGB"				)
							if str(lowercase				).startswith("mps"				):
											A__						=	torch.manual_seed(lowercase				)
							else:
											A__						=	torch.Generator(device=lowercase				).manual_seed(lowercase				)
							A__						=	{
							    "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'''






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

							A__						=	self.get_dummy_inputs(lowercase				)
							A__						=	sd_pipe(**lowercase				).images
							A__						=	image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							A__						=	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				)     ->  Any:
							'''simple docstring'''






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

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

							assert image.shape == (1, 32, 32, 3)
							A__						=	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				)     ->  Tuple:
							'''simple docstring'''






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

							A__						=	self.get_dummy_inputs(lowercase				)
							A__						=	[inputs["prompt"]] * 2

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

							A__						=	sd_pipe(**lowercase				).images
							A__						=	image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							A__						=	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				)     ->  int:
							'''simple docstring'''






							A__						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							A__						=	self.get_dummy_components()
							A__						=	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085					,					beta_end=0.012					,					beta_schedule="scaled_linear"				)
							A__						=	StableDiffusionInstructPixaPixPipeline(**lowercase				)
							A__						=	sd_pipe.to(lowercase				)
							sd_pipe.set_progress_bar_config(disable=lowercase				)

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

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

							assert image.shape == (1, 32, 32, 3)
							A__						=	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				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






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

							A__						=	pipe(**self.get_dummy_inputs_by_type(lowercase					,					input_image_type="pt"				)				)[0]

							A__						=	components["vae"]
							A__						=	self.get_dummy_inputs_by_type(lowercase					,					input_image_type="pt"				)

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

							A__						=	pipe(**lowercase				)[0]

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

@slow
@require_torch_gpu
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase=0				)     ->  List[str]:
							'''simple docstring'''






							A__						=	torch.manual_seed(lowercase				)
							A__						=	load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"				)
							A__						=	{
							    "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'''






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

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

							assert image.shape == (1, 512, 512, 3)
							A__						=	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				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"					,					safety_checker=lowercase				)
							A__						=	LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
							pipe.to(lowercase				)
							pipe.set_progress_bar_config(disable=lowercase				)
							pipe.enable_attention_slicing()

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

							assert image.shape == (1, 512, 512, 3)
							A__						=	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[Any]:
							'''simple docstring'''






							A__						=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    "timbrooks/instruct-pix2pix"					,					safety_checker=lowercase				)
							A__						=	DDIMScheduler.from_config(pipe.scheduler.config				)
							pipe.to(lowercase				)
							pipe.set_progress_bar_config(disable=lowercase				)
							pipe.enable_attention_slicing()

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

							assert image.shape == (1, 512, 512, 3)
							A__						=	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'''






							A__						=	0

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

							A__						=	False

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

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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






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

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

							A__						=	self.get_inputs()
							A__						=	pipe(**lowercase				)

							A__						=	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				)     ->  int:
							'''simple docstring'''






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

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

							A__						=	pipe(**lowercase				)
							A__						=	output.images[0]

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

							assert image.shape == (504, 504, 3)
							A__						=	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




def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_							=    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					snake_case_							=    max(
																					    mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )   ,							mf_knapsack(i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							j - wt[i - 1]      ) + val[i - 1]   ,							)
														snake_case_							=    val
							return f[i][j]





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [[0] * (w + 1) for _ in range(n + 1      )]

							for i in range(1   ,							n + 1      ):
														for w_ in range(1   ,							w + 1      ):
																					if wt[i - 1] <= w_:
																												snake_case_							=    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]   ,							dp[i - 1][w_]      )
																					else:
																												snake_case_							=    dp[i - 1][w_]

							return dp[n][w_], dp





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__   ,							(list, tuple)      ) and isinstance(UpperCamelCase__   ,							(list, tuple)      )):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'      )

							snake_case_							=    len(UpperCamelCase__      )
							if num_items != len(UpperCamelCase__      ):
														snake_case_							=    (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__      )} values'''
														)
														raise ValueError(UpperCamelCase__      )
							for i in range(UpperCamelCase__      ):
														if not isinstance(wt[i]   ,							UpperCamelCase__      ):
																					snake_case_							=    (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]      )} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__      )

							snake_case_				,    snake_case_							=    knapsack(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    set()
							_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							return optimal_val, example_optional_set





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														else:
																					optimal_set.add(UpperCamelCase__      )
																					_construct_solution(UpperCamelCase__   ,							UpperCamelCase__   ,							i - 1   ,							j - wt[i - 1]   ,							UpperCamelCase__      )


if __name__ == "__main__":
					_UpperCAmelCase		:   Tuple										=       [3, 2, 4, 4]
					_UpperCAmelCase		:   Optional[Any]										=       [4, 3, 2, 3]
					_UpperCAmelCase		:   List[str]										=       4
					_UpperCAmelCase		:   str										=       6
					_UpperCAmelCase		:   Tuple										=       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase				,						_UpperCAmelCase		:   List[Any]										=       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase				,						_UpperCAmelCase		:   Any										=       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




