






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





       """simple docstring"""

       A_						  =					StableDiffusionInstructPixaPixPipeline
       A_						  =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
       A_						  =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       A_						  =					IMAGE_TO_IMAGE_IMAGE_PARAMS
       A_						  =					IMAGE_TO_IMAGE_IMAGE_PARAMS

       def       __A (  self:   int   )    ->				List[Any]:
            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=__lowerCamelCase   )
            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=10_00		,     )
            _A								= CLIPTextModel(__lowerCamelCase   )
            _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       __A (  self:   Tuple		,     __A:   Union[str, Any]		,     __A:   Union[str, Any]=0   )    ->				Optional[Any]:
            _A								= floats_tensor((1, 3, 32, 32)		,     rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )
            _A								= image.cpu().permute(0		,     2		,     3		,     1   )[0]
            _A								= Image.fromarray(np.uinta(__lowerCamelCase   )   ).convert('''RGB'''   )
            if str(__lowerCamelCase   ).startswith('''mps'''   ):
                 _A								= torch.manual_seed(__lowerCamelCase   )
            else:
                 _A								= torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
            _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       __A (  self:   Optional[int]   )    ->				List[str]:
            _A								= "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A								= self.get_dummy_components()
            _A								= StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase   )
            _A								= sd_pipe.to(__lowerCamelCase   )
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

            _A								= self.get_dummy_inputs(__lowerCamelCase   )
            _A								= sd_pipe(**__lowerCamelCase   ).images
            _A								= image[0, -3:, -3:, -1]
            assert image.shape == (1, 32, 32, 3)
            _A								= np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]   )

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

       def       __A (  self:   List[str]   )    ->				Optional[Any]:
            _A								= "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A								= self.get_dummy_components()
            _A								= StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase   )
            _A								= sd_pipe.to(__lowerCamelCase   )
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

            assert image.shape == (1, 32, 32, 3)
            _A								= np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]   )

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

       def       __A (  self:   Dict   )    ->				int:
            _A								= "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A								= self.get_dummy_components()
            _A								= StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase   )
            _A								= sd_pipe.to(__lowerCamelCase   )
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

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

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

            assert image.shape == (2, 32, 32, 3)
            _A								= np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]   )

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

       def       __A (  self:   str   )    ->				List[Any]:
            _A								= "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A								= self.get_dummy_components()
            _A								= EulerAncestralDiscreteScheduler(
                beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='''scaled_linear'''   )
            _A								= StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase   )
            _A								= sd_pipe.to(__lowerCamelCase   )
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

            _A								= [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)
            _A								= np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]   )

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

       def       __A (  self:   Optional[int]   )    ->				Tuple:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



       def       __A (  self:   int   )    ->				int:
            _A								= self.get_dummy_components()
            _A								= StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase   )
            _A								= VaeImageProcessor(do_resize=__lowerCamelCase		,     do_normalize=__lowerCamelCase   )
            _A								= pipe.to(__lowerCamelCase   )
            pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

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

            _A								= pipe(**__lowerCamelCase   )[0]

            _A								= 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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





       """simple docstring"""

       def       __A (  self:   Optional[int]   )    ->				Dict:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def       __A (  self:   List[Any]		,     __A:   str=0   )    ->				List[str]:
            _A								= torch.manual_seed(__lowerCamelCase   )
            _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       __A (  self:   str   )    ->				Any:
            _A								= StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''		,     safety_checker=__lowerCamelCase   )
            pipe.to(__lowerCamelCase   )
            pipe.set_progress_bar_config(disable=__lowerCamelCase   )
            pipe.enable_attention_slicing()

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

            assert image.shape == (1, 5_12, 5_12, 3)
            _A								= np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]   )

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

       def       __A (  self:   Dict   )    ->				Tuple:
            _A								= StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''		,     safety_checker=__lowerCamelCase   )
            _A								= LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
            pipe.to(__lowerCamelCase   )
            pipe.set_progress_bar_config(disable=__lowerCamelCase   )
            pipe.enable_attention_slicing()

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

            assert image.shape == (1, 5_12, 5_12, 3)
            _A								= np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]   )

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

       def       __A (  self:   Tuple   )    ->				List[str]:
            _A								= StableDiffusionInstructPixaPixPipeline.from_pretrained(
                '''timbrooks/instruct-pix2pix'''		,     safety_checker=__lowerCamelCase   )
            _A								= DDIMScheduler.from_config(pipe.scheduler.config   )
            pipe.to(__lowerCamelCase   )
            pipe.set_progress_bar_config(disable=__lowerCamelCase   )
            pipe.enable_attention_slicing()

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

            assert image.shape == (1, 5_12, 5_12, 3)
            _A								= np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]   )

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

       def       __A (  self:   List[Any]   )    ->				Optional[int]:
            _A								= 0

            def callback_fn(__A:   int		,     __A:   int		,     __A:   torch.FloatTensor   ) -> 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([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]   )

                      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([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]   )

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

            _A								= False

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

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

       def       __A (  self:   Any   )    ->				Tuple:
            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=__lowerCamelCase		,     torch_dtype=torch.floataa   )
            _A								= pipe.to(__lowerCamelCase   )
            pipe.set_progress_bar_config(disable=__lowerCamelCase   )
            pipe.enable_attention_slicing(1   )
            pipe.enable_sequential_cpu_offload()

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

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



       def       __A (  self:   int   )    ->				Optional[Any]:
            _A								= self.get_inputs()
            # resize to resolution that is divisible by 8 but not 16 or 32
            _A								= inputs["image"].resize((5_04, 5_04)   )

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

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

            _A								= image[2_55:2_58, 3_83:3_86, -1]

            assert image.shape == (5_04, 5_04, 3)
            _A								= np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]   )

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











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

					for values in graph.values():
										for i in values:
															indegree[i] += 1

					for i in range(len(_lowercase ) ):
										if indegree[i] == 0:
															queue.append(_lowercase )

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



