



'''simple docstring'''





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 				snake_case__ (_lowerCamelCase		,      _lowerCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					StableDiffusionDiffEditPipeline
       lowerCAmelCase__  :Union[str, Any]						   =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       lowerCAmelCase__  :Optional[int]						   =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       lowerCAmelCase__  :List[str]						   =					frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       lowerCAmelCase__  :int						   =					frozenset([]   )



       def 							_a      (				self      )  ->							List[Any]:
           torch.manual_seed(0      )
           lowercase__		       =						UNetaDConditionModel(
               block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=32     ,attention_head_dim=(2, 4)     ,use_linear_projection=A__     ,)
           lowercase__		       =						DDIMScheduler(
               beta_start=0.0_00_85     ,beta_end=0.0_12     ,beta_schedule="scaled_linear"     ,clip_sample=A__     ,set_alpha_to_one=A__     ,)
           lowercase__		       =						DDIMInverseScheduler(
               beta_start=0.0_00_85     ,beta_end=0.0_12     ,beta_schedule="scaled_linear"     ,clip_sample=A__     ,set_alpha_to_zero=A__     ,)
           torch.manual_seed(0      )
           lowercase__		       =						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     ,sample_size=128     ,)
           torch.manual_seed(0      )
           lowercase__		       =						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=1_000     ,hidden_act="gelu"     ,projection_dim=512     ,)
           lowercase__		       =						CLIPTextModel(A__      )
           lowercase__		       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

           lowercase__		       =						{
               "unet": unet,
               "scheduler": scheduler,
               "inverse_scheduler": inverse_scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }

           return components



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							Tuple:
           lowercase__		       =						floats_tensor((1, 16, 16)     ,rng=random.Random(A__      )      ).to(A__      )
           lowercase__		       =						floats_tensor((1, 2, 4, 16, 16)     ,rng=random.Random(A__      )      ).to(A__      )
           if str(A__      ).startswith("mps"      ):
               lowercase__		       =						torch.manual_seed(A__      )
           else:
               lowercase__		       =						torch.Generator(device=A__      ).manual_seed(A__      )
           lowercase__		       =						{
               "prompt": "a dog and a newt",
               "mask_image": mask,
               "image_latents": latents,
               "generator": generator,
               "num_inference_steps": 2,
               "inpaint_strength": 1.0,
               "guidance_scale": 6.0,
               "output_type": "numpy",
           }

           return inputs



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							int:
           lowercase__		       =						floats_tensor((1, 3, 32, 32)     ,rng=random.Random(A__      )      ).to(A__      )
           lowercase__		       =						image.cpu().permute(0     ,2     ,3     ,1      )[0]
           lowercase__		       =						Image.fromarray(np.uinta(A__      )      ).convert("RGB"      )
           if str(A__      ).startswith("mps"      ):
               lowercase__		       =						torch.manual_seed(A__      )
           else:
               lowercase__		       =						torch.Generator(device=A__      ).manual_seed(A__      )
           lowercase__		       =						{
               "image": image,
               "source_prompt": "a cat and a frog",
               "target_prompt": "a dog and a newt",
               "generator": generator,
               "num_inference_steps": 2,
               "num_maps_per_mask": 2,
               "mask_encode_strength": 1.0,
               "guidance_scale": 6.0,
               "output_type": "numpy",
           }

           return inputs



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							str:
           lowercase__		       =						floats_tensor((1, 3, 32, 32)     ,rng=random.Random(A__      )      ).to(A__      )
           lowercase__		       =						image.cpu().permute(0     ,2     ,3     ,1      )[0]
           lowercase__		       =						Image.fromarray(np.uinta(A__      )      ).convert("RGB"      )
           if str(A__      ).startswith("mps"      ):
               lowercase__		       =						torch.manual_seed(A__      )
           else:
               lowercase__		       =						torch.Generator(device=A__      ).manual_seed(A__      )
           lowercase__		       =						{
               "image": image,
               "prompt": "a cat and a frog",
               "generator": generator,
               "num_inference_steps": 2,
               "inpaint_strength": 1.0,
               "guidance_scale": 6.0,
               "decode_latents": True,
               "output_type": "numpy",
           }
           return inputs



       def 							_a      (				self      )  ->							str:
           if not hasattr(self.pipeline_class     ,"_optional_components"      ):
               return

           lowercase__		       =						self.get_dummy_components()
           lowercase__		       =						self.pipeline_class(**A__      )
           pipe.to(A__      )
           pipe.set_progress_bar_config(disable=A__      )

           # set all optional components to None and update pipeline config accordingly
           for optional_component in pipe._optional_components:
               setattr(A__     ,A__     ,A__      )
           pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}      )

           lowercase__		       =						self.get_dummy_inputs(A__      )
           lowercase__		       =						pipe(**A__      )[0]

           with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(A__      )
               lowercase__		       =						self.pipeline_class.from_pretrained(A__      )
               pipe_loaded.to(A__      )
               pipe_loaded.set_progress_bar_config(disable=A__      )

           for optional_component in pipe._optional_components:
               self.assertTrue(
                   getattr(A__     ,A__      ) is None     ,F'''`{optional_component}` did not stay set to None after loading.'''     ,)

           lowercase__		       =						self.get_dummy_inputs(A__      )
           lowercase__		       =						pipe_loaded(**A__      )[0]

           lowercase__		       =						np.abs(output - output_loaded      ).max()
           self.assertLess(A__     ,1E-4      )



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						"cpu"

           lowercase__		       =						self.get_dummy_components()
           lowercase__		       =						self.pipeline_class(**A__      )
           pipe.to(A__      )
           pipe.set_progress_bar_config(disable=A__      )

           lowercase__		       =						self.get_dummy_mask_inputs(A__      )
           lowercase__		       =						pipe.generate_mask(**A__      )
           lowercase__		       =						mask[0, -3:, -3:]

           self.assertEqual(mask.shape     ,(1, 16, 16)      )
           lowercase__		       =						np.array([0] * 9      )
           lowercase__		       =						np.abs(mask_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(A__     ,1E-3      )
           self.assertEqual(mask[0, -3, -4]     ,0      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						"cpu"

           lowercase__		       =						self.get_dummy_components()
           lowercase__		       =						self.pipeline_class(**A__      )
           pipe.to(A__      )
           pipe.set_progress_bar_config(disable=A__      )

           lowercase__		       =						self.get_dummy_inversion_inputs(A__      )
           lowercase__		       =						pipe.invert(**A__      ).images
           lowercase__		       =						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape     ,(2, 32, 32, 3)      )
           lowercase__		       =						np.array(
               [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]     ,)
           lowercase__		       =						np.abs(image_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(A__     ,1E-3      )



       def 							_a      (				self      )  ->							int:
           super().test_inference_batch_single_identical(expected_max_diff=5E-3      )







       def 							_a      (				self      )  ->							int:
           lowercase__		       =						"cpu"

           lowercase__		       =						self.get_dummy_components()

           lowercase__		       =						{"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
           lowercase__		       =						DPMSolverMultistepScheduler(**A__      )
           lowercase__		       =						DPMSolverMultistepInverseScheduler(**A__      )

           lowercase__		       =						self.pipeline_class(**A__      )
           pipe.to(A__      )
           pipe.set_progress_bar_config(disable=A__      )

           lowercase__		       =						self.get_dummy_inversion_inputs(A__      )
           lowercase__		       =						pipe.invert(**A__      ).images
           lowercase__		       =						image[0, -1, -3:, -3:]

           self.assertEqual(image.shape     ,(2, 32, 32, 3)      )
           lowercase__		       =						np.array(
               [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]     ,)
           lowercase__		       =						np.abs(image_slice.flatten() - expected_slice      ).max()
           self.assertLessEqual(A__     ,1E-3      )





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



       def 							_a      (				self      )  ->							List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       @classmethod
       def 							_a      (				cls      )  ->							Any:
           lowercase__		       =						load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"      )

           lowercase__		       =						raw_image.convert("RGB"      ).resize((768, 768)      )

           lowercase__		       =						raw_image



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						torch.manual_seed(0      )

           lowercase__		       =						StableDiffusionDiffEditPipeline.from_pretrained(
               "stabilityai/stable-diffusion-2-1"     ,safety_checker=A__     ,torch_dtype=torch.floataa      )
           lowercase__		       =						DDIMScheduler.from_config(pipe.scheduler.config      )
           lowercase__		       =						DDIMInverseScheduler.from_config(pipe.scheduler.config      )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=A__      )

           lowercase__		       =						"a bowl of fruit"
           lowercase__		       =						"a bowl of pears"

           lowercase__		       =						pipe.generate_mask(
               image=self.raw_image     ,source_prompt=A__     ,target_prompt=A__     ,generator=A__     ,)

           lowercase__		       =						pipe.invert(
               prompt=A__     ,image=self.raw_image     ,inpaint_strength=0.7     ,generator=A__      ).latents

           lowercase__		       =						pipe(
               prompt=A__     ,mask_image=A__     ,image_latents=A__     ,generator=A__     ,negative_prompt=A__     ,inpaint_strength=0.7     ,output_type="numpy"     ,).images[0]

           lowercase__		       =						(
               np.array(
                   load_image(
                       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                       "/diffedit/pears.png"      ).resize((768, 768)      )      )
               / 255
           )
           assert np.abs((expected_image - image).max()      ) < 5E-1







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						torch.manual_seed(0      )

           lowercase__		       =						StableDiffusionDiffEditPipeline.from_pretrained(
               "stabilityai/stable-diffusion-2-1"     ,safety_checker=A__     ,torch_dtype=torch.floataa      )
           lowercase__		       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
           lowercase__		       =						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=A__      )

           lowercase__		       =						"a bowl of fruit"
           lowercase__		       =						"a bowl of pears"

           lowercase__		       =						pipe.generate_mask(
               image=self.raw_image     ,source_prompt=A__     ,target_prompt=A__     ,generator=A__     ,)

           lowercase__		       =						pipe.invert(
               prompt=A__     ,image=self.raw_image     ,inpaint_strength=0.7     ,generator=A__     ,num_inference_steps=25     ,).latents

           lowercase__		       =						pipe(
               prompt=A__     ,mask_image=A__     ,image_latents=A__     ,generator=A__     ,negative_prompt=A__     ,inpaint_strength=0.7     ,num_inference_steps=25     ,output_type="numpy"     ,).images[0]

           lowercase__		       =						(
               np.array(
                   load_image(
                       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                       "/diffedit/pears.png"      ).resize((768, 768)      )      )
               / 255
           )
           assert np.abs((expected_image - image).max()      ) < 5E-1









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




