
"""simple docstring"""


from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def  lowercase						(	):
			lowerCamelCase_					=  9, 14  # noqa: F841
			lowerCamelCase_					=  [
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowerCamelCase_					=  defaultdict(__UpperCamelCase						)
			for nodea, nodea, cost in edges:
						adjancency[nodea].append([nodea, cost]						)
						adjancency[nodea].append([nodea, cost]						)

			lowerCamelCase_					=  mst(__UpperCamelCase						)

			lowerCamelCase_					=  [
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			for answer in expected:
						lowerCamelCase_					=  tuple(answer[:2]						)
						lowerCamelCase_					=  tuple(edge[::-1]						)
						assert edge in result or reverse in result









'''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 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				int        =						StableDiffusionDiffEditPipeline
       A						:				str        =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
       A						:				int        =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
       A						:				str        =						frozenset(
           [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       A						:				Union[str, Any]        =						frozenset([] )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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,							)
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_one=A,							)
             SCREAMING_SNAKE_CASE   :      str    					=						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  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						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  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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=1_000,							hidden_act='gelu',							projection_dim=512,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

             SCREAMING_SNAKE_CASE   :      int    					=						{
                 '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        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						floats_tensor((1, 16, 16),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						floats_tensor((1, 2, 4, 16, 16),							rng=random.Random(A  )  ).to(A  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{
                 '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        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						{
                 '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        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      int    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						{
                 '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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not hasattr(self.pipeline_class,							'_optional_components'  ):
                   return

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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}  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe(**A  )[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						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.",							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						pipe_loaded(**A  )[0]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						np.abs(output - output_loaded  ).max()
             self.assertLess(A,							1E-4  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_mask_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.generate_mask(**A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						mask[0, -3:, -3:]

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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],							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_components()

             SCREAMING_SNAKE_CASE   :      Dict    					=						{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DPMSolverMultistepScheduler(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						DPMSolverMultistepInverseScheduler(**A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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],							)
             SCREAMING_SNAKE_CASE   :      Any    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )






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





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						raw_image.convert('RGB'  ).resize((768, 768)  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						raw_image







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						DDIMScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A  ).latents

             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						(
                 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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      int    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A,							num_inference_steps=25,							).latents

             SCREAMING_SNAKE_CASE   :      str    					=						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]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						(
                 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


