


'''simple docstring'''






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 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Any   		=  StableDiffusionInstructPixaPixPipeline
							lowercase			:		Tuple   		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
							lowercase			:		Optional[int]   		=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase			:		Optional[Any]   		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
							lowercase			:		Optional[Any]   		=  IMAGE_TO_IMAGE_IMAGE_PARAMS





							def 		a__   (  self		:Any			):
									torch.manual_seed(0			)
									snake_case_		:							Union[str, Any]			       =						UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)  ,layers_per_block=2  ,sample_size=3_2  ,in_channels=8  ,out_channels=4  ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,cross_attention_dim=3_2  ,)
									snake_case_		:							int			       =						PNDMScheduler(skip_prk_steps=_UpperCamelCase			)
									torch.manual_seed(0			)
									snake_case_		:							Union[str, Any]			       =						AutoencoderKL(
									    block_out_channels=[3_2, 6_4]  ,in_channels=3  ,out_channels=3  ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,latent_channels=4  ,)
									torch.manual_seed(0			)
									snake_case_		:							List[Any]			       =						CLIPTextConfig(
									    bos_token_id=0  ,eos_token_id=2  ,hidden_size=3_2  ,intermediate_size=3_7  ,layer_norm_eps=1E-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_0_0_0  ,)
									snake_case_		:							Union[str, Any]			       =						CLIPTextModel(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

									snake_case_		:							List[Any]			       =						{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Dict=0			):
									snake_case_		:							Any			       =						floats_tensor((1, 3, 3_2, 3_2)  ,rng=random.Random(_UpperCamelCase			)			).to(_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						image.cpu().permute(0  ,2  ,3  ,1			)[0]
									snake_case_		:							List[str]			       =						Image.fromarray(np.uinta(_UpperCamelCase			)			).convert("""RGB"""			)
									if str(_UpperCamelCase			).startswith("""mps"""			):
											snake_case_		:							Optional[Any]			       =						torch.manual_seed(_UpperCamelCase			)
									else:
											snake_case_		:							Optional[Any]			       =						torch.Generator(device=_UpperCamelCase			).manual_seed(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						{
									    """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		:List[str]			):
									snake_case_		:							Any			       =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case_		:							List[str]			       =						self.get_dummy_components()
									snake_case_		:							Any			       =						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						sd_pipe.to(_UpperCamelCase			)
									sd_pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							Tuple			       =						self.get_dummy_inputs(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						sd_pipe(**_UpperCamelCase			).images
									snake_case_		:							Optional[Any]			       =						image[0, -3:, -3:, -1]
									assert image.shape == (1, 3_2, 3_2, 3)
									snake_case_		:							Union[str, Any]			       =						np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]			)

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





							def 		a__   (  self		:Any			):
									snake_case_		:							Any			       =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case_		:							List[Any]			       =						self.get_dummy_components()
									snake_case_		:							int			       =						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase			)
									snake_case_		:							str			       =						sd_pipe.to(_UpperCamelCase			)
									sd_pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							Tuple			       =						self.get_dummy_inputs(_UpperCamelCase			)
									snake_case_		:							Any			       =						"""french fries"""
									snake_case_		:							Dict			       =						sd_pipe(**_UpperCamelCase  ,negative_prompt=_UpperCamelCase			)
									snake_case_		:							str			       =						output.images
									snake_case_		:							Optional[Any]			       =						image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									snake_case_		:							List[Any]			       =						np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]			)

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





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

									snake_case_		:							Optional[Any]			       =						self.get_dummy_inputs(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						[inputs["""prompt"""]] * 2

									snake_case_		:							List[Any]			       =						np.array(inputs["""image"""]			).astype(np.floataa			) / 2_5_5.0
									snake_case_		:							Union[str, Any]			       =						torch.from_numpy(_UpperCamelCase			).unsqueeze(0			).to(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						image / 2 + 0.5
									snake_case_		:							Tuple			       =						image.permute(0  ,3  ,1  ,2			)
									snake_case_		:							List[Any]			       =						image.repeat(2  ,1  ,1  ,1			)

									snake_case_		:							str			       =						sd_pipe(**_UpperCamelCase			).images
									snake_case_		:							Optional[Any]			       =						image[-1, -3:, -3:, -1]

									assert image.shape == (2, 3_2, 3_2, 3)
									snake_case_		:							Dict			       =						np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]			)

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





							def 		a__   (  self		:int			):
									snake_case_		:							Dict			       =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case_		:							Tuple			       =						self.get_dummy_components()
									snake_case_		:							int			       =						EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85  ,beta_end=0.0_12  ,beta_schedule="""scaled_linear"""			)
									snake_case_		:							Optional[int]			       =						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase			)
									snake_case_		:							Tuple			       =						sd_pipe.to(_UpperCamelCase			)
									sd_pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						self.get_dummy_inputs(_UpperCamelCase			)
									snake_case_		:							int			       =						sd_pipe(**_UpperCamelCase			).images
									snake_case_		:							Union[str, Any]			       =						image[0, -3:, -3:, -1]

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

									assert image.shape == (1, 3_2, 3_2, 3)
									snake_case_		:							List[str]			       =						np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]			)

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





							def 		a__   (  self		:Optional[Any]			):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3			)





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						self.get_dummy_components()
									snake_case_		:							str			       =						StableDiffusionInstructPixaPixPipeline(**_UpperCamelCase			)
									snake_case_		:							Any			       =						VaeImageProcessor(do_resize=_UpperCamelCase  ,do_normalize=_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						pipe(**self.get_dummy_inputs_by_type(_UpperCamelCase  ,input_image_type="""pt"""			)			)[0]

									snake_case_		:							Any			       =						components["""vae"""]
									snake_case_		:							Union[str, Any]			       =						self.get_dummy_inputs_by_type(_UpperCamelCase  ,input_image_type="""pt"""			)

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

									snake_case_		:							Optional[Any]			       =						pipe(**_UpperCamelCase			)[0]

									snake_case_		:							List[Any]			       =						np.abs(out - out_latents_inputs			).max()
									self.assertLess(_UpperCamelCase  ,1E-4  ,"""passing latents as image input generate different result from passing image"""			)





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





							def 		a__   (  self		:List[Any]			):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:Optional[int]=0			):
									snake_case_		:							List[Any]			       =						torch.manual_seed(_UpperCamelCase			)
									snake_case_		:							Dict			       =						load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""			)
									snake_case_		:							Tuple			       =						{
									    """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		:Dict			):
									snake_case_		:							Any			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""  ,safety_checker=_UpperCamelCase			)
									pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing()

									snake_case_		:							str			       =						self.get_inputs()
									snake_case_		:							int			       =						pipe(**_UpperCamelCase			).images
									snake_case_		:							Any			       =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case_		:							Optional[Any]			       =						np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]			)

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





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Optional[int]			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""  ,safety_checker=_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
									pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing()

									snake_case_		:							Dict			       =						self.get_inputs()
									snake_case_		:							Dict			       =						pipe(**_UpperCamelCase			).images
									snake_case_		:							Any			       =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case_		:							Optional[Any]			       =						np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]			)

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





							def 		a__   (  self		:str			):
									snake_case_		:							Optional[int]			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""  ,safety_checker=_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						DDIMScheduler.from_config(pipe.scheduler.config			)
									pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing()

									snake_case_		:							Tuple			       =						self.get_inputs()
									snake_case_		:							Optional[Any]			       =						pipe(**_UpperCamelCase			).images
									snake_case_		:							Optional[int]			       =						image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									snake_case_		:							str			       =						np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]			)

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





							def 		a__   (  self		:Dict			):
									snake_case_		:							Any			       =						0

									def callback_fn(_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:torch.FloatTensor			) -> None:
											snake_case_		:							int			       =						True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
													snake_case_		:							Any			       =						latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 6_4)
													snake_case_		:							Optional[int]			       =						latents[0, -3:, -3:, -1]
													snake_case_		:							List[str]			       =						np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]			)

													assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
											elif step == 2:
													snake_case_		:							Tuple			       =						latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 6_4)
													snake_case_		:							Tuple			       =						latents[0, -3:, -3:, -1]
													snake_case_		:							str			       =						np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]			)

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

									snake_case_		:							Any			       =						False

									snake_case_		:							List[Any]			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""  ,safety_checker=_UpperCamelCase  ,torch_dtype=torch.floataa			)
									snake_case_		:							Optional[int]			       =						pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing()

									snake_case_		:							Dict			       =						self.get_inputs()
									pipe(**_UpperCamelCase  ,callback=_UpperCamelCase  ,callback_steps=1			)
									assert callback_fn.has_been_called
									assert number_of_steps == 3





							def 		a__   (  self		:Optional[Any]			):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									snake_case_		:							Optional[int]			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""  ,safety_checker=_UpperCamelCase  ,torch_dtype=torch.floataa			)
									snake_case_		:							Dict			       =						pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

									snake_case_		:							Union[str, Any]			       =						self.get_inputs()
									snake_case_		:							Dict			       =						pipe(**_UpperCamelCase			)

									snake_case_		:							int			       =						torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 1_0**9





							def 		a__   (  self		:Any			):
									snake_case_		:							Any			       =						self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									snake_case_		:							List[Any]			       =						inputs["""image"""].resize((5_0_4, 5_0_4)			)

									snake_case_		:							Tuple			       =						"""timbrooks/instruct-pix2pix"""
									snake_case_		:							str			       =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    _UpperCamelCase  ,safety_checker=_UpperCamelCase  ,)
									pipe.to(_UpperCamelCase			)
									pipe.set_progress_bar_config(disable=_UpperCamelCase			)
									pipe.enable_attention_slicing()

									snake_case_		:							Dict			       =						pipe(**_UpperCamelCase			)
									snake_case_		:							Dict			       =						output.images[0]

									snake_case_		:							List[Any]			       =						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

									assert image.shape == (5_0_4, 5_0_4, 3)
									snake_case_		:							Any			       =						np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]			)

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



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')