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class TrieNode:

    def __init__(self):
        self.value = "_"
        self.children = {}

    def insert(self, str):
        if not str:
            c = ""
            self.children[c] = TrieNode()

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#!/bin/sh

#<your project id (you can find it with inspect element on the project page)>
PROJECT_ID=36944
#<your access token (can be generated in your profile settings)>
ACCESS_TOKEN=xxxxx-XXXXXXXXXXXXXXXXXXXX
#<your gitlab instance hostname (ex. gitlab.lrz.de)>
GL_SERVER=gitlab.lrz.de

for PAGE in $(seq 1 1000); do

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How to get this

Edit init.rc as following:

on early-init
    # Prepare early debugfs
    mount debugfs none /sys/kernel/debug
    chmod 0755 /sys/kernel/debug/tracing

    # Enable i2c tracer

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import numpy as np
import torch
import cv2
from PIL import Image
from transformers import DPTForDepthEstimation, DPTImageProcessor

model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True).to("cuda")
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")

# Start capturing video from the first camera device

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#!/bin/bash

set -e

# Check if zsh is installed
if ! command -v zsh &> /dev/null; then
    echo "zsh is not installed. Please install zsh first."
    exit 1
fi

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from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import torch._dynamo
from torch._dynamo.testing import rand_strided
from torch._dynamo.debug_utils import run_fwd_maybe_bwd

import torch._dynamo.config

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import numpy as np
import torch
import cv2
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
from transformers import DPTForDepthEstimation, DPTImageProcessor


def depth_estimation(model, feature_extractor, image):
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")

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Summary

txt2img sdxl 1024x1024

65.417351÷65.623950=0.996851774

img2img sdxl 1024x1024 (input 1024x1024 image)

54.623950÷55.738662=0.980001099