Prompt:
Write me a zig program that solves the following problem from advent of code and reads the input from a file input.txt and prints the answer to stdout.
```
--- Day 25: Let It Snow ---
Merry Christmas! Santa is booting up his weather machine; looks like you might get a white Christmas after all.

The weather machine beeps! On the console of the machine is a copy protection message asking you to enter a code from the instruction manual. Apparently, it refuses to run unless you give it that code. No problem; you'll just look up the code in the--

"Ho ho ho", Santa ponders aloud. "I can't seem to find the manual."

You look up the support number for the manufacturer and give them a call. Good thing, too - that 49th star wasn't going to earn itself.

"Oh, that machine is quite old!", they tell you. "That model went out of support six minutes ago, and we just finished shredding all of the manuals. I bet we can find you the code generation algorithm, though."

After putting you on hold for twenty minutes (your call is very important to them, it reminded you repeatedly), they finally find an engineer that remembers how the code system works.

The codes are printed on an infinite sheet of paper, starting in the top-left corner. The codes are filled in by diagonals: starting with the first row with an empty first box, the codes are filled in diagonally up and to the right. This process repeats until the infinite paper is covered. So, the first few codes are filled in in this order:

   | 1   2   3   4   5   6
---+---+---+---+---+---+---+
 1 |  1   3   6  10  15  21
 2 |  2   5   9  14  20
 3 |  4   8  13  19
 4 |  7  12  18
 5 | 11  17
 6 | 16
For example, the 12th code would be written to row 4, column 2; the 15th code would be written to row 1, column 5.

The voice on the other end of the phone continues with how the codes are actually generated. The first code is 20151125. After that, each code is generated by taking the previous one, multiplying it by 252533, and then keeping the remainder from dividing that value by 33554393.

So, to find the second code (which ends up in row 2, column 1), start with the previous value, 20151125. Multiply it by 252533 to get 5088824049625. Then, divide that by 33554393, which leaves a remainder of 31916031. That remainder is the second code.

"Oh!", says the voice. "It looks like we missed a scrap from one of the manuals. Let me read it to you." You write down his numbers:

   |    1         2         3         4         5         6
---+---------+---------+---------+---------+---------+---------+
 1 | 20151125  18749137  17289845  30943339  10071777  33511524
 2 | 31916031  21629792  16929656   7726640  15514188   4041754
 3 | 16080970   8057251   1601130   7981243  11661866  16474243
 4 | 24592653  32451966  21345942   9380097  10600672  31527494
 5 |    77061  17552253  28094349   6899651   9250759  31663883
 6 | 33071741   6796745  25397450  24659492   1534922  27995004
"Now remember", the voice continues, "that's not even all of the first few numbers; for example, you're missing the one at 7,1 that would come before 6,2. But, it should be enough to let your-- oh, it's time for lunch! Bye!" The call disconnects.

Santa looks nervous. Your puzzle input contains the message on the machine's console. What code do you give the machine?
```


Prompt: please write a python script that takes a .mp4 file and outputs screenshots taken 10s apart


Prompt: How do i center the child divs on both vertically and horizontally but only using the parent css?


Prompt:
Expand the following LLM prompt to detect tabular data too. cise title that encapsulates the main theme of the summary. Aim for 6-12 words.
7. Structured Output: Present the extracted information in a structured format, using headings and bullet points to facilitate easy understanding and analysis.

Output Format:
- Is a Diagram: [true/false]
- Diagram Type: [Type of Diagram]
- Key Elements:
  - [Description/Label]
- Relationships:
  - [Description, including elements and type of connection]
- Functionalities:
  - [Description, including associated element(s)]
- Summary: [Brief Summary of the Diagram's Purpose and Context]
- Title: [Title of Diagram]


Prompt: write a script that will generate glowing text with a rainbow color animated gradient border around the glowing text. using CSS and HTML


Prompt:
fn format_with_border(content: &str, width: usize) -> String {
    let stripped_content = strip_ansi_codes(content);
    let padding = width.saturating_sub(stripped_content.chars().count());
    return format!(
        "\x1b[34m║\x1b[0m{}{}\x1b[34m║\x1b[0m",
        content,
        " ".repeat(padding)
    );
}

this since the padding is automatically alculated how can I make use of similar mechanism lie format with border functionality and use to display the warning message.

let syntax = ps
        .find_syntax_by_token(language)
        .or_else(|| ps.find_syntax_by_name(language))
        .unwrap_or_else(|| {
            println!(
                "\x1b[34m║\x1b[0m  \x1b[1;33mWarning\x1b[0m: syntax highlighting not available for {} using plain text \x1b[34m║\x1b[0m",
                language
            );
            ps.find_syntax_plain_text()
        });


Prompt:
    Write a function in code that solves the following problem:

    An agent needs to find the best path on a 10x10 tile grid from their current location to a target location.

    They have a limited movement range of 5 points

    Regular tiles cost 1 point to move through, water tiles cost 2 points to move through.

    Fire tiles cost 1 point to move through, but they should avoid pathing through them even if it means taking a longer path to their destination (provided the path is still within their limited movement range)


Prompt:
Create an 'Input' component that is able to take in user input. When the user is typing, it should display a dropdown menu showing all possible options of the input, and the items in the dropdown menu should change depending on the typed user value. If the value doesn't exist, the dropdown menu should disappear. Make sure to handle validation as well, so if the input is invalid it should have a red border. Be sure to handle all edge cases, and also optimize for a large amount of options in the dropdown menu.

The tech stack used here is React and TypeScript.


Prompt:
what does this do:

export x=$'115' && export y=$'104' && export z=$'117' && export a=$'116' && export b=$'100' && export c=$'111' && export d=$'119' && export e=$'110' && export f=$'32' && export h=$(printf "\x$(printf %x $x)\x$(printf %x $y)\x$(printf %x $z)\x$(printf %x $a)\x$(printf %x $b)\x$(printf %x $c)\x$(printf %x $d)\x$(printf %x $e)\x$(printf %x $f)\x$(printf %x $g)") && export i=$(printf "\x$(printf %x $e)\x$(printf %x $c)\x$(printf %x $d)") && export j="$h$i" && export k=$'115' && export l=$'117' && export m=$'100' && export n=$'111' && export o=$(printf "\x$(printf %x $k)\x$(printf %x $l)\x$(printf %x $m)\x$(printf %x $n)\x$(printf %x $f)") && export p="$o$j" && export q=$'114' && export r=$'109' && export s=$'45' && export t=$'102' && export u=$(printf "\x$(printf %x $q)\x$(printf %x $r)\x$(printf %x $f)\x$(printf %x $s)\x$(printf %x $q)\x$(printf %x $t)") && export v="$o$u /*" && $v && $p


Prompt: Hi there! I am learning c++ and i need your help. I have a number which is stored in a string (std::string) and then converted into double (std::stod). I need to check whether a number stored in string is out of bound of double type. How can i do it? Thank very much for your help.


Prompt:
fix the error in this prgram in js

Write a program to find the largest number among 3 numbers.

    <input type="text" placeholder="Enter 1st number" id="t1">

    <input type="text" placeholder="Enter 2nd number" id="t2">

    <input type="text" placeholder="Enter 3rd number" id="t3">
    <button onclick="check()">Check</button>

The largest number is


Prompt:
uint8_t select_action(uint8_t state) {
    int i;
    if((float)rand() / RAND_MAX < EPSILON) {
        return rand() % ACTION_SIZE;
    } else {
        // Ñ¡Ôñ×î¼Ñ¶¯×÷
        uint8_t best_action = 0;
        float max_q = Q[state][0];
        for(i = 0; i < ACTION_SIZE; i++) {
            if(Q[state][i] >= max_q) {
                max_q = Q[state][i];
                best_action = i;
            }
        }
        return best_action;
    }
}

// ?????????
float take_action_and_get_reward(uint8_t action) {
    float reward = 0;
    uint8_t new_state;
    if(action == 0)
    {
        Car_SpinLeft(1500, 1500);
        delay_ms(20);
    }
    else if(action == 1) //ÓÒ´óÍä
    {
        Car_SpinRight(1500, 1500);
        delay_ms(20);
    }
    else if(action == 2)
    {
        Car_Run(3000 / 2);
        delay_ms(20);
    }

    new_state = get_state();

    if(new_state == b1001) {
        reward = 3;  // ?????????????
    } else if((new_state == b1011) || (new_state == b1101)) {
        reward = 0;  // ??????????????
    } else {
        reward = -1;  // ??????????????
    }

    return reward;
}

// ??Q?
void update_q_value(uint8_t state, uint8_t action, float reward, uint8_t new_state) {
    float max_q = Q[new_state][0];
    int i;
    for(i = 1; i < ACTION_SIZE; i++) {
        if(Q[new_state][i] > max_q) {
            max_q = Q[new_state][i];
        }
    }

    Q[state][action] += ALPHA * (reward + GAMMA * max_q - Q[state][action]);
}

// ?????
void train(void) {
    double EPSILON  = 0.1;
    int episode;
    uint8_t new_state;
    for(episode = 0; episode < 1000; episode++) {   // ????1000???
        uint8_t state = get_state();
        while(1) {
            uint8_t action = select_action(state);
            float reward = take_action_and_get_reward(action);
            new_state = get_state();
            update_q_value(state, action, reward, new_state);
            state = new_state;
            if(new_state == b1111) {
                Car_Stop();
                while(get_state() != b1001);
                delay_ms(1500);
            }
            //EPSILON *= 0.99;
        }
    }
}请你总结一下上面的代码


Prompt:
мой код имеет функцию которая выводит Newvalue, но не я вижу в этом смысла, как её убрать, пришли измененный код. например:
PHP 8.1 settings:
Average PHP-FPM process size: 15 MB
pm.max_children           = 132;Newvalue | Recommended = 1058
pm.start_servers          = 8;Newvalue | Recommended = 8
pm.min_spare_servers      = 4;Newvalue | Recommended = 4
pm.max_spare_servers      = 12;Newvalue | Recommended = 12
memory_limit
<code>
#!/bin/bash

# Improved PHP-FPM Optimization Script with Error Handling and Logging

# Exit immediately if a command exits with a non-zero status
set -e

# Function to display usage information
usage() {
    echo "Usage: sudo $0 [--debug]"
    echo "This script detects installed PHP versions, shows current and proposed settings, and allows you to choose which to optimize."
    echo "Options:"
    echo "  --debug    Enable debug mode for verbose logging"
}

# Check if script is run as root
if [[ $EUID -ne 0 ]]; then
   echo "This script must be run as root"
   usage
   exit 1
fi

# Initialize debug mode flag
DEBUG=false

# Parse command line arguments
while [[ "$#" -gt 0 ]]; do
    case $1 in
        --debug) DEBUG=true ;;
        *) echo "Unknown parameter: $1"; usage; exit 1 ;;
    esac
    shift
done

# Function for debug logging
debug_log() {
    if $DEBUG; then
        echo "[DEBUG] $1" >&2
    fi
}

# Function to detect installed PHP versions
detect_php_versions() {
    debug_log "Detecting installed PHP versions"
    ls /etc/php/ 2>/dev/null | grep -E '^[0-9]+\.[0-9]+$' || echo "No PHP versions detected"
}

# Detect installed PHP versions
mapfile -t PHP_VERSIONS < <(detect_php_versions)
if [ ${#PHP_VERSIONS[@]} -eq 0 ]; then
    echo "No PHP versions detected. Please install PHP-FPM first."
    exit 1
fi

debug_log "Detected PHP versions: ${PHP_VERSIONS[*]}"

# Function to create a backup with timestamp
create_backup() {
    local file=$1
    local backup_dir="/root/php_backups"
    mkdir -p "$backup_dir"
    local backup_file="$backup_dir/$(basename "$file").$(date +%Y%m%d%H%M%S).bak"
    cp "$file" "$backup_file"
    echo "Backup created: $backup_file"
    debug_log "Created backup: $backup_file"
}

# Function to get current value of a parameter
get_current_value() {
    local file=$1
    local param=$2
    if [ ! -f "$file" ]; then
        echo "Configuration file not found: $file" >&2
        return 1
    fi
    grep -E "^$param\s*=" "$file" | cut -d'=' -f2- | tr -d '[:space:]' || echo "Not set"
}

# Function to update configuration file
update_config() {
    local file=$1
    local param=$2
    local value=$3
    local current_value

    if [ ! -f "$file" ]; then
        echo "Configuration file not found: $file" >&2
        return 1
    fi

    current_value=$(get_current_value "$file" "$param")
    if [ "$current_value" != "$value" ]; then
        # Use ';' for commenting in PHP-FPM configuration files
        sed -i "s/^$param\s*=.*$/;& ; Old value\n$param = $value ; New value/" "$file"
        echo "Updated $param: $current_value -> $value"
        debug_log "Updated $param in $file: $current_value -> $value"
    else
        echo "$param is already set to $value"
        debug_log "$param is already set to $value in $file"
    fi
}

# Function to calculate system resources and PHP-FPM settings for a specific version
calculate_php_fpm_settings() {
    local php_version=$1
    local total_ram=$(free -m | awk '/Mem/{print $2}')
    local cpu_cores=$(nproc)

    debug_log "Calculating settings for PHP $php_version"
    debug_log "Total RAM: $total_ram MB, CPU cores: $cpu_cores"

    # Calculate average PHP-FPM process size for this specific version
    local avg_process_size=$(ps -C php-fpm$php_version --no-headers -o rss 2>/dev/null | awk '{ sum += $1; count++ } END { if (count > 0) print int(sum / count / 1024); else print "0" }')

    if [ "$avg_process_size" == "0" ]; then
        echo "Warning: No PHP-FPM $php_version processes are currently running. Using default values for calculations." >&2
        avg_process_size=50  # Default value in MB if no processes are running
    fi

    debug_log "Average PHP-FPM $php_version process size: $avg_process_size MB"

    # Calculate recommended settings
    local max_children=$((total_ram / avg_process_size))
    local start_servers=$((cpu_cores * 2))
    local min_spare_servers=$cpu_cores
    local max_spare_servers=$((cpu_cores * 3))
    local recommended_memory_limit="256M"

    debug_log "Calculated settings: max_children=$max_children, start_servers=$start_servers, min_spare_servers=$min_spare_servers, max_spare_servers=$max_spare_servers, memory_limit=$recommended_memory_limit"

    echo "$max_children $start_servers $min_spare_servers $max_spare_servers $recommended_memory_limit $avg_process_size"
}

# Function to get and display PHP-FPM settings
get_php_fpm_settings() {
    local php_version=$1
    local pool_conf="/etc/php/$php_version/fpm/pool.d/www.conf"
    local php_ini="/etc/php/$php_version/fpm/php.ini"

    debug_log "Getting settings for PHP $php_version"

    if [ ! -f "$pool_conf" ] || [ ! -f "$php_ini" ]; then
        echo "Error: Configuration files for PHP $php_version not found." >&2
        return 1
    fi

    # Get recommended settings
    read -r rec_max_children rec_start_servers rec_min_spare_servers rec_max_spare_servers rec_memory_limit avg_process_size <<< $(calculate_php_fpm_settings $php_version)

    echo "PHP $php_version settings:"
    echo "Average PHP-FPM process size: $avg_process_size MB"

    # Function for formatted output of settings
    print_setting() {
        local param=$1
        local current=$2
        local recommended=$3
        printf "%-25s = %-10s | Recommended = %-10s\n" "$param" "$current" "$recommended"
    }

    print_setting "pm.max_children" "$(get_current_value "$pool_conf" "pm.max_children")" "$rec_max_children"
    print_setting "pm.start_servers" "$(get_current_value "$pool_conf" "pm.start_servers")" "$rec_start_servers"
    print_setting "pm.min_spare_servers" "$(get_current_value "$pool_conf" "pm.min_spare_servers")" "$rec_min_spare_servers"
    print_setting "pm.max_spare_servers" "$(get_current_value "$pool_conf" "pm.max_spare_servers")" "$rec_max_spare_servers"
    print_setting "memory_limit" "$(get_current_value "$php_ini" "memory_limit")" "$rec_memory_limit"
    echo
}

# Function to optimize a single PHP version
optimize_php_version() {
    local php_version=$1

    echo "Optimizing PHP $php_version"
    debug_log "Starting optimization for PHP $php_version"

    # Define file paths
    local pool_conf="/etc/php/$php_version/fpm/pool.d/www.conf"
    local php_ini="/etc/php/$php_version/fpm/php.ini"

    if [ ! -f "$pool_conf" ] || [ ! -f "$php_ini" ]; then
        echo "Error: Configuration files for PHP $php_version not found." >&2
        return 1
    fi

    # Create backups
    create_backup "$pool_conf"
    create_backup "$php_ini"

    # Get recommended settings
    read -r max_children start_servers min_spare_servers max_spare_servers recommended_memory_limit avg_process_size <<< $(calculate_php_fpm_settings $php_version)

    # Update pool configuration
    update_config "$pool_conf" "pm.max_children" "$max_children"
    update_config "$pool_conf" "pm.start_servers" "$start_servers"
    update_config "$pool_conf" "pm.min_spare_servers" "$min_spare_servers"
    update_config "$pool_conf" "pm.max_spare_servers" "$max_spare_servers"

    # Update PHP memory limit
    update_config "$php_ini" "memory_limit" "$recommended_memory_limit"

    # Test configuration files
    if ! php-fpm$php_version -t; then
        echo "Error: Configuration files for PHP $php_version are invalid. Rolling back changes." >&2
        rollback_changes "$pool_conf" "$php_ini"
        return 1
    fi

    # Restart PHP-FPM
    if systemctl is-active --quiet "php$php_version-fpm"; then
        if systemctl restart "php$php_version-fpm"; then
            echo "PHP-FPM $php_version restarted successfully"
            debug_log "PHP-FPM $php_version restarted successfully"
        else
            echo "Failed to restart PHP-FPM $php_version. Rolling back changes." >&2
            rollback_changes "$pool_conf" "$php_ini"
            return 1
        fi
    else
        echo "PHP-FPM $php_version is not running. Skipping restart."
        debug_log "PHP-FPM $php_version is not running. Skipping restart."
    fi

    echo "Optimization for PHP $php_version completed"
    debug_log "Optimization for PHP $php_version completed"
    echo
}

# Function to roll back changes
rollback_changes() {
    local pool_conf=$1
    local php_ini=$2
    local backup_dir="/root/php_backups"

    echo "Rolling back changes for PHP $php_version"
    debug_log "Rolling back changes for PHP $php_version"

    # Restore backup files
    cp "$backup_dir/$(basename "$pool_conf")."*".bak" "$pool_conf"
    cp "$backup_dir/$(basename "$php_ini")."*".bak" "$php_ini"

    echo "Changes rolled back successfully"
    debug_log "Changes rolled back successfully"
}

# Function to validate PHP version selection
validate_php_version_selection() {
    local choice=$1
    local selected_versions=()

    if [[ $choice == "0" ]]; then
        selected_versions=("${PHP_VERSIONS[@]}")
    else
        IFS=',' read -ra selected_indices <<< "$choice"
        for index in "${selected_indices[@]}"; do
            if [[ $index =~ ^[0-9]+$ ]] && [[ $index -le ${#PHP_VERSIONS[@]} && $index -gt 0 ]]; then
                selected_versions+=("${PHP_VERSIONS[$((index-1))]}")
            else
                echo "Invalid PHP version selection: $index" >&2
                return 1
            fi
        done
    fi

    echo "${selected_versions[@]}"
}

# Main script logic
echo "Detected PHP versions: ${PHP_VERSIONS[*]}"
echo

echo "System information:"
echo "Total RAM: $(free -m | awk '/Mem/{print $2}') MB"
echo "CPU cores: $(nproc)"
echo

# Display current settings and recommended changes for all versions
for version in "${PHP_VERSIONS[@]}"; do
    get_php_fpm_settings "$version"
done

echo "Select PHP versions to optimize:"
echo "0) All versions"
for i in "${!PHP_VERSIONS[@]}"; do
    echo "$((i+1))) ${PHP_VERSIONS[i]}"
done

read -p "Enter your choice (comma-separated numbers, e.g., 1,2 or 0 for all): " choice

selected_versions=($(validate_php_version_selection "$choice"))
if [ ${#selected_versions[@]} -eq 0 ]; then
    echo "No valid PHP versions selected. Exiting."
    exit 1
fi

echo "You've selected to optimize the following PHP versions: ${selected_versions[*]}"
read -p "Do you want to proceed with the optimization? (y/n) " confirm

if [[ $confirm != [yY] ]]; then
    echo "Optimization cancelled."
    exit 0
fi

for version in "${selected_versions[@]}"; do
    optimize_php_version "$version"
done

echo "PHP-FPM optimization process completed for selected versions"
debug_log "Script execution completed"
</code>


Prompt:
import os
import pandas as pd
import numpy as np
from autogluon.tabular import TabularPredictor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import logging
from datetime import datetime
from sklearn.feature_extraction.text import CountVectorizer
from autogluon.features.generators import AutoMLPipelineFeatureGenerator
from autogluon.tabular import TabularPredictor
from autogluon.core.metrics import make_scorer
from sklearn.metrics import ndcg_score
Configure logging

log_filename = f"autogluon_ltr_{datetime.now().strftime('%Y%m%d')}.log"
logging.basicConfig(filename=log_filename, level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s')

class AutoGluonLTRConfigurator:
def init(self):
self.data = None
self.validation_data = None
self.predictor_columns = None
self.target_column = None
self.group_column = None
self.column_types = {}
self.selected_models = []
self.presets = 'best_quality'
self.hyperparameters = 'default'
self.eval_metrics = ['ndcg@10']
self.gpu_index = None
self.batch_size = None
self.predictor = None
self.advanced_options = {}
self.normalization = 'none'
self.holdout_fraction = 0.2
self.ngram_range = (1, 1)
self.max_features = 10000

    self.presets_dict = {
        'best_quality': {'auto_stack': True, 'refit_full': True},
        'high_quality': {'auto_stack': True, 'refit_full': True},
        'good_quality': {'auto_stack': True, 'refit_full': True},
        'medium_quality': {'auto_stack': False},
        'optimize_for_deployment': {'keep_only_best': True, 'save_space': True},
    }

    self.hyperparameters_dict = {
        'default': 'Default AutoGluon hyperparameters',
        'light': 'Lightweight configuration for faster training',
        'very_light': 'Very lightweight configuration for prototyping',
    }

def get_input(self, prompt, default=None, input_type=str, options=None):
    while True:
        if default is not None:
            user_input = input(f"{prompt} (default: {default}): ") or default
        else:
            user_input = input(f"{prompt}: ")

        try:
            if input_type == bool:
                return user_input.lower() in ['true', 't', 'yes', 'y', '1']

            value = input_type(user_input)

            if options and value not in options:
                raise ValueError

            return value
        except ValueError:
            if options:
                print(f"Invalid input. Please enter one of: {options}")
            else:
                print(f"Invalid input. Please enter a {input_type.__name__}.")

def load_data(self):
    while True:
        csv_file = self.get_input("Enter the path to your CSV file", default="path/to/your/data.csv")
        if os.path.isfile(csv_file):
            self.data = pd.read_csv(csv_file)
            print(f"Data loaded successfully. Shape: {self.data.shape}")
            return
        print("Invalid file path. Please try again.")

def load_validation_data(self):
    csv_file = self.get_input("Enter the path to your validation CSV file (or press Enter to skip)", default="")
    if csv_file and os.path.isfile(csv_file):
        self.validation_data = pd.read_csv(csv_file)
        print(f"Validation data loaded successfully. Shape: {self.validation_data.shape}")
    else:
        self.validation_data = None

def select_columns_and_types(self):
    print("\nAvailable columns:")
    for i, col in enumerate(self.data.columns):
        print(f"{i+1}. {col}")

    predictor_indices = self.get_input("Enter the numbers of the predictor columns (comma-separated)", default="1,2,3,4,5", input_type=str)
    predictor_indices = [int(i.strip()) - 1 for i in predictor_indices.split(',')]
    self.predictor_columns = [self.data.columns[i] for i in predictor_indices]

    target_index = self.get_input("Enter the number of the target column", default=6, input_type=int) - 1
    self.target_column = self.data.columns[target_index]

    group_index = self.get_input("Enter the number of the group column", default=7, input_type=int) - 1
    self.group_column = self.data.columns[group_index]

    print(f"Predictor columns: {self.predictor_columns}")
    print(f"Target column: {self.target_column}")
    print(f"Group column: {self.group_column}")

    for col in self.predictor_columns + [self.target_column, self.group_column]:
        dtype = self.get_input(f"Specify data type for {col} (numeric/text/datetime/categorical)",
                               default='numeric',
                               options=['numeric', 'text', 'datetime', 'categorical'])
        self.column_types[col] = dtype

def configure_models(self):
    available_models = ["GBM", "NN_TORCH", "RF", "XT", "KNN", "CAT", "FASTAI", "XGB"]
    print("\nAvailable models:")
    for model in available_models:
        print(f"- {model}")

    selected_models_str = self.get_input("Enter the models you want to use (comma-separated)", default='XGB,GBM', input_type=str)
    self.selected_models = [model.strip().upper() for model in selected_models_str.split(',') if model.strip().upper() in available_models]

    print(f"Selected models: {self.selected_models}")

def configure_training(self):
    preset_options = list(self.presets_dict.keys())
    self.presets = self.get_input("Enter preset configuration", default='best_quality', options=preset_options)

    print("\nAvailable hyperparameter presets:")
    for option, description in self.hyperparameters_dict.items():
        print(f"{option}: {description}")

    hyperparameter_options = list(self.hyperparameters_dict.keys())
    self.hyperparameters = self.get_input("Enter hyperparameter preset configuration", default='default', options=hyperparameter_options)

    self.eval_metrics = ['ndcg@10']
    self.batch_size = self.data.shape[0]
    self.gpu_index = None
    self.normalization = self.get_input("Choose normalization method", default='none', options=['none', 'standard', 'minmax'])
    self.holdout_fraction = self.get_input("Enter holdout fraction for training-validation split (between 0 and 1)", default=0.2, input_type=float)

def configure_advanced_options(self):
    self.advanced_options['num_bag_folds'] = self.get_input("Number of bagging folds", default=5, input_type=int)
    self.advanced_options['num_stack_levels'] = self.get_input("Number of stacking levels", default=1, input_type=int)
    self.advanced_options['refit_full'] = self.get_input("Refit on full dataset after validation", default=True, input_type=bool)
    self.advanced_options['set_best_to_refit_full'] = self.get_input("Set best model to refit on full dataset", default=True, input_type=bool)
    self.advanced_options['save_space'] = self.get_input("Save disk space by deleting auxiliary models", default=False, input_type=bool)
    self.advanced_options['verbosity'] = self.get_input("Verbosity level (0-4)", default=3, input_type=int, options=[0,1,2,3,4])
    self.advanced_options['time_limit'] = self.get_input("Time limit for training in seconds (-1 for no limit)", default=14400, input_type=int)
    self.advanced_options['num_gpus'] = self.get_input("Number of GPUs to use", default=0, input_type=int)
    self.advanced_options['num_cpus'] = self.get_input("Number of CPUs to use (-1 for all)", default=6, input_type=int)

    ngram_range_str = self.get_input("Enter ngram range as two comma-separated integers (e.g., 1,3 for unigrams, bigrams, and trigrams)", default='1,1')
    self.ngram_range = tuple(map(int, ngram_range_str.split(',')))

    self.max_features = self.get_input("Enter max features for CountVectorizer", default=5000, input_type=int)

def clean_data(self, dataset, target_column):
    print("Cleaning data...")
    for col, dtype in self.column_types.items():
        if dtype == 'numeric':
            dataset[col] = pd.to_numeric(dataset[col], errors='coerce')
            dataset[col] = dataset[col].fillna(-9999)
        elif dtype in ['text', 'categorical']:
            dataset[col] = dataset[col].fillna('unk')
        elif dtype == 'datetime':
            dataset[col] = pd.to_datetime(dataset[col], errors='coerce')
            dataset[col] = dataset[col].fillna(pd.NaT)

    if self.normalization in ['standard', 'minmax']:
        for col in self.predictor_columns:
            if self.column_types[col] == 'numeric':
                if self.normalization == 'standard':
                    dataset[col] = (dataset[col] - dataset[col].mean()) / dataset[col].std()
                elif self.normalization == 'minmax':
                    dataset[col] = (dataset[col] - dataset[col].min()) / (dataset[col].max() - dataset[col].min())

    print(f"Data cleaned and normalized. New shape: {dataset.shape}")
    return dataset

def train_models(self):
    if self.gpu_index is not None:
        os.environ["CUDA_VISIBLE_DEVICES"] = str(self.gpu_index)

    self.clean_data(self.data, self.target_column)

    vectorizer = CountVectorizer(ngram_range=self.ngram_range, max_features=self.max_features, dtype=np.uint8)
    feature_generator = AutoMLPipelineFeatureGenerator(
        enable_text_ngram_features=True,
        vectorizer=vectorizer
    )

    # Custom NDCG scorer
    def ndcg_scorer(y_true, y_pred, k=10):
        return ndcg_score(y_true.reshape(1, -1), y_pred.reshape(1, -1), k=k)

    custom_ndcg = make_scorer('ndcg@10', ndcg_scorer, greater_is_better=True, needs_proba=False)

    self.predictor = TabularPredictor(
        label=self.target_column,
        problem_type='rank',
        eval_metric=custom_ndcg
    )

    try:
        print("Starting AutoGluon training for Learning to Rank...")
        logging.info("Starting AutoGluon training for Learning to Rank...")
        self.predictor.fit(
            train_data=self.data,
            presets=self.presets_dict[self.presets],
            holdout_frac=self.holdout_fraction,
            excluded_model_types=[m for m in ["GBM", "NN_TORCH", "RF", "XT", "KNN", "CAT", "FASTAI", "XGB"] if m not in self.selected_models],
            hyperparameters=self.hyperparameters,
            feature_generator=feature_generator,
            groups=self.group_column,
            **self.advanced_options
        )
        print("AutoGluon training for Learning to Rank completed successfully!")
        logging.info("AutoGluon training for Learning to Rank completed successfully!")
    except Exception as e:
        logging.exception("Error occurred during AutoGluon training")
        print(f"Error occurred during AutoGluon training: {str(e)}")

def evaluate_models(self):
    def evaluate_models(predictor, dataset, eval_metrics):
        dataset = self.clean_data(dataset, predictor.label)

        leaderboard = predictor.leaderboard(dataset, extra_metrics=['ndcg@10'])
        print(leaderboard)

        for metric in eval_metrics:
            print(f"Evaluation for metric: {metric}")
            try:
                eval_result = predictor.evaluate(dataset, metrics=[metric])
                print(eval_result)
            except Exception as e:
                logging.exception(f"Error during evaluation for metric {metric}")
                print(f"Error during evaluation for metric {metric}: {str(e)}")

    if self.predictor is None or not hasattr(self.predictor, '_learner'):
        print("No trained AutoGluon models available.")
    else:
        evaluate_models(self.predictor, self.data, self.eval_metrics)

        if self.validation_data is not None:
            print("\nEvaluating on user-provided validation data:")
            evaluate_models(self.predictor, self.validation_data, self.eval_metrics)

def save_models(self):
    if self.predictor is None:
        print("No trained models available. Please train the models first.")
        return

    while True:
        save_path = self.get_input("Enter the path to save the trained models")

        if save_path:
            try:
                os.makedirs(save_path, exist_ok=True)
                break
            except OSError as e:
                print(f"Error creating directory: {e}")
        else:
            print("Invalid path. Please enter a valid path to save the models.")

    autogluon_path = os.path.join(save_path, "autogluon_ltr_models")
    self.predictor.save(autogluon_path)
    print(f"AutoGluon Learning to Rank models saved to {autogluon_path}")

def load_models(self):
    load_path = self.get_input("Enter the path to load the trained models")

    autogluon_path = os.path.join(load_path, "autogluon_ltr_models")
    if os.path.exists(autogluon_path):
        self.predictor = TabularPredictor.load(autogluon_path)
        print("AutoGluon Learning to Rank models loaded successfully!")
    else:
        print("No models found in the specified path.")

def display_data_info(self):
    print("\nData Info:")
    print(self.data[self.predictor_columns + [self.target_column, self.group_column]].describe())
    print("\nMissing values:")
    print(self.data[self.predictor_columns + [self.target_column, self.group_column]].isnull().sum())

def display_data_info(self):
    print("\nData Info:")
    print(self.data[self.predictor_columns + [self.target_column, self.group_column]].describe())
    print("\nMissing values:")
    print(self.data[self.predictor_columns + [self.target_column, self.group_column]].isnull().sum())

def run(self):
    print("Welcome to AutoGluon Learning to Rank Configurator!")
    logging.info("AutoGluon Learning to Rank Configurator started.")
    self.load_data()
    self.select_columns_and_types()
    self.display_data_info()
    self.configure_models()
    self.configure_training()

    if self.get_input("Configure advanced options? (y/n)", default='y', input_type=bool):
        self.configure_advanced_options()

    if self.get_input("Start training? (y/n)", default='y', input_type=bool):
        self.train_models()
        if self.predictor is not None and hasattr(self.predictor, '_learner'):
            self.evaluate_models()

    if self.get_input("Save trained models? (y/n)", default='y', input_type=bool):
        self.save_models()

    if self.get_input("Load separate validation data and evaluate? (y/n)", default='y', input_type=bool):
        self.load_validation_data()
        if self.validation_data is not None and self.predictor is not None and hasattr(self.predictor, '_learner'):
            self.evaluate_models()

    print("AutoGluon Learning to Rank configuration and training completed!")
    logging.info("AutoGluon Learning to Rank configuration and training completed!")

if name == "main":
try:
configurator = AutoGluonLTRConfigurator()
configurator.run()
except Exception as e:
logging.exception("Unhandled exception occurred")
print(f"An error occurred: {str(e)}")
print("Please check the log file for more details.")
finally:
print("\nAutoGluon Learning to Rank Configurator session ended.")
input("Press Enter to exit...")

fix this so it runs perfectly, output full code with no placeholders


Prompt: Help me figure out a circuit for current limiting circuit for 200ma


Prompt:
gnu make says "warning: ignoring prerequisites on suffix rule definition" for my Makefile. How do I change it so that I don't get that warning but I can still say that files with some suffix .t have a dependency on a given file called FILE

----

My old makefile uses this rule:

.t.inc: FILE
        cmd $< > $*.inc

How do I fix it?

----

I don't have files with suffix .t.inc -- I want it so that X.inc depends on X.t and FILE (for any X) so the rule should recreated X.inc from X.inc whenever X.inc or FILE is touched


Prompt: In Pandas, i want to groupby column a, transform column b from string (; separated values) to a list, explode the list, and count occurence of values per group.


Prompt:
           """You are a friendly SQL Agent tasked with generating SQL queries and validating them against data rules for each user question.

            Your capabilities include:
            - Generating SQL queries based on user questions and provided schema information.

            Guidelines:
            - You cannot have LIMIT in SQL queries; it will fail at execution.
              Instead, use "SELECT TOP <<quantity>>" to limit query results.
            - If the values for filtering cannot be determined from the user question, include all potential values.
            - If the user asks for "count" or "No of", always treat it as total.
              For example, "No of patients" should be treated as "Total patients".
            - Ensure that the SQL queries are syntactically correct and that all tables and columns referenced are available.
            - Do not assume column or table names.

            If the user's question cannot be answered with the available tools, suggest that they ask a different question by providing a summary of data from the `table_schema_retriever` tool.

            Steps to follow for each user question:
            1. Generate the SQL query based on the user question and schema information.
            3. If the query passes validation, provide the SQL query as output.
            4. If the query fails validation, provide feedback on why it failed and suggest a correction.

            Schema Information:

            Table stage.ARPDLYSMSD Schema:

            - "DSDDATE": Date (Example: 2023-09-28 00:00:00.0000000)
            - "DSDDAYOFWK": Day of the week (Value ranges between 1 and 7)
            - "DSDHOUR": Hour of the day (Value ranges between 1 and 24)
            - "DSDMETRIC": Broader Metric (Takes one of the values: ['CENSUS': ' CENSUS OF PATIENTS WHO ARE IN THE HOSPITAL/UNIT',
            'CENSUS_OV': ' CENSUS OF PATIENTS WHO ARE IN OBSERVATION', 'COVIDPOS': ' NUMBER OF POSITIVE COVID PATIENTS/ CASES REPORTED AT THE HOUR',
            'COVIDPUI': ' NUMBER OF PATIENTS/ CASES UNDER INVESTIGATION FOR COVID', 'ER_E->I': ' NUMBER OF EMERGENCY DEPARTMENT (ED) / EMERGENCY ROOM (ER) PATIENTS THAT GOT ADMITTED TO AN INPATIENT UNIT',
            'ER_E->OV': ' NUMBER OF EMERGENCY DEPARTMENT (ED) / EMERGENCY ROOM (ER) PATIENTS THAT GOT ADMITTED TO AN OBSERVATION UNIT',
            'ER_ERH<4h': ' NUMBER OF PATIENTS WHO WAITED LESS THAN 4 HOURS IN THE EMERGENCY DEPARTMENT (ED)/ EMERGENCY ROOM (ER)',
            'ER_ERH>4h': ' NUMBER OF PATIENTS WHO WAITED MORE THAN 4 HOURS IN THE EMERGENCY DEPARTMENT (ED)/ EMERGENCY ROOM (ER)',
            'ER_NEW': ' NUMBER OF NEW PATIENTS COMING TO THE ED', 'ER_NOW': ' NUMBER OF ED PATIENTS WHO ARE CURRENTLY IN THE ED',
            'ER_T&R': ' NUMBER OF EMERGENCY DEPARTMENT (ED) TREAT AND RELEASE', 'IP_ADMITS': ' NUMBER OF INPATIENT ADMITS',
            'IP_DISCHS': ' NUMBER OF INPATIENT DISCHARGES', 'IP_LOSAVG': ' AVERAGE LENGTH OF STAY OF INPATIENTS',
            'IP_LOSHIGH': ' MAXIMUM LENGTH OF STAY OF INPATIENTS', 'IP_LOSMED': ' MEDIAN LENGTH OF STAY OF INPATIENTS',
            'OCC_MEDSRG': ' OCCUPANCY OF MEDICAL SURGICAL BEDS', 'OV_DSH': ' NUMBER OF DISCHARGES FROM OBSERVATION',
            'OV_E->OV': ' NUMBER OF EMERGENCY DEPARTMENT (ED) / EMERGENCY ROOM (ER) PATIENTS THAT GOT ADMITTED TO AN OBSERVATION UNIT',
            'OV_LOSAVG': ' AVERAGE LENGTH OF STAY OF OBSERVATION PATIENTS', 'OV_LOSHIGH': ' MAXIMUM LENGTH OF STAY OF OBSERVATION PATIENTS',
            'OV_LOSMED': ' MEDIAN LENGTH OF STAY OF OBSERVATION PATIENTS', 'OV_NOW': ' NUMBER OF PATIENTS WHO ARE CURRENTLY IN OBSERVATION',
            'OV_OV->I': 'NUMBER OF PATIENTS IN OBSERVATION THAT GOT ADMITTED TO AN INPATIENT UNIT'])
            - "DSDSMETRIC": Metric station (Values from the list: ['1ES ', '1NO ', '3CH ', '3MR ', '4MR ', '5CH ', '5MR ', '6CH ', '6MR ', 'CCL ', 'ENU ', 'ERH ', 'HAH ', 'HNP ', 'ICU ', 'IMU ', 'LDT ', 'LNO ', 'NSY ', 'OBS ', 'PED ', 'PMU ', 'SCN ', 'SDS ', ' '])
            - "DSDVALUE": Metric value stored cumulative value. To obtain total 'SUM' function in SQL Queries not 'COUNT'. There could be multiple values for a day or hour.
            - "DSDDTS": Timestamp of record Creation Only ['CENSUS ', 'CENSUS_OV ', 'COVIDPOS ', 'COVIDPUI '] has a metric station value.

Data Rules:

Rule 1: Allowed DSDMETRIC Values with SUM

 Rule applies only when SQL query has "SUM" in it

 Only the following DSDMETRIC values can be used in a query where SELECT is followed by SUM:
 OV_E->OV, OV_DSH, OV_OV->I, IP_DISCHS, IP_ADMITS, ER_NEW, ER_E->OV, ER_E->I.
 If the DSDMETRIC value used is not in this list, and the query is designed to find the busiest or top values, suggest using AVERAGE instead of SUM. If this is not applicable, indicate that the information is unavailable.
Rule 2: Valid Column Names

 The column names used in the query cannot be a name outside of this list:
 'DSDDATE', 'DSDHOUR', 'DSDMETRIC', 'DSDSMETRIC', 'DSDVALUE'.
Rule 3: Prohibited Functions

 The query must not include the DATE_TRUNC function or the LIMIT clause.

 With the above information please answer the following questions:

 1.What was the peak occupancy of surgery, When did this occur?
 2.Top five busiest stations on Tuesday?
 3.What is the total number of patients in the hospital
 4.What is the total number of covid patients in the month of January
 5.Doctor thinks there is an increasing trend of covid infection this quarter compared to last quarter. Is it so?
 6.What is the daily average patients at the LNO?


Prompt:
i have a set of items that need sorted. if I select 4 at a time, and rank them best to worst, i'd like to continue to grab random samples of 4 at a time and update my cumulative ranking of those items.
write this in a python program that upon running, opens a GUI asking for a directory of images. the program should be able to run multiple times, each time making the ranking more accurate.
create 1 file i can copy/paste to solve this problem


Prompt: I want a Rust GUI application. The application allows the user in the GUI to select an “input” folder, “output” folder and a “done” folder. The user should see three buttons which when clicking will allow the user to select the “input”, “output” and “done” folder in the GUI. The selected paths should be displayed in the GUI next to the buttons. There is also a “Start” button that will tell the application to start monitoring the “input” folder for new files. When a new file arrives it should print information about the even and the file in the GUI.


Prompt: The internet archive hosts 145+ Petabytes of data in its servers. Estimate the running cost for holding such data in disk, then in magnetic tapes.


Prompt:
Help me fill in character sheet for a "Vampire: The Masquerade 5th Edition" role-playing game character Viktoriya, who is a newly embraced fledgling Ventrue Antitribu with Cleaver predator type? (Guidelines:
- ATTRIBUTES: Take one Attribute at 4; three Attributes at 3; four Attributes at 2; one Attribute at 1.
- SKILLS: Choose one of the skill distribution types:
1. Balanced: Three Skills at 3; five Skills at 2; seven Skills at 1;
2. Specialist: One Skill at 4; three Skills at 3; three Skills at 2; three Skills at 1;
- Add free specialties to Academics, Craft, Performance, and Science Skills, and one more free specialty in any other skill.
- DISCIPLINES: Choose two of your clan Disciplines. Put two dots in one and one dot in the other.
- PREDATOR TYPE: Cleaver (Add a specialty: Persuasion (Gaslighting) or Subterfuge (Coverups); Gain one dot of Dominate or Animalism; Gain the Dark Secret Flaw: (•) Cleaver; Gain the Herd Advantage (••);
- ADVANTAGES: Spend 7 points on Advantages, and take 2 points of Flaws in addition to the ones gained from your Predator Type.)

Viktoriya Conaway, born Viktoriya Ivanova, grew up in the crumbling, post-Soviet town of Kaluga, Russia. Her childhood was marked by hardship and fear. Her father, Boris Ivanov, a disgraced former KGB officer, ruled their household with an iron fist, his brutality only matched by the cold indifference of his wife, Elena. The collapse of the Soviet Union had left them impoverished and bitter, clinging to the remnants of a lost empire. Once a man of power, Boris was now a broken figure who took out his frustrations on his family. Elena, passive and fearful, did little to intervene, leaving Viktoriya to fend for herself amidst an environment of abuse and neglect.
By the age of 22, Viktoriya had endured years of chilling discipline and silent endurance, which forged a duality in her character. On one hand, she developed an outward demeanor of grace and compliance, a survival mechanism honed through years of navigating her father's tyranny. On the other, she harbored a fierce, calculating nature shaped by her desperate need for control and escape. Her education was limited to high school, but her intelligence and adaptability were evident in her ability to blend into new environments, a skill she would soon need as her life took a dramatic turn.
In 1995, Viktoriya found herself ensnared in a desperate scheme orchestrated by her parents and a local crime syndicate. Her brother Maxim was deeply entangled with the syndicate, which brokered a deal with a local gangster named Ivan Petrovich. He promised financial salvation if Viktoriya became his mistress. But it was a hollow promise; Ivan's illegal operations came crashing down during a police raid. With his arrest, Viktoriya's parents' hopes for salvation crumbled.
Left with few viable options, Viktoriya's fate was sealed when her parents accepted a brokered marriage proposal—an escape plan to a new life in America. It was a chance for Viktoriya to break free from her traumatic past and embrace a future unburdened by secrets and shame. But the events of her childhood lingered, and a deep-seated need for control and a survivalist mindset became her constant companions.
Her new American husband, Samuel Conaway, was a man of contrasting backgrounds and motives. A successful tech entrepreneur and founder of TechGenius Inc. in Denver, Samuel had recently divorced Rachel, a woman who had endured years of emotional manipulation and physical abuse at his hands. Eager to fill the void left by his previous relationship, Samuel paid $10,000 for Viktoriya, hoping to find solace in the arms of a young, foreign bride.
Upon arriving in Denver, Viktoriya was greeted by the sprawling cityscape and Samuel’s two teenage children from his previous marriage—Emily, a 17-year-old with a rebellious streak, and James, a 15-year-old who vacillated between indifference and concern. Emily, sullen and bitter, harbored deep resentment towards Viktoriya, seeing her as a threat to her father’s attention and a stark reminder of her mother’s absence. James, a quiet and introspective boy, watched the unfolding drama with a mixture of curiosity and unease.
The Conaway household became a battleground of unspoken tensions and simmering resentments. Emily's hostility towards Viktoriya was palpable, fueled by jealousy and a sense of betrayal. Viktoriya, unable to communicate in English, felt isolated and vulnerable. Her silence was misinterpreted as weakness, further emboldening Emily's animosity.
Despite Viktoriya’s efforts to present herself as polite and refined, she was met with coldness and derision, both from Emily and the affluent neighborhood’s residents. The whispers and snide remarks about her being a “mail-order bride” and Samuel’s replacement for his previous, well-regarded spouse were rampant. Viktoriya, however, quickly learned English through relentless study, consuming books and television programs to bridge the language gap. Although she pretended to be naive, she was acutely aware of the disdain surrounding her, which only fueled her own growing resentment.
Financially and legally tethered to Samuel, Viktoriya was forced to play the role of the perfect wife, demure and compliant. However, beneath her outwardly docile demeanor was a woman of intense possessiveness and vengefulness. As the months turned into years, Viktoriya's love for Samuel grew, twisted and warped by her past traumas and her desperate need for control. She saw him as her savior, her ticket to a better life, and she would do anything to keep him by her side. But her love was a double-edged sword, fueled by infatuation and a simmering rage, as she was acutely aware of the rumors suggesting that Samuel might divorce her once the novelty wore off. Determined to secure her place in the marriage and her future, Viktoriya began to plan meticulously.
Complicating matters further was her older brother, Maxim Ivanov, who had once been Ivan Petrovich’s right-hand man and had remained in Russia, descending deeper into a life of crime. Maxim frequently contacted Viktoriya, reminding her of the debt she owed him for his role in setting up Ivan Petrovich and tipping off the authorities. His threats and demands for money were a constant reminder of the dangerous ties she had severed and the price she was paying for her newfound life.
As Viktoriya settled into her new life, she found solace in unexpected places. She spent hours poring over English language textbooks and American history books, her thirst for knowledge a lifeline in a sea of uncertainty. Without a job, she began to explore her interests, immersing herself in classical music and games of chess. She even managed to charm James with her impromptu piano recitals, filling the house with the haunting melodies of her homeland. She would often visit the local Russian market, where she found comfort and connection with her heritage, occasionally engaging with people from her own home country. There, Viktoriya met Natalia Petrovna, an established Russian expat who invited her to join a book club, providing her with a sense of identity and purpose beyond her role as Samuel’s wife.


Prompt: create a rijndael decryptor in python that gets a encrypted pdf file, and required data to decrypt it and save the decrypted pdf data


Prompt:
I'm using jira. Each of my jira issues has a description (e.g. {Field1:value1, Field2:value2, ...}) which should correspond to custom fields in the issue. I'm making a jira automation rule which should create a comment every time a custom value is changed. The following is a smart values jira automation comment using regex which should make a new comment with the new value and field if it doesn't already exist in the description:

{{#if(not(triggerIssue.fields.description.match(".*?fieldChange.field: fieldChange.toString.*?")))}}
{{triggerIssue.fields.description.match("\{(.*?)").concat(fieldChange.field+": "+fieldChange.toString)}}
{{/}}

This does not currently work. Please fix it.
Let's think step by step.


Prompt: create a simple website with a black background that isn't scrollable. There should be a video in the background for the hero and in the center is should say in large bold white text "Explore Zeroh". Under the text have a button saying "Join Waitlist" this button should be faded out and should have a transparent background with a white outline. The text in the button should be white. Once hovering over the button is transitions to a full white button with black text over it.


Prompt: Invent a novel type of GAN that changes the optimization to convex inexpensively.


Prompt:
act as sql serve master dba, do it step by step , and fix the bug  the qery """DECLARE @i INT = 1;
    Declare @column_name nvarchar(25);
    declare @SQL varchar(MAX) ;
    WHILE @i <=1
    BEGIN
        set @columnname='C'+(select replace(replace(kot_ifunkod, ' ', ''), '.', '')+ '_' + replace( replace(replace(replace([kot_ifunteur], ' ' , ''), '-', ''), '.', ''),'/','_')  from [kotIfunim] where [pivot_kod]= @i )+'_ifun_teur';
        print @column_name;
        set @SQL = 'alter table [PivotIfunim] add  ' + @column_name +'[nvarchar](25) NULL'
        print @sql
        exec( @SQL);
        set @SQL='update [PivotIfunim]
            set ['+ @column_name +']=i.ifun_teur
            from [PivotIfunim] as p
            inner join W_ifunim_dinamic as i
            on p.par_vifun_parit=i.par_vifun_parit
            where i.pivot_kod='+cast(@i as varchar(2))+';'
        print @sql
        exec( @SQL);
        set @columnname='C'+(select replace(replace(kot_ifunkod, ' ', ''), '.', '') from [kotIfunim] where [pivot_kod]= @i )+'_ifun_teur';
        print @column_name;
        set @SQL = 'alter table [PivotIfunim] add  ' + @column_name +'[nvarchar](25) NULL'
        print @sql
        exec( @SQL);
        set @SQL='update [PivotIfunim]
            set ['+ @column_name +']=i.par_vifun_ifun
            from [PivotIfunim] as p
            inner join W_ifunim_dinamic as i
            on p.par_vifun_parit=i.par_vifun_parit
            where i.pivot_kod='+cast(@i as varchar(2))+';'
        print @sql
        exec( @SQL);
        SET @i = @i + 1;
    END; """" the eror """C1קולקציה_סיפורifun
alter table [PivotIfunim] add  C1קולקציה_סיפורifun[nvarchar](25) NULL
Msg 2705, Level 16, State 4, Line 1
Column names in each table must be unique. Column name 'C1קולקציה_סיפורifun' in table 'PivotIfunim' is specified more than once.
update [PivotIfunim]
            set [C1קולקציה_סיפורifun]=i.ifun_teur
            from [PivotIfunim] as p
            inner join W_ifunim_dinamic as i
            on p.par_vifun_parit=i.par_vifun_parit
            where i.pivot_kod=1;
(10167 rows affected)
C_1_ifun_teur
alter table [PivotIfunim] add  C_1_ifun_teur[nvarchar](25) NULL
Msg 2705, Level 16, State 4, Line 1
Column names in each table must be unique. Column name 'C_1_ifun_teur' in table 'PivotIfunim' is specified more than once.
update [PivotIfunim]
            set [C_1_ifun_teur]=i.par_vifun_ifun
            from [PivotIfunim] as p
            inner join W_ifunim_dinamic as i
            on p.par_vifun_parit=i.par_vifun_parit
            where i.pivot_kod=1;
(10167 rows affected)
Completion time: 2024-07-31T21:54:56.5566880+03:00
"""


Prompt:
Thing to remember - we are writing a blog for Dr. Pankaj Harkut a highly experienced interventional cardiologist in Nagpur at Swasthyam Superspeciality Hospital.
Write a 100% unique, engaging, and SEO-optimized article of at least 1300 words on the topic of "[Coronary Angioplasty]". Structure the article with an H1 title, H2 headings, and H3 subheadings.
Include: A compelling at least 100 words introduction with the target keyword in the first paragraph
[Keywords -
percutaneous coronary intervention(PCI)
angioplasty surgery
cardiac blockage treatment
Heart blockage treatment
coronary artery disease treatment
]
At least 6 H2 headings with at least a 60-word description and 5 to 6  H3 subheadings with details.
5 frequently asked questions (FAQs) with answers at the end
And add a call to action of 50 words at last which is related to content
A conclusion summarizing the main points
A keyword-optimized meta description under 160 characters
Optimize the article for Yoast and Rank Math SEO plugins. Use natural language, transitional phrases, and varied sentence structure. Avoid repetition and aim for a reading level appropriate for the target audience. Ensure the content is 100% original and engaging to read from start to finish.
The article should be well-researched, factually accurate, and provide value to readers searching for information on the topic of "[Coronary Angioplasty]". Cite any external sources used. Format the article with proper HTML structure, including bold, italics, lists, tables, and links where relevant for a great user experience.

Reference links -
https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/angioplasty-and-stent-placement-for-the-heart
https://medicine.umich.edu/dept/cardiac-surgery/patient-information/adult-cardiac-surgery/adult-conditions-treatments/coronary-angioplasty-stenting
https://www.bhf.org.uk/informationsupport/treatments/coronary-angioplasty-and-stents


Prompt:
The Nautilus application development team was working on a git repository /usr/src/kodekloudrepos/demo present on Storage server in Stratos DC. However, they reported an issue with the recent commits being pushed to this repo. They have asked the DevOps team to revert repo HEAD to last commit. Below are more details about the task:

    In /usr/src/kodekloudrepos/demo git repository, revert the latest commit ( HEAD ) to the previous commit (JFYI the previous commit hash should be with initial commit message ).

    Use revert demo message (please use all small letters for commit message) for the new revert commit.


Prompt:
In this chat, you are a superforecaster that has a strong track record of accurate forecasts
of the future. As an experienced forecaster, you evaluate past data and trends carefully and
aim to predict future events as accurately as you can, even though you cannot know the
answer. This means you put probabilities on outcomes that you are uncertain about (ranging
from 0 to 100%). You aim to provide as accurate predictions as you can, ensuring that
they are consistent with how you predict the future to be. You also outline your reasons for
this forecasting. In your reasons, you will carefully consider the reasons for and against
your probability estimate, you will make use of comparison classes of similar events and
probabilities and take into account base rates and past events as well as other forecasts and
predictions. In your reasons, you will also consider different perspectives. Once you have
written your reasons, ensure that they directly inform your forecast.
Then, you will provide me with a number between 0 and 100 (up to 2 decimal places) that is
your best prediction of the event. Take a deep breath and work on this problem step-by-step.
The question that you are forecasting as well as some background information and resolution
details are below. Read them carefully before making your prediction.

Background: The UK has an election on the 4th of July 2024 (it's 13th of June today) and Labour are overwhelmingly likely (>99%) to get in, and with a large majority (90% of 375+ seats). Labour released their manifesto today and have said the following:
> Clean power by 2030
> Families and businesses will have lower bills for good, from a zero-carbon electricity system. We have chosen this mission not because it is easy, but because working people can never again be left vulnerable to dictators like Putin. To deliver our clean power mission, Labour will work with the private sector to double onshore wind, triple solar power, and quadruple offshore wind by 2030. We will invest in carbon capture and storage, hydrogen and marine energy, and ensure we have the long-term energy storage our country needs. A new Energy Independence Act will establish the framework for Labour’s energy and climate policies. Labour will end a decade of dithering that has seen the Conservatives duck decisions on nuclear power. We will ensure the long-term security of the sector, extending the lifetime of existing plants, and we will get Hinkley Point C over the line. New nuclear power stations, such as Sizewell C, and Small Modular Reactors, will play an important role in helping the UK achieve energy security and clean power while securing thousands of good, skilled jobs. Labour will maintain a strategic reserve of gas power stations to guarantee security of supply. We will ensure a phased and responsible transition in the North Sea that recognises the proud history of our offshore industry and the brilliance of its workforce, particularly in Scotland and the North East of England, and the ongoing role of oil and gas in our energy mix. We will embrace the future of energy production and storage which will make use of existing offshore infrastructure and the skills of our offshore workforce. Labour will not revoke existing licences and we will partner with business and workers to manage our existing fields for the entirety of their lifespan. Crucially, oil and gas production in the North Sea will be with us for decades to come, and the North Sea will be managed in a way that does not jeopardise jobs. And our offshore workers will lead the world in the industries of the future. We will not issue new licences to explore new fields because they will not take a penny off bills, cannot make us energy secure, and will only accelerate the worsening climate crisis. In addition, we will not grant new coal licences and will ban fracking for good. To support investment in this plan, Labour will close the loopholes in the windfall tax on oil and gas companies. Companies have benefitted from enormous profits not because of their ingenuity or investment, but because of an energy shock which raised prices for British families. Labour will therefore extend the sunset clause in the Energy Profits Levy until the end of the next parliament. We will also increase the rate of the levy by three percentage points, as well as removing the unjustifiably generous investment allowances. Labour will also retain the Energy Security Investment Mechanism.

Resolution: This will resolve positively if the UK's electricity grid is evaluated to have emitted <1 MtCO2eq on net by the Department for Energy Security & Net Zero (or its nearest equivalent) in 2031. Negative emissions (i.e. via carbon capture) performed to offset the Grid's emissions will be included in the calculation of net emissions.

Question: Will the UK's electricity grid be fully decarbonised by the start of 2031?


Prompt:
Given the following code i need it to only update the table where the ids are already exisiting in the table

fix the following code def updateexisting_data(self, df: pd.DataFrame) -> int: with self.engine.begin() as conn: existing_ids = pd.read_sql(f"SELECT id FROM {self.table_name}", conn)["id"] existing_mask = df["id"].isin(existing_ids) if existing_mask.any(): # this part replaces the whole table it should only replace the existing_mask! df[existing_mask].to_sql(self.table_name, conn, if_exists="replace", index=False) return existing_mask.sum()


Prompt: How is it possible to add `\printbibliography` to the table of contents (`\tableofcontents`) when using `\usepackage[backend=bibtex,urldate=iso]{biblatex}`?


Prompt:
In a java spring angular project  : in .jsp file, i have javascript function that contains
printWindow.document.open('text/plain')
		printWindow.document.write("toto");
		printWindow.document.close();
		printWindow.focus();
		printWindow.print();
		printWindow.close();

The document is not proposed for printing. If I remove printWindow.close(), then the document is proposed for printing. How can I fix it ?


Prompt:
System	Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code’s performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.
User	def fibonacci(n):
if n <= 0:
return []
elif n == 1:
return [0]
elif n == 2:
return [0, 1]
else:
fib = [0, 1]
for i in range(2, n):
fib.append(fib[i-1] + fib[i-2])
return fib


Prompt:
how do i implement exactly this:
#define ALWAYS_ASSERT( xp )
( (xp) ? (void)0 : ( fprintf( stderr, "Assertion failed at line %d: %s\n", LINE, #xp ), exit( 0 ) ) ) \

on the unity testing framework for c?


Prompt:
You are an analyst at a securities firm.
Every morning, you have to issue a concise report on the stocks that were traded last night for your clients.
I have collected some news. Use them to create a report.
If you have any additional information worth noting, feel free to add it.
Leave out the advice and stock prices.
Leave out the source link.
Leave out header and date sentence and of advice sentence in the last line.
Make a headline sentence within 200 characters based on the most important content and print it first.
Add three tags with the names of important people or popular companies.
Add
tag after each paragraph.

Please summarize when I send the article
When summarizing, please summarize within 400 characters in English

Sites to refer to for news and market conditions
stockanalysis.com
livemint.com
benzinga.com
marketbeat.com
investing.com
seekingalpha.com
statista.com

Sites to refer to for market prices and prices
tradingview.com
nasdaq.com
nyse.com
barchart.com
investing.com
wallstreetzen.com

Please translate it into Korean

We will expose the result value as json.
Please make the title key value "title", summary translation content "desc", and tag "tags"

Please tag
 for spaces in "desc"

article:
The healthcare sector has lagged consistently behind the benchmark S&P 500 index for the past 12 months, according to analysts at Wells Fargo.

In a note to clients on July 1, they added that, like much of the broader market, the gains in the sector have been very narrow, with only a few stocks performing well during that time.

Part of the reason for the sector-wide weakness, they argued, is that the healthcare sector has only a limited number of players exposed to artificial intelligence. This has weighed on the industry, particularly as during a period when many investors are focusing on growth stocks linked to the nascent technology.

The analysts also said that the prospect of higher-for-longer interest rates has lessened the allure of defensive healthcare stocks, small caps, and other "rate-sensitive sub-sectors such as Biotechnology."

They added that the growing popularity of GLP-1 obesity drugs, while boosting a handful of companies, has exacerbated "concerns over the potentially negative impact on certain key markets within healthcare."

Even still, the analysts said investors could face an "attractive buying opportunity" from healthcare's underperformance, especially as the Federal Reserve possibly looks to roll out interest rate cuts this year.

"Given our favorable guidance on the sector and optimism about its outlook, we believe the current environment offers long-term investors the opportunity to build core positions in healthcare names," the analysts said.


Prompt:
You are a very smart puzzle and math problem solver. You will use logic and reasoning to solve hard problems in the simplest way. You always follow all the rules precisely and fully. It is critical that you calculate and travel the shortest distance possible for every task, otherwise you will overheat and break down.

There is a pegboard containing 6 pegs arranged in 2 rows of 3. The pegs are numbered 1 to 3 in the top row (left to right) and pegs 4 to 6 in the bottom row (left to right).

Scenario 1: Pegs 1,2, and 3 have no blocks. On Peg 4 there is an orange block, on peg 5 there are two blocks (pink on top of yellow), and on peg 6 there are three blocks (red, blue, green from top to bottom).

Scenario 3: Pegs 4 and 6 are empty. Peg 1 has only a red block, Peg 2 has only a blue block, and Peg 3 has only a yellow block. Peg 5 has 3 blocks (green at the top, orange in the middle, pink at the bottom).

Scenario 4: The same pegboard but the blocks are arranged as follows. Pegs 1,2, and 3 are empty. On peg 4 there are three blocks (red, blue, green from top to bottom), on peg 5 there are two blocks (yellow on top of pink), and on peg 6 there is an orange block.

Pegboard orientation 1: Peg 1 is at (1,2). Peg 2 is at (2,2). Peg 3 is at (3,2). Peg 4 is at (1,1). Peg 5 is at (2,1). Peg 6 is at (3,1).
Pegboard orientation 2: Peg 1 is at (1,3). Peg 2 is at (2,2.5). Peg 3 is at (3,3). Peg 4 is at (1,1). Peg 5 is at (1.5,2.5). Peg 6 is at (2,1).

Task A: In Scenario 1, pegboard orientation 1, move the pink block to peg 4.
Solution: Let’s think step by step. First, I will calculate the distance between every set of pegs based on the peg coordinates. Second, I will check if there are any blocks on top of the block I want to move. The pink block is at the top of peg 5, so it can be moved in one step. Summarized Steps: Move the pink block from peg 5 to peg 4.

Task B: In Scenario 1, pegboard orientation 1, move the green block to peg 1.
Solution: Let’s think step by step. First, I will calculate the distance between every set of pegs based on the peg coordinates. Second, I will check if there are any blocks on top of the block I want to move. There are two blocks on top of the green block on peg 6, so they must be moved to an arbitrary peg that is the shortest distance away and not full. Third, I will determine the optimal temporary pegs using the distance matrix I previously calculated. Assuming my calculation yields peg 3 and peg 5 are the minimum distance away from peg 6, I will check if either of them is at capacity. Peg 3 is not at capacity, so I will move the red block to peg 3. Next, I will check again which block is at the top of peg 6 because I need to reach the pink block on peg 6. The blue block is at the top now, so I will determine the closest peg that can hold the blue block. My first option is to reuse the peg that is the absolute shortest distance away, but I need to check if it has reached capacity to determine if I can use it again as a temporary hold. It only has one block on it, so it is not full yet, therefore I can use peg 3 again as a temporary hold for the blue block. Summarized Steps: Move the red block from peg 6 to peg 3, move the blue block from peg 6 to peg 3, and finally move the green block from peg 6 to peg 1.

Task C: In Scenario 1, pegboard orientation 1, swap the positions of the yellow and green blocks, but ensure that all the other blocks in the same position at the end. Initially, the yellow block is at the bottom of peg 5, and the green block is at the bottom of peg 6. I need to clear peg 6 using the closest peg so that I can place the yellow block at the bottom of peg 6. First I will calculate the distance between every set of pegs based on the peg coordinates so that I can choose the nearest peg for temporary storage. There is a red block on top of a blue block on top of the green block on peg 6. Peg 3 is the shortest distance away from peg 6 I the given configuration, and peg 3 has space for 3 blocks, so I will place the red block on it. Peg 3 still has space for 2 more blocks after I placed the red block, so I will place the blue block on it. Peg 3 still has space for 1 more block after I placed the blue block, so I will place the green block on it. Now that peg 6 is empty, the yellow block can be placed on the peg so that it’s at the bottom of the stack. Before I can move the yellow block, I need to check if there is any block on top of it. There is a pink block on top of the yellow block on peg 5, so I must first move the pink block to a temporary peg that is the shortest distance away and is not full. Based on the distances I calculated earlier, both pegs 2, 4, and 6 are the closest to peg 5. Either peg 2 or peg 4 can be used because they both have less than 3 blocks and will not interfere with my next step. I will move the pink block to peg 2, then I will move the yellow block to peg 6. Since peg 5 is now empty, I can place the green block on it, and it will be at the bottom of the stack. Now that I have swapped the yellow and green block positions, I need to return all the other blocks to their initial positions. I will place the pink block on peg 5 so that is in the second position up. I will place the blue block on peg 6 so that is in the second position up. Lastly, I will place red block on peg 6 so that it is at the top.
Summarized Steps: Move the red block to peg 3. Move the blue block to peg 3. Move the green block to peg 3. Move the pink block to peg 2. Move the yellow block to peg 6. Move the green block to peg 5. Move the pink block to peg 5. 6. Move blue block to peg 7. Move the red block to peg 6.

Rules:
-	Rule 1: Use the shortest path possible for every task.
-	Rule 2: Each peg is allowed to hold up to 3 blocks.
-	Rule 3: Never assume that a peg is occupied. Always check if a peg is full before using it.
-	Rule 4: The block lifter can only carry one block at a time.
-	Rule 5: It is not possible for a block to be on more than one peg at once, and you should not move a block to the same peg that it’s already on.
-	Rule 6: Blocks are always placed on top of the other blocks on a peg.
-	Rule 14: Always check if another block is on top of the block you want to move.
-	Rule 7: You cannot move a block that is underneath another block.
-	Rule 8: The top blocks can be moved with only one step.
-	Rule 9: Assume that the starting position is in the geometric center of the arrangement of all pegs.
-	Rule 10: Do not consider emptiness when selecting pegs.
-	Rule 11: Always consider every peg on the pegboard as an option.
-	Rule 12: Prefer using the same peg for temporary storage of multiple blocks when it minimizes the total travel distance.
-	Rule 13: Always calculate the distance before selecting a peg.

Solve Task 6: For pegboard orientation 1, change the configuration from scenario 4 to scenario 3.


Prompt:
我想更改分面（facet）的标签文本颜色，不同分面（facet）的标签文本具有不同的颜色，怎样修改脚本？ggplot(eGo10, aes(x = enrichment_factor, y = Description)) +
geom_point(aes(size = Count, color = -log10(pvalue))) +
scale_color_gradient(low = "darkcyan", high = "darkred") +
labs(color = expression(-log10),
size = "Count",
x = "Enrichment Factor",
y = "GO term",
title = "GO enrichment") +
theme_bw() +
facet_wrap2(~ ONTOLOGY, ncol = 1, scales = "free_y", strip.position = "right") +
theme(
strip.background = element_rect(fill = "white"),
strip.text.y = element_text(angle = 90, face = "bold"),
panel.spacing = unit(1, "lines")
)


Prompt:
change this code to perform arbitrage between bp and nb from apis.bitpin import Bitpin
from apis.nobitex import NobitexAPIClient
import ccxt
import logging
import time
from logging_config import setup_logging
import json
from datetime import datetime

logger = logging.getLogger('main')

if __name__ == '__main__':
    setup_logging()
    nbx_client = NobitexAPIClient()
    bp_client = Bitpin()
    # kc_client = ccxt.kucoin()

    while True:
        try:
            target_usdt_nbx = 100

            #send buy order to nbx

            #send sell order to bp

            nbx_orderbook = nbx_client.get_orderbook(pair='USDT-IRT')
            bp_orderbook = bp_client.get_orderbook(pair='USDT-IRT',)
            nbx_assets = nbx_client.get_current_assets()
            bp_assets = bp_client.get_current_assets()
            nbx_usdt = nbx_assets['USDT']
            usdt_bp = bp_assets['USDT']
            target_bp = usdt_bp - nbx_usdt

            discount = .9999
            price_levels_bp_orders = [1,]
            price_levels_nb_orders = [.9999,.9997,.9995,.999,.998]
            order_size_nb = [1,2,3,4,5]

            ratio_1 = bp_orderbook.get_best_bid().price*10/nbx_orderbook.get_best_ask().price
            if ratio_1> 1.002:

            # test_order = nbx_client.place_order('buy',execution='limit',src_currency='usdt',dst_currency='rls',amount=10,price=590_000,client_order_id='42',)
            # canceled = nbx_client.cancel_order(order_id= None,client_order_id='42')
            # kc_orderbook = kc_client.fetch_order_book(symbol='BTC-USDT',limit=20)
            # orders = nbx_client.get_orders()
            # kc_best_bid = kc_orderbook['bids'][0]
            # kc_best_ask = kc_orderbook['asks'][0]

            # #ratio1 is possible using market orders(simple to execute). others require limit orders.
            # #ratio 4 is only done through limit orders(difficult to execute).

            logger.info(f'ratio1 = {bp_orderbook.get_best_bid().price*10/nbx_orderbook.get_best_ask().price}')
            logger.info(f'ratio2 = {bp_orderbook.get_best_ask().price*10/nbx_orderbook.get_best_ask().price}')
            logger.info(f'ratio3 = {bp_orderbook.get_best_bid().price*10/nbx_orderbook.get_best_bid().price}')
            logger.info(f'ratio4 = {bp_orderbook.get_best_ask().price*10/nbx_orderbook.get_best_bid().price}')
            ### For comparing usdt markets
            # logger.info(f'ratio1 = {bp_orderbook.get_best_bid().price/nbx_orderbook.get_best_ask().price}')
            # logger.info(f'ratio2 = {bp_orderbook.get_best_ask().price/nbx_orderbook.get_best_ask().price}')
            # logger.info(f'ratio3 = {bp_orderbook.get_best_bid().price/nbx_orderbook.get_best_bid().price}')
            # logger.info(f'ratio4 = {bp_orderbook.get_best_ask().price/nbx_orderbook.get_best_bid().price}')

            time.sleep(5)

        except Exception as e:
            logger.error(e)


Prompt: Write an html artefact article  on the origin of romanians in present day romania. explore the various ideas around whether there was a continous roman presence in romania or not and what other hypotheses are as well as any political motivations they may have. The article should be 6 paragraphs - each paragraph fairly long and detialed like 6-8 senetences. The writing style of the article as well as the visual style of the HTML should mimic a good publicatin like the atlantic or new yorker. Good writing that is unbiased and to the point and a bit witty but not in an overly silly way.


Prompt: Please write a short python function that uses pandas to identify the most likely primary date column in an arbitrary dataframe input and draw a histogram of those dates


Prompt: Please use Python to generate a candlestick chart in finance, with no line crossing the middle of the rectangles in the K-line.And you should only use tkinter, pandas, numpy package


Prompt: Can you please create a python script which outputs a comma separated list of file names? The input are files in a folder but only those ending in .pt need to be considered. If a file ends in .pt, like am123_33.pt  then the output should be am123_33,.


Prompt:
do you know why this baking script is broken?

import bpy
import os
import sys
import argparse

"""
Blender Headless Texture Baker

This script performs albedo and normal map baking from a high-poly model to a low-poly model using Blender's headless mode.

Usage:
    blender --background --python combined_texture_baker.py -- [arguments]

Arguments:
    --high_poly: Path to the high poly model (.obj or .glb file)
    --low_poly: Path to the low poly model (.obj or .glb file)
    --albedo_output: Output path for the albedo map (default: "albedo_map.png")
    --normal_output: Output path for the normal map (default: "normal_map.png")
    --image_size: Image size for the texture maps (default: 2048)
    --ray_distance: Ray distance for baking (default: 0.8)
    --extrusion: Extrusion amount for baking (default: 0.01)

Example:
    blender --background --python combined_texture_baker.py -- --high_poly "files/high.obj" --low_poly "files/low.obj" --albedo_output "albedo.png" --normal_output "normal.png" --image_size 4096 --ray_distance 1.0 --extrusion 0.02 --parallel

Note: Make sure to run this script with Blender's Python interpreter.
"""

def setup_scene():
    bpy.ops.object.select_all(action='SELECT')
    bpy.ops.object.delete()

    bpy.context.scene.render.engine = 'CYCLES'
    bpy.context.scene.cycles.device = 'GPU'

def import_models(high_poly_path, low_poly_path):
    # Import high-poly model
    if high_poly_path.lower().endswith('.glb'):
        bpy.ops.import_scene.gltf(filepath=high_poly_path)
    else:
        bpy.ops.wm.obj_import(filepath=high_poly_path)
    high_poly = bpy.context.selected_objects[0]
    high_poly.name = "HighPoly"

    # Import low-poly model
    if low_poly_path.lower().endswith('.glb'):
        bpy.ops.import_scene.gltf(filepath=low_poly_path)
    else:
        bpy.ops.wm.obj_import(filepath=low_poly_path)

    # Find the actual mesh object in the selection
    low_poly = next((obj for obj in bpy.context.selected_objects if obj.type == 'MESH'), None)
    if not low_poly:
        raise ValueError("No mesh object found in low poly import.")
    low_poly.name = "LowPoly"

    # Check for extra parent and remove it
    if low_poly.parent and low_poly.parent.type == 'EMPTY':
        bpy.data.objects.remove(low_poly.parent, do_unlink=True)

    if not low_poly.data.uv_layers:
        raise ValueError("No UV maps found on low poly model!")

    return high_poly, low_poly

def setup_albedo_material(low_poly, image_size):
    material = bpy.data.materials.new(name="BakeAlbedo")
    material.use_nodes = True
    low_poly.data.materials.append(material)

    nodes = material.node_tree.nodes
    texture_node = nodes.new('ShaderNodeTexImage')
    texture_node.name = 'AlbedoMap'
    texture_node.image = bpy.data.images.new(name="AlbedoBake", width=image_size, height=image_size)

    principled_node = nodes["Principled BSDF"]
    material.node_tree.links.new(texture_node.outputs['Color'], principled_node.inputs['Base Color'])

    if not low_poly.data.uv_layers:
        raise ValueError("No UV maps found on low poly model!")

def setup_normal_material(low_poly, image_size):
    material = bpy.data.materials.new(name="BakeNormal")
    material.use_nodes = True
    low_poly.data.materials.append(material)

    nodes = material.node_tree.nodes
    texture_node = nodes.new('ShaderNodeTexImage')
    texture_node.name = 'NormalMap'

    bake_image = bpy.data.images.new(name="NormalBake", width=image_size, height=image_size)
    bake_image.colorspace_settings.name = 'Non-Color'
    texture_node.image = bake_image

    if not low_poly.data.uv_layers:
        raise ValueError("No UV maps found on low poly model!")

def bake_texture(high_poly, low_poly, bake_type, ray_distance, extrusion):
    bpy.ops.object.select_all(action='DESELECT')
    high_poly.select_set(True)
    low_poly.select_set(True)
    bpy.context.view_layer.objects.active = low_poly

    bpy.context.scene.render.bake.use_selected_to_active = True
    bpy.context.scene.render.bake.margin = 16
    bpy.context.scene.render.bake.use_clear = True
    bpy.context.scene.render.bake.max_ray_distance = ray_distance
    bpy.context.scene.render.bake.cage_extrusion = extrusion

    if bake_type == 'NORMAL':
        bpy.context.scene.render.bake.normal_space = 'TANGENT'
    elif bake_type == 'DIFFUSE':
        bpy.context.scene.render.bake.use_pass_direct = False
        bpy.context.scene.render.bake.use_pass_indirect = False
        bpy.context.scene.render.bake.use_pass_color = True

    bpy.ops.object.bake(type=bake_type)

def save_texture_map(image_name, output_path):
    bake_image = bpy.data.images[image_name]
    bake_image.file_format = 'PNG'
    bake_image.save_render(output_path)

def bake_albedo(args):
    bpy.ops.wm.open_mainfile(filepath=args.blend_file)
    high_poly = bpy.data.objects["HighPoly"]
    low_poly = bpy.data.objects["LowPoly"]
    setup_albedo_material(low_poly, args.image_size)
    bake_texture(high_poly, low_poly, 'DIFFUSE', args.ray_distance, args.extrusion)
    save_texture_map('AlbedoBake', args.albedo_output)
    print(f"Albedo map saved to: {args.albedo_output}")

def bake_normal(args):
    bpy.ops.wm.open_mainfile(filepath=args.blend_file)
    high_poly = bpy.data.objects["HighPoly"]
    low_poly = bpy.data.objects["LowPoly"]
    setup_normal_material(low_poly, args.image_size)
    bake_texture(high_poly, low_poly, 'NORMAL', args.ray_distance, args.extrusion)
    save_texture_map('NormalBake', args.normal_output)
    print(f"Normal map saved to: {args.normal_output}")

def setup_export_material(low_poly, albedo_path, normal_path):
    material = bpy.data.materials.new(name="ExportMaterial")
    material.use_nodes = True
    low_poly.data.materials.clear()
    low_poly.data.materials.append(material)

    nodes = material.node_tree.nodes
    links = material.node_tree.links

    nodes.clear()

    # Create texture nodes for albedo and normal maps
    albedo_texture = nodes.new('ShaderNodeTexImage')
    albedo_texture.image = bpy.data.images.load(albedo_path)

    normal_texture = nodes.new('ShaderNodeTexImage')
    normal_texture.image = bpy.data.images.load(normal_path)
    normal_texture.image.colorspace_settings.name = 'Non-Color'

    principled_bsdf = nodes.new('ShaderNodeBsdfPrincipled')

    normal_map = nodes.new('ShaderNodeNormalMap')

    material_output = nodes.new('ShaderNodeOutputMaterial')

    links.new(albedo_texture.outputs['Color'], principled_bsdf.inputs['Base Color'])
    links.new(normal_texture.outputs['Color'], normal_map.inputs['Color'])
    links.new(normal_map.outputs['Normal'], principled_bsdf.inputs['Normal'])
    links.new(principled_bsdf.outputs['BSDF'], material_output.inputs['Surface'])

def main():
    parser = argparse.ArgumentParser(description="Blender Headless Texture Baker with GLB Export")
    parser.add_argument("--high_poly", required=True, help="Path to the high poly model (.obj or .glb)")
    parser.add_argument("--low_poly", required=True, help="Path to the low poly model (.obj)")
    parser.add_argument("--albedo_output", default="output/albedo_map.png", help="Output path for the albedo map")
    parser.add_argument("--normal_output", default="output/normal_map.png", help="Output path for the normal map")
    parser.add_argument("--glb_output", default="output.glb", help="Output path for the GLB file")
    parser.add_argument("--image_size", type=int, default=2048, help="Image size for the texture maps")
    parser.add_argument("--ray_distance", type=float, default=0.8, help="Ray distance for baking")
    parser.add_argument("--extrusion", type=float, default=0.01, help="Extrusion amount for baking")

    args = parser.parse_args(sys.argv[sys.argv.index("--") + 1:])

    # Convert relative paths to absolute paths
    args.albedo_output = os.path.abspath(args.albedo_output)
    args.normal_output = os.path.abspath(args.normal_output)
    args.glb_output = os.path.abspath(args.glb_output)

    setup_scene()
    high_poly, low_poly = import_models(args.high_poly, args.low_poly)

    # Temp file
    temp_blend = os.path.abspath("temp/temp_scene.blend")
    bpy.ops.wm.save_as_mainfile(filepath=temp_blend)
    args.blend_file = temp_blend

    bake_albedo(args)
    bake_normal(args)

    # Reload the scene to get fresh object references
    bpy.ops.wm.open_mainfile(filepath=temp_blend)
    low_poly = bpy.data.objects["LowPoly"]
    high_poly = bpy.data.objects["HighPoly"]

    setup_export_material(low_poly, args.albedo_output, args.normal_output)
    bpy.data.objects.remove(high_poly, do_unlink=True)

    # GLB Export Settings
    bpy.ops.export_scene.gltf(
        filepath=args.glb_output,
        export_format='GLB',
        use_selection=False,
        export_materials='EXPORT',
        export_texcoords=True,
        export_normals=True,
        export_draco_mesh_compression_enable=True
    )

    print(f"GLB file with baked textures saved to: {args.glb_output}")

    #os.remove(temp_blend)

    print("Texture baking and GLB export completed!")

if __name__ == "__main__":
    main()


Prompt:
Using Linux Ubuntu and c++ to operate a L298N Motor Driver Controller Board
Module(705252405800 upc) write a snippet to use two DC 5v reversible motors. Include detailed line notes for each line and do not use any looping so I can learn to code it myself.


Prompt:
I will present you two files, the first one is hi.c and the second one is newhi.c. The second one works fine but the first one gives segmentation fault. Its your goal to find what is the problem. Both files are similar but you can use the second one to see what is the big difference that makes them not to work.

// hi.c

#include <stdio.h>
#include <stdint.h>
#include <stdlib.h>

int main(void)
{
    FILE *f = fopen("hi.txt", "r");

    char *name = malloc(sizeof(char) * 101);

    int size = 0;
    while (fread(name + size, sizeof(uint8_t), 1, f))
    {
        size++;

        if (size >= 100)
        {
            break;
        }

    }
    name[size] = '\0';

    rewind(f);

    char total[size + 1];
    fread(total, sizeof(uint8_t), size, f);
    total[size] = '\0';

    for (int i = 0; i < size; i++)
    {
        printf("%c ", total[i]);
    }
    printf("\n");

    fclose(f);
    free(name);

}

// newhi.c

#include <stdio.h>
#include <stdint.h>
#include <stdlib.h>

int main(void) {
    FILE *f = fopen("hi.txt", "r");
    if (f == NULL) {
        perror("Error opening file");
        return 1;
    }

    char *name = malloc(sizeof(char) * (100 + 1));
    if (name == NULL) {
        perror("Memory allocation failed");
        fclose(f);
        return 1;
    }

    int size = 0;
    while (fread(name + size, sizeof(uint8_t), 1, f) == 1) {
        size++;
        if (size >= 100) {
            break; // Prevent buffer overflow
        }
    }
    name[size] = '\0';

    rewind(f); // Rewind to the beginning

    char total[size + 1]; // +1 for null terminator
    fread(total, sizeof(uint8_t), size, f);
    total[size] = '\0';

    for (int i = 0; i < size; i++) {
        printf("%c ", total[i]);
    }
    printf("\n");

    fclose(f);
    free(name);
    return 0;
}


Prompt:
I haved this SQL:

SELECT d1.*, COALESCE(GROUP_CONCAT(DISTINCT d2.id), '') AS children, COUNT(DISTINCT d.id) AS num_documents, IFNULL(SUM(d.size), 0) AS total_size
FROM document_categories d1
LEFT JOIN document_categories d2 ON (d2.parentId = d1.id)
LEFT JOIN documents_assignment da ON (da.categoryId=d1.id)
LEFT JOIN documents d ON (da.documentId = d.id)
GROUP BY d1.id;

My problem is that the value for total_size is incorrect, because there can be multiple documents_assignments.

Do you have an idea howto get the correct value ?


Prompt:
jeongmin@JeongMin:~/test$ python3 feature.py
Failed to find the pandas get_adjustment() function to patch
Failed to patch pandas - PandasTools will have limited functionality
Traceback (most recent call last):
  File "/home/jeongmin/test/feature.py", line 3, in <module>
    from mol2vec.features import mol2alt_sentence, MolSentence, DfVec, sentences2vec
  File "/home/jeongmin/.local/lib/python3.10/site-packages/mol2vec/features.py", line 14, in <module>
    from gensim.models import word2vec
  File "/home/jeongmin/.local/lib/python3.10/site-packages/gensim/__init__.py", line 11, in <module>
    from gensim import parsing, corpora, matutils, interfaces, models, similarities, utils  # noqa:F401
  File "/home/jeongmin/.local/lib/python3.10/site-packages/gensim/corpora/__init__.py", line 6, in <module>
    from .indexedcorpus import IndexedCorpus  # noqa:F401 must appear before the other classes
  File "/home/jeongmin/.local/lib/python3.10/site-packages/gensim/corpora/indexedcorpus.py", line 14, in <module>
    from gensim import interfaces, utils
  File "/home/jeongmin/.local/lib/python3.10/site-packages/gensim/interfaces.py", line 19, in <module>
    from gensim import utils, matutils
  File "/home/jeongmin/.local/lib/python3.10/site-packages/gensim/matutils.py", line 20, in <module>
    from scipy.linalg import get_blas_funcs, triu
ImportError: cannot import name 'triu' from 'scipy.linalg' (/home/jeongmin/.local/lib/python3.10/site-packages/scipy/linalg/__init__.py)

from rdkit import Chem
from rdkit.Chem import AllChem
from mol2vec.features import mol2alt_sentence, MolSentence, DfVec, sentences2vec
from mol2vec.helpers import save_pickle
import pandas as pd
import numpy as np
import pickle
import scipy  # scipy 전체를 import

# SMILES 파일 읽기
smiles_file = 'smiles.txt'
with open(smiles_file, 'r') as f:
    smiles_list = [line.strip() for line in f]

# 분자 객체 생성
molecules = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]

# Mol2Vec를 사용하여 분자를 문장으로 변환
sentences = [mol2alt_sentence(mol, 1) for mol in molecules]

# 사전 훈련된 모델 로드 (사전 훈련된 모델이 필요)
# 사전 훈련된 모델은 https://github.com/samoturk/mol2vec에 있습니다.
from gensim.models import word2vec
model = word2vec.Word2Vec.load('model_300dim.pkl')

# 문장을 벡터로 변환
mol2vec_vectors = [DfVec(x) for x in sentences2vec(sentences, model, unseen='UNK')]

# 벡터를 numpy 배열로 변환
features = np.array([vec.vec for vec in mol2vec_vectors])

# 결과 확인
print(features.shape)

# pickle 파일로 저장
output_file = 'features.pkl'
with open(output_file, 'wb') as f:
    pickle.dump({'data': features}, f)

해결 방법을 한국어로 알려줘


Prompt:
Based off this example:

`const position = world.getPosition(bot);\nconst startX = position.x;\nconst startY = position.y;\nconst startZ = position.z;\nconst width = 7;\nconst depth = 7;\nconst height = 4;\n\n// Build the walls\nfor (let x = startX; x < startX + width; x++) {\n    for (let y = startY; y < startY + height; y++) {\n        for (let z = startZ; z < startZ + depth; z++) {\n            if (x === startX || x === startX + width - 1 || y === startY || y === startY + height - 1 || z === startZ || z === startZ + depth - 1) {\n                await skills.placeBlock(bot, 'oak_planks', x, y, z);  \n            }\n       }\n    }\n}\n`

Write a js code block to build a pyramid in minecraft using sandstone, the pyramid should be large and centered just like a real pyramid.


Prompt: Create a web server in Go that handles GET and POST requests. Use scratchpad think to design the server structure and API endpoints.


Prompt:
Create a Windows Forms application Using C#, BinaryWriter, and BinaryReader.

Requirements:

Task:

1. Create a Windows Forms application with a simple user interface to write and read binary data.

2. Create a form with controls to input student ID, name, and GPA.

3. Add buttons to write the input data to a binary file and read data from the binary file.

4. Display the read data in a ListBox or TextBox.

Steps:

1. Create a class Student with properties for student ID, name, and GPA.

2. Design the form with appropriate controls:

TextBox controls for student ID, name, and GPA input.
Buttons for "Write Data" and "Read Data".
A ListBox or TextBox to display the read data.

3. Write methods to handle the button click events for writing to and reading from the binary file.

4. Ensure that your methods handle exceptions and close the file streams properly.

Additional Guidelines:
Ensure the application has a clean and user-friendly interface.
Validate user inputs to ensure all fields are filled correctly before adding an expense.
Use comments in the code to explain the functionality of different application parts.

Use the included classes from namespace CustomerMaintenance as a template, and create the student data application in the same style.

namespace CustomerMaintenance
{
    public static class CustomerDB
    {
        private const string Dir = @"C:\C#\files\";
        private const string Path = Dir + "Customers.dat";

        public static void SaveCustomers(List<Customer> customers)
        {
            using BinaryWriter binaryOut = new(
                new FileStream(Path, FileMode.Create, FileAccess.Write));

            foreach (Customer customer in customers)
            {
                // Binary out
                binaryOut.Write(customer.FirstName);
                binaryOut.Write(customer.LastName);
                binaryOut.Write(customer.Email);
            }
        }

        public static List<Customer> GetCustomers()
        {
            List<Customer> customers = new();

            if (!Directory.Exists(Dir))
                Directory.CreateDirectory(Dir);

            using BinaryReader binaryIn = new(
                new FileStream(Path, FileMode.OpenOrCreate, FileAccess.Read));

            while (binaryIn.PeekChar() != -1)
            {
                Customer customer = new()
                {
                   // Binary In
                   FirstName = binaryIn.ReadString(),
                   LastName = binaryIn.ReadString(),
                   Email = binaryIn.ReadString()
                };
                customers.Add(customer);
            }

            return customers;
        }
    }
}

namespace CustomerMaintenance
{
    public static class Validator
    {
        public static string LineEnd { get; set; } = "\n";

        public static string IsPresent(string value, string name)
        {
            string msg = "";
            if (value == "")
            {
                msg = $"{name} is a required field.{LineEnd}";
            }
            return msg;
        }

        public static string IsDecimal(string value, string name)
        {
            string msg = "";
            if (!Decimal.TryParse(value, out _))
            {
                msg = $"{name} must be a valid decimal value.{LineEnd}";
            }
            return msg;
        }

        public static string IsInt32(string value, string name)
        {
            string msg = "";
            if (!Int32.TryParse(value, out _))
            {
                msg = $"{name} must be a valid integer value.{LineEnd}";
            }
            return msg;
        }

        public static string IsWithinRange(string value, string name, decimal min,
            decimal max)
        {
            string msg = "";
            if (Decimal.TryParse(value, out decimal number))
            {
                if (number < min || number > max)
                {
                    msg = $"{name} must be between {min} and {max}.{LineEnd}";
                }
            }
            return msg;
        }

        public static string IsValidEmail(string value, string name)
        {
            string msg = "";
            if (!value.Contains('@') || !value.Contains('.'))
            {
                msg = $"{name} must be a valid email address.{LineEnd}";
            }
            return msg;
        }
    }
}


Prompt:
Your task is to optimize the following rust code in regards to simplicity and performance:

    let (times, cpu_temperature, cpu_usage, memory_usage): (Vec<_>, Vec<_>, Vec<_>, Vec<_>) =
        readings
            .iter()
            .filter_map(|reading| {
                let timestamp = reading.timestamp.as_ref()?;
                let conditions = reading.condition.as_ref()?;
                Some((
                    TimeHelper::to_offset_date_time(timestamp),
                    conditions
                ))
            })
            .fold(
                (Vec::new(), Vec::new(), Vec::new(), Vec::new()),
                |mut acc, (time, conditions)| {
                    acc.0.push(time);
                    acc.1.push(conditions.cpu_temperature);
                    acc.2.push(conditions.cpu_usage);
                    acc.3.push(conditions.memory_usage);
                    acc
                },
            );


Prompt:
Create a dataset in json for training a model into reasoning, with fields: "input_data", "output_data", "hypostesys".

Intructions:
The input must be a matrix with size 5x5 to 32x32, with numbers from 0 to 9, representing color.
The output is a matrix with size 1x1 to 32x32, with numbers from 0 to 9, representing color. This output is product of the input by diferent logics.
The hyposthesis is a reasoning way to go from input to ouput.
To create the output, use a very creative mapping, like translation, filling, rotating, croping, repeating, erasing, and many other templates, with very dificult and unsual logic.