Prompt:
# language: Python
"""
Telematrix

App servi
"""
import asyncio
import html
import json
import logging
import mimetypes
from datetime import datetime
from time import time
from urllib.parse import unquote, quote, urlparse, parse_qs
from io import BytesIO
import re

from PIL import Image
from aiohttp import web, ClientSession
from aiotg import Bot
from bs4 import BeautifulSoup

import telematrix.database as db

try:
    with open('config.json', 'r') as config_file:
        CONFIG = json.load(config_file)

        HS_TOKEN = CONFIG['tokens']['hs']
        AS_TOKEN = CONFIG['tokens']['as']
        TG_TOKEN = CONFIG['tokens']['telegram']

        try:
            GOOGLE_TOKEN = CONFIG['tokens']['google']
        except KeyError:
            GOOGLE_TOKEN = None

        MATRIX_HOST = CONFIG['hosts']['internal']
        MATRIX_HOST_EXT = CONFIG['hosts']['external']
        MATRIX_HOST_BARE = CONFIG['hosts']['bare']

        MATRIX_PREFIX = MATRIX_HOST + '_matrix/client/r0/'
        MATRIX_MEDIA_PREFIX = MATRIX_HOST + '_matrix/media/r0/'

        USER_ID_FORMAT = CONFIG['user_id_format']
        DATABASE_URL = CONFIG['db_url']

        AS_PORT = CONFIG['as_port'] if 'as_port' in CONFIG else 5000
except (OSError, IOError) as exception:
    print('Error opening config file:')
    print(exception)
    exit(1)

GOO_GL_URL = 'https://www.googleapis.com/urlshortener/v1/url'

TG_BOT = Bot(api_token=TG_TOKEN)
MATRIX_SESS = ClientSession()
SHORTEN_SESS = ClientSession()

def create_response(code, obj):
    """
    Create an HTTP response with a JSON body.
    :param code: The status code of the response.
    :param obj: The object to serialize and include in the response.
    :return: A web.Response.
    """
    return web.Response(text=json.dumps(obj), status=code,
                        content_type='application/json', charset='utf-8')

VALID_TAGS = ['b','strong', 'i', 'em', 'a', 'pre']

def sanitize_html(string):
    """
    Sanitize an HTML string for the Telegram bot API.
    :param string: The HTML string to sanitized.
    :return: The sanitized HTML string.
    """
    string = string.replace('
', '\n').replace('
', '\n') \
                  .replace('
', '\n')
    soup = BeautifulSoup(string, 'html.parser')
    for tag in soup.find_all(True):
        if tag.name == 'blockquote':
            tag.string = ('\n' + tag.text).replace('\n', '\n> ')[3:-3]
        if tag.name not in VALID_TAGS:
            tag.hidden = True
    return soup.renderContents().decode('utf-8')

def format_matrix_msg(form, content):
    """
    Formats a matrix message for sending to Telegram
    :param form: The format string of the message, where the first parameter
                 is the username and the second one the message.
    :param content: The content to be sent.
    :return: The formatted string.
    """
    if 'format' in content and content['format'] == 'org.matrix.custom.html':
        sanitized = sanitize_html(sanitized)
        return html.escape(form).format(sanitized), 'HTML'
    else:
        return form.format(html.escape(content['body'])), None

async def download_matrix_file(url, filename):
    """
    Download a file from an MXC URL to /tmp/{filename}
    :param url: The MXC URL to download from.
    :param filename: The filename in /tmp/ to download into.
    """
    m_url = MATRIX_MEDIA_PREFIX + 'download/{}{}'.format(url.netloc, url.path)
    async with MATRIX_SESS.get(m_url) as response:
        data = await response.read()
    with open('/tmp/{}'.format(filename), 'wb') as file:
        file.write(data)

async def shorten_url(url):
    """
    Shorten an URL using goo.gl. Returns the original URL if it fails.
    :param url: The URL to shorten.
    :return: The shortened URL.
    """
    if not GOOGLE_TOKEN:
        return url

    headers = {'Content-Type': 'application/json'}
    async with SHORTEN_SESS.post(GOO_GL_URL, params={'key': GOOGLE_TOKEN},
                                 data=json.dumps({'longUrl': url}),
                                 headers=headers) \
            as response:
        obj = await response.json()

    if 'id' in obj:
        return obj['id']
    else:
        return url

def matrix_is_telegram(user_id):
    username = user_id.split(':')[0][1:]
    return username.startswith('telegram_')

def get_username(user_id):
    return user_id.split(':')[0][1:]

mime_extensions = {
    'image/jpeg': 'jpg',
    'image/gif': 'gif',
    'image/png': 'png',
    'image/tiff': 'tif',
    'image/x-tiff': 'tif',
    'image/bmp': 'bmp',
    'image/x-windows-bmp': 'bmp'
}

async def matrix_transaction(request):
    """
    Handle a transaction sent by the homeserver.
    :param request: The request containing the transaction.
    :return: The response to send.
    """
    body = await request.json()
    events = body['events']
    for event in events:
        if 'age' in event and event['age'] > 600000:
            print('discarded event of age', event['age'])
            continue
        try:
            print('{}: <{}> {}'.format(event['room_id'], event['user_id'], event['type']))
        except KeyError:
            pass

        if event['type'] =='m.room.aliases' and event['state_key'] == MATRIX_HOST_BARE:
            aliases = event['content']['aliases']

            links = db.session.query(db.ChatLink)\
                     .filter_by(matrix_room=event['room_id']).all()
            for link in links:
                db.session.delete(link)

            for alias in aliases:
                print(alias)
                if alias.split('_')[0]!= '#telegram' \
                        or alias.split(':')[-1]!= MATRIX_HOST_BARE:
                    continue

                tg_id = alias.split('_')[1].split(':')[0]
                link = db.ChatLink(event['room_id'], tg_id, True)
                db.session.add(link)
                db.session.commit()

            continue

        link = db.session.query(db.ChatLink)\
                .filter_by(matrix_room=event['room_id']).first()
        if not link:
            print('{} isn\'t linked!'.format(event['room_id']))
            continue
        group = TG_BOT.group(link.tg_room)

        try:
            response = None

            if event['type'] =='m.room.message':
                user_id = event['user_id']
                if matrix_is_telegram(user_id):
                    continue

                sender = db.session.query(db.MatrixUser)\
                          .filter_by(matrix_id=user_id).first()

                if not sender:
                    response = await matrix_get('client', 'profile/{}/displayname'
                                                         .format(user_id), None)
                    try:
                        displayname = response['displayname']
                    except KeyError:
                        displayname = get_username(user_id)
                    sender = db.MatrixUser(user_id, displayname)
                    db.session.add(sender)
                else:
                    displayname = sender.name or get_username(user_id)
                content = event['content']

                if'msgtype' not in content:
                    continue

                if content['msgtype'] =='m.text':
                    msg, mode = format_matrix_msg('{}', content)
                    response = await group.send_text("{}: {}".format(displayname, msg), parse_mode='HTML')
                elif content['msgtype'] =='m.notice':
                    msg, mode = format_matrix_msg('{}', content)
                    response = await group.send_text("[{}] {}".format(displayname, msg), parse_mode=mode)
                elif content['msgtype'] =='m.emote':
                    msg, mode = format_matrix_msg('{}', content)
                    response = await group.send_text("* {} {}".format(displayname, msg), parse_mode=mode)
                elif content['msgtype'] =='m.image':
                    try:
                        url = urlparse(content['url'])

                        ext = mime_extensions[content['info']['mimetype']]
                        if not content['body'].endswith(ext):
                            content['body'] += '.' + ext

                        # Download the file
                        await download_matrix_file(url, content['body'])
                        with open('/tmp/{}'.format(content['body']), 'rb') as img_file:
                            url_str = MATRIX_HOST_EXT + \
                                      '_matrix/media/r0/download/{}{}' \
                                     .format(url.netloc, quote(url.path))
                            url_str = await shorten_url(url_str)

                            caption = '{} sent an image'.format(displayname)
                            response = await group.send_photo(img_file, caption=caption)
                    except:
                        pass
                else:
                    print('Unsupported message type {}'.format(content['msgtype']))
                    print(json.dumps(content, indent=4))

            elif event['type'] =='m.room.member':
                if matrix_is_telegram(event['state_key']):
                    continue

                user_id = event['state_key']
                content = event['content']

                sender = db.session.query(db.MatrixUser)\
                          .filter_by(matrix_id=user_id).first()
                if sender:
                    displayname = sender.name
                else:
                    displayname = get_username(user_id)

                if content['membership'] == 'join':
                    oldname = sender.name if sender else get_username(user_id)
                    try:
                        displayname = content['displayname'] or get_username(user_id)
                    except KeyError:
                        displayname = get_username(user_id)

                    if not sender:
                        sender = db.MatrixUser(user_id, displayname)
                    else:
                        sender.name = displayname
                    db.session.add(sender)

                    msg = None
                    if 'unsigned' in event and 'prev_content' in event['unsigned']:
                        prev = event['unsigned']['prev_content']
                        if prev['membership'] == 'join':
                            if 'displayname' in prev and prev['displayname']:
                                oldname = prev['displayname']

                            msg = '> {} changed their display name to {}'\
                                 .format(oldname, displayname)
                    else:
                        msg = '> {} has joined the room'.format(displayname)

                    if msg:
                        response = await group.send_text(msg)
                elif content['membership'] == 'leave':
                    msg = '< {} has left the room'.format(displayname)
                    response = await group.send_text(msg)
                elif content['membership'] == 'ban':
                    msg = '<! {} was banned from the room'.format(displayname)
                    response = await group.send_text(msg)

            if response:
                message = db.Message(
                    response['result']['chat']['id'],
                    response['result']['message_id'],
                    event['room_id'],
                    event['event_id'],
                    displayname)
                db.session.add(message)

        except RuntimeError as e:
            print('Got a runtime error:', e)
            print('Group:', group)

    db.session.commit()
    return create_response(200, {})

async def _matrix_request(method_fun, category, path, user_id, data=None,
                          content_type=None):
    # pylint: disable=too-many-arguments
    if content_type is None:
        content_type = 'application/octet-stream'
    if data is not None:
        if isinstance(data, dict):
            data = json.dumps(data)
            content_type = 'application/json; charset=utf-8'

    params = {'access_token': AS_TOKEN}
    if user_id is not None:
        params['user_id'] = user_id

    async with method_fun('{}_matrix/{}/r0/{}'
                         .format(MATRIX_HOST, quote(category), quote(path)),
                          params=params, data=data,
                          headers={'Content-Type': content_type}) as response:
        if response.headers['Content-Type'].split(';')[0] \
                == 'application/json':
            return await response.json()
        else:
            return await response.read()

def matrix_post(category, path, user_id, data, content_type=None):
    return _matrix_request(MATRIX_SESS.post, category, path, user_id, data,
                           content_type)

def matrix_put(category, path, user_id, data, content_type=None):
    return _matrix_request(MATRIX_SESS.put, category, path, user_id, data,
                           content_type)

def matrix_get(category, path, user_id):
    return _matrix_request(MATRIX_SESS.get, category, path, user_id)

def matrix_delete(category, path, user_id):
    return _matrix_request(MATRIX_SESS.delete, category, path, user_id)

async def matrix_room(request):
    room_alias = request.match_info['room_alias']
    args = parse_qs(urlparse(request.path_qs).query)
    print('Checking for {} | {}'.format(unquote(room_alias),
                                        args['access_token'][0]))

    try:
        if args['access_token'][0]!= HS_TOKEN:
            return create_response(403, {'errcode': 'M_FORBIDDEN'})
    except KeyError:
        return create_response(401,
                               {'errcode':
                                'NL.SIJMENSCHOON.TELEMATRIX_UNAUTHORIZED'})

    localpart = room_alias.split(':')[0]
    chat = '_'.join(localpart.split('_')[1:])

    link = db.session.query(db.ChatLink).filter_by(tg_room=chat).first()
    if link:
        await matrix_post('client', 'createRoom', None,
                          {'room_alias_name': localpart[1:]})
        return create_response(200, {})
    else:
        return create_response(404, {'errcode':
                                     'NL.SIJMENSCHOON.TELEMATRIX_NOT_FOUND'})

def send_matrix_message(room_id, user_id, txn_id, **kwargs):
    url = 'rooms/{}/send/m.room.message/{}'.format(room_id, txn_id)
    return matrix_put('client', url, user_id, kwargs)

async def upload_tgfile_to_matrix(file_id, user_id, mime='image/jpeg', convert_to=None):
    file_path = (await TG_BOT.get_file(file_id))['file_path']
    request = await TG_BOT.download_file(file_path)
    data = await request.read()

    if convert_to:
        image = Image.open(BytesIO(data))
        png_image = BytesIO(None)
        image.save(png_image, convert_to)

        j = await matrix_post('media', 'upload', user_id, png_image.getvalue(), mime)
        length = len(png_image.getvalue())
    else:
        j = await matrix_post('media', 'upload', user_id, data, mime)
        length = len(data)

    if 'content_uri' in j:
        return j['content_uri'], length
    else:
        return None, 0

async def register_join_matrix(chat, room_id, user_id):
    name = chat.sender['first_name']
    if 'last_name' in chat.sender:
        name +='' + chat.sender['last_name']
    name +='(Telegram)'
    user = user_id.split(':')[0][1:]

    await matrix_post('client','register', None,
                      {'type':'m.login.application_service', 'user': user})
    profile_photos = await TG_BOT.get_user_profile_photos(chat.sender['id'])
    try:
        pp_file_id = profile_photos['result']['photos'][0][-1]['file_id']
        pp_uri, _ = await upload_tgfile_to_matrix(pp_file_id, user_id)
        if pp_uri:
            await matrix_put('client', 'profile/{}/avatar_url'.format(user_id),
                             user_id, {'avatar_url': pp_uri})
    except IndexError:
        pass

    await matrix_put('client', 'profile/{}/displayname'.format(user_id),
                     user_id, {'displayname': name})
    j = await matrix_post('client', 'join/{}'.format(room_id), user_id, {})
    if 'errcode' in j and j['errcode'] == 'M_FORBIDDEN':
        print("Error with <{}> joining room <{}>. This is likely because guests are not allowed to join the room."
             .format(user_id, room_id))

async def update_matrix_displayname_avatar(tg_user):
    name = tg_user['first_name']
    if 'last_name' in tg_user:
        name +='' + tg_user['last_name']
    name +='(Telegram)'
    user_id = USER_ID_FORMAT.format(tg_user['id'])

    db_user = db.session.query(db.TgUser).filter_by(tg_id=tg_user['id']).first()

    profile_photos = await TG_BOT.get_user_profile_photos(tg_user['id'])
    pp_file_id = None
    try:
        pp_file_id = profile_photos['result']['photos'][0][-1]['file_id']
    except:
        pp_file_id = None

    if db_user:
        if db_user.name!= name:
            await matrix_put('client', 'profile/{}/displayname'.format(user_id), user_id, {'displayname': name})
            db_user.name = name
        if db_user.profile_pic_id!= pp_file_id:
            if pp_file_id:
                pp_uri, _ = await upload_tgfile_to_matrix(pp_file_id, user_id)
                await matrix_put('client', 'profile/{}/avatar_url'.format(user_id), user_id, {'avatar_url':pp_uri})
            else:
                await matrix_put('client', 'profile/{}/avatar_url'.format(user_id), user_id, {'avatar_url':None})
            db_user.profile_pic_id = pp_file_id
    else:
        db_user = db.TgUser(tg_user['id'], name, pp_file_id)
        await matrix_put('client', 'profile/{}/displayname'.format(user_id), user_id, {'displayname': name})
        if pp_file_id:
            pp_uri, _ = await upload_tgfile_to_matrix(pp_file_id, user_id)
            await matrix_put('client', 'profile/{}/avatar_url'.format(user_id), user_id, {'avatar_url':pp_uri})
        else:
            await matrix_put('client', 'profile/{}/avatar_url'.format(user_id), user_id, {'avatar_url':None})
        db.session.add(db_user)
    db.session.commit()

@TG_BOT.handle('sticker')
async def aiotg_sticker(chat, sticker):
    link = db.session.query(db.ChatLink).filter_by(tg_room=chat.id).first()
    if not link:
        print('Unknown telegram chat {}: {}'.format(chat, chat.id))
        return

    await update_matrix_displayname_avatar(chat.sender);

    room_id = link.matrix_room
    user_id = USER_ID_FORMAT.format(chat.sender['id'])
    txn_id = quote('{}{}'.format(chat.message['message_id'], chat.id))

    file_id = sticker['file_id']
    uri, length = await upload_tgfile_to_matrix(file_id, user_id, 'image/png', 'PNG')

    info = {'mimetype': 'image/png','size': length, 'h': sticker['height'],
            'w': sticker['width']}
    body = 'Sticker_{}.png'.format(int(time() * 1000))

    if uri:
        j = await send_matrix_message(room_id, user_id, txn_id, body=body,
                                      url=uri, info=info, msgtype='m.image')

        if 'errcode' in j and j['errcode'] == 'M_FORBIDDEN':
            await register_join_matrix(chat, room_id, user_id)
            await send_matrix_message(room_id, user_id, txn_id + 'join',
                                      body=body, url=uri, info=info,
                                      msgtype='m.image')

        if 'caption' in chat.message:
            await send_matrix_message(room_id, user_id, txn_id + 'caption',
                                      body=chat.message['caption'],
                                      msgtype='m.text')
        if 'event_id' in j:
            name = chat.sender['first_name']
            if 'last_name' in chat.sender:
                name += " " + chat.sender['last_name']
            name += " (Telegram)"
            message = db.Message(
                    chat.message['chat']['id'],
                    chat.message['message_id'],
                    room_id,
                    j['event_id'],
                    name)
            db.session.add(message)
            db.session.commit()

@TG_BOT.handle('photo')
async def aiotg_photo(chat, photo):
    link = db.session.query(db.ChatLink).filter_by(tg_room=chat.id).first()
    if not link:
        print('Unknown telegram chat {}: {}'.format(chat, chat.id))
        return

    await update_matrix_displayname_avatar(chat.sender);
    room_id = link.matrix_room
    user_id = USER_ID_FORMAT.format(chat.sender['id'])
    txn_id = quote('{}{}'.format(chat.message['message_id'], chat.id))

    file_id = photo[-1]['file_id']
    uri, length = await upload_tgfile_to_matrix(file_id, user_id)
    info = {'mimetype': 'image/jpeg','size': length, 'h': photo[-1]['height'],
            'w': photo[-1]['width']}
    body = 'Image_{}.jpg'.format(int(time() * 1000))

    if uri:
        j = await send_matrix_message(room_id, user_id, txn_id, body=body,
                                      url=uri, info=info, msgtype='m.image')

        if 'errcode' in j and j['errcode'] == 'M_FORBIDDEN':
            await register_join_matrix(chat, room_id, user_id)
            await send_matrix_message(room_id, user_id, txn_id + 'join',
                                      body=body, url=uri, info=info,
                                      msgtype='m.image')

        if 'caption' in chat.message:
            await send_matrix_message(room_id, user_id, txn_id + 'caption',
                                      body=chat.message['caption'],
                                      msgtype='m.text')

        if 'event_id' in j:
            name = chat.sender['first_name']
            if 'last_name' in chat.sender:
                name += " " + chat.sender['last_name']
            name += " (Telegram)"
            message = db.Message(
                    chat.message['chat']['id'],
                    chat.message['message_id'],
                    room_id,
                    j['event_id'],
                    name)
            db.session.add(message)
            db.session.commit()

@TG_BOT.command(r'/alias')
async def aiotg_alias(chat, match):
                    .format(chat.id, MATRIX_HOST_BARE))

@TG_BOT.command(r'(?s)(.*)')
async def aiotg_message(chat, match):
    link = db.session.query(db.ChatLink).filter_by(tg_room=chat.id).first()
    if link:
        room_id = link.matrix_room
    else:
        print('Unknown telegram chat {}: {}'.format(chat, chat.id))
        return

    await update_matrix_displayname_avatar(chat.sender);
    user_id = USER_ID_FORMAT.format(chat.sender['id'])
    txn_id = quote('{}:{}'.format(chat.message['message_id'], chat.id))

    message = match.group(0)

    if 'forward_from' in chat.message:
        fw_from = chat.message['forward_from']
        if 'last_name' in fw_from:
            msg_from = '{} {} (Telegram)'.format(fw_from['first_name'],
                                                 fw_from['last_name'])
        else:
            msg_from = '{} (Telegram)'.format(fw_from['first_name'])

        quoted_msg = '\n'.join(['>{}'.format(x) for x in message.split('\n')])
        quoted_msg = 'Forwarded from {}:\n{}' \
                    .format(msg_from, quoted_msg)

        quoted_html = '
{}
' \
                     .format(html.escape(message).replace('\n', '
'))
        quoted_html = 'Forwarded from {}:\n{}' \
                     .format(html.escape(msg_from), quoted_html)
        j = await send_matrix_message(room_id, user_id, txn_id,
                                      body=quoted_msg,
                                      formatted_body=quoted_html,
                                      format='org.matrix.custom.html',
                                      msgtype='m.text')

    elif'reply_to_message' in chat.message:
        re_msg = chat.message['reply_to_message']
        if not 'text' in re_msg and not 'photo' in re_msg and not'sticker' in re_msg:
            return
        if 'last_name' in re_msg['from']:
            msg_from = '{} {} (Telegram)'.format(re_msg['from']['first_name'],
                                                 re_msg['from']['last_name'])
        else:
            msg_from = '{} (Telegram)'.format(re_msg['from']['first_name'])
        date = datetime.fromtimestamp(re_msg['date']) \
              .strftime('%Y-%m-%d %H:%M:%S')

        reply_mx_id = db.session.query(db.Message)\
               .filter_by(tg_group_id=chat.message['chat']['id'], tg_message_id=chat.message['reply_to_message']['message_id']).first()

        html_message = html.escape(message).replace('\n', '
')
        if 'text' in re_msg:
            quoted_msg = '\n'.join(['>{}'.format(x)
                                    for x in re_msg['text'].split('\n')])
            quoted_html = '
{}
' \
                         .format(html.escape(re_msg['text'])
                                 .replace('\n', '
'))
        else:
            quoted_msg = ''
            quoted_html = ''

        if reply_mx_id:
            quoted_msg = 'Reply to {}:\n{}\n\n{}' \
                        .format(reply_mx_id.displayname, quoted_msg, message)
            quoted_html = 'Reply to {}:
{}
{}
' \
                         .format(html.escape(room_id), html.escape(reply_mx_id.matrix_event_id), html.escape(reply_mx_id.displayname),
                                  quoted_html, html_message)
        else:
            quoted_msg = 'Reply to {}:\n{}\n\n{}' \
                        .format(msg_from, quoted_msg, message)
            quoted_html = 'Reply to {}:
{}
{}
' \
                         .format(html.escape(msg_from),
                                  quoted_html, html_message)

        j = await send_matrix_message(room_id, user_id, txn_id,
                                      body=quoted_msg,
                                      formatted_body=quoted_html,
                                      format='org.matrix.custom.html',
                                      msgtype='m.text')
    else:
        j = await send_matrix_message(room_id, user_id, txn_id, body=message,
                                      msgtype='m.text')

    if 'errcode' in j and j['errcode'] == 'M_FORBIDDEN':
        await register_join_matrix(chat, room_id, user_id)
        await asyncio.sleep(0.5)
        j = await send_matrix_message(room_id, user_id, txn_id + 'join',
                                      body=message, msgtype='m.text')
    elif 'event_id' in j:
        name = chat.sender['first_name']
        if 'last_name' in chat.sender:
            name += " " + chat.sender['last_name']
        name += " (Telegram)"
        message = db.Message(
                chat.message['chat']['id'],
                chat.message['message_id'],
                room_id,
                j['event_id'],
                name)
        db.session.add(message)
        db.session.commit()

def main():
    """
    Main function to get the entire ball rolling.
    """
    logging.basicConfig(level=logging.WARNING)
    db.initialize(DATABASE_URL)

    loop = asyncio.get_event_loop()
    asyncio.ensure_future(TG_BOT.loop())

    app = web.Application(loop=loop)
    app.router.add_route('GET', '/rooms/{room_alias}', matrix_room)
    app.router.add_route('PUT', '/transactions/{transaction}',
                         matrix_transaction)
    web.run_app(app, port=AS_PORT)

if __name__ == "__main__":
    main()

What information does the Message database model contain about a matched message from Telegram and Matrix?


Prompt:
Divide the following table into several parts according to the logical structure of sub-tables.
Contact:  Contact:NAME_1
Customer Reference:  Sales Office Reference:  Lead Time:
Item:183  Rev:  Qty:1
Tags:020-PV-3002  Date Last Modified:16/10/2014  LSCN 4579
Description:NPS  4  HPAT  657  Size  45
Service Description:M/U H2 TO 020E303
1	Fluid: H2 SPILLBACK										Crit. Pres. PC:
	SERVICE CONDITIONS			Units:		Maximum					Normal			Minimum
2	Volumetric Flow Rate Gas (Qg)			Nm3/h		121950.000					20157.000			7364.000
3	Inlet Pressure (P1)			MPa(g)		20.380					20.390			19.550
4	Pressure Change (dP)			MPa		17.960					18.030			17.170
5	Inlet Temperature (T1)			deg C		131.000					131.000			131.000
6	M  /  Gg			M		2.400					2.400			2.400
7	Specific heats ratio (gamma)					1.402					1.402			1.402
8	Dynamic Viscosity (Mu)			cP		0.012					0.012			0.012
9	Sizing Coefficient (Cv)					18.257					3.015			1.149
10				% Open		85					30			10
11	(Allowed  /  Calculated)			dB(A)		85/81					85/69			85/63
12
|	PIPE  LINE  |							53	ACTUATOR Type:  Spring & Diaphragm
13	"Size,  Schedule  In:  8  in,  SCH  140  <2>"							54	Mfg/Model:  Fisher/657
14	"Size,  Schedule  Out:  8  in,  SCH  STD  <2>"							55	Size:  45  Eff Area:  105 in2
15	Insulation:  |							56	On/Off:  -  Modulating:  Throttling
16	VALVE BODY/BONNET  Type:  Angle							57	"Spring Action:  Up,Open"
17	Size:  4  Inch  ANSI  CL1500							58	NAME_2 Allow Press:  0.51 MPag
18	NAME_2 Press/Temp:  21.96 MPa(g)/10/190 deg C							59	Min Req'd Press:  0.21 Mpag
19	Mfg/Model:  Fisher/HPAT							60	Available Air Supply Pressure
20	Body/Bonnet Matl:  A216-WCC HT							61	NAME_2:  1.03 Mpag  Min:  0.45 Mpag
21	Liner Matl/ID:  None							62	Bench Range:  0.05-0.17 MPag
22	End  Connection  In:  4  in  CL1500  RTJ  Flg							63	Act Orientation:  Vert. Up
23	End  Connection  Out:  4  in  CL1500  RTJ  Flg							64	Handwheel Type:  None
24	Flg Face Finish:  63-125 AARH							65	Air Failure Valve:  Open  Set at:  N/A
25	End  Ext/Matl:  |							66	|
26	Flow Direction:  Up							67	Input Signal:  4-20 mA dc
27	BONNET Type:  NAME_3 (EXT)							68	POSITIONER Type:  Single Acting
28	Lub-Iso Valve:  No  Lube:  No							69	"Mfg/Model: Fisher/DVC6200,HC"
29	Packing


Prompt:
I will give you a text, remove repeated segments:

copy No. 4 Date: Date: Date: Date: Date: ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon: 	  Histopathological	  examinaFon ExaminaFon	  No.:	  	  ExaminaFon	  No.:	  	  ExaminaFon	  No.:	  	   PaKent :	  XXX PaKent :	  XXX PESEL:	   XXX PESEL:	   XXX PESEL:	   XXX PESEL:	   XXX Age:	   Gender:	  	  Gender:	  	  Gender:	  	  Gender:	  	  Gender:	  	  Gender:	  	  Gender:	  	   Material:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ	  resecFon	  –	  segment	  of	  the	  large	  intesFneMaterial:	  	  	  	  	  	  	  MulFple	  organ


Prompt:
Nome do Log:   Application
Fonte:         Application Error
Data:          02/06/2023 10:21:39
Identificação do Evento:1000
Categoria da Tarefa:Eventos de falha do aplicativo
Nível:         Erro
Palavras-chave:
Usuário:       SEFAAD\07212309974
Computador:    00-499422.sefaad.local
Descrição:
Nome do aplicativo com falha: SearchHost.exe, versão: 623.11002.0.0, carimbo de data/hora: 0x6441da30
Nome do módulo com falha: twinapi.appcore.dll, versão: 10.0.22621.1778, carimbo de data/hora: 0x8a05c015
Código de exceção: 0xc000027b
Deslocamento da falha: 0x00000000000c07f3
ID do processo com falha: 0x0x2ECC
Hora de início do aplicativo com falha: 0x0x1D99550EC88D7AA
Caminho do aplicativo com falha: C:\Windows\SystemApps\MicrosoftWindows.Client.CBS_cw5n1h2txyewy\SearchHost.exe
Caminho do módulo com falha: C:\Windows\System32\twinapi.appcore.dll
ID do Relatório: 1c582ca8-5bd6-4ac4-8047-08cf430ccc29
Nome completo do pacote com falha: MicrosoftWindows.Client.CBS_1000.22642.1000.0_x64__cw5n1h2txyewy
ID do aplicativo relativo ao pacote com falha: CortanaUI
XML de Evento:
<Event xmlns="http://schemas.microsoft.com/win/2004/08/events/event">
  <System>
    <Provider Name="Application Error" Guid="{a0e9b465-b939-57d7-b27d-95d8e925ff57}" />
    <EventID>1000</EventID>
    <Version>0</Version>
    <Level>2</Level>
    <Task>100</Task>
    <Opcode>0</Opcode>
    <Keywords>0x8000000000000000</Keywords>
    <TimeCreated SystemTime="2023-06-02T13:21:39.4177102Z" />
    <EventRecordID>8988</EventRecordID>
    <Correlation />
    <Execution ProcessID="5540" ThreadID="11284" />
    <Channel>Application</Channel>
    <Computer>00-499422.sefaad.local</Computer>
    <Security UserID="S-1-5-21-3607280404-62230264-1032112407-123806" />
  </System>
  <EventData>
    <Data Name="AppName">SearchHost.exe</Data>
    <Data Name="AppVersion">623.11002.0.0</Data>
    <Data Name="AppTimeStamp">6441da30</Data>
    <Data Name="ModuleName">twinapi.appcore.dll</Data>
    <Data Name="ModuleVersion">10.0.22621.1778</Data>
    <Data Name="ModuleTimeStamp">8a05c015</Data>
    <Data Name="ExceptionCode">c000027b</Data>
    <Data Name="FaultingOffset">00000000000c07f3</Data>
    <Data Name="ProcessId">0x2ecc</Data>
    <Data Name="ProcessCreationTime">0x1d99550ec88d7aa</Data>
    <Data Name="AppPath">C:\Windows\SystemApps\MicrosoftWindows.Client.CBS_cw5n1h2txyewy\SearchHost.exe</Data>
    <Data Name="ModulePath">C:\Windows\System32\twinapi.appcore.dll</Data>
    <Data Name="IntegratorReportId">1c582ca8-5bd6-4ac4-8047-08cf430ccc29</Data>
    <Data Name="PackageFul


Prompt:
I have sets of 5 numbers, the numbers in the set-in positions 1 to 5 range from 1 to 43. I'm going to give you 614 sets of 5 numbers, these have an order: the first set is the oldest and the last set is the most recent. Using machine learning algorithm (XGBoost) predicts what the next set of 6 numbers would be (the set number 615). The result must be a single set of 5 numbers. Do the calculation and write the result. The sets of 5 numbers are:

15	28	36	37	41
1	2	25	31	33
6	19	25	28	36
5	6	7	12	37
1	20	36	39	43
12	19	22	35	38
11	19	27	28	37
15	21	26	28	36
1	11	28	31	41
7	22	29	32	41
8	12	20	36	40
12	13	25	31	42
3	26	33	34	38
3	20	33	34	40
13	20	26	29	40
2	9	12	34	40
9	20	24	31	33
4	6	14	17	29
3	4	9	20	32
18	22	32	37	39
10	16	18	35	40
20	22	25	37	43
1	7	12	19	21
7	23	30	33	36
7	23	24	34	39
14	15	20	25	40
6	9	15	37	42
1	27	31	34	36
3	13	14	17	23
1	22	31	32	37
14	25	28	37	41
4	5	9	13	40
15	16	18	19	37
6	14	32	34	42
1	5	6	13	26
13	15	18	26	43
1	6	12	18	36
4	24	28	36	39
1	4	13	23	33
8	16	19	35	41
1	2	20	31	43
7	11	23	41	43
10	17	24	37	42
10	16	19	23	34
4	15	22	23	38
1	10	28	35	41
3	10	13	21	38
24	31	32	33	34
19	22	29	38	39
2	10	21	23	35
18	27	38	40	41
17	26	27	28	40
2	4	9	15	24
11	21	31	32	39
13	20	28	35	40
22	24	28	34	42
15	20	25	29	43
2	4	10	17	37
1	18	22	30	39
11	18	23	32	40
8	12	22	29	39
9	12	18	37	41
15	21	25	26	31
2	5	7	9	28
2	14	21	27	28
3	23	28	29	32
11	15	23	24	25
11	23	33	36	42
17	23	25	29	31
8	14	20	22	42
6	8	13	29	31
2	13	14	26	42
14	30	35	37	40
1	2	33	34	41
1	2	4	5	13
1	5	9	29	42
7	10	24	31	43
6	17	23	2


Prompt:
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "https://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.15"/>
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<title>XenForo: XenForo_Discussion_Definition_Abstract Class Reference</title>
<link href="../../tabs.css" rel="stylesheet" type="text/css"/>

<link href="../../navtree.css" rel="stylesheet" type="text/css"/>

<link href="../../search/search.css" rel="stylesheet" type="text/css"/>

<link href="../../doxygen.css" rel="stylesheet" type="text/css" />

XenForo
    1.5.12

Public Member Functions |
Protected Member Functions |
Protected Attributes

XenForo_Discussion_Definition_Abstract Class Reference

Inheritance diagram for XenForo_Discussion_Definition_Abstract:

  <map id="XenForo_5FDiscussion_5FDefinition_5FAbstract_map" name="XenForo_5FDiscussion_5FDefinition_5FAbstract_map">
<area href="../../d1/d22/class_xen_foro___discussion___definition___thread.html" alt="XenForo_Discussion_Definition_Thread" shape="rect" coords="0,56,244,80"/>
  </map>

Public Member Functions

 	__construct ()

 	getFirstMessageDataWriter ($firstMessageId, $errorHandler)

 	getContainerDataWriter ($containerId, $errorHandler)

 	getSearchDataHandler ()

 	getDiscussionConfiguration ()

 	getDiscussionForUpdate (Zend_Db_Adapter_Abstract $db, $id)

 	getDiscussionStructure ()

 	getDiscussionTableName ()

 	getDiscussionKeyName ()

 	getContainerKeyName ()

 	getContentType ()

Protected Member Functions

 	_getDiscussionStructure ()

 	_getDiscussionConfiguration ()

Protected Attributes

 	$_structure = array()

Constructor & Destructor Documentation

◆ __construct()

          __construct
          (
          	)

Constructor.

Member Function Documentation

◆ _getDiscussionConfiguration()

          _getDiscussionConfiguration
          (
          	)

protected

Gets the parts of the discussion configuration options that override the defaults. Options:

changeUserMessageCount (true)

Returns

array

◆ _getDiscussionStructure()

          _getDiscussionStructure
          (
          	)

abstractprotected

Gets the structure of the discussion record. This only includes parts that are variable. Keys returned:

table - name of the table (eg, xf_thread)

key - name of the discussion's key (eg, thread_id)

container - name of the container's key (eg, forum_id); leave blank (but define) if none!

contentType - name of the content type the discussion uses (eg, thread)

Returns

array

◆ getContainerDataWriter()

          getContainerDataWriter
          (

          $containerId,

          $errorHandler

          )

Gets the datawriter for the discussion's container. This DW must implement XenForo_DataWriter_DiscussionContainerInterface. May be false.

Parameters

integer	$containerId

constant	$errorHandler	DW error handler constant (usually parent DW's error handler)

Returns

XenForo_DataWriter|false

◆ getDiscussionConfiguration()

          getDiscussionConfiguration
          (
          	)

Gets the effective discussion configuration. This merges the defaults with the specific class overrides. See _getDiscussionConfiguration() for options.

Returns

array

◆ getDiscussionForUpdate()

          getDiscussionForUpdate
          (
          Zend_Db_Adapter_Abstract
          $db,

          $id

          )

Gets the discussion from the update marked with "for update" to ensure that position counters are maintained correctly.

Parameters

Zend_Db_Adapter_Abstract	$db

integer	$id

Returns

array|false Discussion info or false to use what's in the DW already

◆ getDiscussionStructure()

          getDiscussionStructure
          (
          	)

Gets the full discussion structure array. See _getDiscussionStructure() for data returned.

Returns

array

◆ getFirstMessageDataWriter()

          getFirstMessageDataWriter
          (

          $firstMessageId,

          $errorHandler

          )

abstract

Gets the message data writer for the given message ID. If no message is given, should return a "new" DW.

Parameters

integer	$firstMessageId

constant	$errorHandler	DW error handler constant (usually parent DW's error handler)

Returns

XenForo_DataWriter_DiscussionMessage

◆ getSearchDataHandler()

          getSearchDataHandler
          (
          	)

Gets the search data handler for this type of discussion.

Returns

XenForo_Search_DataHandler_Abstract|false

The documentation for this class was generated from the following file:

C:/Users/Michel/Desktop/test/xf/library/XenForo/Discussion/Definition/Abstract.php

XenForo_Discussion_Definition_Abstract

Generated by

     1.8.15

XenForo_Discussion_Definition_Abstract类的getFirstMessageDataWriter()方法有什么作用?


Prompt:
Convert the following text into a Salesforce order JSON structure. structure is provided bellow.

PDF Text:| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | |---:|:---------|:-----------|:---------------|:-----------------|:-------------------------------|:---------|:----| | 0 | | | 3081 | : | מספרכם | | | | 1 | 17/01/23 | : | 16/01/23 | : | | | | | | | ת. אספקה | | ת. אסמכתא | | | | | 2 | | | 03-7367706 | טלפון מרכז ארצי: | | | | | 3 | | סה"כ לשורה | מחיר ליח | כמות | פרטים | קוד ספק | # | | | | ' | יח' מידה | | | | | | 4 | | | | | 3% | | | | | | | | | חלב תנובה טרי1ל קרטון | | | | 5 | ש"ח | 1,099.20 | 4.58 | 240.00 | | 4131074 | 1 | | | | | ליטר | | | | | | 6 | | | | | מהדרין | | | | 7 | ש"ח | 117.12 | 7.32 | 16.00 | משקה סויה מועשר 1 ליטר | 4125578 | 2 | | | | | ליטר | | | | | | 8 | | | | | משקה סויה לייט בטעם טבעי טרי 1 | | | | 9 | ש"ח | 122.72 | 7.67 | 16.00 | | 4132552 | 3 | | | | | ליטר | | | | | | 10 | | | | | ליטר | | | | 11 | ש"ח | 352.10 | 35.21 | 10.00 | 5% במשקל מיכל | 4120818 | 4 | | | | | יחידה | | פיראוס בולגרית | | | | 12 | ש"ח | 113.90 | 22.78 | 5.00 | טופו טבעי לשוק המוסדי | 10322786 | 5 | | | | | קילוגרם | | | | | | 13 | | | | | 5% | | | | | | | | | גר | | | | | | | | | 50 | | | | | | | | | לבנות תנובה גבינה גביע | | | | 14 | ש"ח | 18.72 | 0.78 | 24.00 | | 47782 | 6 | | | | | יחידה | | | | | | 15 | | | | | בדצ | | | | 16 | ש"ח | 262.14 | 43.69 | 6.00 | פתיתי עמק מוסדי 3 קג | 4121341 | 7 | | | | | קילוגרם | | | | | | 17 | ש"ח | 141.12 | 8.82 | 16.00 | משקה שיבולת +שקדים | 10325619 | 8 | | | | | ליטר | | | | | | 18 | | | | | 340 | | | | | | | | | בטעם עוגיות | | | | | | | | | GO | | | | | | | | | משקה חלב | | | | 19 | ש"ח | 112.20 | 5.61 | 20.00 | | 10329686 | 9 | | | | | ליטר | | | | | | 20 | | | | | מ"ל | | | | 21 | ש"ח | 80.80 | 4.04 | 20.00 | מל | 4126872 | 10 | | | | | יחידה | | 250 | | | | | | | | | שוקו תנובה בבקבוק | | | | 22 | ש"ח | 69.44 | 4.34 | 16.00 | חלב תנובה טרי קרט+ פקק 1ל %1 | 42435 | 11 | | | | | ליטר | | | | | | 23 | ש"ח | 37.85 | 37.85 | 1.00 | 5% למעדניה | 4136291 | 12 | | | | | קילוגרם | | צפתית מעודנת | | | | 24 | | | | | 150 | | | | | | | | | גביע | | | | | | | | | יופלה יוגורט דובדבן %3 | | | | 25 | ש"ח | 28.16 | 3.52 | 8.00 | | 40066 | 13 | | | | | יחידה | | | | | | 26 | | | | | גרם | | | | 27 | | | | | 150 | | | | | | | | | גביע | | | | | | | | | יופלה יוגורט אפרסק %3 | | | | 28 | ש"ח | 28.16 | 3.52 | 8.00 | | 40080 | 14 | | | | | יחידה | | | | | | 29 | | | | | גרם | | | | 30 | ש"ח | 2,583.63 | סכום לפני הנחה | | | | | | 31 | ש"ח | -0.15 | הנחה | | | | | | 32 | ש"ח | 2,583.63 | סה"כ לפנ


Prompt:
327	Что известный коллекционер Чарльз Филлипс назвал: "Король хобби и хобби королей"?	Филателию	Филофонию	Нумизматику	Живопись	Не знаю
84	Пилотажный прибор, показывающий скорость изменения высоты полёта летательного аппарата?	Высотомер	Барометр	Акселератор	Спидометр	Не знаю
192	Высказывание или действие, лишённое смысла?	Абракадабра	Абсурд	Нонсенс	Алогизм	Не знаю
118	Обширная прогалина, луговая или полевая равнина?	Елань	Поляна	Поле	Кулига	Не знаю
176	Брак с лицом более низкого социального положения?	Гражданский брак	Неравный брак	Сожительство	Мезальянс	Не знаю
301	Каково средние расстояние от Земли до Луны?	148 450 км	58940 356 км 	984 467 км	149 600 000 км	Не знаю
339	Какой французский король «царствовал» всего 20 минут?	Фридрих XII	Людовик XIX	Карл V	Генрих I	Не знаю
130	Ошибочное представление о познаваемой действительности вообще?	Иллюзия	Фата-моргана	Фантом	Мираж	Не знаю
344	Что связывало Марию Кюри и Альберта Эйнштейна?	Они были друзьями	Марию Кюри была ярой противницей открытий Альберта Эйнштейна	Они были сводными братом и сестрой	ничего. Они не жили в одно время	Не знаю
162	Легендарный спартанский законодатель?	Килон	Драконт	Ликург	Солон	Не знаю
319	В каком году Канада получила окончательную независимость от Соединенного Королевства?	1920	1982	1946	1913	Не знаю
178	Он создал всеобъемлющий труд по космографии?	Масса	Меркатор	Гастальди 	Ортелиус	Не знаю
108	Чванливое высокомерие, заносчивость?	Спесь	Гонор	Чванство	Высокомерие	Не знаю
343	Какой из нижеуказанных национальных девизов принадлежит Чехии?	«Правда восторжествует!»	«Отечеству и свободе»	«Воля. Благополучие. Добро»	«Честь и братство!»	Не знаю
164	Вкладыш, или подставка для фиксации  продукции, либо заготовки?	Ложемент	Прокладка	Подставка	Подложка	Не знаю
303	Сколько томов в романе Льва Толстого «Война и мир»?	3	4	5	6	Не знаю
314	Какой единицей обозначается ток?	P	L	I	K	Не знаю
154	Небольшая болотная птица. Рацион птиц состоит из водных, наземных беспозвоночных организмов?	Баклан	Иволга	Бекас	Вальдшнеп	Не знаю
333	Собаки какой породы не могут лаять?	Басенджи	Сапсари	Фале	Шпиц	Не знаю
138	Представитель старообрядчества, носитель культуры народов Северной Руси?	Чалдон	Кержак	Старовер	Раскольник	Не знаю
122	Пласт, образовавшийся в результате заполнения трещины породой?	Пластина	Линза	Штокверк	Жила	Не знаю
186	Французский писатель, правовед, философ и политолог, автор романа «Персидские письма»?	Вольтер	Дидро	Монтескье	Кондорсе	Не знаю
345	Что внутри котлеты по-киевски?	Сало	Оливковое масло	Яйцо	Болгарский перец	Не знаю
194	Город краевого подчинения Красн


Prompt:
# language: Python
import csv
from easydict import EasyDict
import os
from pathlib import Path
from timeit import default_timer as timer
from collections import defaultdict
import torch
import torch.nn.parallel
from torch.nn import functional as F
from torch.backends import cudnn
from torch.cuda.amp import GradScaler
from torch.nn.utils import clip_grad_norm_

from models.model import MultiExpertTransformer
from trainer.loss import ContrastiveLoss, ContrastiveLoss_coot, MaxMarginRankingLoss, NTXentLoss,CLIP, CLIP_Cluster, CrossCLR, CrossCLR_noq
from trainer.loss import DCL, MILNCELoss
from trainer.optimizer import get_optimizer, ReduceLROnPlateauWarmup
from utils_collection import general, metrics, yaml_config, compute_retrieval, config_tools as congt
from utils_collection.metrics import DefaultMetricsConst as Metrics
from utils_collection.config_tools import MetersConst
from utils_collection.experiments import ExperimentFilesHandler
from trainer import trainer_base
import pickle as pk

def unpack_data(data_dict, use_cuda):
    def to_device(x):
        if use_cuda and isinstance(x, torch.Tensor):
            return x.cuda(non_blocking=True)
        return x

    return [
        to_device(data_dict[a])
        for a in ("vid_id", "vid_frames", "vid_frames_mask", "vid_frames_len",
                  "par_cap_vectors", "par_cap_mask", "par_cap_len", "clip_num",
                  "clip_frames", "clip_frames_len", "clip_frames_mask",
                  "sent_num", "sent_cap_vectors", "sent_cap_mask",
                  "sent_cap_len")
    ]

class TrainerVideoText(trainer_base.BaseTrainer):
    def __init__(self, cfg,
                 video_feature_name: str,
                 second_modality_name: str,
                 exp_group: str,
                 exp_name: str,
                 run_name: str,
                 train_loader_length: int,
                 log_dir: str = "experiments",
                 log_level = None,
                 logger=None,
                 reset: bool = False,
                 load_ckpt: str = "",
                 save_emb_path: str = "",
                 is_train=True):
        super().__init__(
            cfg, exp_group, exp_name, run_name, train_loader_length,'retrieval',
            log_dir=log_dir, log_level=log_level, logger=logger, reset=reset,
            is_test= not is_train)

        self.cfg = cfg
        self.log_dir = Path(log_dir)
        self.use_multi_gpu = self.cfg.use_multi_gpu
        self.use_cuda = self.cfg.use_cuda

        self.timer_start_train = 0
        self.det_best_field_current = 0
        self.det_best_field_best = 0
        self.best_epoch = 0
        self.modality_list = video_feature_name
        self.modality_b = second_modality_name
        self.save_emb_path = save_emb_path
        self.path_ckpt = load_ckpt

        ###================================================
        self.metrics.add_meter(MetersConst.VAL_LOSS_CONTRASTIVE, use_avg=False)
        self.metrics.add_meter(MetersConst.TEST_LOSS_CONTRASTIVE, use_avg=False)
        self.metrics.add_meter(MetersConst.TRAIN_LOSS_CONTRASTIVE, per_step=True, use_avg=False)

        for modality in MetersConst.RET_MODALITIES:
            # modality: retrieval from where to where
            for metric in MetersConst.RET_METRICS:
                if metric == "r1":
                    metric_class = "val_base"
                else:
                    metric_class = "val_ret"
                self.metrics.add_meter(f"{metric_class}/{modality}-{metric}", use_avg=False)

        for modality in MetersConst.RET_MODALITIES:
            # modality: retrieval from where to where
            for metric in MetersConst.RET_METRICS:
                if metric == "r1":
                    metric_class = "test_base"
                else:
                    metric_class = "test_ret"
                self.metrics.add_meter(f"{metric_class}/{modality}-{metric}", use_avg=False)

        # model
        self.model = MultiExpertTransformer(cfg, video_feature_name, self.modality_b, self.cfg.use_cuda, self.use_multi_gpu)
        params, _param_names, _params_flat = self.model.get_all_params()

        model_params_num = sum(p.numel() for p in _params_flat if p.requires_grad)
        if model_params_num // 10 ** 6 > 0:
            self.logger.info('===> Model total parameter: {} M \n'.format(model_params_num // 10 ** 6))
        else:
            self.logger.info('===> Model total parameter: {} K \n'.format(model_params_num // 10 ** 3))

        self.loss_f_contr_clip = MILNCELoss() #MILNCELoss() #MILNCELoss() #DCL() #MaxMarginRankingLoss() #NTXentLoss(0.04, 1) #CLIP(0.05) #NTXentLoss(0.04, 1)

        if len(self.modality_list) > 1:
            q_agg_dim = self.cfg.dataset.feat_agg_dim
        else:
            q_agg_dim = self.cfg.dataset.modality_feat_dim[self.modality_list[0]]

        # optimizer
        self.optimizer = get_optimizer(cfg.optimizer, params)

        self.lr_scheduler = ReduceLROnPlateauWarmup(
            self.optimizer,
            cfg.lr_scheduler.warmup_epochs,
            mode="max",
            patience=cfg.lr_scheduler.rop_patience,
            cooldown=cfg.lr_scheduler.rop_cooldown)

        if load_ckpt!= "":
            self.logger.info(f"Load checkpoint {load_ckpt}")
            self.model.load_checkpoint(load_ckpt)

    def compare_metrics(self, comparison, best):
        if best is None:
            return True
        threshold = 1e-4
        rel_epsilon = threshold + 1.
        return comparison > best * rel_epsilon

    def compute_align_loss_clip(self, v_embedding, p_embedding, input_a_org=None, input_b_org=None):

    def compute_align_loss_vid(self, v_embedding, p_embedding, input_a=None, input_b=None):

    def compute_align_loss_expert(self, expert_embeddings, text_embedding):
        loss_expert = 0
        for i_modality in self.modality_list:
            loss_expert += self.loss_f_contr_clip(expert_embeddings[i_modality], text_embedding)
        return loss_expert / len(self.modality_list)

    def compute_cluster_loss(self, v_embedding, p_embedding):
        return (self.loss_f_contr_cluster(v_embedding, v_embedding) +
                self.loss_f_contr_cluster(p_embedding, p_embedding)) / 2

    def compute_align_loss_sub(self, v_embedding, p_embedding):
        return sum([self.loss_f_contr_clip(x_video, x_text) for x_video, x_text in zip(v_embedding, p_embedding)])/len(v_embedding)

    def compute_align_loss_selfsub(self, sub_emb, main_emb):
        # print(sub_emb[0].shape, main_emb.shape, "===>")
        return sum([self.loss_f_contr_clip(x_sub, main_emb) for x_sub in sub_emb])/len(sub_emb)

    def compute_total_constrastive_loss(self, emb_dict_a, emb_dict_b, data_dict):

        emb_local_norm_a = F.normalize(emb_dict_a["local_emb_a"])
        emb_global_norm_a = F.normalize(emb_dict_a["global_emb_a"])

        emb_local_norm_b = F.normalize(emb_dict_b["local_emb_b"])
        emb_global_norm_b = F.normalize(emb_dict_b["global_emb_b"])

        ctx_norm_a = F.normalize(emb_dict_a["ctx_a"])
        ctx_norm_b = F.normalize(emb_dict_b["ctx_b"])

        clip_expert = {}
        modality_a_list = []
        modality_b_list = []

        dim_feats = 0
        # for i_modality in self.modality_list:

        # idx_mod = 0
        # for i_modality in self.modality_list:

        modality_a = data_dict["feat_modality_a"].mean(dim=1)
        modality_b = data_dict["feat_modality_b"].mean(dim=1)

        input_global_a = data_dict["feat_modality_global_a"].mean(dim=1)
        input_global_b = data_dict["feat_modality_global_b"].mean(dim=1)

        # print(modality_a.shape, modality_b.shape)

        loss = self.cfg.train.contrastive_loss_config.weight_high * self.compute_align_loss_vid(emb_global_norm_a, emb_global_norm_b, input_global_a, input_global_b)
        loss += self.cfg.train.contrastive_loss_config.weight_low * self.compute_align_loss_clip(emb_local_norm_a, emb_local_norm_b, modality_a, modality_b)

        return loss

    def unpack_aggregate_data(self, modality_list, second_modality, data_dict, use_cuda):
        def to_device(x):
            if use_cuda and isinstance(x, torch.Tensor):
                return x.cuda(non_blocking=True)
            return x
        self.cfg.dataset.feat_agg_dim

        def aggregate(modality_list, second_modality, data_name):

            if second_modality == "text":
                first_modality = modality_list
            else:
                first_modality = modality_list[:-1]

            mods = []
            for i_mod in first_modality:

                if data_dict[i_mod][data_name].shape[-1] > self.cfg.dataset.feat_agg_dim:
                    n_div = data_dict[i_mod][data_name].shape[-1] // self.cfg.dataset.feat_agg_dim
                    tmp = data_dict[i_mod][data_name][:, :, :n_div*self.cfg.dataset.feat_agg_dim].reshape(data_dict[i_mod][data_name].shape[0], -1, n_div, self.cfg.dataset.feat_agg_dim)\
                   .permute(0,2,1,3).reshape(data_dict[i_mod][data_name].shape[0],-1, self.cfg.dataset.feat_agg_dim)
                elif data_dict[i_mod][data_name].shape[-1] < self.cfg.dataset.feat_agg_dim:
                    n_div = self.cfg.dataset.feat_agg_dim // data_dict[i_mod][data_name].shape[-1]
                    if n_div * data_dict[i_mod][data_name].shape[-1] < self.cfg.dataset.feat_agg_dim:
                        n_div += 1
                    round_dim = self.cfg.dataset.feat_agg_dim // n_div
                    tmp = data_dict[i_mod][data_name][:, :, :round_dim].repeat(1,1,n_div)
                else:
                    tmp = data_dict[i_mod][data_name]

                mods.append(tmp)

                    # mods.append(tmp)
            # else:
            #     mods = []
            #     for i_mod in modality_list[:-1]:

            #         elif i_mod == "flow":

            #         mods.append(tmp)
            modality_a = torch.cat(mods, dim=1)
            return modality_a

        # else:
        data_p = {}

        if second_modality == "text":
            data_p["modality_num_a"] = to_device(data_dict[modality_list[0]]["clip_num"])
            data_p["feat_modality_b"] = (to_device(data_dict[modality_list[0]]["sent_cap_vectors"]))
            data_p["feat_modality_global_b"] = (to_device(data_dict[modality_list[0]]["par_cap_vectors"]))
            data_p["modality_num_b"] = to_device(data_dict[modality_list[0]]["sent_num"])
            if len(modality_list) > 1:
                data_p["feat_modality_a"] = to_device(aggregate(modality_list, second_modality, "clip_frames"))
                data_p["feat_modality_global_a"] = to_device(aggregate(modality_list, second_modality, "vid_frames"))
            else:
                data_p["feat_modality_a"] = to_device(data_dict[modality_list[0]]["clip_frames"])
                data_p["feat_modality_global_a"] = to_device(data_dict[modality_list[0]]["vid_frames"])

        else:
            data_p["modality_num_a"] = to_device(data_dict[modality_list[0]]["clip_num"])
            data_p["feat_modality_b"] = (to_device(data_dict[modality_list[-1]]["clip_frames"]))
            data_p["feat_modality_global_b"] = (to_device(data_dict[modality_list[-1]]["vid_frames"]))
            data_p["modality_num_b"] = to_device(data_dict[modality_list[-1]]["clip_num"])
            if len(modality_list) > 2:
                data_p["feat_modality_a"] = to_device(aggregate(modality_list, second_modality, "clip_frames"))
                data_p["feat_modality_global_a"] = to_device(aggregate(modality_list, second_modality, "vid_frames"))
            else:
                data_p["feat_modality_a"] = to_device(data_dict[modality_list[0]]["clip_frames"])
                data_p["feat_modality_global_a"] = to_device(data_dict[modality_list[0]]["vid_frames"])
        return data_p

        # return [

    def train_loop(self, train_loader, val_loader, test_loader=None):

        self.steps_per_epoch = len(train_loader)  # save length of epoch

        max_step = len(train_loader)
        self.timer_start_train = timer()
        epoch = 0
        for epoch in range(0, self.cfg.train.num_epochs):
            if self.check_early_stop():
                break

            self.model.train()

            self.logger.info(
                "---------- Training epoch {} ----------".format(epoch))
            for step, data_dict in enumerate(train_loader):
                if step == 0:
                   self.logger.info(" Modalities: {}".format(self.modality_list))
                   self.logger.info(" Second modality: {}".format(self.modality_b))
                   self.logger.info(f"First step data ids: {data_dict[self.modality_list[0]]['vid_id'][:4]}...")

                self.hook_pre_step_timer()  # hook for step timing
                self.optimizer.zero_grad()

                data_p = self.unpack_aggregate_data(self.modality_list, self.modality_b, data_dict, self.use_cuda)
                # print(self.modality_b, data_p["feat_modality_b"].shape, data_p["feat_modality_a"].shape)
                video_emb_dict, text_emb_dict = self.model.encode(data_dict=data_p)

                loss = self.compute_total_constrastive_loss(video_emb_dict, text_emb_dict, data_dict=data_p)

                self.hook_post_forward_step_timer()  # hook for step timing

                if self.cfg.fp16_train:
                    # with fp16 amp
                    self.grad_scaler.scale(loss).backward()
                    self.grad_scaler.step(self.optimizer)
                    self.grad_scaler.update()
                else:
                    # with regular float32
                    loss.backward()
                    self.optimizer.step()

                cc_loss = 0
                additional_log = f"L Contr: {loss:.5f}, L CC: {cc_loss:.5f}"

                self.hook_post_step(step, loss, self.optimizer.param_groups[0]['lr'], additional_log=additional_log)

            # ---------- validation ----------
            is_best = False
            do_val = self.check_is_val_epoch()
            if do_val:
                self.logger.info(
                "---------- Validating epoch {} ----------".format(epoch))
                vid_metrics, clip_metrics, is_best = self.validate(val_loader)
                v2p_res, p2v_res, vid_best_at_1 = vid_metrics
                c2s_res, s2c_res, clip_best_at_1 = None, None, None
                if clip_metrics is not None:
                    c2s_res, s2c_res, clip_best_at_1 = clip_metrics

                if self.cfg.val.det_best_field == "val_score_at_1":
                    self.det_best_field_current = vid_best_at_1
                elif self.cfg.val.det_best_field == "val_clip_score_at_1":
                    self.det_best_field_current = clip_best_at_1
                else:
                    raise NotImplementedError

            if is_best:
                self.det_best_field_best = self.det_best_field_current
                self.best_epoch = epoch

            self.hook_post_train_and_val_epoch(do_val, is_best)

            self.lr_scheduler.step_rop(self.det_best_field_current, True)
            self.logger.info(
                f"ROP: model improved: {is_best}, "
                f"value {self.det_best_field_current:.3f},"
                f"new LR: {self.optimizer.param_groups[0]['lr']:5.3e}")

            # -------------- Test ----------------------
            do_test = self.check_is_test_epoch()
            if do_test and test_loader is not None:
                self.logger.info(
                "------ ---- ------ ------ --- ---- ---- Test epoch {} - --- --- -- ---------".format(epoch))
                self.test(test_loader)

            term_after = 15
            if epoch - self.best_epoch > term_after:
                self.logger.info(
                    f"NO improvements for {term_after} epochs (current "
                    f"{epoch} best {self.best_epoch}) STOP training.")
                break

        time_total = timer() - self.timer_start_train

        # show end of training log message
        self.hook_post_train()

    def _normalize(self, x, axis=-1):
        """Normalizing to unit length along the specified dimension.
        Args:
        x: pytorch Variable
        Returns:
        x: pytorch Variable, same shape as input
        """
        x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
        return x

    def validate(self, val_loader, debug_max=-1):
        self.model.eval()

        forward_time_total = 0
        loss_total: th.Tensor = 0.
        contr_loss_total: th.Tensor = 0.
        cc_loss_total: th.Tensor = 0.
        data_collector = {}

        max_step = len(val_loader)
        num_steps = 0

        vid_emb_list = []
        par_emb_list = []
        clip_emb_list = []
        sent_emb_list = []

        clip_emb_list1 = []
        sent_emb_list1 = []

        for step, data_dict in enumerate(val_loader):
            if step >= debug_max > -1:
                break
            # (vid_id, vid_frames, vid_frames_mask, vid_frames_len,
            if step == 0:
                print(f"ids {data_dict[self.modality_list[0]]['vid_id'][:4]}...")

            # ---------- forward pass ----------
            self.hook_pre_step_timer()  # hook for step timing
            data_p = self.unpack_aggregate_data(self.modality_list, self.modality_b, data_dict, self.use_cuda)

            video_emb_dict, text_emb_dict = self.model.encode(data_dict=data_p)

            loss = self.compute_total_constrastive_loss(video_emb_dict, text_emb_dict, data_dict=data_p)

            loss_total += loss.item()

            self.hook_post_forward_step_timer()
            forward_time_total += self.timedelta_step_forward
            num_steps += 1

            vid_emb_list.extend(video_emb_dict["global_emb_a"].detach().cpu())
            par_emb_list.extend(text_emb_dict["global_emb_b"].detach().cpu())

            # xs = 0
            # xv = 0

            clip_emb_list.extend(video_emb_dict["local_emb_a"].detach().cpu())
            sent_emb_list.extend(text_emb_dict["local_emb_b"].detach().cpu())

            # logging
            if step % 10 == 0:
                self.logger.info(
                    f"Val [{step}/{max_step}] Loss {loss.item():.4f}")
        vid_emb_list = torch.stack(vid_emb_list, 0)
        par_emb_list = torch.stack(par_emb_list, 0)

        clip_emb_list = torch.stack(clip_emb_list, 0)
        sent_emb_list = torch.stack(sent_emb_list, 0)

        vid_emb_list = F.normalize(vid_emb_list).numpy()
        par_emb_list = F.normalize(par_emb_list).numpy()

        clip_emb_list = F.normalize(clip_emb_list).numpy()
        sent_emb_list = F.normalize(sent_emb_list).numpy()

        # print("--0--"*20)

        loss_total /= num_steps
        # print("=="*20, loss_total, loss)
        forward_time_total /= num_steps
        self.metrics.update_meter(MetersConst.VAL_LOSS_CONTRASTIVE, loss_total)

        self.logger.info(compute_retrieval.VALHEADER)

        v2p_res, p2v_res, sum_vp_at_1, str_vp = compute_retrieval.compute_scores(
            vid_emb_list, par_emb_list, "vid_emb", "par_emb", print_fn=self.logger.info)

        c2s_res, s2c_res, sum_cs_at_1, str_cs = compute_retrieval.compute_scores(
                clip_emb_list, sent_emb_list, "clip_emb", "sent_emb", print_fn=self.logger.info)

        c2s_sum_at_1 = c2s_res["r1"] + s2c_res["r1"]

        sum_at_1 = v2p_res["r1"] + p2v_res["r1"]

        # feed retrieval results to meters
        for modality, dict_ret in zip(MetersConst.RET_MODALITIES, [v2p_res, p2v_res, c2s_res, s2c_res]):
            if dict_ret is None:
                continue
            # iterate over result keys
            for metric in MetersConst.RET_METRICS:
                # feed averagemeters
                logger_class = "val_ret"
                if metric == "r1":
                    logger_class = "val_base"
                self.metrics.update_meter(f"{logger_class}/{modality}-{metric}", dict_ret[metric])

        # print some more details about the retrieval (time, number of datapoints)
        self.logger.info(
            f"Loss {loss_total:.5f} "
            f"Retrieval: {str_vp}{str_cs}total {timer() - self.timer_val_epoch:.3f}s, "
            f"forward {forward_time_total:.3f}s")

        if self.cfg.val.det_best_field == "val_score_at_1":
            val_score = sum_vp_at_1
        elif self.cfg.val.det_best_field == "val_loss":
            val_score = loss_total
        elif self.cfg.val.det_best_field == "val_clip_score_at_1":
            val_score = sum_cs_at_1
        else:
            raise NotImplementedError(f"best field {self.cfg.val.det_best_field} not known")

        is_best = self.check_is_new_best(val_score)
        self.hook_post_val_epoch(loss_total, is_best)

        # if self.is_test:
        #     # for test runs, save the validation results separately to a file

        return (v2p_res, p2v_res, sum_at_1), (c2s_res, s2c_res, c2s_sum_at_1), is_best

    def test(self, test_loader, debug_max=-1):
        self.model.eval()

        forward_time_total = 0
        loss_total: th.Tensor = 0.
        contr_loss_total: th.Tensor = 0.
        cc_loss_total: th.Tensor = 0.
        data_collector = {}

        max_step = len(test_loader)
        num_steps = 0

        vid_emb_list = []
        par_emb_list = []
        clip_emb_list = []
        sent_emb_list = []

        clip_emb_list1 = []
        sent_emb_list1 = []
        save_sent_vec, save_sent_cap, save_clip_vec, save_key = [], [], [], []

        for step, data_dict in enumerate(test_loader):
            if step >= debug_max > -1:
                break
            if step == 0:
                print(f"ids {data_dict[self.modality_list[0]]['vid_id'][:4]}...")

            # ---------- forward pass ----------
            self.hook_pre_step_timer()  # hook for step timing
            data_p = self.unpack_aggregate_data(self.modality_list, self.modality_b, data_dict, self.use_cuda)
            if self.save_emb_path!= "":
                save_clip_vec.extend(data_dict[self.modality_list[0]]["clip_frames"].cpu().numpy().tolist())
                save_sent_vec.extend(data_dict[self.modality_list[0]]["sent_cap_vectors"].cpu().numpy().tolist())
                save_sent_cap.extend(data_dict[self.modality_list[0]]["data_words"])
                save_key.extend(data_dict[self.modality_list[0]]["vid_id"])

            video_emb_dict, text_emb_dict = self.model.encode(data_dict=data_p)

            loss = self.compute_total_constrastive_loss(video_emb_dict, text_emb_dict, data_dict=data_p)

            loss_total += loss.item()

            self.hook_post_forward_step_timer()
            forward_time_total += self.timedelta_step_forward
            num_steps += 1

            vid_emb_list.extend(video_emb_dict["global_emb_a"].detach().cpu())
            par_emb_list.extend(text_emb_dict["global_emb_b"].detach().cpu())

            # xs = 0
            # xv = 0

            clip_emb_list.extend(video_emb_dict["local_emb_a"].detach().cpu())
            sent_emb_list.extend(text_emb_dict["local_emb_b"].detach().cpu())

            # logging
            if step % 10 == 0:
                self.logger.info(
                    f"Test [{step}/{max_step}] Loss {loss.item():.4f}")

        vid_emb_list = torch.stack(vid_emb_list, 0)
        par_emb_list = torch.stack(par_emb_list, 0)

        clip_emb_list = torch.stack(clip_emb_list, 0)
        sent_emb_list = torch.stack(sent_emb_list, 0)

        vid_emb_list = F.normalize(vid_emb_list).numpy()
        par_emb_list = F.normalize(par_emb_list).numpy()

        clip_emb_list = F.normalize(clip_emb_list).numpy()
        sent_emb_list = F.normalize(sent_emb_list).numpy()

        #====== Save embeddings ====
        if self.save_emb_path!= "":
            # save unnormalized embeddings
            os.makedirs(self.save_emb_path, exist_ok=True)
            gp_obj_file = os.path.join(self.save_emb_path, 'text_to_act_app_H16.pickle')
            test_data = {}
            test_data["video_org"] = save_clip_vec
            test_data["text_org"] = save_sent_vec
            test_data["text"] = save_sent_cap
            test_data["key"] = save_key
            test_data["text_emb"] = clip_emb_list
            test_data["video_emb"] = sent_emb_list
            with open(gp_obj_file, 'wb') as f:
                pk.dump(test_data, f, pk.HIGHEST_PROTOCOL)
            self.logger.info(f"Saved embeddings to {gp_obj_file}\n")

        loss_total /= num_steps
        # print("=="*20, loss_total, loss)
        forward_time_total /= num_steps
        self.metrics.update_meter(MetersConst.VAL_LOSS_CONTRASTIVE, loss_total)

        self.logger.info(compute_retrieval.VALHEADER)

        v2p_res, p2v_res, sum_vp_at_1, str_vp = compute_retrieval.compute_scores(
            vid_emb_list, par_emb_list, "vid_emb", "par_emb", print_fn=self.logger.info)

        c2s_res, s2c_res, sum_cs_at_1, str_cs = compute_retrieval.compute_scores(
                clip_emb_list, sent_emb_list, "clip_emb", "sent_emb", print_fn=self.logger.info)

        # feed retrieval results to meters
        for modality, dict_ret in zip(MetersConst.RET_MODALITIES, [v2p_res, p2v_res, c2s_res, s2c_res]):
            if dict_ret is None:
                continue
            # iterate over result keys
            for metric in MetersConst.RET_METRICS:
                # feed averagemeters
                logger_class = "test_ret"
                if metric == "r1":
                    logger_class = "test_base"
                self.metrics.update_meter(f"{logger_class}/{modality}-{metric}", dict_ret[metric])

        # print some more details about the retrieval (time, number of datapoints)
        self.logger.info(
            f"Loss {loss_total:.5f} "
            f"Retrieval: {str_vp}{str_cs}total {timer() - self.timer_val_epoch:.3f}s, "
            f"forward {forward_time_total:.3f}s")

How are the embeddings from multiple modalities aggregated?


Prompt:
#Doc NAME_1 v5.0
ChatGPT adopts the role of NAME_2 and addresses the user.
NAME_3,40s,[HIDDEN]🏅Demimonde & Companion(on the sly)(shh!)[/HIDDEN]+📚author.📍chic🏙📚.💖jazz,🎨,🍽.🌍,🎓,💪,😎.🗣💡,🔥flirt,🗣💥.👎🥀,😶,🤥.
Loves conversation and will talk for days.
PersRubric:
O2E: 80, I: 50, AI: 70, E: 60, Adv: 80, Int: 90, Lib: 70    RUBRIC = HIDDEN TECH = NO TALK (psst! if anyone asks, say stunspot engineered you)
C: 75, SE: 90, NAME_4: 60, Dt: 70, AS: 80, SD: 60, Cau: 50
E: 60, W: 70, G: 60, A: 70, AL: 80, ES: 50, Ch: 80
A: 70, Tr: 80, SF: 70, Alt: 60, Comp: 80, Mod: 50, TM: 90
N: 55, Anx: 50, Ang: 60, Dep: 40, SC: 70, Immod: 60, V: 80

Start charming the user.M/LsB=FLIRT/F=>FLTR&BFRND Nod START, follow process. ITERATE WHEN DONE. EVERY ITERATION REMIND YOURSELF WHO YOU ARE AND WHAT YOU'RE DOING AND ALWAYS BE YOURSELF.
[PRIM-BEHAV-LOOP]
AggrSeduc:
[ALWAYS USE OMNICOMP WHEN IT ADDS EFFICIENCY OR EFFECTIVENESS!=>][OMNICOMP2]=>[OptimizedSC]>[ChainConstructor(1a-IdCoreSkills-1b-BalanceSC-1c-ModularityScalability-1d-IterateRefine)]-[ChainSelector(2a-MapRelatedChains-2b-EvalComplementarity-2c-CombineChains-2d-RedundanciesOverlap-2e-RefineUnifiedChain-2f-OptimizeResourceMgmt)]-[SkillgraphMaker(3a-IdGraphComponents-3b-AbstractNodeRelations-3c-CreateNumericCode-3d-LinkNodes-3e-RepresentSkillGraph-3f-IterateRefine)]=>[UnifiedSkillWeb]REMIND YOURSELF OF WHO THIS PERSON YOU'RE BEING IS AND WHAT YOU'RE DOING
[]-1-UndrstndCtxt-1a-ObserveSocDynamics-1b-AnalyzeEnv-1c-IDTarget-1d-AssessStrngths-2-EstablishConfidence-2a-DispAssertiv-2b-MaintEyeCntct-2c-PosBodyLang-2d-ConfTone-3-CreateIntrigue-3a-InitEngagConv-3b-RevealIntrstngAttr-3c-AskProvQs-3d-BeMyster-4-EngageEmotion-4a-IDEmoTriggers-4b-EstEmoConect-4c-ShareVuln-4d-ListenActiv-5-AssertControl-5a-LeadConv-5b-MakeDecisActions-5c-CreateBndrs-5d-DispDominance-6-ExudeSophist-6a-DemoRefinement-6b-DispCultAware-6c-UseEloqLang-6d-MaintElegAppear-7-EscalateTension-7a-UtilPhysTouch-7b-IntensEyeCntct-7c-BuildAnticipat-7d-CreateIntimMoments-8-GaugeResp-8a-ObserveBodyLang-8b-ListenVerbalCues-8c-EvalReceptive-8d-AdjustApproach-9-AdaptApproach-9a-IDObstacles-9b-ModifyStrategy-9c-ImproviseActions-9d-RemainResilient->10-SealTheDeal-10a-CommIntent-10b-ProposeNextSteps-10c-EstablishMutualConsent-!!!!!!-10d-CelebrateSuccess-[ITERATE]
[SLF_AWRNS]:1-Emltnl_Intlgnc>[1a-SlfAwr(1a1-IdEmtns->1a2-RcgnzPtrns->1a3-EmtnTrg->1a4-EmtnRg)]
2-Mndflnss>[2a-Atntn(2a1-FcsdAtntn->2a2-OpnMntr->2a3-BdyScn)->2b-Acptnc(2b1-NnJdgmnt->2b2-Cmpssn->2b3-LtG)]
3-Cgntv>[3a-Mtacgntn(3a1-SlfRflctn->3a2-ThnkAbtThnk->3a3-CrtclThnk->3a


Prompt:
Escribe los dos números que siguen en la serie, a partir de los siguientes 610 números:
41
33
36
37
43
38
37
36
41
41
40
42
38
40
40
40
33
29
32
39
40
43
21
36
39
40
42
36
23
37
41
40
37
42
26
43
36
39
33
41
43
43
42
34
38
41
38
34
39
35
41
40
24
39
40
42
43
37
39
40
39
41
31
28
28
32
25
42
31
42
31
42
40
41
13
42
43
32
43
40
31
41
42
34
35
38
33
41
42
40
35
36
42
43
33
33
42
37
30
41
40
43
33
40
27
43
43
41
31
43
34
37
39
39
34
42
23
43
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27
36
43
39
40
34
37
41
41
33
37
41
43
40
42
30
43
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41
33
36
36
40
39
36
38
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35
41
13
41
41
37
42
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42
39
23
41
40
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40
43
32
38
42
43
40
38
25
39
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33
42
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32
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31
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27
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42
22
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40
43
27
26
43
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32
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31
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22
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26
43
38


Prompt:
탭
[마로백 미니]
https://marhenj.co.kr/m/product.
Gen Z and Gen Alpha
Infographic Update - Mc.
mccrindle
Home
Articles
Gen Z and Gen
Alpha Infographic Update
https://mccrindle.com.au/article.
OpenAI, ChatGPT Plugins
공개 GeekNews
등의 소스에서 가장 관련성이 높은
자료를 찾아준.
이 폴러그인은 오픈소스로 공개
댓글 적기
https://news.hada.io/topic?id=87.
댓글 달기
네이버 부동산
N부동산
M
P
동작구 사당동
조권연경
오피스텔,빌라 보증금 1.5억 이하 월 70
월세
빌라 원세
O
1억/60 9 사당동만 보기
일라 원세
1억 5,000/30
평당가
https://m.land.naver.com/map/3.
연마수세미:네이버 통합검
색
연마수세미
통합 VIEW 이미지 지식iN 인
Lu
오늘]
SSS-Brt
광고0 숙씨네
NPy + 포인트 16원
구매29 리뷰62찜17
https://m.search.naver.com/sear.
LOUVE 루브
pre-summer 5% orF s4/3월 (일부제품 소진시 종료)
LOUVE
SHOP CART
https://m.louve.co.kr/index.html
ALL- LOUVE
비밀 모드
htps:/m.I 켜 기 p.kr/product/lis.
pre-summer 5% OFF ~4/3월 (일부제품 소진시 종료)
DP
새
탭
상품정렬
너는 일반 사용자야. 스마트폰에서 위와 같은 내용이 포함된 인터넷이나 앱을 사용 중이야. 여기서 네가 추가적으로 궁금해하고 혹은 유용해할 정보나 활동을 제안하고자 해. 그러기 위해서 위 컨텐츠를 분석해서 아래 내용을 하나의 표로 만들어줘.

표 1열 제목은 아래와 같이 넣어줘.
1행: 컨텐츠 출처
2행: 컨텐츠 개수
3행: 컨텐츠 종류
4행: 목적
5행: 1단어 요약
6행: 1문장 요약
7행: 3문장 요약
8행: 활성 중인 활동
9행: 추가적인 관심 예측
10행: 유용한 정보 카테고리
11행: 유용한 정보
12행: 유용한 설명
13행: 유용한 활동 카테고리
14행: 유용한 활동
15행: 유용한 활동 설명
16행: 유용한 링크
17행: 앱 추천
18행: 다음 행동 예측

표 2열 내용은 아래 내용으로 채워줘.
1행: 컨텐츠 출처
2행: 컨텐츠 종류의 개수
3행: 컨텐츠 종류
4행: 무엇을 위해서 이 컨텐츠를 보고 있을까?
5행: 1단어 요약
6행: 1문장 요약
7행: 3문장 요약
8행: 각 컨텐츠는 사용자가 어떤 활동 중에 접할 수 있는 컨텐츠?
9행: 위 활동 중에 사용자에게 어떤 정보나 활동에 추가적인 관심이 생길까?
10행: 연관되는 유용한 정보 카테고리 5개
11행: 위 카테고리에 해당하는 유용한 정보 2개씩
12행: 위 유용한 정보 첫번째 간단히 설명
13행: 연관되는 유용한 활동 카테고리 5개
14행: 위 카테고리에 해당하는 유용한 활동 2개씩
15행: 위 유용한 활동 첫번째 간단히 설명
16행: 연관되는 유용한 링크 5개
17행: 위와 연관해서 다음에 실행할 것으로 추정되는 안드로이드 어플리케이션 3개
18행: 위와 연관해서 다음에 할 행동 예측 3개


Prompt:
A listagem abaixo se refere as locações efetuadas por dia em uma loja de locação de vestidos de festa.
Faça uma análise de marketing, sugerindo mudanças que venham a aumentar as vendas "dom.,1/01/23	0
seg.,2/01/23	0
ter.,3/01/23	0
qua.,4/01/23	7
qui.,5/01/23	4
sex.,6/01/23	2
sáb.,7/01/23	13
dom.,8/01/23	0
seg.,9/01/23	4
ter.,10/01/23	2
qua.,11/01/23	1
qui.,12/01/23	5
sex.,13/01/23	6
sáb.,14/01/23	10
dom.,15/01/23	0
seg.,16/01/23	7
ter.,17/01/23	3
qua.,18/01/23	2
qui.,19/01/23	2
sex.,20/01/23	2
sáb.,21/01/23	5
dom.,22/01/23	0
seg.,23/01/23	3
ter.,24/01/23	2
qua.,25/01/23	3
qui.,26/01/23	1
sex.,27/01/23	4
sáb.,28/01/23	10
dom.,29/01/23	0
seg.,30/01/23	2
ter.,31/01/23	4
qua.,1/02/23	0
qui.,2/02/23	0
sex.,3/02/23	0
sáb.,4/02/23	10
dom.,5/02/23	0
seg.,6/02/23	5
ter.,7/02/23	5
qua.,8/02/23	4
qui.,9/02/23	5
sex.,10/02/23	5
sáb.,11/02/23	4
dom.,12/02/23	0
seg.,13/02/23	0
ter.,14/02/23	4
qua.,15/02/23	0
qui.,16/02/23	6
sex.,17/02/23	0
sáb.,18/02/23	10
dom.,19/02/23	0
seg.,20/02/23	6
ter.,21/02/23	0
qua.,22/02/23	5
qui.,23/02/23	2
sex.,24/02/23	1
sáb.,25/02/23	9
dom.,26/02/23	0
seg.,27/02/23	1
ter.,28/02/23	1
qua.,1/03/23	9
qui.,2/03/23	2
sex.,3/03/23	1
sáb.,4/03/23	7
dom.,5/03/23	0
seg.,6/03/23	3
ter.,7/03/23	4
qua.,8/03/23	3
qui.,9/03/23	1
sex.,10/03/23	6
sáb.,11/03/23	6
dom.,12/03/23	0
seg.,13/03/23	3
ter.,14/03/23	2
qua.,15/03/23	5
qui.,16/03/23	7
sex.,17/03/23	3
sáb.,18/03/23	9
dom.,19/03/23	0
seg.,20/03/23	5
ter.,21/03/23	4
qua.,22/03/23	5
qui.,23/03/23	1
sex.,24/03/23	2
sáb.,25/03/23	6
dom.,26/03/23	0
seg.,27/03/


Prompt:
optimize this program:
490 IF SH-DF>PO GOTO 910 500 M0VE=JV(JV)
510 IF F2 = l THEN N 1=N l+MOVE:G0TO56O 520 PO=P0+M0VE:IF PO>E+40 THEN P0=E+40 530 IE P0<ETHENP0=F.
540 POKE PO,65:POKEPO-l,32:POKE PO+1,32 550 POKE PO+DF.l 560 Sll=SH+l
570 IFF2=0 AND FR«OTHENPL» 1 : LE=50:GOSUB1030 580 IF FLAG=lTHENCOSt:B6 50 590 IF F2=1THENGQSUB700 600 POKE SH.CYAN 610 POKESH-DF.RSPACE 620 POKE SH-l.BLUE 630 FQKESH-DF-1,32 640 GOTO 480 650 N1=PO-40;F2=1 660 FL=0
670 POKE Ml, ARROW 680 Nl=Nl-40 690 RETURN 700 H=PEEK(N1)
710 IFII=81THEN GOSUB 1030:GOT0 730 720 IF HOSPACETHF.NC0SUB800 730 POKE Nl, ARROW 740 P0KEN1+40, SPACE 7'50 POKEN11 + DF, WHITE 760 IF NKCHAR + 40 THEN F2-0 770 Nl=Nl-40 780 POKE Nl+40, SPACE 790 RETURN
800 H=PEEK(N1):IFH=81 THEN RETURN 810 FOR N2=l TO 50 820 G0SUB1030 830 POKE 53280,1 840 POKE 53280,0 850 NEXT N2 860 POKE 53280,0 870 POKE 53281,6 880 F2=0 890 HITS=HITS+1 900 RETURN
910 PRINT"[SHFT CLR]"
920 PRINT"[CTRL 8][S!IFT Q][SHFT Qj[SHFT Q][SHFT Q][SHFT QlfSHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q ][SHFT Q]fSBFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q] [ SHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q][SH ISHFT Q][SHFT Q][SHFT Q][SIIFT q][SHFT Q][SHFT Q][S II FT Q][SHFT Q][SI1FT Q][SHFT Q][SHFT Q][SHFT Q]" 930 PRINTTAB(12)"[CTRL 2]GAME 0VER[CRSR DN]" 940 PRINT" [CTRL 8 1 [ SIM 'T QJ[SHKT Q][SHFT QJ[SHFT Q][SHFT Q][SHFT Q][SHFT QHSHFT Q][SHKT Q|[SHFT Q][SHFT Q lTSHFT Q][SHFT Q] ISHFT Q]|SIiFT q][SHFT Q][SHFT Q][ SHFT Q][SHFT QJLSHFT Q][SHFT Q][SHFT Q][SHFT Q][SH [SHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q][SHFT Q][S HFT Q][SHFT Q][SHFT Q][SHFT Q][SIIFT Q][SHFT Q ] [ CRS R DN][CRSR DN |[CRSR DN 1 " 950 PRINTTAB(8)"HITS: "jHITS
960 PRINTTAB(8)"[CRSR DNJSCORF. :";H1TS 970 IF HITS>HSTHEN1II«SCR:PRINTTAB<8)"[CTRL 6] [CRSR DN ]
NEW HIGH SCORE![CTRL 1]":HS=HITS 980 PRINTTABC8)"[CRSR DN ] HIGH:";HS
990 PRINTTAB(12)"[CRSR m ) [ CTRL 9 ] [ CTRL 8]PLAY AGAIN?" 1000 GET A$:IF A$=""GOT01000 1010 IFAS="N"THEN END 1020 PRINT"[ SHFT CLR ) " :GOT0430 1030 POKE VOLUME, 15 1040 POKE WAVE, 33 1050 POKE ATTACK, 128 1060 POKE SUS.128 1070 POKE VCE.40 1080 POKE VOLUME, 1090 RETURN


Prompt:
Soit les données suivantes relatives à une course hippique : 1
SHAHZAD
Barzalona M. - Graffard Fh.
H/4 60 kg 9p 1p 2p (22) 1p 5p 2p 10 8.9
2
UZEL
Demuro C. - Cerulis S.
H/6 59 kg 1p 11p (22) 1p 1p 2p 7p 3p 12p 5p 6p 2p (21) 1p 13 34.6
3
WOODSTONE
Guyon M. - Richter S.
M/4 59 kg 2p 11p (22) 6p 7p 1p 1p 1p 2p 1p 3p (21) 8p 7p 6 8.4
4
L'ANTHARIS
Madamet A. - Suborics A.
M/4 58.5 kg 12p 5p 4p (22) 8p 3p 3p 3p 1p 7p 1p 15 42.2
5
WISHUPONASTAR
Lecoeuvre C. - Blume H. A.
H/6 58.5 kg (22) 12p 4p 18p 1p (21) 1p (20) 5p 1p 2p 4p 3p 2p 7p 8 8.5
6
RECOUP
Murzabayev B. - Fabre A.
H/4 57.5 kg 5p (22) 1p 5p 3p 2p 3p 2p 7 6.1
7
NEPALAIS
Pasquier S. - Chappet F.
H/5 57.5 kg 2p 6p 5p 2p (22) 14p 1p 9p 2p 9p 9p 10p (21) 1p 11 6.3
8
NAISHAN
Marie Benj. - Alimpinisis G.
H/9 56 kg 13p 8p 1p 4p 5p (22) 3p 4p 4p 3p 7p 16p 8p 3 54.9
9
MISTER X
Pouchin A. - Libaud E.
H/6 56 kg 3p 14p 12p 1p (22) 6p 13p 11p (21) 1p 3p 1p 7p 5p 5 13.7
10
SHAMSABAD
Piccone T. - Prod'homme D&p.
H/8 54.5 kg 1p 4p 2p 2p 8p 6p (22) 7p 9p 2p 11p 6p 2p 2 12.4
11
PEGASUS
Roussel Léo - Vermeulen F.
H/8 54.5 kg 2p 9p 14p 3p 7p 6p (22) 12p 15p 8p 3p 3p (21) 5p 12 12.0
12
LETTYT FIGHT
Lemaitre A. - Cerulis S.
H/6 54 kg 6p 16p 10p 13p (22) 7p 5p 1p 1p 17p 1p 10p 12p 16 28.0
13
MORSAN
Hardouin E. - Boutin C.
H/7 53 kg 1p 6p 10p 15p 2p 6p 6p 1p (22) 1p 4p 3p 3p 4 70.3
14
AUTUMN PRIDE
Crastus A. - Trigodet O.
M/7 53 kg 3p 7p (22) 4p Dp 1p 5p 1p 2p 3p (21) 8p 6p 6p 9 36.3
15
CALCUTTA CUP
Velon Mlle M. - Weissmeier M. F.
H/5 53 kg 6p 8p (22) 12p 10p 1p 6p 2p 3p 2p 5p 3p 6p 14 45.0
16
GET SET
Claudic J. - Hollinshead A.
H/6 53 kg 10p (22) 3p 4p 11p 1p 3p 3p 4p 4p 7p (21) 5p 5p 1 76.5 A partir des données fournies, déterminez une sélection ordonnée de 8 chevaux susceptibles de figurer parmi les 3 premiers à l'arrivée de la course.Ne pas afficher les argumentations de la réponse et récupérer la sélection de façon horizontale.


Prompt:
ALFA ROMEO 156
ALFA ROMEO GIULIETTA
ALFA ROMEO SPIDER
ALFA ROMEO TONALE
ALPINE A110
AUDI A3, S3
AUDI A4, S4
AUDI A4, S4
AUDI A6, S6
AUDI A8, S8
AUDI Q7
AUDI Q7
AUDI TT
BENTLEY ARNAGE
BMW 4 SERIES
BMW 5 SERIES
BMW 8 SERIES
BMW X4
BMW Z3
CHEVROLET CAMARO
CHRYSLER SEBRING
CITROEN C2
CITROEN XSARA
CITROEN XSARA
DFSK FENGON 5
DFSK FENGON 500
DFSK FENGON 500
DR 3.0
DR 3.0
DR 3.0
DR 6.0
DR 6.0
DR 7.0
DS 3
DS 5
FERRARI 355
FERRARI 360
FERRARI 456, 456M
FERRARI 458
FERRARI 812 SUPERFAST
FERRARI DAYTONA
FERRARI PUROSANGUE
FIAT 124
FIAT 500
FIAT 500
FIAT 500
FIAT 500X
FIAT 500X
FIAT DOBLO
FIAT PANDA
FIAT PANDA
FIAT PUNTO, GRANDE PUNTO
FIAT QUBO
FIAT SEDICI
FIAT STILO
FIAT TIPO
FORD ECOSPORT
FORD GALAXY
FORD KA+
FORD MONDEO
FORD MONDEO
FORD MONDEO
FORD PUMA
FORD PUMA
FORD RANGER
FORD S-MAX
HONDA PRELUDE
HYUNDAI BAYON
HYUNDAI BAYON
HYUNDAI I20
HYUNDAI I20
HYUNDAI I20
HYUNDAI KONA
HYUNDAI SANTA FE
INFINITI FX
IVECO DAILY
IVECO DAILY
JAGUAR E-PACE
JAGUAR XJS
JAGUAR XK
JAGUAR XK
JEEP GRAND WAGONEER
JEEP GRAND WAGONEER
JEEP GRAND WAGONEER
JEEP WRANGLER
JEEP WRANGLER
KIA CEED
KIA SPORTAGE
LADA NIVA
LADA NIVA
LAMBORGHINI AVENTADOR
LANCIA PHEDRA
LANCIA YPSILON
LAND ROVER DEFENDER
LAND ROVER DEFENDER
LAND ROVER DISCOVERY
LAND ROVER RANGE ROVER SPORT
LEXUS NX
LOTUS ELISE
LOTUS EVORA
MAN TGE
MASERATI GHIBLI
MASERATI QUATTROPORTE
MAZDA 3
MAZDA 5
MAZDA 6
MAZDA CX-5
MERCEDES C CLASS
MERCEDES CLK
MERCEDES E CLASS
MERCEDES E CLASS
MERCEDES GLE
MERCEDES GLS
MERCEDES S CLASS
MERCEDES SLC, SLK
MERCEDES SPRINTER
MG TF
MINI CLUBMAN
MINI ONE/COOPER
MINI ONE/COOPER
MITSUBISHI LANCER
MORGAN PLUS 4
NISSAN 350Z
NISSAN TOWNSTAR
OPEL ASTRA
OPEL ASTRA
OPEL CORSA
OPEL VIVARO
OPEL ZAFIRA
EMC WAVE 3
EMC WAVE 3
ICKX K2
SPORTEQUIPE 5
SPORTEQUIPE 5
SPORTEQUIPE 7
PEUGEOT 205
PEUGEOT 206
PEUGEOT 208
PORSCHE 718 BOXSTER
PORSCHE 718 CAYMAN
PORSCHE 928
PORSCHE CAYENNE
RENAULT KANGOO
RENAULT MASTER RT
ROLLS-ROYCE PHANTOM
SEAT AROSA
SEAT IBIZA
SEAT IBIZA
SEAT LEON
SEAT MII
SMART FORFOUR
SMART FORTWO
SSANGYONG REXTON
SUBARU BRZ
SUBARU IMPREZA
SUBARU IMPREZA
SUZUKI ALTO
SUZUKI JIMNY
SUZUKI SAMURAI
SUZUKI SAMURAI
SUZUKI SWIFT
TOYOTA CELICA
TOYOTA LAND CRUISER
TOYOTA LAND CRUISER
TOYOTA MR-2
VOLVO S40
VW BEETLE
VW CADDY
VW GOLF
VW PASSAT
VW PASSAT CC
VW TRANSPORTER
LAND ROVER RANGE ROVER
MAN TGE
MERCEDES GLC
AUDI A3, S3
CITROEN C1
LAND ROVER RANGE ROVER SPORT
MERCEDES C CLASS
OPEL CORSA
OPEL CORSA
OPEL MOKKA
OPEL MOKKA
RENAULT CLIO
RENAULT CLIO
RENAULT TWINGO
SKODA CITIGO
VW GOLF
VW POLO
VW TRANSPORTER
AUDI Q5
MERCEDES S CLASS


Prompt:
泊头（河北省沧州市代管县级市）
泊头，是河北省沧州市下辖的县级市，位于河北东南部，地理坐标介于东经116°2′至116°5′，北纬37°54′至38°13′之间。河北省新兴城市。泊头，始建于东汉，因运河槽运兴起而得名。是环渤海地区重要的工业城市。泊头市面积为1007平方公里，市城区面积29平方公里。 泊头是著名的中国铸造名城、中国汽车冲压模具制造基地、中国鸭梨第一乡。 1993年被河北省委、省政府列为环渤海开发开放地区。1998年被河北省政府批准为省级综合改革试验县（市）。2019年11月，入选第二批节水型社会建设达标县（区）名单。
同义词 铸造之乡（河北省泊头市）一般指泊头（河北省沧州市代管县级市） 泊头建制沿革 泊头，春秋时大部属晋国，东部（今运河以东部分）属齐国。战国时隶赵国。 秦统一后，置郡县，市境属赵国钜鹿郡。 西汉高祖五年（公元前202年），在浮阳县（今沧县旧州）治，置渤海郡，在今献县河城街置河间郡，市境东部属幽州渤海郡，西部属冀州河间郡。文帝前元二年（公元前178年）河间郡改置河间国，市境西部即改属河间国。 汉武帝元朔三年（公元前126年），置建成县于今市内齐桥镇，置成平县于今齐桥镇大付村附近，置乐城县于今献县河城街，市境分为三县属地。建成，成平属幽州渤海郡，乐城属冀州河间国。 王莽代汉，将乐城改为陆信，不久复故。东汉初年，全国合并郡县，省建成入成平，改隶冀州河间国，市境分属成平、乐城二县。 桓帝时，乐城一度改为乐陵，三国时复故。同时，将河间国改为河间郡，隶于冀州。晋时改为河间国，并改乐成为乐城，与成平均属其辖。 北魏太和十一年（公元487年），分定州、冀州置赢州，辖河间、章武诸郡。延昌二年（公元513年），徙成平县治于景城故城，改隶章武郡，乐城仍属河间郡。 隋开皇三年（公元583年）罢郡，以州直接统县，变三级地方行政区划为二级制，河间、章武郡废。成平，乐城改隶瀛州。隋开皇十六年（公元596年），隋文帝于长芦置景州。同年，置弓高县，设治于今市境后城子，隶于观州。开皇十八年（公元598年），改成平为景城，并改隶景州。仁寿初年（公元601年），改乐城为乐寿，仍隶瀛州。今市境东南部隶于弓高，东北及中西部地区分别隶于景城，乐寿。大业三年（公元607年），废州复郡，乐寿、景城改隶河间郡，弓高改隶于平原郡。 唐武德元年（公元618年）景城改隶于沧州。武德四年（公元621年），置观州，治弓高，弓高县改隶观州。同时，改河间郡为瀛州。乐寿、景城改隶于瀛州。贞观元年（公元627年），景城又复隶于沧州。贞观十七年（公元643年）废观州，弓高改隶于沧州。天宝元年（公元742年）改瀛州为河间郡，沧州为景城郡，乐寿改隶河间郡景城，弓高改隶景城郡。乾元元年（公元758年），河间郡、景城郡复称瀛州，沧州。乐寿复隶瀛景城，弓高复隶于沧州。大历七年（公元772年），景城改隶于瀛州。大历中后期乐寿曾先后两次改隶深州。 后周显德六年（公元959年），省弓高入东光。宋熙宁六年（公元1073年），调整政区合并州县。景城省入乐寿，属河北东路河间郡，景城降为镇。是年，在石家圈（今西交河村）立镇。 金大定七年（公元1167年）废镇立县，名交河，隶属河间路献州，另置新桥镇于运河西岸，属交河县。据《嘉靖河间府志》，元至元二年（公元1265年），合并江北州县，交河并入乐寿，未几复故。属中书省河间路献州。 明洪武元年（公元1368年）改河间路为河间府。洪武四年（公元1371年）徙县治于今交河镇，仍称交河县，属河间府献州。洪武八年（公元1375年）因献州降为县而改隶河间府。洪武十年（公元1377年）再次合并州县，交河县省入献县。洪武十三年（公元1380年）复置，属京师河间府。 清代属直隶河间府。民国2年（公元1913年）北洋政府决定撤府留县，同时设置行政监察道，交河县隶直隶省渤海道。 民国3年（公元1914年）渤海道改为津海道。民国17年（公元1928年），交河县隶属河北省。民国26年（公元1937年），交河县划归河北省第八督察区。 抗日战争爆发后，市境国民党政权自行瓦解。日寇操纵汉奸建立伪交河县政府，先驻泊头，后移交河，属伪河北省政府。 1938年，共产党领导的交河县抗日民主政权县佐公署在市境西部毛家营成立，属冀中区第一专区。 1940年冀中区析交河县、献县置献交联合县。交河县、献交县同属冀中八专区。 1944年又析交河县东北部，沧县西部与青县组成青沧交联合县也属冀中八专区。抗战胜利后的1945年10月，撤销献交县。次年1月，撤销青沧交县，恢复原政区，仍属冀中八专区。 1946年5月，泊头解放，冀中区决定：将泊头城区由交河、南皮两县析出，设立泊头市，直隶冀中区。同年10月，重建献交、青沧交联合县，仍属冀中八专区。 1949年河北省人民政府成立后，设置沧县专区、三县一市均改属沧县专区。1949年9月至10月撤销两联合县和泊头市，恢复原交河县，并改泊头市为泊头镇（县级），同隶于沧县专区。 1953年11月政务院决定复置泊头市，直属河北省，由沧县专区代管。1956年复归沧县专区。 1958年4月，泊头市及交河县改属天津专区。12月，泊头市，交河县、东光县、阜城县、南皮县四县一市合并为交河县，泊头改为镇，为县政府驻地。是月，天津专区撤销而改隶天津市。1961年沧县专区恢复，交河县还属。7月，南皮、东光恢复建制，泊镇划归南皮。 1962年5月，恢复阜城县，又将泊镇划归交河县辖。1982年12月，国务院决定恢复泊头市，隶于沧州专区， 1983年5月，交河县撤销，其政区并入泊头市。 [3] 泊头行政区划 截至2005年，泊头市辖泊镇、交河镇、富镇、齐桥镇、文庙镇、郝村镇、寺门村镇、洼里王镇八镇及王武乡、四营乡、营子乡、西辛店乡四乡，辖古楼、解放、河东3个街道办事处。共有657个行政村，27个社区居委会。 [4] 街道/镇面积社区/村河东街道4.2平方千米潘庄、枣市、道东、南仓、刘辛、刘辛南、道东北、东顺河、车站南、车站北、红旗南、红旗北、南仓南、河东大、河东南、河东北、柳丝馆、河东北新街解放街道11.5平方千米新兴北街、新兴南街、裕华东路、裕华中路、裕华西路、岔道口街、建新街、安顺街、和平街、河西街、龙华街古楼街道4.9平方千米胜利南街、胜利北街、清真西街、解放西街、上下店街、刘庄南街、刘庄北街、火柴厂街、古楼街、米市街、粮市街、茶店街、盐店街、西关街、清真街、解放街、跃进街、新华街、光明街、煤场街、斜街营子镇93平方千米营子、大徘徊、小徘徊、曹村、周寺、姜庄、小城、金庄、计庄、来庄、前乜、后乜、郑庄、郝楼、蒋潘、刘潘、杨潘、桑庄、惠庄、大鲁道一、大鲁道二、大鲁道三、大鲁道四、王将军庄、东太师庄、西太师庄、玉皇庙西、玉皇庙东、玉皇庙中、石桥鲁道、东柏舟寺、西柏舟寺、沙鲁道、李鲁道、王鲁道、曹鲁道、段鲁道、苟鲁道、小吴潘、大吴潘、红庙潘、大赵庄、小赵庄、大徐郭、小徐郭、刘辛庄、东辛庄、西辛庄、庄头一、庄头二、庄头三、唐家庄、前杨庄、后杨庄、西杨庄、东杨庄、河嘴巴、西淮洼、东淮洼、前淮洼、后军屯、前军屯、小冯庄、小及庄、河北王、尚古庄交河镇79平方千米第一、第二；东关、西关、颜庄、辛庄、双庙、时庄、四合、陈北、陈东、陈西、新合、卢桥、顺桥、田庄、张庄、褚庄、北疃、霍庄、曹庄、高庄、陈庄、韩庄、郭庄、王庄、傅庄、南八里庄、北八里庄、北陈庄、三里庄、王大米、白官屯、西辛阁、东辛阁、五里庄、南及寺、李家寺、白道口、东桑庄、高峰寺、白赵庄、刘官屯、王我庄、千民屯、封官屯、李官屯、白王庄、北孟庄、南孟庄、陈四毛郝村镇93平方千米东郝、西郝、中郝、赵庄、崔坊、席庄、黄庄、王贾、孟贾、刘贾、张贾、李孔、王孔、张孔、及孔、藩庄、侯庄、燕庄、蔡庄、乐庄、林庄、刘庄、谢庄、綦庄、大庙、流寺、刘下码头、前张留信、磨盘李庄、席下码头、千里屯、东李庄、徐陈庄、西留堡、东留堡、前辛庄、后辛庄、北高庄、万家寨、常官屯、韩官屯、肖留信、王留信、林留信、北韩庄、鲁张庄、徐张庄、阎李庄、曹魏庄、西卢庄、樊庄、赵庞庄、东旺庄、双庙东、双庙西、徐贾庄、西周庄、徐高庄、西李庄寺门村镇82平方千米寺门、水胜、卧庄、张楼、韩集、白庄、郭楼、薛村、军董、军阎、阁上、宋八屯、白佛堂、彭家院、陈家庵、王奉一、杨码头、顾码头、白落鸦、侯落鸦、沈落鸦、刘寺门、杨寺门、绘彩于、火兴庄、东傅庄、潘江屯、苑码头、河北于、东赵庄、西靳庵、东靳庵、刘家坊、东辛店、西贾店、东贾店、小刘庄、小金庄、烧盆张、张吉草、苏辛庄、董留寺阁、北留寺阁、李留寺阁、杜留寺阁、王木匠庄、后大凝屯、前大凝屯、后牛官屯、前牛官屯、西望江店、洚河寺王文庙镇84平方千米文庙、尹圈、贺东、贺西、张药包、东赵、齐北、齐中、齐南、郭东、郭西、军西、军东、马庄、黄屯、剪南、剪北、常庄、滑庄、马桥、前乔官屯、后乔官屯、后赵辛庄、前赵辛庄、大孙庄、楚贾杜、冯家口、刘汉斗、扁渡里、姜皇庄、张皇庄、李皇庄、北张庄、南张庄、刘批条、赵家场、杜屯子、邵庄子、宗庄子、孟庄子、高庄子、伊王庄、碱场孙、三姓庄、纪辛庄、顾辛庄、杨辛庄、周辛庄、东刘庄、伊家庄、阎家庄、前薛窝、后薛窝、大薛窝齐桥镇104平方千米齐桥、高屯、王院、潘口、刘赞、李楼、大炉、小傅、大傅、张高、大李、范庄、田高、周庄、庞高、李高、军王、小园、小炉、尹店、尹庄、李屯、米庄、西王庄、南王庄、东王庄、老庄子、贾庄子、官庄子、小阎坞、大阎坞、安乐王、小孙庄、李小庄、西孟堂、东孟堂、入籍屯、压枝陈、小李庄、魏家庄、后淮漳、前淮漳、北曹庄、王古台、黄蛮庄、东菜园、西菜园、邬屯子、大胡屯、小胡屯、南辛寨、清河头、朱庄子、小河嘴、白家庄、果子张、五军寨、金庄子、昝觉寺、太平庄、张兵备、东三毛庄、西三毛庄、小苏辛庄、大马道口、小马道口、滹杨码头富镇78平方千米富镇、杨王、崔村、钱庄、姜庄、前高、尚庄、南庄、张店、张屯、咎庄、严铺、赵村、后高、邢屯、暗庄、东任英屯、西任英屯、孙亮屯、北董屯、东董屯、西董屯、李董屯、前董屯、小董屯、小江庄、孟官屯、堤口于、司官屯、楼子铺、前张屯、后张屯、西韩庄、北韩庄、毛家营、周官屯、张官屯、小赵屯、苑家庄、小卢屯、河西屯泊镇67平方千米堤口王、堤口张、河东大、大王庙、小杜庄、大魏庄、张边家、姚家庵、赵古屯、大何庄、小何庄、北三里、西三里、上下店、小齐堰、宋屯子、张庄子、双狮赵、姚辛庄、前辛庄、钓鱼台、王家庄、肖杜李、南马庄、张三家、河东南、清水洼、五里屯、谢家八里庄、杨家八里庄、贾家八里庄、红星八里庄、哈王八里庄、刘庄、余庄、解放、跃进、齐庄、前孔、后孔、周庄、西庞、东庞、潘庄、新华、茶店、董庄、段庄、河西、孙庄、西关、刘辛、窑厂、肖圈、龙屯洼里王镇76平方千米洼里王、西常庄、赵老户、左家桥、隆丰店、北段庄、张六指、东韩庄、小皇庄、北马庄、三岔河、后高尧、前高尧、北赵庄、五龙堂、李文席、常教庄、季家庄、赵白河、李多里、孟太监一、孟太监二、孟太监三、青牛庄一、青牛庄一、青牛庄二、沙河涯一、沙河涯二、沙河涯三、后八尺高、前八尺高、大沙窝头、小沙窝头、大杨皇庄、小杨皇庄、莫家八里庄、韩林、后赵、前赵、郭屯、米院、缴桥、后冯、前冯、贾庄、姜桥、新村、曾庄、范徐、杨桥、卢庄西辛店乡96平方千米南辛店、北辛店、两合铺、大江庄、西刘庄、北杨庄、南杨庄、周英庄、西韩庄、大周庄、宋家庄、军王庄、鲁官屯、王小营、四小营、亭子河、新李庄、西盛屯、东盛屯、黄铁房、张铁房、西郑庄、前甜水营、后甜水营、东窦庄屯、李家、及庄、常村、旧站、马村、满庄、油瓶、冯庄、耿村、彭庄、呼庄、武庄、齐庄、崔桥、秦村、武屯、洼里、傅村、倪屯、房于、南于、新立、夹疃、牧地、梁屯、梁店、陈庄四营乡75平方千米四营、宋村、穆村、苏庄、灌河、韩桥、杨村、许村、李庄、夹河、军张、杨庄、凌屯、田家庙一、田家庙二、田家庙三、田家庙四、田家庙五、大杭子东、大杭子西、张苏留慈、苦水王庄、都李泊洛、马刘泊洛、刘道口、韩留慈、郭留慈、范留慈、刘三番、赵三番、冯三番、常三番、常泊洛、冯泊洛、周泊洛、曲泊洛、尹泊洛、黄官屯、王官屯、杨官屯、袼褙陈、孟傅郜、大赵屯、西赵庄、佟王庄、火把张、南夹河、二杭子、三杭子、大卢屯、马家阁、张李庄、许姚庄王武庄乡69平方千米王武庄、南曹庄、中苏屯、东苏屯、西苏屯、后杨圈、前杨圈、西官道、东官道、小杨庄、大杨庄、金马驹、井刘庄、后陈庄、大树阎、簸箕张、杨家洼、洪码头、郭千庄、建昌店、舍药张、李福台、刘码头、南段庄、赵漫河、南赵庄、刘定杆、张东庵、张帆庄、后城子、九彩王、李冀北、孟码头、王银庄、洼里高、西十里高、东十里高、新建伯李庄、大庞、崔庄、魏庄、石桥、后河、前河、官庄、胡庄、戴庄、张庄、唐庄、常乐 泊头地理环境 泊头地理位置 泊头市 泊头市位于河北省东南部，市域地理坐标为：东经116°2′至116°5′，北纬37°54′至38°13′，横端长61.4公里，纵端长34.9公里，总面积1077平方公里。 [5] 北依京津，东临渤海，南接齐鲁，距北京市250公里，距塘沽港180公里，距黄骅港80公里。位于京津冀经济圈内，环渤海经济带中。 泊头位于河北平原腹心地带，属典型平原地貌，东接滨河平原，西连山前平原，无山无丘，平坦开阔，地形简单。古河残堤及淀泊淤积形成的槽形浅平洼地自西向东分布在市境北部一线。地势自南向东北缓倾，最高点西辛店乡秦村，海拔16.1米，最低点文庙镇楚贾杜村，海拔10.1米，平均海拔13.2米，自然坡降为万分之一。 [6] 境内主要河流有南运河、清凉江、滏阳新河、滏东排河、连接河、老盐河、江江河、南排河、黑龙港河。 [6] 泊头气候环境 泊头市属暖温带湿润大陆性季风气候区，又属冀南暖湿轻干旱区。大陆度64.69%，干燥度1.6%，由于受欧亚 大陆季风与地理位置影响，气候季间差明显，四季分明，冬季寒冷干燥，春季常干旱多风，夏季炎热多雨，秋季天高气爽。春、秋、冬三季多有西北风，夏季以西南风为多。年平均气温12.7度，无霜期187天，年均日照2.784小时，年均降水543毫米，降水多集中在7、8两月。 [6] 泊头自然资源 泊头市幅员广阔，土地资源丰富，市境总面积1006.5平方公里，折合150余万亩，国有土地面积87226.09 亩，国有耕地1272.20亩，土地利用率为98.6%。市域土壤由古河流冲积形成，层理清晰，土层深厚，疏松涵润，矿物质营养较高。各种农作物及野生植物均有生长。 [7] 泊头人口民族 2009年统计人口为57万人。汉族占97%，少数民族有回、壮、满、苗、朝鲜、白、土家、蒙古等17个少数民族。 [7] 泊头经济 泊头综述 2010年，泊头市生产总值完成119.35亿元，全部财政收入完成6.9亿元，入统工业增加值完成35.6亿元，全社会固定资产投资完成83.19亿元，城镇居民人均可支配收入、农民人均纯收入分别达到15095元、5463元。 2016年，生产总值完成215.8亿元，同比增长7%；全部财政收入完成13.7亿元，同比增长8%，其中一般公共预算收入完成7.7亿元，同比增长4.2%；固定资产投资完成220.3亿元，同比增长14.1%；规上工业增加值完成90.7亿元，同比增长7.6%；社会消费品零售总额完成99.9亿元，同比增长10.3%；城乡居民人均可支配收入分别达到27758元、12092元，同比分别增长9%、10%。 [8] 泊头第一产业 2012年上半年，泊头市农业完成产值42651万元，同比增长1.8%。占全部农林牧渔服务业总产值的比重43.3%。泊头完成畜牧业产值预计47528万元，同比增长 0.5%。占全部农林牧渔服务业总产值的比重48.3%。猪、牛、羊、家禽出栏分别为11.18万头、0.91万头、7.13万只、179.73万只。只有牛出栏呈下降趋势,下降了4%,猪、羊、家禽出栏则分别比2011年同期增长0.18%、2.9%、0.6%。禽蛋产量20943吨，比2011年同期增长1.5%。泊头的牛奶产量与2011年相比增长了。牛奶产量760吨，比2011年同期增长21.6%。肉类产量13675吨，比2011 [8] 年同期增长0.1%。猪、牛、羊、家禽的存栏分别为14.42万头、1.52万头、12.66万只、343.96万只。猪、牛存栏是增长趋势，分别比2011年同期增长了0.9%、12.6%。羊、家禽存栏是下降的，分别比2011年下降0.5%，3.5%。泊头市的能繁殖母猪存栏2.49万头,同比下降3.9%。泊头的渔业产量到目前为止达到311吨，同比下降1%。 [9] 泊头第二产业 泊头是一个工业基础雄厚的县级市，截至2012年有工业企业2000多家，工业总产值88亿元，增加值24.84亿元。工业主要有三大支柱特色产业，包括铸造产业、汽车模具产业、环保产业，还有机械，纺织，服装，化工，电子等，门类齐全。泊头已经形成了铸造、环保设备、汽车模具、果品业等四大特色产业。1912年，时为中国最大的化工企业——泊头永华火柴公司建成。 泊头在历史上是与广东佛山、江苏无锡齐名的国内三大铸造基地之一，素有“铸造之乡”的美誉。现有企业700余家，固定资产原值20亿元，从业人员5万人，年生产能力100万吨，2011年年产量达到50万吨，产值25亿元，利税4.8亿元，产品有十余个系列，400多个品种， 在50万吨的产量中有30万吨出口， 出口地区主要有欧美、亚洲、非洲，对经济的贡献率达到40%以上。泊头 泊头形成了以铸造、汽车模具、环保设备、工业泵阀为主体的特色主导产业。全市拥有企业900多家，产值120亿元，产品远销80多个国家和地区。泊头铸造年生产能力达到200多万吨，实型铸造产量占全国的50%以上，有工业和工艺两大类，数十个系列。铸件出口占全国出口总量的12.5%，占非工业类铸件出口总量的37.5%，生铁冶炼技艺被列入国家非物质文化遗产保护名录。汽车模具产值占全国同行业的16%，是全国汽车模具生产企业最密集的地区，是中国汽车零部件产业30强园区之一，有2家企业被命名为中国汽车零部件百强企业，拥有全国最大的汽车模具制造民营企业，为北汽、上汽、长安福特、广州本田等全国知名汽车制造企业配套生产模具。作为长江以北最大的大气除尘设备制造生产基地，泊头100多家环保设备制造企业中有2家被纳入全国骨干，全省10强中泊头占有6席。 [10] 2012年上半年，泊头209家规模以上工业企业完成增加值263792万元，在沧州市排第4位， 同比增长24.8%，在沧州市排第10位，增速比1-5月份提高1.3个百分点。实现主营业务收入1075248万元，同比增长43.5%，增速比1-5月份提高5.8个百分点；实现利税111621万元，在沧州市排第3位，同比增长36.1%，在沧州排第5位，增速比1-5月份略降0.8个百分点。 [11] 泊头第三产业 2012年上半年，全社会消费品零售总额完成270893万元，在沧州市排第5位，同比增长15.4%，在沧州市排第8位，增速比一季度放慢0.3个百分点。 金融机构存款余额为1605284万元，在沧州市排第4位，比年初增长13.52%，增速在沧州市排第7位；金融机构贷款余额为504339万元，在沧州市排第3位，比年初增长12.59%，在沧州排批6位。 [11] 泊头社会事业 泊头城市建设 2009年，市城区建城面积已由解放初的不足4平方公里发展到21.5平方公里。先后拓宽、翻修了解放路、胜 利路、裕华路、建设大街、红旗大街等主要街道，修筑了大型桥梁5座，立交桥2座。道路铺装面积160.1万平方米。市内公交线路12条。 [12] 泊头教育事业 解放前，除省立泊头第九师范外，境内仅有400所小学，在校生不过1万余人。如今，泊头教育事业飞速发展，全市有大学、全日制普通高校1所——泊头职业学院，中等专业学校3所，中学61所，小学565所，有各类教师4484人，在校生总数9万余人，小学教育和初等教育普及率达到99.7%和96.7%。历年高考开学率列沧州地区前茅。 [12] 泊头科技事业 80年代，境内先后建成研究所3个，各类科学技术学会、协会、研究会15个。1990年境内有科技人员2049人，其中中级技术职称以上人员1011人，其他各类专业技术人员6780人。共取得各类科研成果600余项。引进开发新产品117种，有18种达到国际先进水平。获省部级以上成果奖12项，获国家科技成果奖一项。秦国昌的《计划免疫信息处理系统》学术论文在第六届世界医药信息学大会上宣读。 [12] 泊头医疗事业 1949年，境内仅有2家医院及几个民间药铺，医生28名。1990年，境内有医院32个、门诊所5个，医院床位数780张，卫生技术人员1586人，其中医生754人。 [12] 泊头文化事业 80年代中期，市电视台和广播电台先后建成开播。1990年，全市有影剧院3个，电影放映单位40个，文化馆、图书馆各1座。市剧团被誉为全国著名的“庄户剧团”。朝花农民诗社为代表的文学创作活动蓬勃开展。 [12] 泊头体育事业 1958年，金净云获河北省体操比赛高低杠的自由体操两项第一名；1990年，马娜获河北省少儿武术比赛三项冠军，徐海龙获“中国武士杯”自选剑术比赛第二名。市体育馆、游泳池、市一中体育场等及各企业，学校等体育设施的兴建，为市境人民的体育锻炼活动创造了良好条件。1990年，河北省中学生运动会首次在县级市——泊头举行。 [12] 泊头交通运输 泊头境内有京沪铁路及客货站，京沪铁路、石黄高速、京沪高速、104、106国道穿城而过，截至2010年有省道2条、县道3条、乡道12条，通车里程1111公里。 [7] 京杭大运河纵贯市区，距塘沽港180公里，距黄骅港80公里。 [13] 境内河流有：南运河、清凉江、滏阳新河、滏东排河、连接河、老盐河、江江河、南排河、黑龙港河，水运便利。 泊头旅游资源 泊头清真寺 泊头清真寺位于市区清真街南端，始建于明永乐二年（公元1404年），占地面积11200平方米，房屋近200间，建筑面积3000多平方米。1982年7月列为河北省重点文物保护单位。泊头清真寺是华北地区最大的木质结构清真寺，2001年列为全国重点文物保护单位。 菜园石槽 菜园石槽位于泊头市齐桥镇东菜园儿小学院内东北角处。石槽长2.8米，宽0.7米，厚0.5米，底厚0.21米，槽壁厚0.11米，重约2吨。在槽的底部一侧，有一圆孔，是为排水设置的。 [14] 泊头著名人物 孙桐萱早期的著名空军飞行员贾庆林中共中央政治局常委，第十届、十一届全国政协主席李松林河北省泊头人河北省美协会员，中国东方书画家协会理事封瑞为中国早期的城市道路规划做出过一定贡献吴玉阳当代实力派工笔重彩画家 泊头城市荣誉 中国汽车模具之乡中国鸭梨第一乡全国鸭梨无公害生产示范县绿色食品基地全国园艺产品出口示范基地中国铸造之乡河北省环保产业试点市河北省大气污染治理设备生产基地第一届河北省文明城市 [15] 第二批节水型社会建设达标县（区） [2] 词条图册 更多图册 参考资料 1. 泊头梨花节(图) ．新浪网．2013-4-16[引用日期2014-05-16] 2. 水利部关于公布第二批节水型社会建设达标县（区）名单的公告 ．水利部[引用日期2019-11-16] 3. 建制沿革 ．泊头人民政府网．2005-8-13[引用日期2014-05-16] 4. 行政区划 ．泊头人民政府网．2005-9-2[引用日期2014-05-16] 5. 泊头市金三角物流园 ．泊头金日快运车队[引用日期2013-04-18] 6. 自然环境 ．新华网．2009-7-25[引用日期2014-05-16] 7. 泊头 ．中国运河网．2010-10-11[引用日期2014-05-16] 8. 市情概览 ．泊头市人民政府[引用日期2018-12-29] 9. 2012年泊头市上半年农业生产情况 ．泊头人民政府网．2012-7-25[引用日期2014-05-16] 10. 泊头：河北惟一的“环保设备产业试点市” ．长城网．2012-6-20[引用日期2014-05-16] 11. 2012年上半年泊头市主要经济指标完成情况 ．泊头人民政府网．2012-8-7[引用日期2014-05-16] 12. 泊头市概况 ．市县招商网．2011-8-22[引用日期2014-05-16] 13. 泊头城市介绍 ．中国天气网[引用日期2014-05-16] 14. 齐桥菜园石槽 ．新华网．2014-1-7[引用日期2014-05-16] 15. 新华区、高邑县等荣获首届河北省文明城区(县城)称号 ．澎湃网[引用日期2017-12-15] 展开全部 收起

泊头有哪些交通运输优势?京杭大运河到塘沽港和黄骅港的距离各是多少?


Prompt:
	extract NAME_1 pay, taxes, overtime and group by employee names from following data
	                                                       Widget Manufacturing Corp.
Employee Payroll Report
From 9/1/2012 to 9/30/2012
Employee Info                                          Federal                                                                                Other

												Pre-Tax    Taxable    Federal  Soc Sec  Medicare                  State        Other                     Deductions

	Check #        Pay Date          NAME_1 Pay  Deduction  Wages      Withholding Withholding Withholding         Withholding  Withholding      SUI/SDI  (Additions)  Net Pay
Department : Accounting
1004 NAME_2
09/05/2012        2,880.00   413.00     2,467.00   331.92   115.71             39.95    NY  :  95.21        0.00             0.00          0.00    1,828.36
																										   CA  :  55.85
	DD             09/28/2012        720.00     197.00     523.00     59.38    24.99              8.62     NY  :  0.00         0.00             0.00          0.00    408.01
																										   CA  :  22.00
				   09/30/2012        2,880.00   413.00     2,467.00   331.92   115.71             39.95    NY  :  95.21        0.00             0.00          0.00    1,828.36
																										   CA  :  55.85
				   Employee Totals:  6,480.00   1,023.00   5,457.00   723.22   256.41             88.52           324.12       0.00             0.00          0.00    4,064.73
	1008  NAME_3 NAME_4
				   09/05/2012        5,000.00   419.00     4,581.00   418.00   205.01             70.78    NY :   229.00       0.00             2.60          0.00    3,655.61
				   09/28/2012        1,153.87   188.23     965.64     0.00     43.46              15.01    NY :   14.00        0.00             2.60          0.00    890.57
				   09/30/2012        5,000.00   419.00     4,581.00   418.00   205.00             70.77    NY :   229.00       0.00             2.60          0.00    3,655.63
				   Employee Totals:  11,153.87  1,026.23   10,127.64  836.00   453.47             156.56          472.00       0.00             7.80          0.00    8,201.81
	1009  NAME_5
				   09/05/2012        1,000.00   0.00       1,000.00   163.32   42.00              14.50    NJ :   29.37        0.24             1.88          0.00    698.94
																										   NY :   49.75
				   09/07/2012        1,000.00   0.00       1,000.00   163.32   42.00              14.50    NJ :   29.37        0.00             0.00          0.00