College Life 4 - Problem Code - COLGLF4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### https://www.codechef.com/MARCH21C/problems/COLGLF4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N , E, H, A, B, C."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 4 4 2 2 2\n",
    "\n",
    "4 5 5 1 2 3\n",
    "\n",
    "4 5 5 3 2 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 10 10 2 4 5\n"
     ]
    }
   ],
   "source": [
    "n, e, h, a, b, c = [int(i) for i in input().split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['om', 'msk', 'ck']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_price = {\n",
    "    'om':a,\n",
    "    'msk':b,\n",
    "    'ck':c\n",
    "}\n",
    "\n",
    "s_d = list(dict(sorted(item_price.items(), key=lambda x:x[1])).keys())  # min ---> max\n",
    "s_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "possible_items = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prep_om():\n",
    "    global e, possible_items\n",
    "    possible_om = int(e/2)\n",
    "    e = e%2\n",
    "    possible_items['om'] = (possible_om)\n",
    "    print(f'omelette:egg = {e}, possible = {possible_om}')\n",
    "\n",
    "def prep_choko_msk():\n",
    "    global h, possible_items\n",
    "    possible_msk = int(h/3)\n",
    "    h = h%3\n",
    "    possible_items['msk'] = (possible_msk)\n",
    "    print(f'milkshake:choko = {h}, possible = {possible_msk}')\n",
    "    \n",
    "\n",
    "def prep_cake():\n",
    "    global e, h, possible_items\n",
    "    possible_cake = int(min(e, h))\n",
    "    e = e-possible_cake\n",
    "    h = h-possible_cake\n",
    "    possible_items['ck'] = (possible_cake)\n",
    "    print(f'cake:egg = {e}, choko={h}, possible = {possible_cake}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "prep_items = {\n",
    "    'om':prep_om,\n",
    "    'msk':prep_choko_msk,\n",
    "    'ck':prep_cake\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "omelette:egg = 0, possible = 5\n",
      "milkshake:choko = 1, possible = 3\n",
      "cake:egg = 0, choko=1, possible = 0\n"
     ]
    }
   ],
   "source": [
    "for item in s_d:\n",
    "    prep_items[item]()\n",
    "    remaining = n-sum(possible_items.values())\n",
    "    if sum(possible_items.values())>=n:\n",
    "        possible_items[item]+=remaining\n",
    "        break    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(possible_items.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "if sum(possible_items.values())<n:\n",
    "    total = -1\n",
    "else:\n",
    "    for item in s_d:\n",
    "        try:\n",
    "            total = total+(item_price[item]*possible_items[item])\n",
    "        except:\n",
    "            pass    \n",
    "    \n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "10 10 10 2 4 5\n",
      "-1\n",
      "50 60 80 9 5 3\n",
      "150\n",
      "900 1500 700 10 13 11\n",
      "9150\n"
     ]
    }
   ],
   "source": [
    "test_case = int(input())\n",
    "\n",
    "for _ in range(test_case):\n",
    "    n, e, h, a, b, c = [int(i) for i in input().split()]\n",
    "\n",
    "    item_price = {\n",
    "        'om':a,\n",
    "        'msk':b,\n",
    "        'ck':c\n",
    "    }\n",
    "\n",
    "    s_d = list(dict(sorted(item_price.items(), key=lambda x:x[1])).keys())  # min ---> max\n",
    "\n",
    "    possible_items = {}\n",
    "\n",
    "    def prep_om():\n",
    "        global e, possible_items\n",
    "        possible_om = int(e/2)\n",
    "        e = e%2\n",
    "        possible_items['om'] = (possible_om)\n",
    "\n",
    "    def prep_choko_msk():\n",
    "        global h, possible_items\n",
    "        possible_msk = int(h/3)\n",
    "        h = h%2\n",
    "        possible_items['msk'] = (possible_msk)\n",
    "\n",
    "\n",
    "    def prep_cake():\n",
    "        global e, h, possible_items\n",
    "        possible_cake = int(max(e, h))\n",
    "        e = e-possible_cake\n",
    "        h = h-possible_cake\n",
    "        possible_items['ck'] = (possible_cake)\n",
    "\n",
    "    prep_items = {\n",
    "        'om':prep_om,\n",
    "        'msk':prep_choko_msk,\n",
    "        'ck':prep_cake\n",
    "    }\n",
    "\n",
    "    for item in s_d:\n",
    "        prep_items[item]()\n",
    "        remaining = n-sum(possible_items.values())\n",
    "        if sum(possible_items.values())>=n:\n",
    "            possible_items[item]+=remaining\n",
    "            break    \n",
    "\n",
    "    total = 0\n",
    "    if sum(possible_items.values())<n:\n",
    "        total = -1\n",
    "    else:\n",
    "        for item in s_d:\n",
    "            try:\n",
    "                total = total+(item_price[item]*possible_items[item])\n",
    "            except:\n",
    "                pass    \n",
    "\n",
    "    print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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