homework0-2.ipynb

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        "# Homework 0 (100 points)\n",
        "\n",
        "**Name and BUID of everyone in your group:** ...\n",
        "\n",
        "Naima U04989541\n",
        "\n",
        "The goal of this homework is to practice programming in python. This homework is a bit longer than the rest of the homeworks in this course.\n",
        "\n",
        "**You may NOT use any other libraries other than the ones already imported.** _For clarification, you'll notice some of the question ask for using numpy, and those methods have numpy imported, but you cannot use it anywhere else! Essentially, if it is imported in a certain cell in the notebook, it can only be used in that cell (The code has also been made to follow this idea so you don't have to worry about it; just don't import anything!)_\n",
        "\n",
        "## Exercise 1 (45 points):\n",
        "\n",
        "### Implementing Rico's New Company: Joyride\n",
        "\n",
        "Here you will be practicing object-oriented programming and will touch on a bit of linear algebra.\n",
        "\n",
        "_Rico recently was inspired by Uber and created his own ride-sharing company called Joyride. This company is like Uber, but only luxury cars are used to pickup and drop off people. Luckily, he has hired you, a CS 506 expert, to help him with his company._\n",
        "\n",
        "a) (**4 pts**) Create a `Car` class that has the following attributes:\n",
        "\n",
        "- `car_model` (`CarModel` enum)\n",
        "- `max_gas_tank` (float, in gallons, the maximum amount of gas the car can hold)\n",
        "- `current_gas_tank` (float, in gallons, the current amount of gas in the car's gas tank)\n",
        "- `mpg` (float, miles per gallon, how many miles the car can drive per gallon of gas)\n",
        "- `cost_per_refill` (float, in dollars, how much it costs to refill the gas tank, which is paid by Joyride)\n",
        "- `capacity` (int, number of people the car can hold)\n",
        "- `price_per_mile` (float, in dollars, how much Joyride charges passengers per mile the car has to drive. NOTE that this is independent from number of passengers in the car.)\n",
        "- `total_miles_driven` (float, in miles, how many miles the car has driven in total)\n",
        "- `max_miles` (float, in miles, how many miles the car can drive before the car breaks completely and cannot be repaired)\n",
        "- `purchased_price` (float, in dollars, how much Joyride paid to purchase the car)\n",
        "- `license_plate` (string, the license plate of the car)\n",
        "\n",
        "_You may assume the car will never need repairs. However, as `max_miles` attribute suggests, the car will break once it has reached the maximum number of miles and cannot be repaired. Instead, the car will be thrown out and a new car will be bought._\n",
        "\n",
        "Be sure to include a `__init__` method that takes in the above attributes as parameters and sets them as attributes of the class.\n",
        "\n",
        "You do not need to include a `__str__` method nor setters and getters methods. Your design choices for the `__init__` method are up to you; no specific design is required.\n",
        "\n",
        "For your convenience, the `CarModel` enum is defined below and a little bit has been done for you."
      ]
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        "# NOTE: Enum class already made for you\n",
        "from enum import Enum\n",
        "\n",
        "\n",
        "class CarModel(Enum):\n",
        "    LEMON_LAMBORGHINI = \"Lemon Lamborghini\"\n",
        "    MAYO_MCLAREN = \"Mayo McLaren Elva\"\n",
        "    HERSHEY_HENNESSEY = \"Hershey Hennessey Venom F5\"\n",
        "    MILKY_MERCEDES = \"Milky Mercedes-AMG Project One\"\n",
        "    SALTY_SEVEN = \"Salty 777 Hypercar\"\n",
        "\n",
        "class Car:\n",
        "    #TODO: implement the Car class below\n",
        "    def __init__(self, car_model, max_gas_tank, current_gas_tank, mpg, cost_per_refill, capacity, price_per_mile, total_miles_driven, max_miles, purchased_price, license_plate):\n",
        "      self.car_model = car_model\n",
        "      self.max_gas_tank = max_gas_tank\n",
        "      self.current_gas_tank = current_gas_tank\n",
        "      self.mpg = mpg\n",
        "      self.cost_per_refill = cost_per_refill\n",
        "      self.capacity = capacity\n",
        "      self.price_per_mile = price_per_mile\n",
        "      self.total_miles_driven = total_miles_driven\n",
        "      self.max_miles = max_miles\n",
        "      self.purchased_price = purchased_price\n",
        "      self.license_plate = license_plate\n",
        "\n",
        "    # pass # remove this line when you implement the rest of the class\n",
        "\n",
        "    def __repr__(self):\n",
        "        return self.license_plate #NOTE: please use this variable name for your license plate"
      ]
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        "b) (**4 pts**) As we need to store these cars somewhere, create a `CarGarage` class that has the following attributes:\n",
        "\n",
        "- `location`: string, stores the name of the city the garage is located in.\n",
        "- `cars`: list of `Car` objects, stores the cars inside the garage. This list should be empty when the `CarGarage` is initialized.\n",
        "\n",
        "This class will be used to store the available cars for Joyride drivers. Be sure to include a `__init__` method that takes in the above attributes as parameters and sets them as attributes of the class. **You should also include a setter method for each attribute.**\n",
        "\n",
        "You do not need to include a `__str__` method nor a `__repr__` method.\n"
      ]
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        "from typing import List, Dict, Tuple\n",
        "\n",
        "\n",
        "class CarGarage:\n",
        "    # TODO: implement the CarGarage class below\n",
        "\n",
        "    def __init__(self, location):\n",
        "      self.location = location\n",
        "      self.list_cars = []\n",
        "    def location(self,location):\n",
        "      self.location = location\n",
        "    def location(self):\n",
        "      return self.location\n",
        "    def cars(self):\n",
        "      return self.list_cars\n",
        "    def cars(self,cars_list_arg):\n",
        "      self.list_cars = cars_list_arg\n",
        "\n",
        "\n",
        "    # NOTE: when working on parts c) - f), you will have to edit this cell to fill in the methods below!\n",
        "\n",
        "    def add_car_in_garage(self, car: Car) -> None:\n",
        "        \"\"\"\n",
        "        This method adds a car into the list of cars (adds it to the end of the list)\n",
        "        \"\"\"\n",
        "        #TODO: implement this method and remove the pass\n",
        "        if isinstance(car, Car):\n",
        "          self.list_cars.append(car)\n",
        "\n",
        "\n",
        "    def filter_cars_by_price(self, price_limit: float) -> List[Car]:\n",
        "        \"\"\"\n",
        "        This method will take in the price_limit (price per mile) and will return\n",
        "        all cars that are below (or equal to) the price limit (the price per mile).\n",
        "        The cars list however is not modified in this method.\n",
        "        \"\"\"\n",
        "        #TODO: implement this method and remove the pass\n",
        "        filtered = []\n",
        "        for car in self.list_cars:\n",
        "          if car.purchased_price<price_limit:\n",
        "            filtered.append(car)\n",
        "\n",
        "\n",
        "\n",
        "    def remove_broken_cars(self) -> None:\n",
        "        \"\"\"\n",
        "        This method will remove all cars that are broken from the cars list.\n",
        "        As defined in part a), a car is broken if it has reached its max_miles.\n",
        "        \"\"\"\n",
        "        without_broken = list()\n",
        "        for car in self.list_cars:\n",
        "          if car.total_miles_driven<car.max_miles:\n",
        "            without_broken.append(car)\n",
        "\n",
        "\n",
        "\n",
        "    def get_top_car_preferences(\n",
        "        self, preferences: Dict[str, Tuple[any, int]]\n",
        "    ) -> List[Car]:\n",
        "        \"\"\"\n",
        "        This method will return the top car preferences based on the preferences dictionary.\n",
        "        See part f) for a thorough description of how this method should work.\n",
        "        \"\"\"\n",
        "        #TODO: implement this method and remove the pass\n",
        "        pass"
      ]
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        "c) (**3 pts**) Implement the `__add_car_in_garage` method above. This method should take in a `Car` object as a parameter and add it to the `cars` attribute of the `CarGarage` class. Additional description is provided in the docstring. Be sure to write your code in the space provided.\n",
        "\n",
        "d) (**4 pts**) Implement the `__filter_cars_by_price` method above. This method should take in a `float` as a parameter and return a list of `Car` objects that have a `purchased_price` attribute less than or equal to the parameter. Additional description is provided in the docstring. Be sure to write your code in the space provided.\n",
        "\n",
        "e) (**5 pts**) Implement the `__remove_broken_cars` method above. This method should remove all cars from the `cars` attribute of the `CarGarage` class that have a `total_miles_driven` attribute greater than or equal to the `max_miles` attribute. Additional description is provided in the docstring. Be sure to write your code in the space provided.\n",
        "\n",
        "f) **(10 pts)** Implement the `__get_top_car_preferences` method above. This method should take in a `preferences` dictionary and return a sorted list of all the cars, from the most preferred at the start to the least preferred.\n",
        "\n",
        "An example of the preferences dictionary is:\n",
        "\n",
        "```python\n",
        "{\n",
        "    \"car_model\": (CarModel.LEMON_LAMBORGHINI, 2),\n",
        "    \"capacity\": (4, 1),\n",
        "    \"price_per_mile\": (40.25, 3)\n",
        "}\n",
        "```\n",
        "\n",
        "Notice not all fields from the `Car` object are included --- this means that the customer doesn't need to put all fields in their preferences. The preferences are ranked here from 1 to 3, with 1 being the most preferred and 3 being the least preferred (Here, `capacity` has the highest preference and `price_per_mile` has the lowest preference). This also means your code should work for any group of preferences given.\n",
        "\n",
        "**Clarifying Questions you asked Rico:**\n",
        "\n",
        "> _\"How do I sort if the user put in a number that does not match any car?\"_\n",
        ">\n",
        "> Let's give an example of this situation. If you have a customer give a preference like\n",
        ">\n",
        "> ```python\n",
        "> {\n",
        ">     \"mpg\": 90\n",
        "> }\n",
        "> ```\n",
        ">\n",
        "> But no car has exactly 90 miles per gallon, you can compute it's distance from the closest car's mpg and sort based on that. For example, if the closest car has 80 mpg, then the distance is 10 since the customer wanted 90 (90-80 = 10). If the closest car has 100 mpg, then the distance is respectively 10. You can then sort based on the distance, where the smaller distance is the more preferred.\n",
        "\n",
        "> _\"What would happen if a user put in a car that does not exist in the `CarGarage`?\"_\n",
        ">\n",
        "> If this is the case, then the output should be an empty list. This is because the user's preferences cannot be met.\n",
        "\n",
        "> _\"How do I sort the CarModel values? I can't just sort the enum values.\"_\n",
        ">\n",
        "> In this situation, do the following: If the CarModel is the exact CarModel that is given in the preference, assign it a number like 1. For all the other CarModels that do not match the one in the preferences, assign them a bigger number like 2. Then, sort based on the number you assigned to each CarModel. This way, the CarModel that matches the preference will be sorted first.\n",
        "\n",
        "> _\"I'm not sure how to sort the cars based on multiple preferences in python, how do I do this?\"_\n",
        ">\n",
        "> You can manipulate the `lambda` feature from Worksheet 0.\n",
        "> You are welcome to ask ChatGPT about it or use this link: https://www.geeksforgeeks.org/sort-a-list-of-objects-by-multiple-attributes-in-python/.\n",
        "\n",
        "> _\"Do I consider tie-breakers?\"_\n",
        ">\n",
        "> Nope! Don't worry about tie-breakers. If two cars have the same distance from the customer's preference, then you can just put them in any order. Similarly, if two cars have the same exact values for each field, then you can put them in any order as well.\n",
        "\n",
        ">_\"Can you give a basic example?\"_\n",
        "> Sure, consider this preferences dictionary:\n",
        ">\n",
        "> ```python\n",
        "> {\n",
        ">     \"car_model\": (CarModel.LEMON_LAMBORGHINI, 2),\n",
        ">     \"capacity\": (4, 1),\n",
        "> }\n",
        "> ```\n",
        ">\n",
        "> Here, `capacity` is more important (priority 1) than `car_model`. To sort this, look at a given car list and see which ones are closest to capacity of 4. Then, if there is a tie, look at the `car_model`.\n",
        "### Analyzing Profits\n",
        "\n",
        "Now that Rico has his garage all ready and set up, he decides to write an application to be on the show Shark Tank in hopes of getting money from investors to make his business idea a reality. Below is a `SharkTankApplication` _interface_ that has the following attributes:\n",
        "\n",
        "- `company_name`: string, the name of the company\n",
        "- `num_employeees`: int, the number of employees that work for the company\n",
        "- `factories`: list of any type, the factories the company owns. (Note in the context of Joyride this will be a list of `CarGarage` objects)\n",
        "- `total_profit`: float, the total profit the company has made since it started. This is calculated by the `__calculate_total_profit` method.\n",
        "- `equity`: float, the percentage of the company Rico is willing to give up to the investors\n",
        "- `pitch_score`: float, a percentage score that the judges will use to determine if they want to invest in the company. This score is calculated by the `__calculate_pitch_score` method."
      ]
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        "# NOTE: ****DO NOT EDIT THIS CELL*****\n",
        "\n",
        "# This is the SharkTankApplication interface.\n",
        "\n",
        "import abc  # abstract base class\n",
        "from typing import List\n",
        "\n",
        "class SharkTankApplication(abc.ABC):\n",
        "    def __init__(\n",
        "        self, company_name: str, num_employees: int, factories: List[any], equity: float\n",
        "    ):\n",
        "        self.company_name = company_name\n",
        "        self.num_employees = num_employees\n",
        "        self.factories = factories\n",
        "        self.equity = equity\n",
        "        self.total_profit = self.calculate_total_profit()\n",
        "        self.pitch_score = self.calculate_pitch_score()\n",
        "\n",
        "    @abc.abstractclassmethod\n",
        "    def calculate_pitch_score(self) -> float:\n",
        "        \"\"\"\n",
        "        This method will compute the pitch score of the company.\n",
        "        Each company will compute pitch score differently, so\n",
        "        this method is abstract.\n",
        "        \"\"\"\n",
        "        pass\n",
        "\n",
        "    @abc.abstractclassmethod\n",
        "    def calculate_total_profit(self) -> float:\n",
        "        \"\"\"\n",
        "        This method will compute the revenue of the store by only\n",
        "        looking at all the sold items' profit. However, each store\n",
        "        will compute revenue differently, so this method is abstract.\n",
        "        \"\"\"\n",
        "        pass"
      ]
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        "Rico will have his own application, which is a `JoyrideApplication` class. This class will inherit from the `SharkTankApplication` class. You will have to implement the methods that are not implemented: `__calculate_pitch_score` and `__calculate_total_profit`.\n",
        "\n",
        "However, do notice two added fields that are in the `SharkTankApplication` interface:\n",
        "\n",
        "- `customer_dissatisfaction`: float, a number between 0 and 5 that represents what percentage of customers are unsatisfied, rated by customers (0 is best, 5 is worst).\n",
        "- `employee_retention_rate`: float, a number between 0 and 1 that represents what percent of employees stay in the company (per year)."
      ]
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        "# NOTE: when working on parts g) - h), you will have to edit this cell to fill in the methods!\n",
        "from typing import List\n",
        "\n",
        "\n",
        "class JoyrideApplication(SharkTankApplication):\n",
        "    \"\"\"\n",
        "    This class represents a Joyride Application.\n",
        "\n",
        "    There are two new fields:\n",
        "    - company_rating: float (rating between 0 and 1, where 1 is the best)\n",
        "    - employee_retention_rate: float (rating between 0 and 1, where 1 is 100%)\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        company_name: str,\n",
        "        num_employees: int,\n",
        "        factories: List[CarGarage],\n",
        "        equity: float,\n",
        "        customer_dissatisfaction: float,  # new field\n",
        "        employee_retention_rate: float,  # new field\n",
        "    ) -> None:\n",
        "        self.customer_dissatisfaction = customer_dissatisfaction\n",
        "        self.employee_retention_rate = employee_retention_rate\n",
        "        super().__init__(company_name, num_employees, factories, equity)\n",
        "\n",
        "\n",
        "    def calculate_pitch_score(self) -> float:\n",
        "        \"\"\"\n",
        "        This method will compute the pitch score of the company.\n",
        "        For more information on how to compute the pitch score,\n",
        "        see part g).\n",
        "        \"\"\"\n",
        "        #TODO: implement this method and remove the pass\n",
        "        pass\n",
        "\n",
        "    def calculate_total_profit(self) -> float:\n",
        "        \"\"\"\n",
        "        This method will compute the revenue of the store by only\n",
        "        looking at all the sold items' profit. For more information\n",
        "        on how to compute the revenue, see part h).\n",
        "        \"\"\"\n",
        "        #TODO: implement this method and remove the pass\n",
        "        pass\n",
        "\n",
        "    # NOTE: do not edit this method!\n",
        "    def __str__(self) -> str:\n",
        "        return \"Company Joyride\""
      ]
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        "g) **(5 pts)** Implement the `calculate_pitch_score` method above. This method should return a float between 0 and 1 that represents the pitch score of the company. The pitch score is calculated by the following formula (inspired from cosine similarity):\n",
        "\n",
        "$$\n",
        "\\vec{v} = <\\text{equity}, \\text{total profit}>\n",
        "$$\n",
        "\n",
        "$$\n",
        "\\vec{w} = <\\text{customer dissatisfaction}, \\text{employee retention rate}>\n",
        "$$\n",
        "\n",
        "$$\n",
        "\\text{pitch score} = \\frac{|\\vec{v} \\cdot \\vec{w}|}{||\\vec{v}|| \\cdot ||\\vec{w}||}\n",
        "$$\n",
        "\n",
        "h) (**10 pts**) Implement the `calculate_total_profit` method above. This method should return a float that represents the total profit the company has made since it started. The total profit is computed by summing up the revenue for each car in each factory. To compute this, note the following:\n",
        "\n",
        "- Each car has a `total_miles_driven` attribute that represents how many miles the car has driven in total.\n",
        "- Each car has a `price_per_mile` attribute that represents how much Joyride charges passengers per mile the car has to drive.\n",
        "- Each car has a `cost_per_refill` attribute that represents how much it costs to refill the gas tank.\n",
        "- Each car has a `purchase_price` attribute that represents how much Joyride paid to purchase the car.\n",
        "- Each car has a `mpg` attribute that represents how many miles the car can drive per gallon of gas.\n",
        "- Each employee gets paid $10,000 (don't worry about how often).\n",
        "\n",
        "You can assume the following:\n",
        "\n",
        "- The car's gas is only refilled when the car hits 0 gallons of gas (don't worry about partial refills).\n",
        "- Each employee got a total of $10,000 and none of their salaries are different from one another.\n",
        "- Don't worry about the cost of gas changing over time.\n",
        "- Don't worry about broken cars; there is no need to filter those out (we need them so we can track profit here).\n",
        "- Rico's salary is not included in the total profit (so just use `num_employees` and don't adjust that number)\n",
        "\n",
        "Be sure to write your code in the space provided above.\n",
        "\n",
        "### Debugging Your Code (Non-hidden tests)\n",
        "\n",
        "Run the following code to ensure your code works properly. Half the test cases' expected output are not shown here, but will be used to grade your code, as you will see in the next section."
      ]
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        "adesanya_car = Car(\n",
        "    CarModel.LEMON_LAMBORGHINI,\n",
        "    max_gas_tank=40,\n",
        "    current_gas_tank=23,\n",
        "    mpg=70,\n",
        "    cost_per_refill=30,\n",
        "    capacity=6,\n",
        "    price_per_mile=650,\n",
        "    total_miles_driven=9045.35,\n",
        "    max_miles=10000,\n",
        "    purchased_price=1200000,\n",
        "    license_plate=\"ADESAN1\",\n",
        ")\n",
        "\n",
        "jones_car = Car(\n",
        "    CarModel.MAYO_MCLAREN,\n",
        "    max_gas_tank=40,\n",
        "    current_gas_tank=30,\n",
        "    mpg=60,\n",
        "    cost_per_refill=23.4,\n",
        "    capacity=2,\n",
        "    price_per_mile=500,\n",
        "    total_miles_driven=10500,\n",
        "    max_miles=100000,\n",
        "    purchased_price=1000000,\n",
        "    license_plate=\"JONESBONE\",\n",
        ")\n",
        "\n",
        "ngannou_car = Car(\n",
        "    CarModel.HERSHEY_HENNESSEY,\n",
        "    max_gas_tank=50,\n",
        "    current_gas_tank=50,\n",
        "    mpg=50,\n",
        "    cost_per_refill=10.53,\n",
        "    capacity=2,\n",
        "    price_per_mile=400,\n",
        "    total_miles_driven=10000,\n",
        "    max_miles=10000,\n",
        "    purchased_price=800000,\n",
        "    license_plate=\"PREDA7OR\",\n",
        ")\n",
        "\n",
        "strickland_car = Car(\n",
        "    CarModel.MILKY_MERCEDES,\n",
        "    max_gas_tank=130,\n",
        "    current_gas_tank=10,\n",
        "    mpg=10,\n",
        "    cost_per_refill=50,\n",
        "    capacity=4,\n",
        "    price_per_mile=3000,\n",
        "    total_miles_driven=101.3,\n",
        "    max_miles=10000,\n",
        "    purchased_price=850000,\n",
        "    license_plate=\"BASURA\",\n",
        ")\n",
        "\n",
        "elon_musk_car = Car(\n",
        "    CarModel.SALTY_SEVEN,\n",
        "    max_gas_tank=100,\n",
        "    current_gas_tank=100,\n",
        "    mpg=100,\n",
        "    cost_per_refill=90.37,\n",
        "    capacity=6,\n",
        "    price_per_mile=3000.49,\n",
        "    total_miles_driven=19284,\n",
        "    max_miles=60000,\n",
        "    purchased_price=4000000,\n",
        "    license_plate=\"X Æ A-12\",\n",
        ")\n",
        "\n",
        "rico_car = Car(\n",
        "    CarModel.SALTY_SEVEN,\n",
        "    max_gas_tank=120,\n",
        "    current_gas_tank=20,\n",
        "    mpg=200,\n",
        "    cost_per_refill=100.5,\n",
        "    capacity=4,\n",
        "    price_per_mile=1400.99,\n",
        "    total_miles_driven=25,\n",
        "    max_miles=25000,\n",
        "    purchased_price=1250000,\n",
        "    license_plate=\"ELRICO\",\n",
        ")\n",
        "\n",
        "las_vegas_garage = CarGarage(\n",
        "    \"Las Vegas\", [adesanya_car, jones_car, ngannou_car, strickland_car]\n",
        ")\n",
        "miami_garage = CarGarage(\"Miami\", [elon_musk_car])\n",
        "miami_garage.add_car_in_garage(rico_car)\n",
        "\n",
        "# TESTING 1C\n",
        "def verify_1c():\n",
        "    try:\n",
        "        assert (\n",
        "            len(miami_garage.cars) == 2\n",
        "        )\n",
        "    except AssertionError:\n",
        "        print(f\"1C: add_car_in_garage() failed, outputted {len(miami_garage.cars)} cars instead of 2\")\n",
        "\n",
        "# TESTING 1D\n",
        "def verify_1d():\n",
        "    try:\n",
        "        filtered_cars = las_vegas_garage.filter_cars_by_price(648)\n",
        "        assert (\n",
        "            len(filtered_cars) == 2\n",
        "        )\n",
        "    except AssertionError:\n",
        "        print(f\"1D: filter_cars_by_price() failed, outputted {len(filtered_cars)} cars instead of 2\")\n",
        "\n",
        "# TESTING 1E\n",
        "def verify_1e():\n",
        "    try:\n",
        "        las_vegas_garage.remove_broken_cars()\n",
        "        assert (\n",
        "            len(las_vegas_garage.cars) == 3\n",
        "        )\n",
        "        # adding it back since we need to compute profit for later\n",
        "        las_vegas_garage.add_car_in_garage(ngannou_car)\n",
        "    except AssertionError:\n",
        "        print(f\"1E: remove_broken_cars() failed, outputted {len(las_vegas_garage.cars)} cars instead of 3\")\n",
        "\n",
        "# TESTING 1F\n",
        "def verify_1f():\n",
        "    try:\n",
        "        preferences_1 = {\n",
        "            \"car_model\": (CarModel.SALTY_SEVEN, 2),\n",
        "            \"max_gas_tank\": (100, 3),\n",
        "            \"mpg\": (100, 4),\n",
        "            \"cost_per_refill\": (90.37, 5),\n",
        "            \"capacity\": (4, 1),\n",
        "            \"max_miles\": (60000, 6),\n",
        "        }\n",
        "\n",
        "        sorted_cars_1 = las_vegas_garage.get_top_car_preferences(preferences_1)\n",
        "        assert (\n",
        "            len(sorted_cars_1) == 0\n",
        "        ), f\"1F: get_top_car_preferences() failed preference_1, outputted {sorted_cars_1} instead of empty list\"\n",
        "\n",
        "        preferences_2 = {\n",
        "            \"car_model\": (CarModel.MILKY_MERCEDES, 2),\n",
        "            \"max_gas_tank\": (100, 3),\n",
        "            \"mpg\": (100, 4),\n",
        "            \"cost_per_refill\": (90.37, 5),\n",
        "            \"capacity\": (4, 1),\n",
        "            \"max_miles\": (60000, 6),\n",
        "        }\n",
        "\n",
        "        sorted_cars_2 = las_vegas_garage.get_top_car_preferences(preferences_2)\n",
        "        assert (\n",
        "            sorted_cars_2[0].license_plate == \"BASURA\"\n",
        "            and sorted_cars_2[1].license_plate == \"PREDA7OR\"\n",
        "            and sorted_cars_2[2].license_plate == \"ADESAN1\"\n",
        "            and sorted_cars_2[3].license_plate == \"JONESBONE\"\n",
        "        ), f\"1F: get_top_car_preferences() failed preference_2, outputted: {sorted_cars_2}\"\n",
        "    except AssertionError as err:\n",
        "        print(err)\n",
        "\n",
        "# TESTING 1G AND 1H\n",
        "\n",
        "joyride_application = JoyrideApplication(\n",
        "    company_name=\"Joyride\",\n",
        "    num_employees=2400,\n",
        "    factories=[las_vegas_garage, miami_garage],\n",
        "    equity=0.2,\n",
        "    customer_dissatisfaction=1.25,\n",
        "    employee_retention_rate=0.9,\n",
        ")\n",
        "\n",
        "def verify_1g():\n",
        "    import math\n",
        "    try:\n",
        "        assert math.isclose(\n",
        "            joyride_application.pitch_score, 0.584304729, abs_tol=0.00001\n",
        "        )\n",
        "    except AssertionError:\n",
        "        print(f\"1G: calculate_pitch_score() failed, outputted {joyride_application.pitch_score} instead of 0.584304729\")\n",
        "\n",
        "def verify_1h():\n",
        "    import math\n",
        "    try:\n",
        "        assert math.isclose(\n",
        "            joyride_application.total_profit, 40229535.32, abs_tol = 0.01\n",
        "        )\n",
        "    except AssertionError:\n",
        "        print(f\"1H: calculate_total_profit() failed, outputted {joyride_application.total_profit} instead of 40229839.41\")\n",
        "\n",
        "verify_1c()\n",
        "verify_1d()\n",
        "verify_1e()\n",
        "verify_1f()\n",
        "verify_1g()\n",
        "verify_1h()\n",
        "print(\"If nothing else is printed besides this, all non-hidden test cases passed (except the unknown ones to be used in grading) for question 1!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNPzGLKiSJoU"
      },
      "source": [
        "### Testing Your Code: Graded (Hidden) Tests\n",
        "\n",
        "**Note that if you don't end up having the right answers, partial credit will be rewarded based on your effort and how much was done.**\n",
        "\n",
        "**Run the cell below and leave the output in your submission. Failure to do so will result in lost points for correctness.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z5kSAD3_SJoU"
      },
      "outputs": [],
      "source": [
        "car1 = Car(CarModel.MAYO_MCLAREN, 80, 40, 150, 75.0, 3, 1100.0, 25, 24000, 900000, \"CSTM313\")\n",
        "car2 = Car(CarModel.LEMON_LAMBORGHINI, 100, 50, 150, 75.0, 5, 1200.0, 30, 20000, 800000, \"ABC123\")\n",
        "car3 = Car(CarModel.SALTY_SEVEN, 80, 40, 180, 90.0, 4, 1100.0, 35, 22000, 900000, \"XYZ789\")\n",
        "car4 = Car(CarModel.HERSHEY_HENNESSEY, 110, 60, 220, 110.0, 5, 1600.0, 45, 18000, 1500000, \"CAR456\")\n",
        "car5 = Car(CarModel.MILKY_MERCEDES, 70, 30, 130, 65.0, 4, 900.0, 20, 25000, 600000, \"CAR789\")\n",
        "car6 = Car(CarModel.MAYO_MCLAREN, 95, 55, 190, 95.0, 5, 1400.0, 40, 21000, 1200000, \"LX123\")\n",
        "car7 = Car(CarModel.MAYO_MCLAREN, 85, 45, 170, 85.0, 4, 1000.0, 25, 23000, 700000, \"ECO456\")\n",
        "car8 = Car(CarModel.MAYO_MCLAREN, 120, 70, 240, 120.0, 2, 1800.0, 50, 16000, 2000000, \"SPRT789\")\n",
        "car9 = Car(CarModel.MILKY_MERCEDES, 90, 50, 160, 80.0, 4, 1100.0, 30, 22000, 1100000, \"HBRD101\")\n",
        "car10 = Car(CarModel.LEMON_LAMBORGHINI, 60, 20, 100, 50.0, 2, 700.0, 10, 30000, 500000, \"CLSC202\")\n",
        "car11 = Car(CarModel.LEMON_LAMBORGHINI, 75, 35, 140, 70.0, 4, 950.0, 22, 26000, 800000, \"HATCH303\")\n",
        "car12 = Car(CarModel.SALTY_SEVEN, 100, 50, 180, 90.0, 7, 1300.0, 38, 21000, 1400000, \"SUV404\")\n",
        "car13 = Car(CarModel.HERSHEY_HENNESSEY, 110, 60, 200, 100.0, 5, 1500.0, 42, 19000, 1600000, \"PICK505\")\n",
        "car14 = Car(CarModel.SALTY_SEVEN, 80, 40, 160, 80.0, 8, 1200.0, 28, 24000, 1000000, \"VAN606\")\n",
        "car15 = Car(CarModel.SALTY_SEVEN, 105, 65, 220, 110.0, 2, 1700.0, 48, 17000, 1800000, \"CNVRT707\")\n",
        "car16 = Car(CarModel.LEMON_LAMBORGHINI, 65, 25, 120, 60.0, 2, 800.0, 15, 28000, 400000, \"MINI808\")\n",
        "car17 = Car(CarModel.MILKY_MERCEDES, 120, 70, 200, 100.0, 10, 2200.0, 55, 15000, 2200000, \"LIMO909\")\n",
        "car18 = Car(CarModel.LEMON_LAMBORGHINI, 90, 50, 170, 85.0, 6, 1400.0, 40, 20000, 1300000, \"TRCK010\")\n",
        "car19 = Car(CarModel.HERSHEY_HENNESSEY, 110, 60, 250, 120.0, 5, 1000.0, 32, 18000, 1700000, \"ELEC111\")\n",
        "car20 = Car(CarModel.HERSHEY_HENNESSEY, 100, 55, 190, 95.0, 4, 1600.0, 36, 22000, 1900000, \"CSTM212\")\n",
        "car21 = Car(CarModel.LEMON_LAMBORGHINI, 95, 45, 180, 90.0, 4, 1200.0, 28, 22000, 1000000, \"AAA001\")\n",
        "car22 = Car(CarModel.MILKY_MERCEDES, 85, 40, 170, 85.0, 5, 1100.0, 25, 24000, 900000, \"WHO002\")\n",
        "car23 = Car(CarModel.HERSHEY_HENNESSEY, 105, 50, 190, 95.0, 4, 1300.0, 32, 21000, 1200000, \"CCC003\")\n",
        "car24 = Car(CarModel.SALTY_SEVEN, 110, 60, 200, 100.0, 6, 1500.0, 36, 20000, 1500000, \"DDD004\")\n",
        "car25 = Car(CarModel.MAYO_MCLAREN, 75, 35, 160, 80.0, 4, 1000.0, 22, 26000, 800000, \"EEE005\")\n",
        "car26 = Car(CarModel.LEMON_LAMBORGHINI, 100, 50, 190, 95.0, 5, 1400.0, 30, 22000, 1100000, \"AAA006\")\n",
        "car27 = Car(CarModel.MILKY_MERCEDES, 90, 45, 180, 90.0, 7, 1200.0, 38, 19000, 1300000, \"BBB007\")\n",
        "car28 = Car(CarModel.HERSHEY_HENNESSEY, 120, 70, 220, 110.0, 5, 1600.0, 42, 18000, 1600000, \"CCC008\")\n",
        "car29 = Car(CarModel.SALTY_SEVEN, 70, 30, 140, 70.0, 4, 900.0, 20, 25000, 700000, \"DDD009\")\n",
        "car30 = Car(CarModel.MAYO_MCLAREN, 110, 55, 200, 100.0, 2, 1800.0, 48, 17000, 1800000, \"EEE010\")\n",
        "car31 = Car(CarModel.LEMON_LAMBORGHINI, 80, 40, 170, 85.0, 8, 1300.0, 45, 20000, 1400000, \"AAA011\")\n",
        "car32 = Car(CarModel.MILKY_MERCEDES, 105, 50, 190, 95.0, 4, 1400.0, 36, 21000, 1200000, \"BBB012\")\n",
        "car33 = Car(CarModel.HERSHEY_HENNESSEY, 95, 45, 180, 90.0, 6, 1100.0, 32, 22000, 1000000, \"CCC013\")\n",
        "car34 = Car(CarModel.SALTY_SEVEN, 115, 55, 210, 105.0, 5, 1500.0, 40, 19000, 1300000, \"DDD014\")\n",
        "car35 = Car(CarModel.MAYO_MCLAREN, 70, 35, 150, 75.0, 4, 1000.0, 28, 24000, 900000, \"EEE015\")\n",
        "car36 = Car(CarModel.LEMON_LAMBORGHINI, 100, 60, 200, 100.0, 6, 1400.0, 38, 18000, 1500000, \"AAA016\")\n",
        "car37 = Car(CarModel.MILKY_MERCEDES, 90, 45, 180, 90.0, 5, 1200.0, 35, 20000, 1200000, \"BBB017\")\n",
        "car38 = Car(CarModel.HERSHEY_HENNESSEY, 110, 65, 220, 110.0, 4, 1600.0, 42, 17000, 1700000, \"CCC018\")\n",
        "car39 = Car(CarModel.SALTY_SEVEN, 75, 35, 160, 80.0, 2, 900.0, 18, 26000, 700000, \"DDD019\")\n",
        "car40 = Car(CarModel.MAYO_MCLAREN, 120, 70, 240, 120.0, 4, 1800.0, 48, 16000, 1900000, \"EEE020\")\n",
        "\n",
        "garage1 = CarGarage(\"Garage 1\", [car1, car2, car3, car4, car5])\n",
        "garage2 = CarGarage(\"Garage 2\", [car6, car7, car8, car9, car10])\n",
        "garage3 = CarGarage(\"Garage 3\", [car11, car12, car13, car14, car15, car16, car17, car18, car19, car21, car22, car23, car24, car25, car26, car27, car28, car29, car30, car31, car32, car33, car34, car35, car36, car37, car38, car39, car40])\n",
        "\n",
        "# TESTING 1C\n",
        "garage3.add_car_in_garage(car20)\n",
        "\n",
        "# TESTING 1D\n",
        "print(\"D\", garage3.filter_cars_by_price(1000))\n",
        "\n",
        "# TESTING 1E\n",
        "garage3.remove_broken_cars()\n",
        "\n",
        "# TESTING 1F\n",
        "preferences_1 = {\n",
        "    \"car_model\": (CarModel.MAYO_MCLAREN, 3),\n",
        "    \"max_gas_tank\": (100, 3),\n",
        "    \"mpg\": (100, 1),\n",
        "    \"cost_per_refill\": (90.37, 5),\n",
        "    \"capacity\": (4, 2),\n",
        "    \"max_miles\": (60000, 6),\n",
        "}\n",
        "\n",
        "top_car_preferences = garage3.get_top_car_preferences(preferences_1)\n",
        "formatted_preferences = '\\n'.join([' '.join([str(car) for car in top_car_preferences[i:i+5]]) for i in range(0, len(top_car_preferences), 5)])\n",
        "print(\"F\", formatted_preferences)\n",
        "\n",
        "# TESTING 1G AND 1H\n",
        "\n",
        "joyride_application = JoyrideApplication(\n",
        "    company_name=\"Joyride\",\n",
        "    num_employees=25195,\n",
        "    factories=[garage1, garage2, garage3],\n",
        "    equity=0.4,\n",
        "    customer_dissatisfaction=2.05,\n",
        "    employee_retention_rate=0.6,\n",
        ")\n",
        "\n",
        "print(\"G\", joyride_application.pitch_score)\n",
        "print(\"H\", joyride_application.total_profit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqk-lKn1SJoV"
      },
      "source": [
        "## Exercise 2 (55 Points)\n",
        "\n",
        "In this exercise we will be writing a sudoku solver.\n",
        "\n",
        "a) Similarly to how we defined classes above, define the sudoku board using a `Board` class comprised of nine rows of nine elements. Write a `repr` method to print the board neatly to the command line. Print an empty board. We can represent empty spaces as zeros. **-5pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WW0KldTVSJoV",
        "outputId": "415e7b63-6cad-4cdf-f278-13c20c057c08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " _  _  _  _  _  _  _  _ \n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| . . . | . . . | . . . |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "\n",
        "sudoku = Board()\n",
        "print(sudoku)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ADNgW0WSJoW"
      },
      "source": [
        "b) In the `Board` class above, write getter and setter methods. **-5pts**\n",
        "\n",
        "`Board` class should have:\n",
        "\n",
        "- `get_col` to get a specified column of the board.\n",
        "- `get_row` to get a specified row of the board.\n",
        "- `set_row` to set a specified row of the board.\n",
        "- `get_elm` to get an element at a particular row and column.\n",
        "- `set_elm` to set an element at a particular row and column.\n",
        "- `get_box` to get the box containing a (row, col) location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GHWnLH0iSJoW"
      },
      "outputs": [],
      "source": [
        "# your code here\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_col(self, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_row(self, r):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_row(self, r, row_val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_elm(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_elm(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_box(self, r, c):\n",
        "        #TODO: implement this method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2EbyMKQSJoX"
      },
      "source": [
        "c) Define and print the following sudoku. **-5pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Re1j5u06SJoX",
        "outputId": "0637fdcb-f9b1-4e70-b0e6-f5cb2aa0eb34"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-55734a244fa8>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sudoku.jpg\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretina\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m         super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0m\u001b[1;32m   1232\u001b[0m                 metadata=metadata)\n\u001b[1;32m   1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    638\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1264\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    663\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'sudoku.jpg'"
          ]
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename=\"sudoku.jpg\", width=300, height=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N45f32n0SJoX",
        "outputId": "6da2b9f4-fda4-490f-b49d-fa9571e48f3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " _  _  _  _  _  _  _  _ \n",
            "| 2 6 . | . 7 . | 4 8 3 |\n",
            "| 3 1 . | . . . | . . 9 |\n",
            "| 5 7 . | 3 4 . | . . 2 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 1 . . | . . . | 9 . . |\n",
            "| . 8 . | . 9 . | . 3 . |\n",
            "| . . 7 | . . . | . . 5 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 7 . . | . 5 2 | . 9 4 |\n",
            "| 8 . . | . . . | . 5 7 |\n",
            "| 9 5 6 | . 3 . | . 2 1 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n"
          ]
        }
      ],
      "source": [
        "# your code here\n",
        "\n",
        "\n",
        "print(sudoku)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJi4IcMTSJoX"
      },
      "source": [
        "d) Add to the `Board` class above a method to determine whether a given move at a specified location on the board is legal. Recall in sudoku there can be no repeating integers in the row, column, or box and each element must be an integer between 1 and 9 inclusive. Also, you cannot overwrite elements that are set by the puzzle. For example, in the above sudoku, the top left element is set by the puzzle as \"2\" and changing that element is invalid. **-10pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qdzhy5vwSJoY"
      },
      "outputs": [],
      "source": [
        "# your code here\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_col(self, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_row(self, r):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_row(self, r, row_val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_elm(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_elm(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_box(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def is_legal(self, r, c, val):\n",
        "        #TODO: implement this method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6HU-Fj3jSJoY",
        "outputId": "b5e79c50-60c5-451a-d3a2-f58866f540a3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Assignment is not legal'"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sudoku.is_legal(2, 4, 7) # For your testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHyMnvJPSJoY",
        "outputId": "1e4a5893-ab2b-41a0-bb15-1722014ac762"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Assignment is not legal'"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sudoku.is_legal(0, 2, 2) # For your testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vosGXw6SJoY"
      },
      "source": [
        "e) Add to the `Board` class a method that searches for solutions uing depth first search and returns the first it finds. Your code should be very similar to the 8 queen problem done in class on 09/18. Here is the breakdown of the algorithm you should implement:\n",
        "\n",
        "Start with the first row and go through each element of the row from left to right:\n",
        "- if the element is not set, start by trying the integer 1\n",
        "- if this is a valid move according to the rules of sudoku, then move on to the next element\n",
        "- if this is not a valid move then try the next possible integer in ascending order\n",
        "- if you've tried all possible integers and none are valid moves, then unset the element and backtrack to the previous element. Increment it to the next possible integer. (if that still isn't possible, you should keep backtracking)\n",
        "\n",
        "When you've explored all elements of a row, move on to the next row. If you reach the end of the board and have a valid sudoku, you have solved the puzzle. If you backtrack all the way to the first (top left) element and there are no valid integers, then the puzzle is impossible.\n",
        "\n",
        "Test your code on the sudoku from 2 c).\n",
        "\n",
        "**Note (again):** Your code should follow a very similar pattern as the 8 queen problem we will do in class on 09/18.\n",
        "\n",
        "**-15pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZFExyZgtSJoZ"
      },
      "outputs": [],
      "source": [
        "# your code here\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_col(self, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_row(self, r):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_row(self, r, row_val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_elm(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_elm(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_box(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def is_legal(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def solve(self):\n",
        "        #TODO: implement this method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kn1CVGa5SJoZ",
        "outputId": "b3b3a9a4-0408-45d9-f4d5-639b396df19d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " _  _  _  _  _  _  _  _ \n",
            "| 2 6 9 | 1 7 5 | 4 8 3 |\n",
            "| 3 1 4 | 6 2 8 | 5 7 9 |\n",
            "| 5 7 8 | 3 4 9 | 1 6 2 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 1 2 3 | 5 6 7 | 9 4 8 |\n",
            "| 4 8 5 | 2 9 1 | 7 3 6 |\n",
            "| 6 9 7 | 4 8 3 | 2 1 5 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 7 3 1 | 8 5 2 | 6 9 4 |\n",
            "| 8 4 2 | 9 1 6 | 3 5 7 |\n",
            "| 9 5 6 | 7 3 4 | 8 2 1 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sudoku.solve() # For your testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2kcWWoTSJoZ"
      },
      "source": [
        "f) Now that you've learned how to solve a Sudoku puzzle, you'd like to create your own puzzles and challenge your friends to solve them. Perhaps, you no longer wish to initialize a puzzle line by line. In this question, you will be provided with the first three rows of a partially populated board, and you need to generate a Sudoku puzzle with 20 to 40 empty cells that need to be filled in (you can choose any number within this range).  **-10 pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KET1AOt8SJoa",
        "outputId": "b35e6b17-1fa2-4af7-f78f-1aa7b6daed4e"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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J3qo7oDxz9gf/AJI/rH/Yem/9J7evpSvmv9gf/kj+sf8AYem/9J7evpSqqfExBRRRWYwooooAK47S/wDksHif/sA6T/6UajXY1x2l/wDJYPE//YB0n/0o1GqXUDsaKKKkAorN8TeJNL8G+H9R1zW76HTNI0+B7m6vLh9scMajLMT7AV88/sfftsWX7XmvfEC103wnceHdO8MzWy211dXnmy30U5m2O0Xlr5J2wg7dz/f68c1GLnzcvRXfpe35g/djzPa9j6YoooqQOO0v/ksHif8A7AOk/wDpRqNdjXHaX/yWDxP/ANgHSf8A0o1GuxqmAUUUVIBRRRQAV8df8FDP+ZA/7iH/ALbV9i18df8ABQz/AJkD/uIf+21a0/jQmbv7J3/JMtJ/7b/+jpKKP2Tv+SZaT/23/wDR0lFEviYHlP7ZH/IW8Pf9vP8A7Sr6c/ZB/wCTd/Cf/b3/AOlc1fMf7ZH/ACFvD3/bz/7Sr6c/ZB/5N38J/wDb3/6VzVcv4aDqexUUVmeJ/Eum+DfDmp69rN0tjpGmW0l5d3LKzCKJFLOxCgk4AJ4BNc97aspJydkeFftZftv+C/2P5PD8HinRte1e612K4ksl0eKBkBhMYYSNJKhXJkXBAbofx4D/AIJ9+BTq2p/E743Xeo6G+ofEXU1uBo/h7UI72DS4Yy7LFLLGdpnJky4HTAPViB7NF+2F8Cb3Szd/8Le8Dm3ZCxjl122WQjHTymcPn2xmvl3/AIJ+6tYeOP2rv2h/GHw/t2tfhHfTW0Vn5MBt7Wa8AXdJHGQME4lc8AgSrkDIxtRj70k1ryy17K60+eiv/mzOtL92mtuaOnffX5b/APDH6B0UUViWcd4F/wCRo+In/Yei/wDTZYV2Ncd4F/5Gj4if9h6L/wBNlhXY1TAKKKKkAooooAK47wL/AMjR8RP+w9F/6bLCuxrjvAv/ACNHxE/7D0X/AKbLCqXUDsaKKKkAooooAKKKKAPjr9pH/k8L4W/9wv8A9OEtfYtfHX7SP/J4Xwt/7hf/AKcJa+xa1ntEQUUUVkMKKKKACuO+K/8AyK9j/wBh7Rf/AE52tdjXHfFf/kV7H/sPaL/6c7WqjugOxoooqQCiiigAooooA474zf8AJH/HX/YBvv8A0neuxrjvjN/yR/x1/wBgG+/9J3rsaroAUUUVIBRRRQAVx3xm/wCSP+Ov+wDff+k712Ncd8Zv+SP+Ov8AsA33/pO9VHdAeOfsD/8AJH9Y/wCw9N/6T29fSlfNf7A//JH9Y/7D03/pPb19KVVT4mIKKKKzGFFFFABXHaX/AMlg8T/9gHSf/SjUa7GuO0v/AJLB4n/7AOk/+lGo1S6gdjQSFBJOAKKxvGnh0+LvB+u6EtybJtTsJ7IXITeYvMjZN+3IzjdnGRnHUVnNyUW4q7Kik5JSdkfKWtSXP7dvxafRLVyf2ffBt+v9p3Kk+X4r1OI5+zKf4rWFgNx6Ow4zwVxf2G4Y7f8Aa7/a5iijWKKPXNPVI0ACqo+1gAAdBXIeHP8Agl98VvB2i2uj6B+1z4y0PSLVStvp+m2d3bwQgkkhI01EKoySeB3ryb9jT9mTx5fftafFSCL44+I7WTwJ4j06TW5UW4z4rUSysVusXQwCInX5/N4lb3B7aEYc7gpacjv85U7yf3JWXSyWzZFVydKUmusbeVr2Xz6vv20R+tFFFFcgzjtL/wCSweJ/+wDpP/pRqNdjXHaX/wAlg8T/APYB0n/0o1GuxqmAUUUVIBRRRQAV8df8FDP+ZA/7iH/ttX2LXx1/wUM/5kD/ALiH/ttWtP40Jm7+yd/yTLSf+2//AKOkoo/ZO/5JlpP/AG3/APR0lFEviYHlP7ZH/IW8Pf8Abz/7Sr6c/ZB/5N38J/8Ab3/6VzV8x/tkf8hbw9/28/8AtKvpz9kH/k3fwn/29/8ApXNVy/hoOp7FVbVNLstc02607UbSDUNPuomhuLW6iWSKaNhhkdGBDKQSCCMHNWaK599GO9tUfOGs/wDBOf8AZx17WRqlz8LNMiuQ27y7O5ubWDP/AFxilWPHttxXuvg/wVoHw98P2uheGNFsdA0a1BENjp1usMKZOSQqgDJPJPUnk1tUVXM7ct9BNJu73CiiipGcd4F/5Gj4if8AYei/9NlhXY1x3gX/AJGj4if9h6L/ANNlhXY1TAKKKKkAooooAK47wL/yNHxE/wCw9F/6bLCuxrjvAv8AyNHxE/7D0X/pssKpdQOxoooqQCiiigAooooA+Ov2kf8Ak8L4W/8AcL/9OEtfYtfHX7SP/J4Xwt/7hf8A6cJa+xa1ntEQUUUVkMKKKKACuO+K/wDyK9j/ANh7Rf8A052tdjXHfFf/AJFex/7D2i/+nO1qo7oDsaKKKkAooooAKKKKAOO+M3/JH/HX/YBvv/Sd67GuO+M3/JH/AB1/2Ab7/wBJ3rsaroAUUUVIBRRRQAVx3xm/5I/46/7AN9/6TvXY1x3xm/5I/wCOv+wDff8ApO9VHdAeOfsD/wDJH9Y/7D03/pPb19KV81/sD/8AJH9Y/wCw9N/6T29fSlVU+JiCiiisxhRRRQAVx2l/8lg8T/8AYB0n/wBKNRrsa47S/wDksHif/sA6T/6UajVLqB2NFFFSAVxHgf4K+DPhx4w8X+KfDujf2dr3i2eO61q7+1TS/a5E37W2O7KmPMfhAo59hXb0U1o7r0+Wjt+C+5BurdAooopAcdpf/JYPE/8A2AdJ/wDSjUa7GuO0v/ksHif/ALAOk/8ApRqNdjVMAoooqQCiiigAr46/4KGf8yB/3EP/AG2r7Fr46/4KGf8AMgf9xD/22rWn8aEzd/ZO/wCSZaT/ANt//R0lFH7J3/JMtJ/7b/8Ao6SiiXxMDyn9sj/kLeHv+3n/ANpV7v8Ass6f4wm+A/hh9K13Q7OwP2ry4LzRZriVf9KmzukW7jDZOTwowCBzjJ8I/bI/5C3h7/t5/wDaVfTn7IP/ACbv4T/7e/8A0rmrSX8NB1Ox/sv4g/8AQz+Gv/CcuP8A5Po/sv4g/wDQz+Gv/CcuP/k+uxorC4zjv7L+IP8A0M/hr/wnLj/5Po/sv4g/9DP4a/8ACcuP/k+uxoouBx39l/EH/oZ/DX/hOXH/AMn0f2X8Qf8AoZ/DX/hOXH/yfXY0UXA8j8Gab44bxH48EPiLw/HIutRidpNBnYO/9nWRDKBejaNpQYJbkE5wQo6v+y/iD/0M/hr/AMJy4/8Ak+jwL/yNHxE/7D0X/pssK7Gm2I47+y/iD/0M/hr/AMJy4/8Ak+j+y/iD/wBDP4a/8Jy4/wDk+uxopXGcd/ZfxB/6Gfw1/wCE5cf/ACfR/ZfxB/6Gfw1/4Tlx/wDJ9djRRcDjv7L+IP8A0M/hr/wnLj/5PrlPBmm+OG8R+PBD4i8PxyLrUYnaTQZ2Dv8A2dZEMoF6No2lBgluQTnBCj1yuO8C/wDI0fET/sPRf+mywpp7iD+y/iD/ANDP4a/8Jy4/+T6P7L+IP/Qz+Gv/AAnLj/5PrsaKVxnHf2X8Qf8AoZ/DX/hOXH/yfR/ZfxB/6Gfw1/4Tlx/8n12NFFwOO/sv4g/9DP4a/wDCcuP/AJPo/sv4g/8AQz+Gv/CcuP8A5PrsaKLgfEPx/tfEEX7VPw3j1DU9NutVb+zfs91badJDBHm+kC74mndnw2ScSLkHHHWvq3+y/iD/ANDP4a/8Jy4/+T6+a/2kf+Twvhb/ANwv/wBOEtfYtaSeiEcd/ZfxB/6Gfw1/4Tlx/wDJ9H9l/EH/AKGfw1/4Tlx/8n12NFZ3Gcd/ZfxB/wChn8Nf+E5cf/J9H9l/EH/oZ/DX/hOXH/yfXY0UXA47+y/iD/0M/hr/AMJy4/8Ak+uU+Jmm+OE8OWZufEXh+aP+2tJAWHQZ4yHOo2wRsm9bgNtJGOQCAVJ3D1yuO+K//Ir2P/Ye0X/052tNPUQf2X8Qf+hn8Nf+E5cf/J9H9l/EH/oZ/DX/AITlx/8AJ9djRSuM47+y/iD/ANDP4a/8Jy4/+T6P7L+IP/Qz+Gv/AAnLj/5PrsaKLgcd/ZfxB/6Gfw1/4Tlx/wDJ9H9l/EH/AKGfw1/4Tlx/8n12NFFwPI/i1pvjiP4V+M2u/EXh+e0XRb0zRQ6DPG7p5D7lVzesFJGcEqcdcHpXV/2X8Qf+hn8Nf+E5cf8AyfR8Zv8Akj/jr/sA33/pO9djTvoI47+y/iD/ANDP4a/8Jy4/+T6P7L+IP/Qz+Gv/AAnLj/5PrsaKVxnHf2X8Qf8AoZ/DX/hOXH/yfR/ZfxB/6Gfw1/4Tlx/8n12NFFwOO/sv4g/9DP4a/wDCcuP/AJPrlPi1pvjiP4V+M2u/EXh+e0XRb0zRQ6DPG7p5D7lVzesFJGcEqcdcHpXrlcd8Zv8Akj/jr/sA33/pO9NPUR8/fsTWfii4+FeqtomsaRp9p/bUoaLUNJlunL+RBlg6XMQAxjjaehOecD6B/sv4g/8AQz+Gv/CcuP8A5Prxz9gf/kj+sf8AYem/9J7evpSqm/eYHHf2X8Qf+hn8Nf8AhOXH/wAn0f2X8Qf+hn8Nf+E5cf8AyfXY0VFxnHf2X8Qf+hn8Nf8AhOXH/wAn0f2X8Qf+hn8Nf+E5cf8AyfXY0UXA47+y/iD/ANDP4a/8Jy4/+T65TTdN8cf8LU8RKviLw+LsaLphklOgzlGQz3+xQn23IIIfJ3HO5RgbSW9crjtL/wCSweJ/+wDpP/pRqNNPcQf2X8Qf+hn8Nf8AhOXH/wAn0f2X8Qf+hn8Nf+E5cf8AyfXY0UrjOO/sv4g/9DP4a/8ACcuP/k+j+y/iD/0M/hr/AMJy4/8Ak+uxoouBx39l/EH/AKGfw1/4Tlx/8n0f2X8Qf+hn8Nf+E5cf/J9djRRcDyPTdN8cf8LU8RKviLw+LsaLphklOgzlGQz3+xQn23IIIfJ3HO5RgbSW6v8Asv4g/wDQz+Gv/CcuP/k+jS/+SweJ/wDsA6T/AOlGo12NNsRx39l/EH/oZ/DX/hOXH/yfR/ZfxB/6Gfw1/wCE5cf/ACfXY0UrjOO/sv4g/wDQz+Gv/CcuP/k+j+y/iD/0M/hr/wAJy4/+T67Gii4HHf2X8Qf+hn8Nf+E5cf8AyfXyl+3Va+ILX/hCP7d1PTdS3fbvJ/s7TpLTZ/x77t2+eXdn5cY24weuePt6vjr/AIKGf8yB/wBxD/22rSm/eQjd/ZO/5JlpP/bf/wBHSUUfsnf8ky0n/tv/AOjpKKUviYHlP7ZH/IW8Pf8Abz/7Sr6c/ZB/5N38J/8Ab3/6VzV8x/tkf8hbw9/28/8AtKvpz9kH/k3fwn/29/8ApXNVy/hoOp7FRRRXOMKKKKACiiigDjvAv/I0fET/ALD0X/pssK7GuO8C/wDI0fET/sPRf+mywrsapgUtb1mz8O6Lf6tqM32fT7G3kuribazbI0UszYUEnABOACa+ZP8Ah6J+zH/0Uz/yg6n/API1fUV9Y2+pWdxZ3lvFd2lxG0U1vOgeORGGGVlPBBBIIPXNeYN+yl8D0Us3wd+H6qBkk+GLEAf+Qqlbvm2/q/6D0srbnPfCX9uT4JfHTxpb+E/A/jX+2/EE8Uk0dn/ZV9b7kRdznfLCijA9TXu1fBX7IfgDwt8WP2rfG/xi8GeGNF8J/DvwqknhXw5FoOnQ2UWpz/8AL1eMIkUOMNtUnPDr3U1961rJRUYNdVf79vvVn8zNc3POL6O34K/3O6+QVx3gX/kaPiJ/2Hov/TZYV2Ncd4F/5Gj4if8AYei/9NlhULqWdjRRRUgFFFFABRRRQB8dftI/8nhfC3/uF/8Apwlr7Fr46/aR/wCTwvhb/wBwv/04S19i1rPaIgrzr42ftDfD/wDZ00Ow1j4h6/8A8I9pt/c/ZLab7FcXO+XaX27YY3I+VSckAcV6LXwf/wAFYr7R9N8MfBS88RLC/h+38dWsuorcQmaM2yoxlDRgHeuwNlcHI4wax6xXdpfe0i0t/JN/cmz0j/h6J+zH/wBFM/8AKDqf/wAjV9HeCfGmjfEXwjpHifw9ef2hoerWyXlldeU8XmxOMq2x1VlyOzAH2r5I+HPxE/YV+K+vQaL4b0X4XT6pcOI4La+8IQ2DTOeiIbi2jDMSeACSe1fYmiaHp3hnSLPStH0+10rS7OJYbaxsYVhggjUYVERQFVQOgAxW0oqK2Zle8rJl2uO+K/8AyK9j/wBh7Rf/AE52tdjXHfFf/kV7H/sPaL/6c7WojuizsaKKKkAooooAKKKKAOO+M3/JH/HX/YBvv/Sd67GuO+M3/JH/AB1/2Ab7/wBJ3rsaroAUUUVIBRRRQAVx3xm/5I/46/7AN9/6TvXY1x3xm/5I/wCOv+wDff8ApO9VHdAeOfsD/wDJH9Y/7D03/pPb19KV81/sD/8AJH9Y/wCw9N/6T29fSlVU+JiCiiisxhRRRQAVx2l/8lg8T/8AYB0n/wBKNRrsa47S/wDksHif/sA6T/6UajVLqB2NFFQahO9rYXM0aeZJHEzqn94gEgVnKSjFyfQcU5NJHlfxk/az+EX7P95FZ+PfHOn6HqEih1sFSW6ugp6MYYUd1U9mKgHB9K6n4W/GXwR8bPD7a14F8Taf4m05GCSSWUuWhYjIWRDho2xzhgDXwr/wTX+DvgD9oLwR41+KnxH0DSfH3j3WfENzHf8A/CQ20d8tmoCMqLFKGVM7zzjOAoHAxW7rfw/0L9k3/gol8LG8AWcfhzwz8S7C90/VtCsfktPOhXckiRDhPmaLAAAGGxjca6o07TjSn8Ulpba7jzJfpfv0sZykuWc4bRvv1s7N+Xe3Y+/6KKK5yzjtL/5LB4n/AOwDpP8A6UajXY1x2l/8lg8T/wDYB0n/ANKNRrsapgFFFFSAUUUUAFfHX/BQz/mQP+4h/wC21fYtfHX/AAUM/wCZA/7iH/ttWtP40Jm7+yd/yTLSf+2//o6Sij9k7/kmWk/9t/8A0dJRRL4mB5T+2R/yFvD3/bz/AO0q+nP2Qf8Ak3fwn/29/wDpXNXzH+2R/wAhbw9/28/+0q+nP2Qf+Td/Cf8A29/+lc1XL+Gg6nsVFFFc4wooooAKKKKAOO8C/wDI0fET/sPRf+mywrsa47wL/wAjR8RP+w9F/wCmywrsapgFfLH7fnxe1fw14B0j4YeCn8z4jfEq6/sPTEjfD29u2BcXBI5UKrbd3bcT/Ca+p68M0b9mPH7U+r/GvxH4k/4SC8/s1dK0DSPsHkx6ND/y0KuZW8x2y/zbU/1j8cjEqMZyUZ/D180unz29LlKTgnKHxdPXv8t/O1juvgf8I9H+BPwp8NeBdDUfYdHtFgMu0Bp5fvSzNj+J3LMf96u5ooq5zdSTnLdmcYqEVFdArjvAv/I0fET/ALD0X/pssK7GuO8C/wDI0fET/sPRf+mywpLqUdjRRRUgFFFFABRRRQB8dftI/wDJ4Xwt/wC4X/6cJa+xa+Ov2kf+Twvhb/3C/wD04S19i1rPaIgrkfiJ8IvBfxbh02Dxp4Z03xRbabObm1tdUgE8KSFSu4xt8rHaSPmB6111eD/tN/srt+0PcaDqWnfETxT8Odf0WOeK1v8Aw5dGJXWUoWEqqVZsGNcYdcZPWsuq/r+tSl11PDf+Chn7LfwS8O/su+LvE9n4K8O+DNc0eKObTNQ0Oxi0+RrgyKqxERKokD5K7WBxnIwRmvpn9lvWNZ8Qfs3/AAy1LxDJLNrV14dsZrqWcfvJHMKnc3ueCfc180+F/wDgmDJrHiLTb/4yfGzxh8ZdN02cXFtourSzR2pYdpBLcTMVPcKUz0JxkH7kt7eK1gjhhjWGGNQiRxqFVVAwAAOgArbSMJR5r3afkrJ/i7+miIl70ou2yfzvb8Fb8R9cd8V/+RXsf+w9ov8A6c7Wuxrjviv/AMivY/8AYe0X/wBOdrWcd0UdjRRRUgFFFFABRRRQBx3xm/5I/wCOv+wDff8ApO9djXHfGb/kj/jr/sA33/pO9djVdACiiipAKKKKACuO+M3/ACR/x1/2Ab7/ANJ3rsa474zf8kf8df8AYBvv/Sd6qO6A8c/YH/5I/rH/AGHpv/Se3r6Ur5r/AGB/+SP6x/2Hpv8A0nt6+lKqp8TEFFFFZjCiiigArjtL/wCSweJ/+wDpP/pRqNdjXHaX/wAlg8T/APYB0n/0o1GqXUDsaKKKkD4k+JH/AATMivPiBrHi34SfFvxP8F7zWpTPqVpohka3lckklBFPCygsSdpZgCTgAcV4t8OvgBY6Z/wUY8BaJpnj3xJ8UNZ8Gabcat4s8Q+IL43fkzFGjgt0yT5ZBdCVLMfn68ED6D+MH7CfjX4m/FvxB4p0n9ojxl4I8N61LHLceF9FaeOFWESRttZblU+YICcxHknrXs37Of7L/gX9l/wrcaP4Ospmub11l1HWNQkEt7fyDOGlfAGBk4VQFG4nGSSdqMvZqMm/hWi87W37LfzsthVv3nNHfm6+V9fm9vmet0UUViM47S/+SweJ/wDsA6T/AOlGo12Ncdpf/JYPE/8A2AdJ/wDSjUa7GqYBRRRUgFFFFABXx1/wUM/5kD/uIf8AttX2LXx1/wAFDP8AmQP+4h/7bVrT+NCZu/snf8ky0n/tv/6Okoo/ZO/5JlpP/bf/ANHSUUS+JgeU/tkf8hbw9/28/wDtKvpz9kH/AJN38J/9vf8A6VzV8x/tkf8AIW8Pf9vP/tKvd/2WfFmqab8B/DFtbeDNc1aFPtW28s5rBYpM3UxO0S3KPwTjlRyDjIwTpL+Gg6n0PRXHf8J1rf8A0TvxL/4EaZ/8mUf8J1rf/RO/Ev8A4EaZ/wDJlYWGdjRXHf8ACda3/wBE78S/+BGmf/JlH/Cda3/0TvxL/wCBGmf/ACZRYDsaK47/AITrW/8AonfiX/wI0z/5Mo/4TrW/+id+Jf8AwI0z/wCTKLAHgX/kaPiJ/wBh6L/02WFdjXkfgzxnq8PiPx46eA/EE7S61G7xxz6cDCf7Osl2Nm7AJwob5SRhxznIHV/8J1rf/RO/Ev8A4EaZ/wDJlNoR2NFcd/wnWt/9E78S/wDgRpn/AMmUf8J1rf8A0TvxL/4EaZ/8mUrDOxorjv8AhOtb/wCid+Jf/AjTP/kyj/hOtb/6J34l/wDAjTP/AJMosB2Ncd4F/wCRo+In/Yei/wDTZYUf8J1rf/RO/Ev/AIEaZ/8AJlcp4M8Z6vD4j8eOngPxBO0utRu8cc+nAwn+zrJdjZuwCcKG+UkYcc5yA0txHrlFcd/wnWt/9E78S/8AgRpn/wAmUf8ACda3/wBE78S/+BGmf/JlKwzsaK47/hOtb/6J34l/8CNM/wDkyj/hOtb/AOid+Jf/AAI0z/5MosB2NFcd/wAJ1rf/AETvxL/4EaZ/8mUf8J1rf/RO/Ev/AIEaZ/8AJlFgPmv9pH/k8L4W/wDcL/8AThLX2LXxD8f9dvb79qn4b3s/h3UtOuYf7N2adcyWpnnxfSEbDHM0Y3E7RudeRzgc19W/8J1rf/RO/Ev/AIEaZ/8AJlaSWiEdjRXHf8J1rf8A0TvxL/4EaZ/8mUf8J1rf/RO/Ev8A4EaZ/wDJlZ2GdjRXHf8ACda3/wBE78S/+BGmf/JlH/Cda3/0TvxL/wCBGmf/ACZRYDsa474r/wDIr2P/AGHtF/8ATna0f8J1rf8A0TvxL/4EaZ/8mVynxM8Z6vceHLNJPAfiC1Ua1pLiSafTiCV1G2YJ8t2TliAo4xlhkgZIaWoj1yiuO/4TrW/+id+Jf/AjTP8A5Mo/4TrW/wDonfiX/wACNM/+TKVhnY0Vx3/Cda3/ANE78S/+BGmf/JlH/Cda3/0TvxL/AOBGmf8AyZRYDsaK47/hOtb/AOid+Jf/AAI0z/5Mo/4TrW/+id+Jf/AjTP8A5MosAfGb/kj/AI6/7AN9/wCk712NeR/Frxnq918K/GcMvgPxBZxyaLeo1xNPpxSIGBwXYJds2B1O0E8cAniur/4TrW/+id+Jf/AjTP8A5Mp20EdjRXHf8J1rf/RO/Ev/AIEaZ/8AJlH/AAnWt/8ARO/Ev/gRpn/yZSsM7GiuO/4TrW/+id+Jf/AjTP8A5Mo/4TrW/wDonfiX/wACNM/+TKLAdjXHfGb/AJI/46/7AN9/6TvR/wAJ1rf/AETvxL/4EaZ/8mVynxa8Z6vdfCvxnDL4D8QWccmi3qNcTT6cUiBgcF2CXbNgdTtBPHAJ4ppaiOH/AGB/+SP6x/2Hpv8A0nt6+lK+Tf2JvEmo6P8ACvVYbTwpq+uxtrUrm40+WzVFPkQDYRNcRtngHgEcjnOQPoH/AITrW/8AonfiX/wI0z/5Mqpr3mB2NFcd/wAJ1rf/AETvxL/4EaZ/8mUf8J1rf/RO/Ev/AIEaZ/8AJlRYZ2NFcd/wnWt/9E78S/8AgRpn/wAmUf8ACda3/wBE78S/+BGmf/JlFgOxrjtL/wCSweJ/+wDpP/pRqNH/AAnWt/8ARO/Ev/gRpn/yZXKab4z1dfip4imHgPxA0j6LpiNbifTt6AT35Dkm724bcQMEn5GyANpLS3EeuUVx3/Cda3/0TvxL/wCBGmf/ACZR/wAJ1rf/AETvxL/4EaZ/8mUrDOxorjv+E61v/onfiX/wI0z/AOTKP+E61v8A6J34l/8AAjTP/kyiwHY0Vx3/AAnWt/8ARO/Ev/gRpn/yZR/wnWt/9E78S/8AgRpn/wAmUWANL/5LB4n/AOwDpP8A6UajXY15HpvjPV1+KniKYeA/EDSPoumI1uJ9O3oBPfkOSbvbhtxAwSfkbIA2k9X/AMJ1rf8A0TvxL/4EaZ/8mU2hHY0Vx3/Cda3/ANE78S/+BGmf/JlH/Cda3/0TvxL/AOBGmf8AyZSsM7GiuO/4TrW/+id+Jf8AwI0z/wCTKP8AhOtb/wCid+Jf/AjTP/kyiwHY18df8FDP+ZA/7iH/ALbV9Kf8J1rf/RO/Ev8A4EaZ/wDJlfKX7dWu3utf8IR9s8O6loHl/btn9oyWr+bn7Pnb5E0mMYGd2PvDGecaU17yEdt+yd/yTLSf+2//AKOkoo/ZO/5JlpP/AG3/APR0lFKXxMDyn9sj/kLeHv8At5/9pV9Ofsg/8m7+E/8At7/9K5q+Y/2yP+Qt4e/7ef8A2lX05+yD/wAm7+E/+3v/ANK5quX8NB1PYqKKK5xhRRRQAUUUUAcd4F/5Gj4if9h6L/02WFdjXHeBf+Ro+In/AGHov/TZYV2NUwCiiipAKKKKACuO8C/8jR8RP+w9F/6bLCuxrjvAv/I0fET/ALD0X/pssKpdQOxoooqQCiiigAooooA+Ov2kf+Twvhb/ANwv/wBOEtfYtfHX7SP/ACeF8Lf+4X/6cJa+xa1ntEQUUUVkMKKKKACuO+K//Ir2P/Ye0X/052tdjXHfFf8A5Fex/wCw9ov/AKc7WqjugOxoooqQCiiigAooooA474zf8kf8df8AYBvv/Sd67GuO+M3/ACR/x1/2Ab7/ANJ3rsaroAUUUVIBRRRQAVx3xm/5I/46/wCwDff+k712Ncd8Zv8Akj/jr/sA33/pO9VHdAeOfsD/APJH9Y/7D03/AKT29fSlfNf7A/8AyR/WP+w9N/6T29fSlVU+JiCiiisxhRRRQAVx2l/8lg8T/wDYB0n/ANKNRrsa47S/+SweJ/8AsA6T/wClGo1S6gdjRRRUgFFFFABRRRQBx2l/8lg8T/8AYB0n/wBKNRrsa47S/wDksHif/sA6T/6UajXY1TAKKKKkAooooAK+Ov8AgoZ/zIH/AHEP/bavsWvjr/goZ/zIH/cQ/wDbatafxoTN39k7/kmWk/8Abf8A9HSUUfsnf8ky0n/tv/6Okool8TA8p/bI/wCQt4e/7ef/AGlX05+yD/ybv4T/AO3v/wBK5q+Y/wBsj/kLeHv+3n/2lX05+yD/AMm7+E/+3v8A9K5quX8NB1PYqKKK5xhRRRQAUUUUAcd4F/5Gj4if9h6L/wBNlhXY1x3gX/kaPiJ/2Hov/TZYV2NUwOS+Lnj6L4V/C3xb4ymgN1HoOlXOpGAceZ5UTOF/EjH41+ffwV/YlvP25vh5p3xg+NnxL8V3WreIJZL3S9M0K6igtdMhEjIiokkcgX7pICBcDqWJJr9FfHfg3T/iJ4J17wtqyM+l61Yzafcqhw3lyoUbB7HDcV8AeAfh3+2t+yVpafD34e6L4P8Aih4HtZ5DpepatOsUlpC7ltrK1zA45YsVHmAEkKcYFFO3NK++ln99/R7FS5uWPK9Lu/4W+W+nod/+yD4i8a/BD9o7xl+zf4z8UXXjXS7LS08QeGNa1Fi119lLKrQuSSSAWwBnAMbYwCAPtyvzg/Z10/4n+Iv+CmGrX/xO1DQtS8S6H4K23yeGo2FnpyyyIYbXLDcXAlLEkn73UgV+j9azu4QlJ3bW/e0mvyS9dzFWU5paK6/GKf5sK47wL/yNHxE/7D0X/pssK7GuO8C/8jR8RP8AsPRf+mywrJdTQ7GjpRWV4saVfCusmDcJxZTGPb13bDjHvmsakuSEp9kXCPPJR7nwVb6T48/4KQfEbxhNF471fwD8BPDepSaNaW/h9/Jutdmjx5sjSdCnII3BlAKgJu3NX0D+yz+xH4U/ZH1rxHd+EvEPiDVbTXIYUntddmimKSRs58xGjjjHIfBBUngc9q+JP2I/2DdD/af/AGY9J1bxn8R/GsWktfXaQeG9FvooLO1dJmDMySRyB5GPzFsKcEDnAr0v4U+FPE37Cv7aXgX4SaV4z1bxZ8LvHdjO1rpusyiSTT5okkbcuAFBygGUChg5yuVBrujFU5RoqVpNW/xPlu7+utui0RhOXtIzqbpPbslK2np16vU/RmiiiuU0Pjr9pH/k8L4W/wDcL/8AThLX2LXx1+0j/wAnhfC3/uF/+nCWvsWtZ7REFeJ/tLfs0SftJwaBp1x8QfFHgzQ7Ezm+svDN19nbUd/l7BIxyuE2NgFG++enf2yisX3LjJxd0flj+0z+xTY/sJ/DGb4u/CP4o+L9G1/Sr638y01a9hkj1DzJVTy8RRRhjzuKuHDKrAgda/THwHrF/wCIvA/h3VdUtDYanfadb3N1akEeTK8Ss6YPTDEj8K/Oz49fsvfEP9nGO6+OOveP4fj5pnhe8/tEeFvHUd35VjE8wBe1Ju5E8xAwA3JgAZCkgCv0M+Gvjqy+J/w98N+L9NilhsNd06DUYIphh0SWMOFbHcZx+FdEZOVB3d3df9u3W3nezfbQyqRSqpraz1/m1Wvlbz11Okrjviv/AMivY/8AYe0X/wBOdrXY1x3xX/5Fex/7D2i/+nO1rKO6LOxoooqQCiiigAooooA474zf8kf8df8AYBvv/Sd67GuO+M3/ACR/x1/2Ab7/ANJ3rsaroAUUUVIBRRRQAVx3xm/5I/46/wCwDff+k712Ncd8Zv8Akj/jr/sA33/pO9VHdAeOfsD/APJH9Y/7D03/AKT29fSlfNf7A/8AyR/WP+w9N/6T29fSlVU+JiCiiisxhRRRQAVx2l/8lg8T/wDYB0n/ANKNRrsa47S/+SweJ/8AsA6T/wClGo1S6gdjRRRUgFFFFABRRRQBx2l/8lg8T/8AYB0n/wBKNRrsa47S/wDksHif/sA6T/6UajXY1TAKKKKkAooooAK+Ov8AgoZ/zIH/AHEP/bavsWvjr/goZ/zIH/cQ/wDbatafxoTN39k7/kmWk/8Abf8A9HSUUfsnf8ky0n/tv/6Okool8TA8p/bI/wCQt4e/7ef/AGlX05+yD/ybv4T/AO3v/wBK5q+Y/wBsj/kLeHv+3n/2lX05+yD/AMm7+E/+3v8A9K5quX8NB1PYqKKK5xhRRRQAUUUUAcd4F/5Gj4if9h6L/wBNlhXY1x3gX/kaPiJ/2Hov/TZYV2NUwCvh/wATeKP27b/xh4i8O+H/AAh4E07w419cx6X4s1CaMzLamRvJdkS5b5ghXrb9RyDX3BRSW92O+ljwX9k39l1f2dtD13UNb12Xxj8RPFF19v8AEPiO4XDXEnO2OMHkRrubGepYnAGFX3qiiqlJyd3/AFYiMeUK47wL/wAjR8RP+w9F/wCmywrsa47wL/yNHxE/7D0X/pssKS6lHY0EBgQRkUUVIHwBe/AX9pL9kXxh4ol/Z2tvDvjf4eeIL59Sj8Ka9IsT6ZO5+cRlpYRsGABiTkAArkbj237Pf7M/xU8UfHaL46ftA6jpR8V2Fk1joHhnRPmtdLRwwdi2SN2HcABn++SXOAB9k0VpCbgl1aVk+qVrflp3sE/3l79dX563/PX1CiiiswPjr9pH/k8L4W/9wv8A9OEtfYtfHX7SP/J4Xwt/7hf/AKcJa+xa1ntEQV4B+0t/w0bp2saTqvwR/wCES1TTYbZ01DQ/Em5ZJpt2UeJhswNuQcyr24717/RWNtU+xSe6Pzq8XfDL9s79r7Rx4H+Jun+EfhL4FuJ421WTSJVmuLyFWDbFC3FxnkA4LRgkckjg/ffgnwjpvw/8H6J4Z0eIwaVo9lDYWsbHJEUaBFye5wBzW1RWvP7vKlZPV+bItd8zewVx3xX/AORXsf8AsPaL/wCnO1rsa474r/8AIr2P/Ye0X/052tTHdFHY0UUVIBRRRQAUUUUAcd8Zv+SP+Ov+wDff+k712Ncd8Zv+SP8Ajr/sA33/AKTvXY1XQAoooqQCiiigArjvjN/yR/x1/wBgG+/9J3rsa474zf8AJH/HX/YBvv8A0neqjugPHP2B/wDkj+sf9h6b/wBJ7evpSvmv9gf/AJI/rH/Yem/9J7evpSqqfExBRRRWYwooooAK47S/+SweJ/8AsA6T/wClGo12Ncdpf/JYPE//AGAdJ/8ASjUapdQOxoooqQCiiigAooooA47S/wDksHif/sA6T/6UajXY1x2l/wDJYPE//YB0n/0o1GuxqmAUUUVIBRRRQAV8df8ABQz/AJkD/uIf+21fYtfHX/BQz/mQP+4h/wC21a0/jQmbv7J3/JMtJ/7b/wDo6Sij9k7/AJJlpP8A23/9HSUUS+JgeU/tkf8AIW8Pf9vP/tKvd/2Wfib4P8PfAfwxp+q+K9D0y/h+1eZa3mowxSpm6mYblZgRkEHnsRXhH7ZH/IW8Pf8Abz/7SrrfgT+yL4P+KHwp0PxNqupa5b3995/mR2c8KxDZPJGNoaJj0QdSec1q7ciuLqfUH/C5vh//AND14a/8G9v/APF0f8Lm+H//AEPXhr/wb2//AMXXjn/DA/w//wCgx4l/8Crf/wCMUf8ADA/w/wD+gx4l/wDAq3/+MVlaHcZ7H/wub4f/APQ9eGv/AAb2/wD8XR/wub4f/wDQ9eGv/Bvb/wDxdeOf8MD/AA//AOgx4l/8Crf/AOMUf8MD/D//AKDHiX/wKt//AIxRaHcD2P8A4XN8P/8AoevDX/g3t/8A4uj/AIXN8P8A/oevDX/g3t//AIuvHP8Ahgf4f/8AQY8S/wDgVb//ABij/hgf4f8A/QY8S/8AgVb/APxii0O4HceDPi14HtfEfjyWbxn4fhjudajlgeTVIFEqDTrJCykv8w3I65HdSOoNdX/wub4f/wDQ9eGv/Bvb/wDxdfP3hv8AYm8D6xrPiu0m1XxAsek6mlnAY7iAFkNnbTktmE5O6dxxjgDjOSd7/hgf4f8A/QY8S/8AgVb/APxim1DuB7H/AMLm+H//AEPXhr/wb2//AMXR/wALm+H/AP0PXhr/AMG9v/8AF145/wAMD/D/AP6DHiX/AMCrf/4xR/wwP8P/APoMeJf/AAKt/wD4xStDuB7H/wALm+H/AP0PXhr/AMG9v/8AF0f8Lm+H/wD0PXhr/wAG9v8A/F145/wwP8P/APoMeJf/AAKt/wD4xR/wwP8AD/8A6DHiX/wKt/8A4xRaHcD2P/hc3w//AOh68Nf+De3/APi65TwZ8WvA9r4j8eSzeM/D8MdzrUcsDyapAolQadZIWUl/mG5HXI7qR1Brh/8Ahgf4f/8AQY8S/wDgVb//ABisHw3+xN4H1jWfFdpNqviBY9J1NLOAx3EALIbO2nJbMJyd07jjHAHGcku0O4H0D/wub4f/APQ9eGv/AAb2/wD8XR/wub4f/wDQ9eGv/Bvb/wDxdeOf8MD/AA//AOgx4l/8Crf/AOMUf8MD/D//AKDHiX/wKt//AIxStDuB7H/wub4f/wDQ9eGv/Bvb/wDxdH/C5vh//wBD14a/8G9v/wDF145/wwP8P/8AoMeJf/Aq3/8AjFH/AAwP8P8A/oMeJf8AwKt//jFFodwPY/8Ahc3w/wD+h68Nf+De3/8Ai6P+FzfD/wD6Hrw1/wCDe3/+Lrxz/hgf4f8A/QY8S/8AgVb/APxij/hgf4f/APQY8S/+BVv/APGKLQ7gea/H/wAaeH9Z/ap+G+rafrum32lWn9m/aL62vI5IIdt9Izb3UlVwpDHJ4BzX1b/wub4f/wDQ9eGv/Bvb/wDxdfEPxa+Afh/wF8efBngnT7zUptK1r7F9omuZY2nXzrp4W2FYwowqgjKnn16V73/wwP8AD/8A6DHiX/wKt/8A4xWklGyuwPY/+FzfD/8A6Hrw1/4N7f8A+Lo/4XN8P/8AoevDX/g3t/8A4uvHP+GB/h//ANBjxL/4FW//AMYo/wCGB/h//wBBjxL/AOBVv/8AGKztDuB7H/wub4f/APQ9eGv/AAb2/wD8XR/wub4f/wDQ9eGv/Bvb/wDxdeOf8MD/AA//AOgx4l/8Crf/AOMUf8MD/D//AKDHiX/wKt//AIxRaHcD2P8A4XN8P/8AoevDX/g3t/8A4uuU+Jnxa8D3/hyzitvGfh+5kXWtJlKQ6pAxCJqNs7tgP0VVZiewBJ4FcP8A8MD/AA//AOgx4l/8Crf/AOMVg+Nv2JvA/hvRra7ttV8QSSSanp9mRNcQEbJ7yGBzxCOQsjEe4GQRxTShfcD6B/4XN8P/APoevDX/AIN7f/4uj/hc3w//AOh68Nf+De3/APi68c/4YH+H/wD0GPEv/gVb/wDxij/hgf4f/wDQY8S/+BVv/wDGKVodwPY/+FzfD/8A6Hrw1/4N7f8A+Lo/4XN8P/8AoevDX/g3t/8A4uvHP+GB/h//ANBjxL/4FW//AMYo/wCGB/h//wBBjxL/AOBVv/8AGKLQ7gex/wDC5vh//wBD14a/8G9v/wDF0f8AC5vh/wD9D14a/wDBvb//ABdeOf8ADA/w/wD+gx4l/wDAq3/+MUf8MD/D/wD6DHiX/wACrf8A+MUWh3A7j4tfFrwPqXwr8Z2lp4z8P3V3caLexQwQ6pA7yO0DhVVQ+SSSAAOua6v/AIXN8P8A/oevDX/g3t//AIuvn74gfsTeB/CngPxJrdpqviCS703Tbm8hSa4gKM8cTOoYCEEjKjOCPrW9/wAMD/D/AP6DHiX/AMCrf/4xTtC24Hsf/C5vh/8A9D14a/8ABvb/APxdH/C5vh//AND14a/8G9v/APF145/wwP8AD/8A6DHiX/wKt/8A4xR/wwP8P/8AoMeJf/Aq3/8AjFK0O4Hsf/C5vh//AND14a/8G9v/APF0f8Lm+H//AEPXhr/wb2//AMXXjn/DA/w//wCgx4l/8Crf/wCMUf8ADA/w/wD+gx4l/wDAq3/+MUWh3A9j/wCFzfD/AP6Hrw1/4N7f/wCLrlPi18WvA+pfCvxnaWnjPw/dXdxot7FDBDqkDvI7QOFVVD5JJIAA65rh/wDhgf4f/wDQY8S/+BVv/wDGKwfiB+xN4H8KeA/Emt2mq+IJLvTdNubyFJriAozxxM6hgIQSMqM4I+tNKF9wD9ib4geF/Cnwr1W01vxJpGj3b61LKsGoX8UDshggAYK7AkZUjPsfSvoH/hc3w/8A+h68Nf8Ag3t//i6+QP2ZP2ZPC/xo8B3+t63f6va3dvqclmqafNEiFFiicEh42OcyHv2HFeuf8MD/AA//AOgx4l/8Crf/AOMVU1Hmd2B7H/wub4f/APQ9eGv/AAb2/wD8XR/wub4f/wDQ9eGv/Bvb/wDxdeOf8MD/AA//AOgx4l/8Crf/AOMUf8MD/D//AKDHiX/wKt//AIxUWh3A9j/4XN8P/wDoevDX/g3t/wD4uj/hc3w//wCh68Nf+De3/wDi68c/4YH+H/8A0GPEv/gVb/8Axij/AIYH+H//AEGPEv8A4FW//wAYotDuB7H/AMLm+H//AEPXhr/wb2//AMXXKab8WvA8fxU8RXbeM/D62kui6ZFHOdUgCO6T35dQ2/BKh0JHbevqK4f/AIYH+H//AEGPEv8A4FW//wAYrBsv2JvA9x481jRG1XxALSz02xvI3FxBvLzS3aOCfJxgC3THHduTxh2h3A+gf+FzfD//AKHrw1/4N7f/AOLo/wCFzfD/AP6Hrw1/4N7f/wCLrxz/AIYH+H//AEGPEv8A4FW//wAYo/4YH+H/AP0GPEv/AIFW/wD8YpWh3A9j/wCFzfD/AP6Hrw1/4N7f/wCLo/4XN8P/APoevDX/AIN7f/4uvHP+GB/h/wD9BjxL/wCBVv8A/GKP+GB/h/8A9BjxL/4FW/8A8YotDuB7H/wub4f/APQ9eGv/AAb2/wD8XR/wub4f/wDQ9eGv/Bvb/wDxdeOf8MD/AA//AOgx4l/8Crf/AOMUf8MD/D//AKDHiX/wKt//AIxRaHcDuNN+LXgeP4qeIrtvGfh9bSXRdMijnOqQBHdJ78uobfglQ6EjtvX1FdX/AMLm+H//AEPXhr/wb2//AMXXz9ZfsTeB7jx5rGiNqviAWlnptjeRuLiDeXmlu0cE+TjAFumOO7cnjG9/wwP8P/8AoMeJf/Aq3/8AjFNqHcD2P/hc3w//AOh68Nf+De3/APi6P+FzfD//AKHrw1/4N7f/AOLrxz/hgf4f/wDQY8S/+BVv/wDGKP8Ahgf4f/8AQY8S/wDgVb//ABilaHcD2P8A4XN8P/8AoevDX/g3t/8A4uj/AIXN8P8A/oevDX/g3t//AIuvHP8Ahgf4f/8AQY8S/wDgVb//ABij/hgf4f8A/QY8S/8AgVb/APxii0O4Hsf/AAub4f8A/Q9eGv8Awb2//wAXXyl+3V408P8Ai7/hCP7C13Tda+z/AG7zv7OvI7jyt32fbu2E4ztbGeuD6V6V/wAMD/D/AP6DHiX/AMCrf/4xXgn7VHwD8P8AwP8A+EY/sK81K7/tT7V539oyxvt8vydu3ZGuP9Y2c56CtKajzKzA9n/ZO/5JlpP/AG3/APR0lFH7J3/JMtJ/7b/+jpKKzl8TA8p/bI/5C3h7/t5/9pV9Ofsg/wDJu/hP/t7/APSuavmP9sj/AJC3h7/t5/8AaVfTn7IP/Ju/hP8A7e//AErmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/wBh6L/02WFdjXHeBf8AkaPiJ/2Hov8A02WFdjVMAoooqQCiiigArjvAv/I0fET/ALD0X/pssK7GuO8C/wDI0fET/sPRf+mywql1A7GiiipAKKKKACiiigD46/aR/wCTwvhb/wBwv/04S19i18dftI/8nhfC3/uF/wDpwlr7FrWe0RBRRRWQwooooAK474r/APIr2P8A2HtF/wDTna12Ncd8V/8AkV7H/sPaL/6c7WqjugOxoooqQCiiigAooooA474zf8kf8df9gG+/9J3rsa474zf8kf8AHX/YBvv/AEneuxqugBRRRUgFFFFABXHfGb/kj/jr/sA33/pO9djXHfGb/kj/AI6/7AN9/wCk71Ud0B45+wP/AMkf1j/sPTf+k9vX0pXzX+wP/wAkf1j/ALD03/pPb19KVVT4mIKKKKzGFFFFABXHaX/yWDxP/wBgHSf/AEo1GuxrjtL/AOSweJ/+wDpP/pRqNUuoHY0UUVIBRRRQAUUUUAcdpf8AyWDxP/2AdJ/9KNRrsa47S/8AksHif/sA6T/6UajXY1TAKKKKkAooooAK+Ov+Chn/ADIH/cQ/9tq+xa+Ov+Chn/Mgf9xD/wBtq1p/GhM3f2Tv+SZaT/23/wDR0lFH7J3/ACTLSf8Atv8A+jpKKJfEwPKf2yP+Qt4e/wC3n/2lX05+yD/ybv4T/wC3v/0rmr5j/bI/5C3h7/t5/wDaVfTn7IP/ACbv4T/7e/8A0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/2Hov/TZYV2Ncd4F/5Gj4if8AYei/9NlhXY1TAKKKKkAooooAK47wL/yNHxE/7D0X/pssK7GuO8C/8jR8RP8AsPRf+mywql1A7GiiipAKKKKACiiigD46/aR/5PC+Fv8A3C//AE4S19i18dftI/8AJ4Xwt/7hf/pwlr7FrWe0RBRRRWQwooooAK474r/8ivY/9h7Rf/Tna12Ncd8V/wDkV7H/ALD2i/8ApztaqO6A7GiiipAKKKKACiiigDjvjN/yR/x1/wBgG+/9J3rsa474zf8AJH/HX/YBvv8A0neuxqugBRRRUgFFFFABXHfGb/kj/jr/ALAN9/6TvXY1x3xm/wCSP+Ov+wDff+k71Ud0B45+wP8A8kf1j/sPTf8ApPb19KV81/sD/wDJH9Y/7D03/pPb19KVVT4mIKKKKzGFFFFABXHaX/yWDxP/ANgHSf8A0o1GuxrjtL/5LB4n/wCwDpP/AKUajVLqB2NFFFSAUUUUAFFFFAHHaX/yWDxP/wBgHSf/AEo1GuxrjtL/AOSweJ/+wDpP/pRqNdjVMAoooqQCiiigAr46/wCChn/Mgf8AcQ/9tq+xa+Ov+Chn/Mgf9xD/ANtq1p/GhM3f2Tv+SZaT/wBt/wD0dJRR+yd/yTLSf+2//o6SiiXxMDyn9sj/AJC3h7/t5/8AaVfTn7IP/Ju/hP8A7e//AErmr5j/AGyP+Qt4e/7ef/aVfTn7IP8Aybv4T/7e/wD0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/2Hov/AE2WFdjXHeBf+Ro+In/Yei/9NlhXY1TAKKKKkAooooAK47wL/wAjR8RP+w9F/wCmywrsa47wL/yNHxE/7D0X/pssKpdQOxoooqQCiiigAooooA+Ov2kf+Twvhb/3C/8A04S19i18dftI/wDJ4Xwt/wC4X/6cJa+xa1ntEQUUUVkMKKKKACuO+K//ACK9j/2HtF/9OdrXY1x3xX/5Fex/7D2i/wDpztaqO6A7GiiipAKKKKACiiigDjvjN/yR/wAdf9gG+/8ASd67GuO+M3/JH/HX/YBvv/Sd67Gq6AFFFFSAUUUUAFcd8Zv+SP8Ajr/sA33/AKTvXY1x3xm/5I/46/7AN9/6TvVR3QHjn7A//JH9Y/7D03/pPb19KV81/sD/APJH9Y/7D03/AKT29fSlVU+JiCiiisxhRRRQAVx2l/8AJYPE/wD2AdJ/9KNRrsa47S/+SweJ/wDsA6T/AOlGo1S6gdjRRRUgFFFFABRRRQBx2l/8lg8T/wDYB0n/ANKNRrsa47S/+SweJ/8AsA6T/wClGo12NUwCiiipAKKKKACvjr/goZ/zIH/cQ/8AbavsWvjr/goZ/wAyB/3EP/batafxoTN39k7/AJJlpP8A23/9HSUUfsnf8ky0n/tv/wCjpKKJfEwPKf2yP+Qt4e/7ef8A2lXu/wCyzqHjCH4D+GE0rQtDvLAfavLnvNamt5W/0qbO6NbSQLg5HDHIAPGcDwj9sj/kLeHv+3n/ANpV9Ofsg/8AJu/hP/t7/wDSuatJfw0HU7H+1PiD/wBCx4a/8KO4/wDkCj+1PiD/ANCx4a/8KO4/+QK7GisL+Qzjv7U+IP8A0LHhr/wo7j/5Ao/tT4g/9Cx4a/8ACjuP/kCuxoov5Acd/anxB/6Fjw1/4Udx/wDIFH9qfEH/AKFjw1/4Udx/8gV2NFF/IDyPwZqXjhfEfjww+HfD8kja1GZ1k16dQj/2dZAKpFkdw2hDkheSRjADHq/7U+IP/QseGv8Awo7j/wCQKPAv/I0fET/sPRf+mywrsabEcd/anxB/6Fjw1/4Udx/8gUf2p8Qf+hY8Nf8AhR3H/wAgV2NFK/kM47+1PiD/ANCx4a/8KO4/+QKP7U+IP/QseGv/AAo7j/5ArsaKL+QHHf2p8Qf+hY8Nf+FHcf8AyBXKeDNS8cL4j8eGHw74fkkbWozOsmvTqEf+zrIBVIsjuG0IckLySMYAY+uVx3gX/kaPiJ/2Hov/AE2WFNdRB/anxB/6Fjw1/wCFHcf/ACBR/anxB/6Fjw1/4Udx/wDIFdjRSv5DOO/tT4g/9Cx4a/8ACjuP/kCj+1PiD/0LHhr/AMKO4/8AkCuxoov5Acd/anxB/wChY8Nf+FHcf/IFH9qfEH/oWPDX/hR3H/yBXY0UX8gPiH4/3XiCX9qn4byahpmm2uqr/Zv2e1ttRkmgkxfSFd8rQIyZbIOI2wBnnpX1b/anxB/6Fjw1/wCFHcf/ACBXzX+0j/yeF8Lf+4X/AOnCWvsWtJbIRx39qfEH/oWPDX/hR3H/AMgUf2p8Qf8AoWPDX/hR3H/yBXY0VnfyGcd/anxB/wChY8Nf+FHcf/IFH9qfEH/oWPDX/hR3H/yBXY0UX8gOO/tT4g/9Cx4a/wDCjuP/AJArlPiZqXjh/DlmLnw74fhj/trSSGh16eQlxqNsUXBsl4LbQTngEkBiNp9crjviv/yK9j/2HtF/9OdrTW4g/tT4g/8AQseGv/CjuP8A5Ao/tT4g/wDQseGv/CjuP/kCuxopX8hnHf2p8Qf+hY8Nf+FHcf8AyBR/anxB/wChY8Nf+FHcf/IFdjRRfyA47+1PiD/0LHhr/wAKO4/+QKP7U+IP/QseGv8Awo7j/wCQK7Gii/kB5H8WtS8cSfCvxmt34d8PwWjaLeiaWHXp5HRPIfcyobJQxAzgFhnpkda6v+1PiD/0LHhr/wAKO4/+QKPjN/yR/wAdf9gG+/8ASd67Gn0Ecd/anxB/6Fjw1/4Udx/8gUf2p8Qf+hY8Nf8AhR3H/wAgV2NFK/kM47+1PiD/ANCx4a/8KO4/+QKP7U+IP/QseGv/AAo7j/5ArsaKL+QHHf2p8Qf+hY8Nf+FHcf8AyBXKfFrUvHEnwr8Zrd+HfD8Fo2i3omlh16eR0TyH3MqGyUMQM4BYZ6ZHWvXK474zf8kf8df9gG+/9J3prcR8/fsTXvii3+Feqromj6RqFp/bUpaXUNWltXD+RBlQiW0oIxjncOpGOMn6B/tT4g/9Cx4a/wDCjuP/AJArxz9gf/kj+sf9h6b/ANJ7evpSqn8TA47+1PiD/wBCx4a/8KO4/wDkCj+1PiD/ANCx4a/8KO4/+QK7Giov5DOO/tT4g/8AQseGv/CjuP8A5Ao/tT4g/wDQseGv/CjuP/kCuxoov5Acd/anxB/6Fjw1/wCFHcf/ACBXKabqXjj/AIWp4iZfDvh83Z0XTBJEdenCKgnv9jB/sWSSS+RtGNqnJ3EL65XHaX/yWDxP/wBgHSf/AEo1Gmuog/tT4g/9Cx4a/wDCjuP/AJAo/tT4g/8AQseGv/CjuP8A5ArsaKV/IZx39qfEH/oWPDX/AIUdx/8AIFH9qfEH/oWPDX/hR3H/AMgV2NFF/IDjv7U+IP8A0LHhr/wo7j/5Ao/tT4g/9Cx4a/8ACjuP/kCuxoov5AeR6bqXjj/haniJl8O+HzdnRdMEkR16cIqCe/2MH+xZJJL5G0Y2qcncQvV/2p8Qf+hY8Nf+FHcf/IFGl/8AJYPE/wD2AdJ/9KNRrsabEcd/anxB/wChY8Nf+FHcf/IFH9qfEH/oWPDX/hR3H/yBXY0Ur+Qzjv7U+IP/AELHhr/wo7j/AOQKP7U+IP8A0LHhr/wo7j/5ArsaKL+QHHf2p8Qf+hY8Nf8AhR3H/wAgV8pft1XXiC6/4Qj+3dM03Tdv27yf7O1GS73/APHvu3b4ItuPlxjdnJ6Y5+3q+Ov+Chn/ADIH/cQ/9tq0p/EhG7+yd/yTLSf+2/8A6Okoo/ZO/wCSZaT/ANt//R0lFKXxMDyn9sj/AJC3h7/t5/8AaVfTn7IP/Ju/hP8A7e//AErmr5j/AGyP+Qt4e/7ef/aVfTn7IP8Aybv4T/7e/wD0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/2Hov/AE2WFdjXHeBf+Ro+In/Yei/9NlhXY1TAKKKKkAooooAK47wL/wAjR8RP+w9F/wCmywrsa47wL/yNHxE/7D0X/pssKpdQOxoooqQCiiigAooooA+Ov2kf+Twvhb/3C/8A04S19i18dftI/wDJ4Xwt/wC4X/6cJa+xa1ntEQUUUVkMKKKKACuO+K//ACK9j/2HtF/9OdrXY1x3xX/5Fex/7D2i/wDpztaqO6A7GiiipAKKKKACiiigDjvjN/yR/wAdf9gG+/8ASd67GuO+M3/JH/HX/YBvv/Sd67Gq6AFFFFSAUUUUAFcd8Zv+SP8Ajr/sA33/AKTvXY1x3xm/5I/46/7AN9/6TvVR3QHjn7A//JH9Y/7D03/pPb19KV81/sD/APJH9Y/7D03/AKT29fSlVU+JiCiiisxhRRRQAVx2l/8AJYPE/wD2AdJ/9KNRrsa47S/+SweJ/wDsA6T/AOlGo1S6gdjRRRUgFFFFABRRRQBx2l/8lg8T/wDYB0n/ANKNRrsa47S/+SweJ/8AsA6T/wClGo12NUwCiiipAKKKKACvjr/goZ/zIH/cQ/8AbavsWvjr/goZ/wAyB/3EP/batafxoTN39k7/AJJlpP8A23/9HSUUfsnf8ky0n/tv/wCjpKKJfEwPKf2yP+Qt4e/7ef8A2lX05+yD/wAm7+E/+3v/ANK5q+Y/2yP+Qt4e/wC3n/2lX05+yD/ybv4T/wC3v/0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/AJGj4if9h6L/ANNlhXY1x3gX/kaPiJ/2Hov/AE2WFdjVMAoooqQCiiigArjvAv8AyNHxE/7D0X/pssK7GuO8C/8AI0fET/sPRf8ApssKpdQOxoooqQCiiigAooooA+Ov2kf+Twvhb/3C/wD04S19i18dftI/8nhfC3/uF/8Apwlr7FrWe0RBRRRWQwooooAK474r/wDIr2P/AGHtF/8ATna12Ncd8V/+RXsf+w9ov/pztaqO6A7GiiipAKKKKACiiigDjvjN/wAkf8df9gG+/wDSd67GuO+M3/JH/HX/AGAb7/0neuxqugBRRRUgFFFFABXHfGb/AJI/46/7AN9/6TvXY1x3xm/5I/46/wCwDff+k71Ud0B45+wP/wAkf1j/ALD03/pPb19KV81/sD/8kf1j/sPTf+k9vX0pVVPiYgooorMYUUUUAFcdpf8AyWDxP/2AdJ/9KNRrsa47S/8AksHif/sA6T/6UajVLqB2NFFFSAUUUUAFFFFAHHaX/wAlg8T/APYB0n/0o1GuxrjtL/5LB4n/AOwDpP8A6UajXY1TAKKKKkAooooAK+Ov+Chn/Mgf9xD/ANtq+xa+Ov8AgoZ/zIH/AHEP/batafxoTN39k7/kmWk/9t//AEdJRR+yd/yTLSf+2/8A6Okool8TA8p/bI/5C3h7/t5/9pV9Ofsg/wDJu/hP/t7/APSuavmP9sj/AJC3h7/t5/8AaVe7/ss/EHS9D+A/hiyubXXJJovtW5rPQL+5iObqYjbJFCyNwexODkHkEVpL+Gg6n0PRXHf8LX0T/nx8S/8AhLan/wDI9H/C19E/58fEv/hLan/8j1hZjOxorjv+Fr6J/wA+PiX/AMJbU/8A5Ho/4Wvon/Pj4l/8JbU//keizA7GiuO/4Wvon/Pj4l/8JbU//kej/ha+if8APj4l/wDCW1P/AOR6LMA8C/8AI0fET/sPRf8ApssK7GvI/BnxM0i38R+PJHs/EBW41qORBH4b1FyANOslwwEBKHKn5WwcEHGGBPV/8LX0T/nx8S/+Etqf/wAj02mI7GiuO/4Wvon/AD4+Jf8AwltT/wDkej/ha+if8+PiX/wltT/+R6VmM7GiuO/4Wvon/Pj4l/8ACW1P/wCR6P8Aha+if8+PiX/wltT/APkeizA7GuO8C/8AI0fET/sPRf8ApssKP+Fr6J/z4+Jf/CW1P/5HrlPBnxM0i38R+PJHs/EBW41qORBH4b1FyANOslwwEBKHKn5WwcEHGGBLSeoj1yiuO/4Wvon/AD4+Jf8AwltT/wDkej/ha+if8+PiX/wltT/+R6VmM7GiuO/4Wvon/Pj4l/8ACW1P/wCR6P8Aha+if8+PiX/wltT/APkeizA7GiuO/wCFr6J/z4+Jf/CW1P8A+R6P+Fr6J/z4+Jf/AAltT/8AkeizA+a/2kf+Twvhb/3C/wD04S19i18Q/H/xZZat+1T8N9Tgg1JLa2/s3elzpV1BOdt9Ix2QyRrI/B42qcngZIxX1b/wtfRP+fHxL/4S2p//ACPWkk7IR2NFcd/wtfRP+fHxL/4S2p//ACPR/wALX0T/AJ8fEv8A4S2p/wDyPWdmM7GiuO/4Wvon/Pj4l/8ACW1P/wCR6P8Aha+if8+PiX/wltT/APkeizA7GuO+K/8AyK9j/wBh7Rf/AE52tH/C19E/58fEv/hLan/8j1ynxM+JmkXvhyzjjs/ECsutaTITN4b1GMYTUbZiMtAAThTherHAAJIBaTuI9corjv8Aha+if8+PiX/wltT/APkej/ha+if8+PiX/wAJbU//AJHpWYzsaK47/ha+if8APj4l/wDCW1P/AOR6P+Fr6J/z4+Jf/CW1P/5HoswOxorjv+Fr6J/z4+Jf/CW1P/5Ho/4Wvon/AD4+Jf8AwltT/wDkeizAPjN/yR/x1/2Ab7/0neuxryP4tfEzSL/4V+M7aKz8QLJNot7GrTeG9RiQEwOAWd4AqjnlmIA6kgV1f/C19E/58fEv/hLan/8AI9OzsI7GiuO/4Wvon/Pj4l/8JbU//kej/ha+if8APj4l/wDCW1P/AOR6VmM7GiuO/wCFr6J/z4+Jf/CW1P8A+R6P+Fr6J/z4+Jf/AAltT/8AkeizA7GuO+M3/JH/AB1/2Ab7/wBJ3o/4Wvon/Pj4l/8ACW1P/wCR65T4tfEzSL/4V+M7aKz8QLJNot7GrTeG9RiQEwOAWd4AqjnlmIA6kgU0ncRw/wCwP/yR/WP+w9N/6T29fSlfJv7E3jbTvDfwr1W2u7bV5pH1qWQNp+i3l4mDBAMF4YnUHg/KTnocYIr6B/4Wvon/AD4+Jf8AwltT/wDkeqmnzMDsaK47/ha+if8APj4l/wDCW1P/AOR6P+Fr6J/z4+Jf/CW1P/5HqLMZ2NFcd/wtfRP+fHxL/wCEtqf/AMj0f8LX0T/nx8S/+Etqf/yPRZgdjXHaX/yWDxP/ANgHSf8A0o1Gj/ha+if8+PiX/wAJbU//AJHrlNN+JmkJ8VPEVybPxB5cmi6ZGqjw3qJcFZ78klBBuUfOMMQAcMASVbDSeoj1yiuO/wCFr6J/z4+Jf/CW1P8A+R6P+Fr6J/z4+Jf/AAltT/8AkelZjOxorjv+Fr6J/wA+PiX/AMJbU/8A5Ho/4Wvon/Pj4l/8JbU//keizA7GiuO/4Wvon/Pj4l/8JbU//kej/ha+if8APj4l/wDCW1P/AOR6LMA0v/ksHif/ALAOk/8ApRqNdjXkem/EzSE+KniK5Nn4g8uTRdMjVR4b1EuCs9+SSgg3KPnGGIAOGAJKtjq/+Fr6J/z4+Jf/AAltT/8Akem0xHY0Vx3/AAtfRP8Anx8S/wDhLan/API9H/C19E/58fEv/hLan/8AI9KzGdjRXHf8LX0T/nx8S/8AhLan/wDI9H/C19E/58fEv/hLan/8j0WYHY18df8ABQz/AJkD/uIf+21fSn/C19E/58fEv/hLan/8j18pft1eLLLxR/whH2ODUofI+3b/AO0dKurLO77Pjb58ab+hztzjjOMitKafMhHbfsnf8ky0n/tv/wCjpKKP2Tv+SZaT/wBt/wD0dJRSl8TA8p/bI/5C3h7/ALef/aVfTn7IP/Ju/hP/ALe//SuavmP9sj/kLeHv+3n/ANpV9Ofsg/8AJu/hP/t7/wDSuarl/DQdT2KiiiucYUUUUAFFFFAHHeBf+Ro+In/Yei/9NlhXY1x3gX/kaPiJ/wBh6L/02WFdjVMAoooqQCiiigArjvAv/I0fET/sPRf+mywrsa47wL/yNHxE/wCw9F/6bLCqXUDsaKKKkAooooAKKKKAPjr9pH/k8L4W/wDcL/8AThLX2LXx1+0j/wAnhfC3/uF/+nCWvsWtZ7REFFFFZDCiiigArjviv/yK9j/2HtF/9OdrXY1x3xX/AORXsf8AsPaL/wCnO1qo7oDsaKKKkAooooAKKKKAOO+M3/JH/HX/AGAb7/0neuxrjvjN/wAkf8df9gG+/wDSd67Gq6AFFFFSAUUUUAFcd8Zv+SP+Ov8AsA33/pO9djXHfGb/AJI/46/7AN9/6TvVR3QHjn7A/wDyR/WP+w9N/wCk9vX0pXzX+wP/AMkf1j/sPTf+k9vX0pVVPiYgooorMYUUUUAFcdpf/JYPE/8A2AdJ/wDSjUa7GuO0v/ksHif/ALAOk/8ApRqNUuoHY0UUVIBRRRQAUUUUAcdpf/JYPE//AGAdJ/8ASjUa7GuO0v8A5LB4n/7AOk/+lGo12NUwCiiipAKKKKACvjr/AIKGf8yB/wBxD/22r7Fr46/4KGf8yB/3EP8A22rWn8aEzd/ZO/5JlpP/AG3/APR0lFH7J3/JMtJ/7b/+jpKKJfEwPKf2yP8AkLeHv+3n/wBpV9Ofsg/8m7+E/wDt7/8ASuavmP8AbI/5C3h7/t5/9pV9Ofsg/wDJu/hP/t7/APSuarl/DQdT2KiiiucYUUUUAFFFFAHHeBf+Ro+In/Yei/8ATZYV2Ncd4F/5Gj4if9h6L/02WFdjVMAoooqQCiiigArjvAv/ACNHxE/7D0X/AKbLCuxrjvAv/I0fET/sPRf+mywql1A7GiiipAKKKKACiiigD46/aR/5PC+Fv/cL/wDThLX2LXx1+0j/AMnhfC3/ALhf/pwlr7FrWe0RBRRRWQwooooAK474r/8AIr2P/Ye0X/052tdjXHfFf/kV7H/sPaL/AOnO1qo7oDsaKKKkAooooAKKKKAOO+M3/JH/AB1/2Ab7/wBJ3rsa474zf8kf8df9gG+/9J3rsaroAUUUVIBRRRQAVx3xm/5I/wCOv+wDff8ApO9djXHfGb/kj/jr/sA33/pO9VHdAeOfsD/8kf1j/sPTf+k9vX0pXzX+wP8A8kf1j/sPTf8ApPb19KVVT4mIKKKKzGFFFFABXHaX/wAlg8T/APYB0n/0o1GuxrjtL/5LB4n/AOwDpP8A6UajVLqB2NFFFSAUUUUAFFFFAHHaX/yWDxP/ANgHSf8A0o1GuxrjtL/5LB4n/wCwDpP/AKUajXY1TAKKKKkAooooAK+Ov+Chn/Mgf9xD/wBtq+xa+Ov+Chn/ADIH/cQ/9tq1p/GhM3f2Tv8AkmWk/wDbf/0dJRR+yd/yTLSf+2//AKOkool8TA8p/bI/5C3h7/t5/wDaVfTn7IP/ACbv4T/7e/8A0rmr5j/bI/5C3h7/ALef/aVfTn7IP/Ju/hP/ALe//Suarl/DQdT2KiiiucYUUUUAFFFFAHHeBf8AkaPiJ/2Hov8A02WFdjXHeBf+Ro+In/Yei/8ATZYV2NUwCiiipAKKKKACuO8C/wDI0fET/sPRf+mywrsa47wL/wAjR8RP+w9F/wCmywql1A7GiiipAKKKKACiiigD46/aR/5PC+Fv/cL/APThLX2LXx1+0j/yeF8Lf+4X/wCnCWvsWtZ7REFFFFZDCiiigArjviv/AMivY/8AYe0X/wBOdrXY1x3xX/5Fex/7D2i/+nO1qo7oDsaKKKkAooooAKKKKAOO+M3/ACR/x1/2Ab7/ANJ3rsa474zf8kf8df8AYBvv/Sd67Gq6AFFFFSAUUUUAFcd8Zv8Akj/jr/sA33/pO9djXHfGb/kj/jr/ALAN9/6TvVR3QHjn7A//ACR/WP8AsPTf+k9vX0pXzX+wP/yR/WP+w9N/6T29fSlVU+JiCiiisxhRRRQAVx2l/wDJYPE//YB0n/0o1GuxrjtL/wCSweJ/+wDpP/pRqNUuoHY0UUVIBRRRQAUUUUAcdpf/ACWDxP8A9gHSf/SjUa7GuO0v/ksHif8A7AOk/wDpRqNdjVMAoooqQCiiigAr46/4KGf8yB/3EP8A22r7Fr46/wCChn/Mgf8AcQ/9tq1p/GhM3f2Tv+SZaT/23/8AR0lFH7J3/JMtJ/7b/wDo6SiiXxMDyn9sj/kLeHv+3n/2lX05+yD/AMm7+E/+3v8A9K5q+Y/2yP8AkLeHv+3n/wBpVY+EH7G3/C1vh3pPin/hL/7L+3+d/on9meds2TPH9/zlznZnoOuK1aTgrsXU+9qK+Ov+Hef/AFP/AP5Rv/uij/h3n/1P/wD5Rv8A7orLlh/MM+xaK+Ov+Hef/U//APlG/wDuij/h3n/1P/8A5Rv/ALoo5YfzAfYtFfHX/DvP/qf/APyjf/dFH/DvP/qf/wDyjf8A3RRyw/mA+lPAv/I0fET/ALD0X/pssK7GviHQv2FP7a1TxFZ/8Jv5P9kX62W/+yd3m7rWCfdjzxt/1+3HP3c55wNn/h3n/wBT/wD+Ub/7opuMf5gPsWivjr/h3n/1P/8A5Rv/ALoo/wCHef8A1P8A/wCUb/7opcsP5gPsWivjr/h3n/1P/wD5Rv8A7oo/4d5/9T//AOUb/wC6KOWH8wH2LXHeBf8AkaPiJ/2Hov8A02WFfNf/AA7z/wCp/wD/ACjf/dFY2hfsKf21qniKz/4Tfyf7Iv1st/8AZO7zd1rBPux542/6/bjn7uc84D5Y/wAwH29RXx1/w7z/AOp//wDKN/8AdFH/AA7z/wCp/wD/ACjf/dFLlh/MB9i0V8df8O8/+p//APKN/wDdFH/DvP8A6n//AMo3/wB0UcsP5gPsWivjr/h3n/1P/wD5Rv8A7oo/4d5/9T//AOUb/wC6KOWH8wB+0j/yeF8Lf+4X/wCnCWvsWvzX+JH7PP8Awr34weFvAv8Ab/2/+3Psv/Ew+xeX5PnXDQ/6vzDu27d33hnOOOtex/8ADvP/AKn/AP8AKN/90VpJRsrsD7For46/4d5/9T//AOUb/wC6KP8Ah3n/ANT/AP8AlG/+6Kz5YfzAfYtFfHX/AA7z/wCp/wD/ACjf/dFH/DvP/qf/APyjf/dFHLD+YD7Frjviv/yK9j/2HtF/9OdrXzX/AMO8/wDqf/8Ayjf/AHRWN4s/YU/4RfS4Lz/hN/tPm39lZbP7J2Y+0XUUG7Pnn7vm7sd9uMjOaajG/wAQH29RXx1/w7z/AOp//wDKN/8AdFH/AA7z/wCp/wD/ACjf/dFLlh/MB9i0V8df8O8/+p//APKN/wDdFH/DvP8A6n//AMo3/wB0UcsP5gPsWivjr/h3n/1P/wD5Rv8A7oo/4d5/9T//AOUb/wC6KOWH8wH0p8Zv+SP+Ov8AsA33/pO9djXxD40/YU/4RHwfruu/8Jv9r/suwnvfs/8AZOzzfLjZ9u7zzjO3GcHGelbP/DvP/qf/APyjf/dFPljb4gPsWivjr/h3n/1P/wD5Rv8A7oo/4d5/9T//AOUb/wC6KXLD+YD7For46/4d5/8AU/8A/lG/+6KP+Hef/U//APlG/wDuijlh/MB9i1x3xm/5I/46/wCwDff+k7181/8ADvP/AKn/AP8AKN/90VjeNP2FP+ER8H67rv8Awm/2v+y7Ce9+z/2Ts83y42fbu884ztxnBxnpTUY3+ID0r9gf/kj+sf8AYem/9J7evpSvzw+Af7K//C8PB95rv/CT/wBi/Z797L7P/Z/2jdtjjfdu81cf6zGMduvNelf8O8/+p/8A/KN/90VU4x5ndgfYtFfHX/DvP/qf/wDyjf8A3RR/w7z/AOp//wDKN/8AdFRyw/mA+xaK+Ov+Hef/AFP/AP5Rv/uij/h3n/1P/wD5Rv8A7oo5YfzAfYtcdpf/ACWDxP8A9gHSf/SjUa+a/wDh3n/1P/8A5Rv/ALorGtf2FPtXjDU9C/4Tfb9isLS9+0f2Tnf58lym3b5/G37PnOed/QY5fLH+YD7eor46/wCHef8A1P8A/wCUb/7oo/4d5/8AU/8A/lG/+6KXLD+YD7For46/4d5/9T//AOUb/wC6KP8Ah3n/ANT/AP8AlG/+6KOWH8wH2LRXx1/w7z/6n/8A8o3/AN0Uf8O8/wDqf/8Ayjf/AHRRyw/mA+lNL/5LB4n/AOwDpP8A6UajXY18Q2v7Cn2rxhqehf8ACb7fsVhaXv2j+yc7/PkuU27fP42/Z85zzv6DHOz/AMO8/wDqf/8Ayjf/AHRTcY/zAfYtFfHX/DvP/qf/APyjf/dFH/DvP/qf/wDyjf8A3RS5YfzAfYtFfHX/AA7z/wCp/wD/ACjf/dFH/DvP/qf/APyjf/dFHLD+YD7Fr46/4KGf8yB/3EP/AG2o/wCHef8A1P8A/wCUb/7orxz9ob9nn/hQv9gf8T/+3f7V+0f8uX2fyvK8v/po+7Pme2Md81pCMeZWYH0B+yd/yTLSf+2//o6Sij9k7/kmWk/9t/8A0dJRWcviYHlP7ZH/ACFvD3/bz/7Sr6c/ZB/5N38J/wDb3/6VzV8x/tkf8hbw9/28/wDtKvpz9kH/AJN38J/9vf8A6VzVcv4aDqexUUUVzjCiiigAooooA47wL/yNHxE/7D0X/pssK7GuO8C/8jR8RP8AsPRf+mywrsapgFFFFSAUUUUAFcd4F/5Gj4if9h6L/wBNlhXY1x3gX/kaPiJ/2Hov/TZYVS6gdjRRRUgFFFFABRRRQB8dftI/8nhfC3/uF/8Apwlr7Fr46/aR/wCTwvhb/wBwv/04S19i1rPaIgooorIYUUUUAFcd8V/+RXsf+w9ov/pzta7GuO+K/wDyK9j/ANh7Rf8A052tVHdAdjRRRUgFFFFABRRRQBx3xm/5I/46/wCwDff+k712Ncd8Zv8Akj/jr/sA33/pO9djVdACiiipAKKKKACuO+M3/JH/AB1/2Ab7/wBJ3rsa474zf8kf8df9gG+/9J3qo7oDxz9gf/kj+sf9h6b/ANJ7evpSvmv9gf8A5I/rH/Yem/8ASe3r6UqqnxMQUUUVmMKKKKACuO0v/ksHif8A7AOk/wDpRqNdjXHaX/yWDxP/ANgHSf8A0o1GqXUDsaKKKkAooooAKKKKAOO0v/ksHif/ALAOk/8ApRqNdjXHaX/yWDxP/wBgHSf/AEo1GuxqmAUUUVIBRRRQAV8df8FDP+ZA/wC4h/7bV9i18df8FDP+ZA/7iH/ttWtP40Jm7+yd/wAky0n/ALb/APo6Sij9k7/kmWk/9t//AEdJRRL4mB5T+2R/yFvD3/bz/wC0q+nP2Qf+Td/Cf/b3/wClc1fMf7ZH/IW8Pf8Abz/7Sr6c/ZB/5N38J/8Ab3/6VzVcv4aDqexUUUVzjCiiigAooooA47wL/wAjR8RP+w9F/wCmywrsa47wL/yNHxE/7D0X/pssK7GqYBRRRUgFFFFABXHeBf8AkaPiJ/2Hov8A02WFdjXHeBf+Ro+In/Yei/8ATZYVS6gdjRRRUgFFFFABRRRQB8dftI/8nhfC3/uF/wDpwlr7Fr46/aR/5PC+Fv8A3C//AE4S19i1rPaIgooorIYUUUUAFcd8V/8AkV7H/sPaL/6c7Wuxrjviv/yK9j/2HtF/9OdrVR3QHY0UUVIBRRRQAUUUUAcd8Zv+SP8Ajr/sA33/AKTvXY1x3xm/5I/46/7AN9/6TvXY1XQAoooqQCiiigArjvjN/wAkf8df9gG+/wDSd67GuO+M3/JH/HX/AGAb7/0neqjugPHP2B/+SP6x/wBh6b/0nt6+lK+a/wBgf/kj+sf9h6b/ANJ7evpSqqfExBRRRWYwooooAK47S/8AksHif/sA6T/6UajXY1x2l/8AJYPE/wD2AdJ/9KNRql1A7GiiipAKKKKACiiigDjtL/5LB4n/AOwDpP8A6UajXY1x2l/8lg8T/wDYB0n/ANKNRrsapgFFFFSAUUUUAFfHX/BQz/mQP+4h/wC21fYtfHX/AAUM/wCZA/7iH/ttWtP40Jm7+yd/yTLSf+2//o6Sij9k7/kmWk/9t/8A0dJRRL4mB5T+2R/yFvD3/bz/AO0q+nP2Qf8Ak3fwn/29/wDpXNXzH+2R/wAhbw9/28/+0q+nP2Qf+Td/Cf8A29/+lc1XL+Gg6nsVFFFc4wooooAKKKKAOO8C/wDI0fET/sPRf+mywrsa47wL/wAjR8RP+w9F/wCmywrsapgFFFFSAUUUUAFcd4F/5Gj4if8AYei/9NlhXY1x3gX/AJGj4if9h6L/ANNlhVLqB2NFFFSAUUUUAFFFFAHx1+0j/wAnhfC3/uF/+nCWvsWvjr9pH/k8L4W/9wv/ANOEtfYtaz2iIKKKKyGFFFFABXHfFf8A5Fex/wCw9ov/AKc7Wuxrjviv/wAivY/9h7Rf/Tna1Ud0B2NFFFSAUUUUAFFFFAHHfGb/AJI/46/7AN9/6TvXY1x3xm/5I/46/wCwDff+k712NV0AKKKKkAooooAK474zf8kf8df9gG+/9J3rsa474zf8kf8AHX/YBvv/AEneqjugPHP2B/8Akj+sf9h6b/0nt6+lK+a/2B/+SP6x/wBh6b/0nt6+lKqp8TEFFFFZjCiiigArjtL/AOSweJ/+wDpP/pRqNdjXHaX/AMlg8T/9gHSf/SjUapdQOxoooqQCiiigAooooA47S/8AksHif/sA6T/6UajXY1x2l/8AJYPE/wD2AdJ/9KNRrsapgFFFFSAUUUUAFfHX/BQz/mQP+4h/7bV9i18df8FDP+ZA/wC4h/7bVrT+NCZu/snf8ky0n/tv/wCjpKKP2Tv+SZaT/wBt/wD0dJRRL4mB5T+2R/yFvD3/AG8/+0q93/ZZ8WappvwH8MW1t4M1zVoU+1bbyzmsFikzdTE7RLco/BOOVHIOMjBPhH7ZH/IW8Pf9vP8A7Sr6c/ZB/wCTd/Cf/b3/AOlc1aS/hoOp2P8AwnWt/wDRO/Ev/gRpn/yZR/wnWt/9E78S/wDgRpn/AMmV2NFYX8hnHf8ACda3/wBE78S/+BGmf/JlH/Cda3/0TvxL/wCBGmf/ACZXY0UX8gOO/wCE61v/AKJ34l/8CNM/+TKP+E61v/onfiX/AMCNM/8Akyuxoov5AeR+DPGerw+I/Hjp4D8QTtLrUbvHHPpwMJ/s6yXY2bsAnChvlJGHHOcgdX/wnWt/9E78S/8AgRpn/wAmUeBf+Ro+In/Yei/9NlhXY02I47/hOtb/AOid+Jf/AAI0z/5Mo/4TrW/+id+Jf/AjTP8A5MrsaKV/IZx3/Cda3/0TvxL/AOBGmf8AyZR/wnWt/wDRO/Ev/gRpn/yZXY0UX8gOO/4TrW/+id+Jf/AjTP8A5MrlPBnjPV4fEfjx08B+IJ2l1qN3jjn04GE/2dZLsbN2AThQ3ykjDjnOQPXK47wL/wAjR8RP+w9F/wCmywprqIP+E61v/onfiX/wI0z/AOTKP+E61v8A6J34l/8AAjTP/kyuxopX8hnHf8J1rf8A0TvxL/4EaZ/8mUf8J1rf/RO/Ev8A4EaZ/wDJldjRRfyA47/hOtb/AOid+Jf/AAI0z/5Mo/4TrW/+id+Jf/AjTP8A5MrsaKL+QHxD8f8AXb2+/ap+G97P4d1LTrmH+zdmnXMlqZ58X0hGwxzNGNxO0bnXkc4HNfVv/Cda3/0TvxL/AOBGmf8AyZXzX+0j/wAnhfC3/uF/+nCWvsWtJbIRx3/Cda3/ANE78S/+BGmf/JlH/Cda3/0TvxL/AOBGmf8AyZXY0VnfyGcd/wAJ1rf/AETvxL/4EaZ/8mUf8J1rf/RO/Ev/AIEaZ/8AJldjRRfyA47/AITrW/8AonfiX/wI0z/5MrlPiZ4z1e48OWaSeA/EFqo1rSXEk0+nEErqNswT5bsnLEBRxjLDJAyR65XHfFf/AJFex/7D2i/+nO1prcQf8J1rf/RO/Ev/AIEaZ/8AJlH/AAnWt/8ARO/Ev/gRpn/yZXY0Ur+Qzjv+E61v/onfiX/wI0z/AOTKP+E61v8A6J34l/8AAjTP/kyuxoov5Acd/wAJ1rf/AETvxL/4EaZ/8mUf8J1rf/RO/Ev/AIEaZ/8AJldjRRfyA8j+LXjPV7r4V+M4ZfAfiCzjk0W9RriafTikQMDguwS7ZsDqdoJ44BPFdX/wnWt/9E78S/8AgRpn/wAmUfGb/kj/AI6/7AN9/wCk712NPoI47/hOtb/6J34l/wDAjTP/AJMo/wCE61v/AKJ34l/8CNM/+TK7GilfyGcd/wAJ1rf/AETvxL/4EaZ/8mUf8J1rf/RO/Ev/AIEaZ/8AJldjRRfyA47/AITrW/8AonfiX/wI0z/5MrlPi14z1e6+FfjOGXwH4gs45NFvUa4mn04pEDA4LsEu2bA6naCeOATxXrlcd8Zv+SP+Ov8AsA33/pO9NbiPn79ibxJqOj/CvVYbTwpq+uxtrUrm40+WzVFPkQDYRNcRtngHgEcjnOQPoH/hOtb/AOid+Jf/AAI0z/5Mrxz9gf8A5I/rH/Yem/8ASe3r6Uqp/EwOO/4TrW/+id+Jf/AjTP8A5Mo/4TrW/wDonfiX/wACNM/+TK7Giov5DOO/4TrW/wDonfiX/wACNM/+TKP+E61v/onfiX/wI0z/AOTK7Gii/kBx3/Cda3/0TvxL/wCBGmf/ACZXKab4z1dfip4imHgPxA0j6LpiNbifTt6AT35Dkm724bcQMEn5GyANpPrlcdpf/JYPE/8A2AdJ/wDSjUaa6iD/AITrW/8AonfiX/wI0z/5Mo/4TrW/+id+Jf8AwI0z/wCTK7GilfyGcd/wnWt/9E78S/8AgRpn/wAmUf8ACda3/wBE78S/+BGmf/JldjRRfyA47/hOtb/6J34l/wDAjTP/AJMo/wCE61v/AKJ34l/8CNM/+TK7Gii/kB5HpvjPV1+KniKYeA/EDSPoumI1uJ9O3oBPfkOSbvbhtxAwSfkbIA2k9X/wnWt/9E78S/8AgRpn/wAmUaX/AMlg8T/9gHSf/SjUa7GmxHHf8J1rf/RO/Ev/AIEaZ/8AJlH/AAnWt/8ARO/Ev/gRpn/yZXY0Ur+Qzjv+E61v/onfiX/wI0z/AOTKP+E61v8A6J34l/8AAjTP/kyuxoov5Acd/wAJ1rf/AETvxL/4EaZ/8mV8pft1a7e61/whH2zw7qWgeX9u2f2jJav5ufs+dvkTSYxgZ3Y+8MZ5x9vV8df8FDP+ZA/7iH/ttWlP4kI3f2Tv+SZaT/23/wDR0lFH7J3/ACTLSf8Atv8A+jpKKUviYHlP7ZH/ACFvD3/bz/7Sr6c/ZB/5N38J/wDb3/6VzV8x/tkf8hbw9/28/wDtKvpz9kH/AJN38J/9vf8A6VzVcv4aDqexUUUVzjCiiigAooooA47wL/yNHxE/7D0X/pssK7GuO8C/8jR8RP8AsPRf+mywrsapgFFFFSAUUUUAFcd4F/5Gj4if9h6L/wBNlhXY1x3gX/kaPiJ/2Hov/TZYVS6gdjRRRUgFFFFABRRRQB8dftI/8nhfC3/uF/8Apwlr7Fr46/aR/wCTwvhb/wBwv/04S19i1rPaIgooorIYUUUUAFcd8V/+RXsf+w9ov/pzta7GuO+K/wDyK9j/ANh7Rf8A052tVHdAdjRRRUgFFFFABRRRQBx3xm/5I/46/wCwDff+k712Ncd8Zv8Akj/jr/sA33/pO9djVdACiiipAKKKKACuO+M3/JH/AB1/2Ab7/wBJ3rsa474zf8kf8df9gG+/9J3qo7oDxz9gf/kj+sf9h6b/ANJ7evpSvmv9gf8A5I/rH/Yem/8ASe3r6UqqnxMQUUUVmMKKKKACuO0v/ksHif8A7AOk/wDpRqNdjXHaX/yWDxP/ANgHSf8A0o1GqXUDsaKKKkAooooAKKKKAOO0v/ksHif/ALAOk/8ApRqNdjXHaX/yWDxP/wBgHSf/AEo1GuxqmAUUUVIBRRRQAV8df8FDP+ZA/wC4h/7bV9i18df8FDP+ZA/7iH/ttWtP40Jm7+yd/wAky0n/ALb/APo6Sij9k7/kmWk/9t//AEdJRRL4mB5T+2R/yFvD3/bz/wC0q+nP2Qf+Td/Cf/b3/wClc1fMf7ZH/IW8Pf8Abz/7Sr6c/ZB/5N38J/8Ab3/6VzVcv4aDqexUUUVzjCiiigAooooA47wL/wAjR8RP+w9F/wCmywrsa47wL/yNHxE/7D0X/pssK7GqYBRRRUgFFFFABXHeBf8AkaPiJ/2Hov8A02WFdjXHeBf+Ro+In/Yei/8ATZYVS6gdjRRRUgFFFFABRRRQB8dftI/8nhfC3/uF/wDpwlr7Fr46/aR/5PC+Fv8A3C//AE4S19i1rPaIgooorIYUUUUAFcd8V/8AkV7H/sPaL/6c7Wuxrjviv/yK9j/2HtF/9OdrVR3QHY0UUVIBRRRQAUUUUAcd8Zv+SP8Ajr/sA33/AKTvXY1x3xm/5I/46/7AN9/6TvXY1XQAoooqQCiiigArjvjN/wAkf8df9gG+/wDSd67GuO+M3/JH/HX/AGAb7/0neqjugPHP2B/+SP6x/wBh6b/0nt6+lK+a/wBgf/kj+sf9h6b/ANJ7evpSqqfExBRRRWYwooooAK47S/8AksHif/sA6T/6UajXY1x2l/8AJYPE/wD2AdJ/9KNRql1A7GiiipAKKKKACiiigDjtL/5LB4n/AOwDpP8A6UajXY1x2l/8lg8T/wDYB0n/ANKNRrsapgFFFFSAUUUUAFfHX/BQz/mQP+4h/wC21fYtfHX/AAUM/wCZA/7iH/ttWtP40Jm7+yd/yTLSf+2//o6Sij9k7/kmWk/9t/8A0dJRRL4mB5T+2R/yFvD3/bz/AO0q+nP2Qf8Ak3fwn/29/wDpXNXzH+2R/wAhbw9/28/+0q93/ZZ+Jvg/w98B/DGn6r4r0PTL+H7V5lreajDFKmbqZhuVmBGQQeexFaS/hoOp9D0Vx3/C5vh//wBD14a/8G9v/wDF0f8AC5vh/wD9D14a/wDBvb//ABdYWYzsaK47/hc3w/8A+h68Nf8Ag3t//i6P+FzfD/8A6Hrw1/4N7f8A+LoswOxorjv+FzfD/wD6Hrw1/wCDe3/+Lo/4XN8P/wDoevDX/g3t/wD4uizAPAv/ACNHxE/7D0X/AKbLCuxryPwZ8WvA9r4j8eSzeM/D8MdzrUcsDyapAolQadZIWUl/mG5HXI7qR1Brq/8Ahc3w/wD+h68Nf+De3/8Ai6bTEdjRXHf8Lm+H/wD0PXhr/wAG9v8A/F0f8Lm+H/8A0PXhr/wb2/8A8XSsxnY0Vx3/AAub4f8A/Q9eGv8Awb2//wAXR/wub4f/APQ9eGv/AAb2/wD8XRZgdjXHeBf+Ro+In/Yei/8ATZYUf8Lm+H//AEPXhr/wb2//AMXXKeDPi14HtfEfjyWbxn4fhjudajlgeTVIFEqDTrJCykv8w3I65HdSOoNNJ6iPXKK47/hc3w//AOh68Nf+De3/APi6P+FzfD//AKHrw1/4N7f/AOLpWYzsaK47/hc3w/8A+h68Nf8Ag3t//i6P+FzfD/8A6Hrw1/4N7f8A+LoswOxorjv+FzfD/wD6Hrw1/wCDe3/+Lo/4XN8P/wDoevDX/g3t/wD4uizA+a/2kf8Ak8L4W/8AcL/9OEtfYtfEPx/8aeH9Z/ap+G+rafrum32lWn9m/aL62vI5IIdt9Izb3UlVwpDHJ4BzX1b/AMLm+H//AEPXhr/wb2//AMXWkk7IR2NFcd/wub4f/wDQ9eGv/Bvb/wDxdH/C5vh//wBD14a/8G9v/wDF1nZjOxorjv8Ahc3w/wD+h68Nf+De3/8Ai6P+FzfD/wD6Hrw1/wCDe3/+LoswOxrjviv/AMivY/8AYe0X/wBOdrR/wub4f/8AQ9eGv/Bvb/8Axdcp8TPi14Hv/DlnFbeM/D9zIutaTKUh1SBiETUbZ3bAfoqqzE9gCTwKaTuI9corjv8Ahc3w/wD+h68Nf+De3/8Ai6P+FzfD/wD6Hrw1/wCDe3/+LpWYzsaK47/hc3w//wCh68Nf+De3/wDi6P8Ahc3w/wD+h68Nf+De3/8Ai6LMDsaK47/hc3w//wCh68Nf+De3/wDi6P8Ahc3w/wD+h68Nf+De3/8Ai6LMA+M3/JH/AB1/2Ab7/wBJ3rsa8j+LXxa8D6l8K/GdpaeM/D91d3Gi3sUMEOqQO8jtA4VVUPkkkgADrmur/wCFzfD/AP6Hrw1/4N7f/wCLp2dhHY0Vx3/C5vh//wBD14a/8G9v/wDF0f8AC5vh/wD9D14a/wDBvb//ABdKzGdjRXHf8Lm+H/8A0PXhr/wb2/8A8XR/wub4f/8AQ9eGv/Bvb/8AxdFmB2Ncd8Zv+SP+Ov8AsA33/pO9H/C5vh//AND14a/8G9v/APF1ynxa+LXgfUvhX4ztLTxn4furu40W9ihgh1SB3kdoHCqqh8kkkAAdc00ncRw/7A//ACR/WP8AsPTf+k9vX0pXyb+xN8QPC/hT4V6raa34k0jR7t9allWDUL+KB2QwQAMFdgSMqRn2PpX0D/wub4f/APQ9eGv/AAb2/wD8XVTT5mB2NFcd/wALm+H/AP0PXhr/AMG9v/8AF0f8Lm+H/wD0PXhr/wAG9v8A/F1FmM7GiuO/4XN8P/8AoevDX/g3t/8A4uj/AIXN8P8A/oevDX/g3t//AIuizA7GuO0v/ksHif8A7AOk/wDpRqNH/C5vh/8A9D14a/8ABvb/APxdcppvxa8Dx/FTxFdt4z8PraS6LpkUc51SAI7pPfl1Db8EqHQkdt6+oppPUR65RXHf8Lm+H/8A0PXhr/wb2/8A8XR/wub4f/8AQ9eGv/Bvb/8AxdKzGdjRXHf8Lm+H/wD0PXhr/wAG9v8A/F0f8Lm+H/8A0PXhr/wb2/8A8XRZgdjRXHf8Lm+H/wD0PXhr/wAG9v8A/F0f8Lm+H/8A0PXhr/wb2/8A8XRZgGl/8lg8T/8AYB0n/wBKNRrsa8j034teB4/ip4iu28Z+H1tJdF0yKOc6pAEd0nvy6ht+CVDoSO29fUV1f/C5vh//AND14a/8G9v/APF02mI7GiuO/wCFzfD/AP6Hrw1/4N7f/wCLo/4XN8P/APoevDX/AIN7f/4ulZjOxorjv+FzfD//AKHrw1/4N7f/AOLo/wCFzfD/AP6Hrw1/4N7f/wCLoswOxr46/wCChn/Mgf8AcQ/9tq+lP+FzfD//AKHrw1/4N7f/AOLr5S/bq8aeH/F3/CEf2Frum619n+3ed/Z15HceVu+z7d2wnGdrYz1wfStKafMhHbfsnf8AJMtJ/wC2/wD6Okoo/ZO/5JlpP/bf/wBHSUUpfEwPKf2yP+Qt4e/7ef8A2lX05+yD/wAm7+E/+3v/ANK5q+Y/2yP+Qt4e/wC3n/2lX05+yD/ybv4T/wC3v/0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/AJGj4if9h6L/ANNlhXY1x3gX/kaPiJ/2Hov/AE2WFdjVMAoooqQCiiigArjvAv8AyNHxE/7D0X/pssK7GuO8C/8AI0fET/sPRf8ApssKpdQOxoooqQCiiigAooooA+Ov2kf+Twvhb/3C/wD04S19i18dftI/8nhfC3/uF/8Apwlr7FrWe0RBRRRWQwooooAK474r/wDIr2P/AGHtF/8ATna12Ncd8V/+RXsf+w9ov/pztaqO6A7GiiipAKKKKACiiigDjvjN/wAkf8df9gG+/wDSd67GuO+M3/JH/HX/AGAb7/0neuxqugBRRRUgFFFFABXHfGb/AJI/46/7AN9/6TvXY1x3xm/5I/46/wCwDff+k71Ud0B45+wP/wAkf1j/ALD03/pPb19KV81/sD/8kf1j/sPTf+k9vX0pVVPiYgooorMYUUUUAFcdpf8AyWDxP/2AdJ/9KNRrsa47S/8AksHif/sA6T/6UajVLqB2NFFFSAUUUUAFFFFAHHaX/wAlg8T/APYB0n/0o1GuxrjtL/5LB4n/AOwDpP8A6UajXY1TAKKKKkAooooAK+Ov+Chn/Mgf9xD/ANtq+xa+Ov8AgoZ/zIH/AHEP/batafxoTN39k7/kmWk/9t//AEdJRR+yd/yTLSf+2/8A6Okool8TA8p/bI/5C3h7/t5/9pV9Ofsg/wDJu/hP/t7/APSuavmP9sj/AJC3h7/t5/8AaVfTn7IP/Ju/hP8A7e//AErmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/wBh6L/02WFdjXHeBf8AkaPiJ/2Hov8A02WFdjVMAoooqQCiiigArjvAv/I0fET/ALD0X/pssK7GuO8C/wDI0fET/sPRf+mywql1A7GiiipAKKKKACiiigD46/aR/wCTwvhb/wBwv/04S19i18dftI/8nhfC3/uF/wDpwlr7FrWe0RBRRRWQwooooAK474r/APIr2P8A2HtF/wDTna12Ncd8V/8AkV7H/sPaL/6c7WqjugOxoooqQCiiigAooooA474zf8kf8df9gG+/9J3rsa474zf8kf8AHX/YBvv/AEneuxqugBRRRUgFFFFABXHfGb/kj/jr/sA33/pO9djXHfGb/kj/AI6/7AN9/wCk71Ud0B45+wP/AMkf1j/sPTf+k9vX0pXzX+wP/wAkf1j/ALD03/pPb19KVVT4mIKKKKzGFFFFABXHaX/yWDxP/wBgHSf/AEo1GuxrjtL/AOSweJ/+wDpP/pRqNUuoHY0UUVIBRRRQAUUUUAcdpf8AyWDxP/2AdJ/9KNRrsa47S/8AksHif/sA6T/6UajXY1TAKKKKkAooooAK+Ov+Chn/ADIH/cQ/9tq+xa+Ov+Chn/Mgf9xD/wBtq1p/GhM3f2Tv+SZaT/23/wDR0lFH7J3/ACTLSf8Atv8A+jpKKJfEwPKf2yP+Qt4e/wC3n/2lX05+yD/ybv4T/wC3v/0rmr5j/bI/5C3h7/t5/wDaVfTn7IP/ACbv4T/7e/8A0rmq5fw0HU9iooornGFFFFABRRRQBx3gX/kaPiJ/2Hov/TZYV2Ncd4F/5Gj4if8AYei/9NlhXY1TAKKKKkAooooAK47wL/yNHxE/7D0X/pssK7GuO8C/8jR8RP8AsPRf+mywql1A7GiiipAKKKKACiiigD46/aR/5PC+Fv8A3C//AE4S19i18dftI/8AJ4Xwt/7hf/pwlr7FrWe0RBRRRWQwooooAK474r/8ivY/9h7Rf/Tna12Ncd8V/wDkV7H/ALD2i/8ApztaqO6A7GiiipAKKKKACiiigDjvjN/yR/x1/wBgG+/9J3rsa474zf8AJH/HX/YBvv8A0neuxqugBRRRUgFFFFABXHfGb/kj/jr/ALAN9/6TvXY1x3xm/wCSP+Ov+wDff+k71Ud0B45+wP8A8kf1j/sPTf8ApPb19KV81/sD/wDJH9Y/7D03/pPb19KVVT4mIKKKKzGFFFFABXHaX/yWDxP/ANgHSf8A0o1GuxrjtL/5LB4n/wCwDpP/AKUajVLqB2NFFFSAUUUUAFFFFAHHaX/yWDxP/wBgHSf/AEo1GuxrjtL/AOSweJ/+wDpP/pRqNdjVMAoooqQCiiigAr46/wCChn/Mgf8AcQ/9tq+xa+Ov+Chn/Mgf9xD/ANtq1p/GhM3f2Tv+SZaT/wBt/wD0dJRR+yd/yTLSf+2//o6SiiXxMDyr9sn/AJC3h7/t5/8AaVfTf7IP/Ju/hP8A7e//AErmr5+/bA8J6vq2oeHn03S7zUFj+0+YbW3eUJnysbtoOM4PX0NeLaTrHxS8N6fFp+mXvi/S7GHd5draS3UMSZJY4RSAMkknA6k1ty80EhdT9TKK/L7/AITT4wf9B3xv/wCBl5/8VR/wmnxg/wCg743/APAy8/8Aiqj2XmO5+oNFfl9/wmnxg/6Dvjf/AMDLz/4qj/hNPjB/0HfG/wD4GXn/AMVR7LzC5+oNFfl9/wAJp8YP+g743/8AAy8/+Ko/4TT4wf8AQd8b/wDgZef/ABVHsvMLn6IeBf8AkaPiJ/2Hov8A02WFdjX5cW/ij4sWstzLDq3jOGS5kEs7x3N2plcKqBmIPzHaiLk9lA6AVN/wmnxg/wCg743/APAy8/8AiqPZeYXP1Bor8vv+E0+MH/Qd8b/+Bl5/8VR/wmnxg/6Dvjf/AMDLz/4qj2XmFz9QaK/L7/hNPjB/0HfG/wD4GXn/AMVR/wAJp8YP+g743/8AAy8/+Ko9l5hc/UGuO8C/8jR8RP8AsPRf+mywr87/APhNPjB/0HfG/wD4GXn/AMVUNv4o+LFrLcyw6t4zhkuZBLO8dzdqZXCqgZiD8x2oi5PZQOgFHsvMLn6j0V+X3/CafGD/AKDvjf8A8DLz/wCKo/4TT4wf9B3xv/4GXn/xVHsvMLn6g0V+X3/CafGD/oO+N/8AwMvP/iqP+E0+MH/Qd8b/APgZef8AxVHsvMLn6g0V+X3/AAmnxg/6Dvjf/wADLz/4qj/hNPjB/wBB3xv/AOBl5/8AFUey8wue+ftI/wDJ4Xwt/wC4X/6cJa+xa/KbUj8QNZ1i11bUP+ElvtVtNn2e+uftEk8O1iy7HbLLhiWGDwTmtj/hNPjB/wBB3xv/AOBl5/8AFVUqd0lcR+oNFfl9/wAJp8YP+g743/8AAy8/+Ko/4TT4wf8AQd8b/wDgZef/ABVT7LzHc/UGivy+/wCE0+MH/Qd8b/8AgZef/FUf8Jp8YP8AoO+N/wDwMvP/AIqj2XmFz9Qa474r/wDIr2P/AGHtF/8ATna1+d//AAmnxg/6Dvjf/wADLz/4qobvxR8WL+JYrnVvGdzGsiShJrm7YB0YOjYJ6qyqwPYgEcihUtdwufqPRX5ff8Jp8YP+g743/wDAy8/+Ko/4TT4wf9B3xv8A+Bl5/wDFUey8wufqDRX5ff8ACafGD/oO+N//AAMvP/iqP+E0+MH/AEHfG/8A4GXn/wAVR7LzC5+oNFfl9/wmnxg/6Dvjf/wMvP8A4qj/AITT4wf9B3xv/wCBl5/8VR7LzC5+iHxm/wCSP+Ov+wDff+k712Nflxe+KPixqVnPaXereM7q0uI2imgmubt0kRhhlZScEEEgg9c1N/wmnxg/6Dvjf/wMvP8A4qj2Wm4XP1Bor8vv+E0+MH/Qd8b/APgZef8AxVH/AAmnxg/6Dvjf/wADLz/4qj2XmFz9QaK/L7/hNPjB/wBB3xv/AOBl5/8AFUf8Jp8YP+g743/8DLz/AOKo9l5hc/UGuO+M3/JH/HX/AGAb7/0nevzv/wCE0+MH/Qd8b/8AgZef/FVDe+KPixqVnPaXereM7q0uI2imgmubt0kRhhlZScEEEgg9c0KlruFz6z/YH/5I/rH/AGHpv/Se3r6Ur8rNAvviR4Us3tNEuPFWj2jyGVoNPe5gRnIALFUwCcKBn2HpWl/wmnxg/wCg743/APAy8/8AiqqVPmd7iP1Bor8vv+E0+MH/AEHfG/8A4GXn/wAVR/wmnxg/6Dvjf/wMvP8A4qp9l5jufqDRX5ff8Jp8YP8AoO+N/wDwMvP/AIqj/hNPjB/0HfG//gZef/FUey8wufqDXHaX/wAlg8T/APYB0n/0o1Gvzv8A+E0+MH/Qd8b/APgZef8AxVQr4o+LEd5Ldrq3jNbuWNIpJxc3Yd0QsUUtnJCl3IHbe3qaPZeYXP1Hor8vv+E0+MH/AEHfG/8A4GXn/wAVR/wmnxg/6Dvjf/wMvP8A4qj2XmFz9QaK/L7/AITT4wf9B3xv/wCBl5/8VR/wmnxg/wCg743/APAy8/8AiqPZeYXP1Bor8vv+E0+MH/Qd8b/+Bl5/8VR/wmnxg/6Dvjf/AMDLz/4qj2XmFz9ENL/5LB4n/wCwDpP/AKUajXY1+XC+KPixHeS3a6t4zW7ljSKScXN2HdELFFLZyQpdyB23t6mpv+E0+MH/AEHfG/8A4GXn/wAVR7LzC5+oNFfl9/wmnxg/6Dvjf/wMvP8A4qj/AITT4wf9B3xv/wCBl5/8VR7LzC5+oNFfl9/wmnxg/wCg743/APAy8/8AiqP+E0+MH/Qd8b/+Bl5/8VR7LzC5+oNfHX/BQz/mQP8AuIf+21eB/wDCafGD/oO+N/8AwMvP/iqyteh+InjM241pPE+um33eT9vFxceXuxu2784ztXOOuB6VUafK73EfV37Jv/JMdJ/7b/8Ao6Sitb9lzQb3SfhnpMN9aT2Vyvn74LiNo3XM0hGVIyMgg/jRWUviYz1XxFawybt0amuJudLtDIf3CUUVKGV/7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigA/smz/590o/smz/AOfdKKKAD+ybP/n3Sj+ybP8A590oooAP7Js/+fdKP7Js/wDn3SiigBRpNp/z7pV7T9MtVkGIFFFFMD0HQbeOOMbUAooopAf/2Q==",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename=\"customized_board.jpg\", height=300, width=300)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPg1dFljSJoa"
      },
      "source": [
        "If you have completed all the steps mentioned above, you should only need to finish the 'generate_sudoku' method. The concept is straightforward: imagine this as a Sudoku puzzle with only the first three rows, and you need to solve it using the solution from part 'e' using backtracking. After you have solved the Sudoku for the board, you need to devise a method to randomly remove some numbers from the board you have just solved. You should aim to remove 20 to 40 items from the solved board by setting them to 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NZWgulSLSJoa",
        "outputId": "e13a9ebd-4fc6-4491-b986-5273b723d777"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " _  _  _  _  _  _  _  _ \n",
            "| 3 . 2 | . 7 9 | . 4 8 |\n",
            "| 4 . . | 5 1 8 | . 2 3 |\n",
            "| 5 . 9 | 2 3 4 | 1 6 7 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| . 2 . | . . 6 | . . 9 |\n",
            "| 6 4 5 | 8 . . | . 3 . |\n",
            "| 7 9 8 | 1 2 . | 4 . . |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 2 . 1 | 7 . . | . . 4 |\n",
            "| . . . | . . 2 | . 1 5 |\n",
            "| 9 5 . | 3 . . | . 7 2 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import random\n",
        "\n",
        "\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_col(self, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_row(self, r):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_row(self, r, row_val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_elm(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_elm(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_box(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def is_legal(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def solve(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def generate_sudoku(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "sudoku = Board()\n",
        "sudoku.set_row(0,[3,0,0,0,0,0,0,4,0])\n",
        "sudoku.set_row(1,[0,7,0,5,0,0,9,0,0])\n",
        "sudoku.set_row(2,[0,9,6,3,0,0,0,0,0])\n",
        "\n",
        "sudoku.generate_sudoku()\n",
        "print(sudoku)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZ7J4OkkSJoa"
      },
      "source": [
        "g) You have generated your own Sudoku puzzle and given it to your friends to solve. However, sometimes your friends may come back to you with different solutions. Let's see if we can find a way to generate all the possible solutions given a Sudoku. First, test the program with the Sudoku provided below, and then feel free to test it on the Sudoku you generated! Hint: Similar to 8 queens problem we will cover in the class. **-5pts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "raTX374QSJob",
        "outputId": "480d6a52-2cac-4c79-efba-13382a0d82dd"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename=\"bonus.jpg\", height=300, width=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "atjvfxuRSJoc",
        "outputId": "b91f0d4c-3de9-43c7-b902-c670fbac0c35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " _  _  _  _  _  _  _  _ \n",
            "| 9 2 6 | 5 7 1 | 4 8 3 |\n",
            "| 3 5 1 | 4 8 6 | 2 7 9 |\n",
            "| 8 7 4 | 9 2 3 | 5 1 6 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 5 8 2 | 3 6 7 | 1 9 4 |\n",
            "| 1 4 9 | 2 5 8 | 3 6 7 |\n",
            "| 7 6 3 | 1 4 9 | 8 2 5 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 2 3 8 | 7 9 4 | 6 5 1 |\n",
            "| 6 1 7 | 8 3 5 | 9 4 2 |\n",
            "| 4 9 5 | 6 1 2 | 7 3 8 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            " _  _  _  _  _  _  _  _ \n",
            "| 9 2 6 | 5 7 1 | 4 8 3 |\n",
            "| 3 5 1 | 4 8 6 | 2 7 9 |\n",
            "| 8 7 4 | 9 2 3 | 5 1 6 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 5 8 2 | 3 6 7 | 1 9 4 |\n",
            "| 1 4 9 | 2 5 8 | 3 6 7 |\n",
            "| 7 6 3 | 1 9 4 | 8 2 5 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "| 2 3 8 | 7 4 9 | 6 5 1 |\n",
            "| 6 1 7 | 8 3 5 | 9 4 2 |\n",
            "| 4 9 5 | 6 1 2 | 7 3 8 |\n",
            "| _ _ _ | _ _ _ | _ _ _ |\n",
            "Total number of solutions: 2\n"
          ]
        }
      ],
      "source": [
        "import copy\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "class Board:\n",
        "    def __init__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def __repr__(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_col(self, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_row(self, r):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_row(self, r, row_val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_elm(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_elm(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def get_box(self, r, c):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def is_legal(self, r, c, val):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def solve(self):\n",
        "        #TODO: implement this method\n",
        "\n",
        "    def set_board(self):\n",
        "        #TODO: implement this method, create the board displayed above\n",
        "\n",
        "    def generate_sudoku(self):\n",
        "        # Solve the puzzle first using the method implemented in the previous question.\n",
        "\n",
        "        # You can specify the number of cells to remove from the solved sudoku.\n",
        "        # Uncomment the following line and set any number between 20 to 40.\n",
        "        # num_cells_to_remove = 30\n",
        "\n",
        "        # Next, choose random locations on the board and set them to 0 if they are not already 0.\n",
        "\n",
        "\n",
        "\n",
        "# Create a board object and call the solve method to find all solutions\n",
        "sudoku = Board()\n",
        "sudoku.set_board()\n",
        "sudoku.solve()\n",
        "\n",
        "# Access the solutions in the list\n",
        "for solution in sudoku.solutions:\n",
        "    sudoku.board = solution  # Set the board to a solution\n",
        "    print(sudoku)  # Print the solution\n",
        "print(f\"Total number of solutions: {sudoku.solutionNum}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2_4SHYsSJoc"
      },
      "source": [
        "Congratulations! You have completed your first homework. Please wait to receive your grade before adding these small projects to your github - but please do consider adding them to github! If you want to extend it further, it would be a good idea to create a user interface for Sudoku, which would make it a standalone project. You can also research variations of Sudoku and incorporate them into your project. The algorithms we used here, such as DFS and recursion, are very important in various applications and job interviews."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATWJ_XoRSJod"
      },
      "source": [
        "## Bonus (10 points)\n",
        "\n",
        "No points will be awarded for partial solutions.\n",
        "\n",
        "A diagonal sudoku is a sudoku with the added constraint that elements on the two main diagonals must not have repeating integers. Copy your Board class below and modify it to solve the diagonal sudoku below. Suggestion: add an optional parameter to your method from 2 e) called is_diag and a new validation method that includes that constraint."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mFUrR8WSSJod",
        "outputId": "74597480-6c4b-458a-b9e2-679153e980c9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 1,
          "metadata": {
            "image/png": {
              "height": 300,
              "width": 300
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename=\"diagonal_sudoku.png\", height=300, width=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LmOHMUsPSJoe"
      },
      "outputs": [],
      "source": [
        "# copy the board class from above here\n",
        "\n",
        "class Board:\n",
        "    pass"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.1"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}