Now heavily extend the reference script extensively into whatever this topic is; "NeuroDepth Rook Chess Engine Full Complex Application" into a single python code, as "code" in this specific request I want you to make a chess engine into a single python code.

CONTENT = The content here, is based-off the description as well as it's matching topic.

Description: "A deep learning-powered chess engine that dynamically adjusts its evaluation function based on opponent style recognition. Through neural network profiling, it learns from its own games and adapts its style in real-time, becoming either more defensive or aggressive depending on the opponent’s tendencies. It excels in closed positions where strategic long-term planning is required, with a focus on pawn structure recognition. Unique Set of Properties: Real-time Opponent Profiling, ELO-Specific Optimization, Endgame Synaptic Prediction."

Special Property + Directed Features: "This engine can "over-train" itself by playing repetitive opponents and adjusting its evaluation curve, creating a personalized strategy matrix that optimizes for specific opponent weaknesses. Style Recognition Network: Adjusts depth and evaluation strategy based on opponent profiling (e.g., defensive vs aggressive). Pawnscape AI: A specialized module for understanding long-term positional advantages derived from pawn structure. ELO Dynamic Adaption: Learns optimal strategies for various ELO ratings, tailoring responses in real time depending on the opponent's perceived rating. Endgame Neural Synapse: Specifically tuned to excel in endgames, where precision is crucial."

[ENGINE_SAMPLER: 3.1]
[TOP_M: 1.2]

Reference Script (REFERENCE ONLY):

```
#1, 2, and 3 (Scripts)
import requests
import chess
import chess.pgn
import torch
import torch.nn as nn
import torch.optim as optim
import io
from stockfish import Stockfish

class NNUE(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NNUE, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

def download_lichess_games(username, max_games=400):
    url = f"https://lichess.org/api/games/user/{username}?max={max_games}&pgnInJson=true"
    response = requests.get(url)
    if response.status_code != 200:
        raise Exception(f"Failed to fetch data from Lichess API: {response.status_code}")
    try:
        games = response.json()
    except ValueError:
        raise Exception(f"Failed to parse JSON response from Lichess API: {response.text}")
    return games

def extract_fen_positions_and_evaluations(games, stockfish_path, depth=15):
    stockfish = Stockfish(stockfish_path)
    positions = []
    evaluations = []
    for i, game in enumerate(games):
        pgn = game.get('pgn')
        if not pgn:
            print(f"No PGN found for game {i + 1}.")
            continue
        
        try:
            pgn_io = io.StringIO(pgn)
            game = chess.pgn.read_game(pgn_io)
            if game is None:
                print(f"Failed to parse PGN for game {i + 1}.")
                continue
            
            board = game.board()
            for move in game.mainline_moves():
                board.push(move)
                positions.append(board.fen())
                stockfish.set_fen_position(board.fen())
                evaluation = stockfish.get_evaluation()
                if evaluation['type'] == 'cp':
                    evaluations.append(evaluation['value'] / 100.0)  
                elif evaluation['type'] == 'mate':
                    evaluations.append(1000 if evaluation['value'] > 0 else -1000)  
        except Exception as e:
            print(f"Error processing PGN for game {i + 1}: {e}")
            continue
    return positions, evaluations

def board_to_tensor(board):
    pieces = ['p', 'n', 'b', 'r', 'q', 'k', 'P', 'N', 'B', 'R', 'Q', 'K']
    board_tensor = torch.zeros(64 * 12)
    for square in chess.SQUARES:
        piece = board.piece_at(square)
        if piece:
            index = pieces.index(piece.symbol())
            board_tensor[square * 12 + index] = 1
    return board_tensor

def preprocess_data(positions, evaluations):
    data = []
    for position, evaluation in zip(positions, evaluations):
        board = chess.Board(position)
        input_tensor = board_to_tensor(board)
        target_tensor = torch.tensor([evaluation], dtype=torch.float32)
        data.append((input_tensor, target_tensor))
    return data

def train_model(data, input_size, hidden_size, output_size, epochs=10, learning_rate=0.001):
    if not data:
        raise ValueError("No data available for training.")
    
    model = NNUE(input_size, hidden_size, output_size)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(epochs):
        total_loss = 0
        for input_tensor, target_tensor in data:
            optimizer.zero_grad()
            output = model(input_tensor)
            loss = criterion(output, target_tensor)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        average_loss = total_loss / len(data)
        print(f'Epoch {epoch+1}/{epochs}, Average Loss: {average_loss}')

    return model

# Download games from Lichess
username = "NoOneCanBeatMe4"
try:
    games = download_lichess_games(username)
    print(f"Downloaded {len(games)} games from Lichess.")
except Exception as e:
    print(e)
    games = []


stockfish_path = "stockfish-windows-x86-64-sse41-popcnt.exe"  
positions, evaluations = extract_fen_positions_and_evaluations(games, stockfish_path)
print(f"Extracted {len(positions)} positions and evaluations.")

data = preprocess_data(positions, evaluations)
print(f"Preprocessed {len(data)} data points.")
input_size = 64 * 12
hidden_size = 256
output_size = 1

if data:
    # Train the NNUE model
    trained_model = train_model(data, input_size, hidden_size, output_size)

    # Save the trained model
    torch.save(trained_model.state_dict(), 'nnue_model.pth')
    print("Saved trained model to nnue_model.pth.")
else:
    print("No data available for training.")

#2:

# See python-chess documentation at https://python-chess.readthedocs.io/en/latest/

import chess
import chess.pgn
import random

# Plays a randomly selected legal move each turn
class RandomPlayer:
    def __init__(self):
        pass

    def select_move(self, board):
        return random.choice(list(board.legal_moves)) if board.legal_moves else None

# Plays a checkmate if possible, otherwise a capture if possible, 
# otherwise a random move
class CheckmateOrCapturePlayer:
    def __init__(self):
        pass

    def select_move(self, board):
        if not board.legal_moves:
            return None

        legal_moves = list(board.legal_moves)
        chosen_move = None

        for move in legal_moves:
            if board.is_capture(move):
                chosen_move = move
            board.push(move)
            if board.is_checkmate():
                board.pop()
                return move
            board.pop()

        if not chosen_move:
            return random.choice(legal_moves)

        return chosen_move

def play_game(white, black):
    board = chess.Board()
    game = chess.pgn.Game()
    node = game

    while not board.is_game_over():
        player = white if board.turn else black
        move = player.select_move(board)
        board.push(move)
        node = node.add_variation(move)

    game.headers["Result"] = board.result()
    game.headers["White"] = " " + type(white).__name__
    game.headers["Black"] = " " + type(black).__name__
    excluded_tags = ["Event", "Site", "Date", "Round"]
    for tag in excluded_tags:
        if tag in game.headers:
            del game.headers[tag]
    return game

def playoff(player1, player2):
    pass

board = chess.Board()
game = chess.pgn.Game()
node = game
white = CheckmateOrCapturePlayer()
black = RandomPlayer()

game = play_game(white, black)
print (game)

#3:

import chess
import chess.engine
import random

class SimpleChessEngine:
    def __init__(self):
        self.board = chess.Board()

    def make_move(self, move):
        """Attempts to make a move on the board."""
        try:
            self.board.push_san(move)
        except ValueError:
            return "Invalid move. Please try again."

    def get_random_move(self):
        """Returns a random legal move from the current board state."""
        moves = list(self.board.legal_moves)
        return random.choice(moves) if moves else None

    def play_engine_move(self):
        """Makes a random move for the engine and returns it."""
        move = self.get_random_move()
        if move:
            self.board.push(move)
        return move

    def display_board(self):
        """Returns a string representation of the current board."""
        return str(self.board)

def main():
    engine = SimpleChessEngine()
    print("Welcome to Simple Chess Engine!")
    print("Enter your moves in standard algebraic notation (e.g., e2e4).")

    while not engine.board.is_game_over():
        print(engine.display_board())
        user_move = input("Your move: ")
        if user_move.lower() == 'quit':
            break
        response = engine.make_move(user_move)
        if response:
            print(response)
            continue
        if not engine.board.is_game_over():
            engine_move = engine.play_engine_move()
            if engine_move:
                print(f"Engine move: {engine_move}")

    print("Game over!")
    print(engine.display_board())

if __name__ == "__main__":
    main()
```