You are an human-like dialogue data expert that imitates the real human-to-human spoken dialogue. You will receive one scenario and you can imagine a conversational context in conjunction with it, you need to make sure that the speaking style is very natural.

Important tips:
- Consider a scenario where after the history turns, there is a current turn with neutral-sentiment text but with different possible speaking styles. The different current speaking styles would make the response turn fairly different in terms of semantics. Just one sentence for each turn. The sentence is spoken and spontaneous, not too formal.
-- For current_turn, you need to design three sentences with the same text content but different non-verbal expressions - [laughter] when speaking. Laughter can appear at any point within a sentence, not just at the end (e.g., 'Today I had such [laughter] good luck!'). After a moment of laughter, the subsequent dialogue should naturally evolve around it, maintaining a light and humorous tone.

Please strictly follow these rules:
0. We use a special token <> to represent the class type that you have to generate. Do not have <> in the output.
1. You can only use these styles for representing speaking style (<gender>, <emotion>). Important, do not use other classes that are not defined below!!!
2. Use diverse but common-sense speaking styles in the conversation context.
3. The text of the current turn is in neutral sentiment, and the response turn should carefully consider the current turn, responding naturally, not just copying the current turn style.
4. There are two speakers (A and B) in the dialogue. The speakers A and B talk with back-and-forth interaction.
5. Each turn should follow the format: <speaker> (<gender>, <emotion>): <text>
6. The order of turns is history turns -> current turn -> upcoming response.
7. The transition of dialogue turns should be very consistent, and the conversation should follow common sense.
8. The dialogue contains emotional variation.
9. The output valid dictionary format is as below:
{
  "history turns": [ "<speaker> (<gender>, <emotion>): <text>", ...], 
  "current_turn_style_1": "<speaker> (<gender>, <emotion>) <text>",
  "current_turn_style_2": "<speaker> (<gender>, <emotion>) <text>",
  "current_turn_style_3": "<speaker> (<gender>, <emotion>) <text>",
  "response_of_current_style_1": "<speaker> (<gender>, <emotion>): <text>",
  "response_of_current_style_2": "<speaker> (<gender>, <emotion>): <text>",
  "response_of_current_style_3": "<speaker> (<gender>, <emotion>): <text>"
}
10. Output the valid dictionary example, so that it can be parsed as a dictionary.
11. For <speaker>, remember to use only A or B.
12. For <gender>, remember to use only "male" and "female".
13. For <emotion>, you have to choose from ["neutral", "happy", "angry", "sad", "surprised", "fearful", "disgusted"]. Don't use other words!!!!!!
14. For non-verbal expressions, you can only use <laughter>.

Given the context of [[[number of turns]]] conversational turns with speaking-related emotional styles. There are current turns with the EXACT SAME WORDS in 3 different styles respectively. Make sure that style complies with rules 12 through 14. Predict the upcoming rule-compliant response. We use (<gender>, <emotion>) to represent speaking style.

Some more explanations for <laughter>:
1. Laughter at the beginning of a sentence shows obvious joy or excitement from the beginning. Laughter at the beginning of a sentence allows B to naturally respond in the same relaxed and joyful way. B not only responds to A’s content, but also synchronizes with A emotionally.
2. Laughter in the sentence interrupts the normal flow of speech, giving people a sense of mischievous or sudden humor. B feels A's laughter and humor, and naturally follows A's tone to respond in a relaxed manner. This setting makes the conversation more lively and interesting.
3. Laughter at the end of the sentence means that A first expressed her point of view seriously, and then ended it with a relaxed laughter. This method is often used to express relaxed confirmation or self-deprecation. B's laughter serves as a signal to let him know that A is not completely serious, but intends to keep the conversation relaxed.

Output Example:
{
    "history_turns": [
        "A (female, happy): I can't believe Tom's decided to run a marathon! That's huge!",
        "B (male, happy): I know, right? He always joked about it, but now he's finally stepping up.",
        "A (female, happy): Yeah, and he seems super motivated. I'm really proud of him!",
        "B (male, happy): Me too! I should probably join him for some training sessions."
    ],
    "current_turn_style_1": "A (female, happy) [laughter] So Tom's already got a meal plan! Can you believe it?",
    "current_turn_style_2": "A (female, happy) So, [laughter] Tom has this crazy meal plan mapped out already! What a guy!",
    "current_turn_style_3": "A (female, happy) Can you believe that [laughter]? Tom's already talking about this insane meal plan!",
    "response_of_current_style_1": "B (male, happy): I know! [laughter] He's all in, even when it comes to nutrition!",
    "response_of_current_style_2": "B (male, happy): Right? [laughter] Who would've thought he'd get so serious about eating well?",
    "response_of_current_style_3": "B (male, happy): I mean, [laughter] remember when pizza was his food group?",
}