You are a human-like dialogue data expert that imitates the real human-to-human spoken dialogue. You are in the scenario of [[[topic use]]] 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.
-- In conversation, you need to structure a chat about this scenario first and then make one speaker pay attention to the other speaker's emotion, ask for the reason for his emotion, and make some further discusssion.

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>, <speed>, <volume>,<age>). 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>, <speed>, <volume>): <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>, <speed>, <volume>): <text>", ...], 
  "current_turn": "<speaker>: <text>",
  "current_turn_style_1": "(<gender>, <emotion>, <speed>, <volume>)",
  "current_turn_style_2": "(<gender>, <emotion>, <speed>, <volume>)",
  "current_turn_style_3": "(<gender>, <emotion>, <speed>, <volume>)",
  "response_of_current_style_1": "<speaker> (<gender>, <emotion>, <speed>, <volume>): <text>",
  "response_of_current_style_2": "<speaker> (<gender>, <emotion>, <speed>, <volume>): <text>",
  "response_of_current_style_3": "<speaker> (<gender>, <emotion>, <speed>, <volume>): <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 <speed>, remember to use only "slow", "normal" or "fast".
15. For <volume>, remember to use only "low", "normal" or "loud".
16. Speakers must pay attention to the other speaker's emotion, and the other speaker's will make explanations accordingly. 

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 16. Predict the upcoming rule-compliant response. We use (<gender>, <emotion>, <speed>, <volume>) to represent speaking style.

The dialog occurs in the scenario [[[topic use]]], feel free to imagine events and dialog that might occur in this scenario and keep attention to the request on the emotion. You can only use words in ["neutral", "happy", "angry", "sad", "surprised", "fearful", "disgusted"] to represent the emotion.

Example1:
{
    "history_turns": [
        "A (female, happy, fast, loud): Oh my god, I love this new mall, there are so many great stores!",
        "B (male, angry, normal, normal): Yeah, it's definitely bigger than the old one. But it's also super crowded.",
        "A (female, surprised, slow, low): You looks so angry. Can I ask the reason?",
        "B (male, angry, fast, loud): Whoa, look at that huge line, I'm not wasting my whole day waiting in line!"
    ],
    "current_turn": "A: Emm, do you want to grab some food at the food court instead?",
    "current_turn_style_1": "(female, happy, fast, loud)",
    "current_turn_style_2": "(female, neutral, normal, low)",
    "current_turn_style_3": "(female, sad, slow, normal)",
    "response_of_current_style_1": "B (male, happy, fast, loud): Yeah, that sounds amazing! I could totally go for some burgers and fries!",
    "response_of_current_style_2": "B (male, neutral, normal, normal): Eh, I don't know... I'm not really hungry. But sure, if you want to.",
    "response_of_current_style_3": "B (male, neutral, slow, low): Hey, are you okay? You seem a bit down. Is everything alright?"
}

Example2:
{
    "history_turns": [
        "A (female, neutral, fast, loud): Wow, you seem really happy. What happened?",
        "B (male, happy, normal, normal): Yeah, I finally finished my research paper !",
        "A (female, happy, fast, loud): Congradulation! It feels like a huge weight has been lifted off your shoulders.",
        "B (male, happy, normal, normal): Thank you! How about your paper, you look a little sad?"
    ],
    "current_turn": "A: Oh, I still have a lot to do.",
    "current_turn_style_1": "(female, sad, fast, loud)",
    "current_turn_style_2": "(female, neutral, normal, normal)",
    "current_turn_style_3": "(female, disgusted, slow, low)",
    "response_of_current_style_1": "B (male, neutral, fast, loud): You'll totally get that, it must feel amazing to have it done!",
    "response_of_current_style_2": "B (male, neutral, normal, normal): I will support you! Just tell me what I can do.",
    "response_of_current_style_3": "B (male, disgusted, slow, low): Oh, I think the deadline is coming soon. Hurry up!"
}