import queue

from helper.api_messages import QuestionType
from helper.openai_interface import make_openai_instruct_call
from helper.token_counter import count_tokens
from helper.constants import *

def divide_number_into_parts(number, parts):
    if number < parts:
        return None

    part_size = number // parts
    remaining = number % parts

    q = queue.Queue()

    for i in range(parts):
        if i < remaining:
            q.put(part_size + 1)
        else:
            q.put(part_size)

    return q

def fix_exercise_ids(exercises):
    # Initialize the starting ID for the first exercise
    current_id = 1

    # Iterate through exercises
    for exercise in exercises:
        questions = exercise["questions"]

        # Iterate through questions and update the "id" value
        for question in questions:
            question["id"] = str(current_id)
            current_id += 1

    return exercises


def generate_reading_passage(type: QuestionType, topic: str):
    gen_reading_passage_1 = "Generate an extensive text for IELTS " + type.READING_PASSAGE_1.value + ", of at least 1500 words, on the topic " \
                            "of " + topic + ". The passage should offer a substantial amount of " \
                                                            "information, analysis, or narrative " \
                                                            "relevant to the chosen subject matter. This text passage aims to serve as the primary reading " \
                                                            "section of an IELTS test, providing an in-depth and comprehensive exploration of the topic." \
                                                            "Provide your response in this json format: {'title': 'title of the text', 'text': 'generated text'}"
    token_count = count_tokens(gen_reading_passage_1)["n_tokens"]
    return make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_reading_passage_1, token_count, GEN_TEXT_FIELDS,
                                        GEN_QUESTION_TEMPERATURE)

def gen_multiple_choice_exercise(text: str, quantity: int):
    gen_multiple_choice_for_text = "Generate" + str(quantity) + "multiple choice questions for this text: " \
                                   "'" + text + "'\n" \
                                                           "Use this format: 'questions': [{'id': '9', 'options': [{'id': 'A', 'text': " \
                                                           "'Economic benefits'}, {'id': 'B', 'text': 'Government regulations'}, {'id': 'C', 'text': " \
                                                           "'Concerns about climate change'}, {'id': 'D', 'text': 'Technological advancement'}], " \
                                                           "'prompt': 'What is the main reason for the shift towards renewable energy sources?', " \
                                                           "'solution': 'C', 'variant': 'text'}]"
    token_count = count_tokens(gen_multiple_choice_for_text)["n_tokens"]
    mc_questions = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, gen_multiple_choice_for_text, token_count,
                                             None,
                                             GEN_QUESTION_TEMPERATURE)
    parse_mc_questions = "Parse this '" + mc_questions + "' into this json format: 'questions': [{'id': '9', 'options': [{'id': 'A', 'text': " \
                                                         "'Economic benefits'}, {'id': 'B', 'text': 'Government regulations'}, {'id': 'C', 'text': " \
                                                         "'Concerns about climate change'}, {'id': 'D', 'text': 'Technological advancement'}], " \
                                                         "'prompt': 'What is the main reason for the shift towards renewable energy sources?', " \
                                                         "'solution': 'C', 'variant': 'text'}]"
    token_count = count_tokens(parse_mc_questions)["n_tokens"]
    return make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, parse_mc_questions, token_count,
                                                   ["questions"],
                                                   GEN_QUESTION_TEMPERATURE)