import json
import os
import pickle
import re
import time

import cv2
import openai
from word2number import w2n


def create_dir(output_dir):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)


def read_csv(file):
    data = []
    with open(file, 'r') as f:
        for line in f:
            data.append(line.strip())
    return data


def read_pandas_csv(csv_path):
    # read a pandas csv sheet
    import pandas as pd
    df = pd.read_csv(csv_path)
    return df


def read_json(path):
    with open(path, 'r', encoding='utf-8') as f:
        return json.load(f)


def read_jsonl(file):
    with open(file, 'r') as f:
        data = [json.loads(line) for line in f]
    return data


def read_pickle(path):
    with open(path, 'rb') as f:
        return pickle.load(f)


def save_json(data, path):
    with open(path, 'w') as f:
        json.dump(data, f, indent=4)


def save_array_img(path, image):
    cv2.imwrite(path, image)


def contains_digit(text):
    # check if text contains a digit
    if any(char.isdigit() for char in text):
        return True
    return False


def contains_number_word(text):
    # check if text contains a number word
    ignore_words = ['a', 'an', 'point']
    words = re.findall(r'\b\w+\b', text)  # This regex pattern matches any word in the text
    for word in words:
        if word in ignore_words:
            continue
        try:
            w2n.word_to_num(word)
            return True  # If the word can be converted to a number, return True
        except ValueError:
            continue  # If the word can't be converted to a number, continue with the next word

    # check if text contains a digit
    if any(char.isdigit() for char in text):
        return True

    return False  # If none of the words could be converted to a number, return False


def contains_quantity_word(text, special_keep_words=[]):
    # check if text contains a quantity word
    quantity_words = ['most', 'least', 'fewest'
                                       'more', 'less', 'fewer',
                      'largest', 'smallest', 'greatest',
                      'larger', 'smaller', 'greater',
                      'highest', 'lowest', 'higher', 'lower',
                      'increase', 'decrease',
                      'minimum', 'maximum', 'max', 'min',
                      'mean', 'average', 'median',
                      'total', 'sum', 'add', 'subtract',
                      'difference', 'quotient', 'gap',
                      'half', 'double', 'twice', 'triple',
                      'square', 'cube', 'root',
                      'approximate', 'approximation',
                      'triangle', 'rectangle', 'circle', 'square', 'cube', 'sphere', 'cylinder', 'cone', 'pyramid',
                      'multiply', 'divide',
                      'percentage', 'percent', 'ratio', 'proportion', 'fraction', 'rate',
                      ]

    quantity_words += special_keep_words  # dataset specific words

    words = re.findall(r'\b\w+\b', text)  # This regex pattern matches any word in the text
    if any(word in quantity_words for word in words):
        return True

    return False  # If none of the words could be converted to a number, return False


def is_bool_word(text):
    if text in ['Yes', 'No', 'True', 'False',
                'yes', 'no', 'true', 'false',
                'YES', 'NO', 'TRUE', 'FALSE']:
        return True
    return False


def is_digit_string(text):
    # remove ".0000"
    text = text.strip()
    text = re.sub(r'\.0+$', '', text)
    try:
        int(text)
        return True
    except ValueError:
        return False


def is_float_string(text):
    # text is a float string if it contains a "." and can be converted to a float
    if '.' in text:
        try:
            float(text)
            return True
        except ValueError:
            return False
    return False


def copy_image(image_path, output_image_path):
    from shutil import copyfile
    copyfile(image_path, output_image_path)


def copy_dir(src_dir, dst_dir):
    from shutil import copytree

    # copy the source directory to the target directory
    copytree(src_dir, dst_dir)


import PIL.Image as Image


def get_image_size(img_path):
    img = Image.open(img_path)
    width, height = img.size
    return width, height


def get_chat_response(promot, api_key, model='gpt-3.5-turbo', temperature=0, max_tokens=256, n=1, patience=10000000,
                      sleep_time=0):
    messages = [
        {'role': 'user', 'content': promot},
    ]
    while patience > 0:
        patience -= 1
        try:
            completion = openai.chat.completions.create(model=model,
                                                        messages=messages,
                                                        temperature=temperature,
                                                        max_tokens=max_tokens,
                                                        n=n)
            if n == 1:
                prediction = completion.choices[0].message.content.strip()
                if prediction != '' and prediction is not None:
                    return prediction
            else:
                prediction = [choice.message.content.strip() for choice in completion.choices]
                if prediction[0] != '' and prediction[0] is not None:
                    return prediction

        except Exception as e:
            if 'Rate limit' not in str(e):
                print(e)

            if 'Please reduce the length of the messages' in str(e):
                print('!!Reduce promot size')
                # reduce input prompt and keep the tail
                new_size = int(len(promot) * 0.9)
                new_start = len(promot) - new_size
                promot = promot[new_start:]
                messages = [
                    {'role': 'user', 'content': promot},
                ]

            if sleep_time > 0:
                time.sleep(sleep_time)
    return ''
