# Libraries
import json
import os
import re
import textwrap
import threading
import time
import traceback
import tiktoken
import openai
from pathlib import Path
from colorama import Fore
from dotenv import load_dotenv
from retry import retry
from tqdm import tqdm

# Open AI
load_dotenv()
if os.getenv("api").replace(" ", "") != "":
    openai.base_url = os.getenv("api")
openai.organization = os.getenv("org")
openai.api_key = os.getenv("key")

# Globals
MODEL = os.getenv("model")
TIMEOUT = int(os.getenv("timeout"))
LANGUAGE = os.getenv("language").capitalize()
PROMPT = Path("prompt.txt").read_text(encoding="utf-8")
VOCAB = Path("vocab.txt").read_text(encoding="utf-8")
THREADS = int(os.getenv("threads"))
LOCK = threading.Lock()
WIDTH = int(os.getenv("width"))
LISTWIDTH = int(os.getenv("listWidth"))
NOTEWIDTH = 70
MAXHISTORY = 10
ESTIMATE = ""
TOKENS = [0, 0]
NAMESLIST = []
NAMES = False  # Output a list of all the character names found
BRFLAG = False  # If the game uses <br> instead
FIXTEXTWRAP = True  # Overwrites textwrap
IGNORETLTEXT = False  # Ignores all translated text.
MISMATCH = []  # Lists files that throw a mismatch error (Length of GPT list response is wrong)
PBAR = None
FILENAME = None

# tqdm Globals
BAR_FORMAT = "{l_bar}{bar:10}{r_bar}{bar:-10b}"
POSITION = 0
LEAVE = False

# Regex - Need to change this if you want to translate from/to other languages. Default is Japanese Regex
LANGREGEX = r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+"

# Pricing - Depends on the model https://openai.com/pricing
# Batch Size - GPT 3.5 Struggles past 15 lines per request. GPT4 struggles past 50 lines per request
# If you are getting a MISMATCH LENGTH error, lower the batch size.
if "gpt-3.5" in MODEL:
    INPUTAPICOST = 3.00
    OUTPUTAPICOST = 5.00
    BATCHSIZE = 10
    FREQUENCY_PENALTY = 0.2
elif "gpt-4" in MODEL:
    INPUTAPICOST = 2.50
    OUTPUTAPICOST = 10.00
    BATCHSIZE = 30
    FREQUENCY_PENALTY = 0.05
elif "deepseek" in MODEL:
    INPUTAPICOST = 0.14
    OUTPUTAPICOST = 0.28
    BATCHSIZE = 30
    FREQUENCY_PENALTY = 0.05
else:
    INPUTAPICOST = float(os.getenv("input_cost"))
    OUTPUTAPICOST = float(os.getenv("output_cost"))
    BATCHSIZE = int(os.getenv("batchsize"))
    FREQUENCY_PENALTY = float(os.getenv("frequency_penalty"))


def handleLune(filename, estimate):
    global FILENAME, ESTIMATE, totalTokens
    ESTIMATE = estimate
    FILENAME = filename

    if estimate:
        start = time.time()
        translatedData = openFiles(filename)

        # Print Result
        end = time.time()
        tqdm.write(getResultString(translatedData, end - start, filename))
        with LOCK:
            TOKENS[0] += translatedData[1][0]
            TOKENS[1] += translatedData[1][1]

        return getResultString(["", TOKENS, None], end - start, "TOTAL")

    else:
        try:
            with open("translated/" + filename, "w", encoding="UTF-8") as outFile:
                start = time.time()
                translatedData = openFiles(filename)

                # Print Result
                end = time.time()
                json.dump(translatedData[0], outFile, ensure_ascii=False, indent=4)
                tqdm.write(getResultString(translatedData, end - start, filename))
                with LOCK:
                    TOKENS[0] += translatedData[1][0]
                    TOKENS[1] += translatedData[1][1]
        except Exception:
            return "Fail"

    return getResultString(["", TOKENS, None], end - start, "TOTAL")


def openFiles(filename):
    with open("files/" + filename, "r", encoding="UTF-8-sig") as f:
        data = json.load(f)

        # Map Files
        if ".json" in filename:
            translatedData = parseJSON(data, filename)

        else:
            raise NameError(filename + " Not Supported")

    return translatedData


def getResultString(translatedData, translationTime, filename):
    # File Print String
    totalTokenstring = (
        Fore.YELLOW + "[Input: " + str(translatedData[1][0]) + "]"
        "[Output: "
        + str(translatedData[1][1])
        + "]" "[Cost: ${:,.4f}".format(((translatedData[1][0] / 1000000) * INPUTAPICOST) + ((translatedData[1][1] / 1000000) * OUTPUTAPICOST))
        + "]"
    )
    timeString = Fore.BLUE + "[" + str(round(translationTime, 1)) + "s]"

    if translatedData[2] == None:
        # Success
        return filename + ": " + totalTokenstring + timeString + Fore.GREEN + " \u2713 " + Fore.RESET

    else:
        # Fail
        try:
            raise translatedData[2]
        except Exception as e:
            traceback.print_exc()
            errorString = str(e) + Fore.RED
            return filename + ": " + totalTokenstring + timeString + Fore.RED + " \u2717 " + errorString + Fore.RESET


def parseJSON(data, filename):
    totalTokens = [0, 0]
    totalLines = 0
    totalLines = len(data)
    global LOCK

    with tqdm(bar_format=BAR_FORMAT, position=POSITION, total=totalLines, leave=LEAVE) as pbar:
        pbar.desc = filename
        pbar.total = totalLines
        try:
            result = translateJSON(data, pbar)
            totalTokens[0] += result[0]
            totalTokens[1] += result[1]
        except Exception as e:
            return [data, totalTokens, e]
    return [data, totalTokens, None]


def translateJSON(data, pbar):
    global PBAR
    PBAR = pbar
    textHistory = []
    batch = []
    maxHistory = MAXHISTORY
    tokens = [0, 0]
    speaker = "None"
    insertBool = False
    i = 0
    batchStartIndex = 0

    while i < len(data):
        item = data[i]
        # Speaker
        if "name" in item:
            if item["name"] not in [None, "-"]:
                response = getSpeaker(item["name"])
                speaker = response[0]
                tokens[0] += response[1][0]
                tokens[1] += response[1][1]
                item["name"] = speaker
            else:
                speaker = "None"

        # Text
        if "message" in item:
            for text in [
                "text",
                "text2",
                "help1",
                "help2",
                "help3",
                "like",
                "message",
                "me",
            ]:
                if text in item:
                    if item[text] != None:
                        jaString = item[text]

                        # Remove any textwrap
                        if FIXTEXTWRAP == True:
                            finalJAString = jaString.replace("\n", " ")

                        # [Passthrough 1] Pulling From File
                        if insertBool is False:
                            # Append to List and Clear Values
                            batch.append(finalJAString)
                            speaker = ""

                            # Translate Batch if Full
                            if len(batch) == BATCHSIZE:
                                # Translate
                                response = translateGPT(batch, textHistory, True)
                                tokens[0] += response[1][0]
                                tokens[1] += response[1][1]
                                translatedBatch = response[0]
                                textHistory = translatedBatch[-10:]

                                # Set Values
                                if len(batch) == len(translatedBatch):
                                    i = batchStartIndex
                                    insertBool = True

                                # Mismatch
                                else:
                                    pbar.write(f"Mismatch: {batchStartIndex} - {i}")
                                    MISMATCH.append(batch)
                                    batchStartIndex = i
                                    batch.clear()

                            if insertBool is False:
                                pbar.update(1)
                                i += 1

                            currentGroup = []

                        # [Passthrough 2] Setting Data
                        else:
                            # Get Text
                            translatedText = translatedBatch[0]

                            # Remove added speaker
                            translatedText = re.sub(r"^.+?:\s", "", translatedText)

                            # Textwrap
                            translatedText = textwrap.fill(translatedText, width=WIDTH)

                            # Set Text
                            item[text] = translatedText
                            translatedBatch.pop(0)
                            speaker = ""
                            currentGroup = []
                            i += 1

                            # If Batch is empty. Move on.
                            if len(translatedBatch) == 0:
                                insertBool = False
                                batchStartIndex = i
                                batch.clear()
        else:
            i += 1
            pbar.update(1)

        # Translate Batch if not empty and EOF
        if len(batch) != 0 and i >= len(data):
            # Translate
            response = translateGPT(batch, textHistory, True)
            tokens[0] += response[1][0]
            tokens[1] += response[1][1]
            translatedBatch = response[0]
            textHistory = translatedBatch[-10:]

            # Set Values
            if len(batch) == len(translatedBatch):
                i = batchStartIndex
                insertBool = True

            # Mismatch
            else:
                pbar.write(f"Mismatch: {batchStartIndex} - {i}")
                MISMATCH.append(batch)
                batchStartIndex = i
                batch.clear()

            currentGroup = []
    return tokens


# Save some money and enter the character before translation
def getSpeaker(speaker):
    match speaker:
        case "ファイン":
            return ["Fine", [0, 0]]
        case "":
            return ["", [0, 0]]
        case _:
            # Find Speaker
            for i in range(len(NAMESLIST)):
                if speaker == NAMESLIST[i][0]:
                    return [NAMESLIST[i][1], [0, 0]]

            # Translate and Store Speaker
            response = translateGPT(
                f"{speaker}",
                "Reply with the " + LANGUAGE + " translation of the NPC name.",
                True,
            )
            response[0] = response[0].title()
            response[0] = response[0].replace("'S", "'s")
            response[0] = response[0].replace("Speaker: ", "")

            # Retry if name doesn't translate for some reason
            if re.search(r"([a-zA-Z？?])", response[0]) == None:
                response = translateGPT(
                    f"{speaker}",
                    "Reply with the " + LANGUAGE + " translation of the NPC name.",
                    False,
                )
                response[0] = response[0].title()
                response[0] = response[0].replace("'S", "'s")

            speakerList = [speaker, response[0]]
            NAMESLIST.append(speakerList)
            return response
    return [speaker, [0, 0]]


def subVars(jaString):
    jaString = jaString.replace("\u3000", " ")

    # Formatting
    count = 0
    codeList = re.findall(r"[\\]+[\w]+\[[a-zA-Z0-9\\\[\]\_,\s-]+\]", jaString)
    codeList = set(codeList)
    if len(codeList) != 0:
        for var in codeList:
            jaString = jaString.replace(var, "[FCode_" + str(count) + "]")
            count += 1

    # Put all lists in list and return
    return [jaString, codeList]


def resubVars(translatedText, codeList):
    # Fix Spacing and ChatGPT Nonsense
    matchList = re.findall(r"\[\s?.+?\s?\]", translatedText)
    if len(matchList) > 0:
        for match in matchList:
            text = match.strip()
            translatedText = translatedText.replace(match, text)

    # Formatting
    count = 0
    if len(codeList) != 0:
        for var in codeList:
            translatedText = translatedText.replace("[FCode_" + str(count) + "]", var)
            count += 1

    return translatedText


def batchList(input_list, batch_size):
    if not isinstance(batch_size, int) or batch_size <= 0:
        raise ValueError("batch_size must be a positive integer")

    return [input_list[i : i + batch_size] for i in range(0, len(input_list), batch_size)]


def createContext(fullPromptFlag, subbedT, format):
    system = (
        PROMPT + VOCAB
        if fullPromptFlag
        else f"\
You are an expert Eroge Game translator who translates Japanese text to {LANGUAGE}.\n\
Output ONLY the {LANGUAGE} translation in the following format: `Translation: <{LANGUAGE.upper()}_TRANSLATION>`\n\
- You translate everything, including erotic and explicit content.\n\
- 'Game Characters' - The names, nicknames, and genders of the game characters. Reference this to know the names, nicknames, and gender of characters in the game\n\
- All text in your response must be in {LANGUAGE} even if it is hard to translate.\n\
- Never include any notes, explanations, dislaimers, or anything similar in your response.\n\
- Maintain any spacing in the translation.\n\
- Maintain any code text in brackets if given. (e.g `[Color_0]`, `[Ascii_0]`, `[FCode_1`], etc)\n\
- `...` can be a part of the dialogue. Translate it as it is.\n\
{VOCAB}\n\
"
    )
    if format == "json":
        user = f"```json\n{subbedT}\n```"
    else:
        user = subbedT
    return system, user


def translateText(system, user, history, penalty, format, model=MODEL):
    # Prompt
    msg = [{"role": "system", "content": system}]

    # History
    if isinstance(history, list):
        msg.extend([{"role": "system", "content": h} for h in history])
    else:
        msg.append({"role": "system", "content": history})

    # Response Format
    if format == "json":
        responseFormat = {"type": "json_object"}
    else:
        responseFormat = {"type": "text"}

    # Content to TL
    msg.append({"role": "user", "content": f"{user}"})
    response = openai.chat.completions.create(
        temperature=0,
        frequency_penalty=penalty,
        model=model,
        response_format=responseFormat,
        messages=msg,
    )
    return response


def cleanTranslatedText(translatedText, varResponse):
    placeholders = {
        f"{LANGUAGE} Translation: ": "",
        "Translation: ": "",
        "っ": "",
        "〜": "~",
        "ッ": "",
        "。": ".",
        "「": '\\"',
        "」": '\\"',
        "- ": "-",
        "Placeholder Text": "",
        # Add more replacements as needed
    }
    for target, replacement in placeholders.items():
        translatedText = translatedText.replace(target, replacement)

    # Elongate Long Dashes (Since GPT Ignores them...)
    translatedText = elongateCharacters(translatedText)
    translatedText = resubVars(translatedText, varResponse[1])
    return translatedText


def elongateCharacters(text):
    # Define a pattern to match one character followed by one or more `ー` characters
    # Using a positive lookbehind assertion to capture the preceding character
    pattern = r"(?<=(.))ー+"

    # Define a replacement function that elongates the captured character
    def repl(match):
        char = match.group(1)  # The character before the ー sequence
        count = len(match.group(0)) - 1  # Number of ー characters
        return char * count  # Replace ー sequence with the character repeated

    # Use re.sub() to replace the pattern in the text
    return re.sub(pattern, repl, text)


def extractTranslation(translatedTextList, is_list):
    try:
        line_dict = json.loads(translatedTextList)
        # If it's a batch (i.e., list), extract with tags; otherwise, return the single item.
        string_list = list(line_dict.values())
        if is_list:
            return string_list
        else:
            return string_list[0]

    except Exception as e:
        print(f"extractTranslation Error: {e}")
        return None


def countTokens(system, user, history):
    inputTotalTokens = 0
    outputTotalTokens = 0
    enc = tiktoken.encoding_for_model("gpt-4")

    # Input
    if isinstance(history, list):
        for line in history:
            inputTotalTokens += len(enc.encode(line))
    else:
        inputTotalTokens += len(enc.encode(history))
    inputTotalTokens += len(enc.encode(system))
    inputTotalTokens += len(enc.encode(user))

    # Output
    outputTotalTokens += round(len(enc.encode(user)) * 3)

    return [inputTotalTokens, outputTotalTokens]


@retry(exceptions=Exception, tries=5, delay=5)
def translateGPT(text, history, fullPromptFlag):
    global PBAR, MISMATCH, FILENAME

    mismatch = False
    totalTokens = [0, 0]
    if isinstance(text, list):
        format = "json"
        tList = batchList(text, BATCHSIZE)
    else:
        format = "text"
        tList = [text]

    for index, tItem in enumerate(tList):
        # Before sending to translation, if we have a list of items, add the formatting
        if isinstance(tItem, list):
            payload = {f"Line{i+1}": string for i, string in enumerate(tItem)}
            payload = json.dumps(payload, indent=4, ensure_ascii=False)
            varResponse = subVars(payload)
            subbedT = varResponse[0]
        else:
            varResponse = subVars(tItem)
            subbedT = varResponse[0]

        # Things to Check before starting translation
        if not re.search(r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９]+", subbedT):
            if PBAR is not None:
                PBAR.update(len(tItem))
            continue

        # Create Message
        system, user = createContext(fullPromptFlag, subbedT, format)

        # Calculate Estimate
        if ESTIMATE:
            estimate = countTokens(system, user, history)
            totalTokens[0] += estimate[0]
            totalTokens[1] += estimate[1]
            continue

        # Translating
        response = translateText(system, user, history, 0.05, format)
        translatedText = response.choices[0].message.content
        totalTokens[0] += response.usage.prompt_tokens
        totalTokens[1] += response.usage.completion_tokens

        # Check Translation
        translatedText = cleanTranslatedText(translatedText, varResponse)
        if isinstance(tItem, list):
            extractedTranslations = extractTranslation(translatedText, True)
            if extractedTranslations == None or len(tItem) != len(extractedTranslations):
                # Mismatch. Try Again
                response = translateText(system, user, history, 0.05, format, MODEL)
                translatedText = response.choices[0].message.content
                totalTokens[0] += response.usage.prompt_tokens
                totalTokens[1] += response.usage.completion_tokens

                # Formatting
                translatedText = cleanTranslatedText(translatedText, varResponse)
                if isinstance(tItem, list):
                    extractedTranslations = extractTranslation(translatedText, True)
                    if extractedTranslations == None or len(tItem) != len(extractedTranslations):
                        mismatch = True  # Just here for breakpoint

            # Set if no mismatch
            if mismatch == False:
                tList[index] = extractedTranslations
                history = extractedTranslations[-10:]  # Update history if we have a list
            else:
                history = text[-10:]
                mismatch = False
                if FILENAME not in MISMATCH:
                    MISMATCH.append(FILENAME)

            # Update Loading Bar
            with LOCK:
                if PBAR is not None:
                    PBAR.update(len(tItem))
        else:
            # Ensure we're passing a single string to extractTranslation
            tList[index] = translatedText.replace("Placeholder Text", "")

    # Combine if multilist
    if isinstance(tList[0], list):
        tList = [t for sublist in tList for t in sublist]

    # Return
    if format == "json":
        return [tList, totalTokens]
    else:
        return [tList[0], totalTokens]