import shutil
import time
import json

import requests
import os
from itertools import groupby
from subprocess import Popen, PIPE
import re

from src.aligner import Aligner

import nltk
import glob
from nltk.tokenize import sent_tokenize, word_tokenize

nltk.download('punkt')
nltk.download('punkt_tab')

ip = "192.168.20.216"
port = "8000"


def translate(text, ip, port):
    myobj = {
        'id': '1',
        'src': text,
    }
    port = str(int(port))
    url = 'http://' + ip + ':' + port + '/translate'
    x = requests.post(url, json=myobj)
    json_response = json.loads(x.text)
    return json_response['tgt']


def doc_to_plain_text(input_file: str, source_lang: str, target_lang: str, tikal_folder: str,
                      original_xliff_file_path: str) -> str:
    """
    Given a document, this function generates an xliff file and then a plain text file with the text contents
    while keeping style and formatting using tags like <g id=1> </g>

    Parameters:
    input_file: Path to document to process
    source_lang: Source language of the document
    target_lang: Target language of the document
    tikal_folder: Folder where tikal.sh is located
    original_xliff_file_path: Path to xliff file to generate, which will be use later

    Returns:
    string: Path to plain text file
    """

    tikal_xliff_command = [os.path.join(tikal_folder, "tikal.sh"), "-x", input_file, "-nocopy", "-sl", source_lang,
                           "-tl", target_lang]
    Popen(tikal_xliff_command).wait()

    tikal_moses_command = [os.path.join(tikal_folder, "tikal.sh"), "-xm", original_xliff_file_path, "-sl", source_lang,
                           "-tl", target_lang]
    Popen(tikal_moses_command).wait()

    return os.path.join(original_xliff_file_path + f".{source_lang}")


def get_runs_from_paragraph(text: str, paragraph_index: int) -> list[dict[str, str]]:
    """
    Given some text that may or may not contain some chunks tagged with something like <g id=1> </g>, extract each
    of the runs of text and convert them into dictionaries to keep this information

    Parameters:
    text: Text to process
    paragraph_index: Index of the paragraph in the file

    Returns:
    list[dict]: Where each element is a run with text, tag id (if any, if not None) and paragraph_index
    """

    pattern = r'<g id="(\d+)">(.*?)</g>'
    chunks = []
    last_index = 0

    for match in re.finditer(pattern, text):
        start, end = match.span()
        id_ = match.group(1)
        content = match.group(2)

        # Add plain text before the tag, if any
        if start > last_index:
            plain_text = text[last_index:start]
            chunks.append({"text": plain_text, "id": None, "paragraph_index": paragraph_index})

        # Add tagged content
        if content != " ":
            chunks.append({"text": content, "id": id_, "paragraph_index": paragraph_index})
        last_index = end

    # Add any remaining plain text after the last tag
    if last_index < len(text):
        chunks.append({"text": text[last_index:], "id": None, "paragraph_index": paragraph_index})

    return chunks


def tokenize_with_runs(runs: list[dict[str, str]], detokenizer) -> list[list[dict[str, str]]]:
    """
    Given a list of runs, we need to tokenize them by sentence and token while keeping the style of each token according
    to its original run

    Parameters:
    runs: List of runs, where each item is a chunk of text (possibly various tokens) and some style/formatting information
    detokenizer: Detokenizer object to merge tokens back together

    Returns:
    list[list[dict]]: A list of tokenized sentences where each token contains the style of its original run
    """
    text_paragraph = detokenizer.detokenize([run["text"] for run in runs])
    sentences = sent_tokenize(text_paragraph)
    tokenized_sentences = [word_tokenize(sentence) for sentence in sentences]

    tokens_with_style = []
    for run in runs:
        tokens = word_tokenize(run["text"])
        for token in tokens:
            tokens_with_style.append(run.copy())
            tokens_with_style[-1]["text"] = token

    token_index = 0
    tokenized_sentences_with_style = []
    for sentence in tokenized_sentences:
        sentence_with_style = []
        for word in sentence:
            if word == tokens_with_style[token_index]["text"]:
                sentence_with_style.append(tokens_with_style[token_index])
                token_index += 1
            else:
                if word.startswith(tokens_with_style[token_index]["text"]):
                    # this token might be split into several runs
                    word_left = word

                    while word_left:
                        sentence_with_style.append(tokens_with_style[token_index])
                        word_left = word_left.removeprefix(tokens_with_style[token_index]["text"])
                        token_index += 1
                else:
                    raise "Something unexpected happened I'm afraid"
        tokenized_sentences_with_style.append(sentence_with_style)
    return tokenized_sentences_with_style


def generate_alignments(original_paragraphs_with_runs: list[list[dict[str, str]]],
                        translated_paragraphs: list[str], aligner, temp_folder: str,
                        detokenizer) -> list[list[dict[str, str]]]:
    """
    Given some original paragraphs with style and formatting and its translation without formatting, try to match
    the translated text formatting with the original. Since we only want to run fastalign once we have to temporarily
    forget about paragraphs and work only in sentences, so the output is a list of sentences but with information about
    from which paragraph that sentence came from

    Parameters:
    original_paragraphs_with_runs: Original text split into paragraphs and runs
    translated_paragraphs: Translated text, split into paragraphs
    aligner: Object of the aligner class, uses fastalign
    temp_folder: Path to folder where to put all the intermediate files
    detokenizer: Detokenizer object to merge tokens back together

    Returns:
    list[list[dict]]: A list of tokenized sentences where each translated token contains the style of the associated
                        original token
    """
    # clean temp folder
    for f in glob.glob(os.path.join(temp_folder, "*align*")):
        os.remove(f)

    # tokenize the original text by sentence and words while keeping the style
    original_tokenized_sentences_with_style = [tokenize_with_runs(runs, detokenizer) for runs in
                                               original_paragraphs_with_runs]

    # flatten all the runs so we can align with just one call instead of one per paragraph
    original_tokenized_sentences_with_style = [item for sublist in original_tokenized_sentences_with_style for item in
                                               sublist]

    # tokenize the translated text by sentence and word
    translated_tokenized_sentences = [word_tokenize(sentence) for
                                      translated_paragraph in translated_paragraphs for sentence in
                                      sent_tokenize(translated_paragraph)]

    assert len(translated_tokenized_sentences) == len(
        original_tokenized_sentences_with_style), "The original and translated texts contain a different number of sentence, likely due to a translation error"

    original_sentences = []
    translated_sentences = []
    for original, translated in zip(original_tokenized_sentences_with_style, translated_tokenized_sentences):
        original_sentences.append(' '.join(item['text'] for item in original))
        translated_sentences.append(' '.join(translated))

    alignments = aligner.align(original_sentences, translated_sentences)

    # using the alignments generated by fastalign, we need to copy the style of the original token to the translated one
    translated_sentences_with_style = []
    for sentence_idx, sentence_alignments in enumerate(alignments):

        # reverse the order of the alignments and build a dict with it
        sentence_alignments = {target: source for source, target in sentence_alignments}

        translated_sentence_with_style: list[dict[str, str]] = []
        for token_idx, translated_token in enumerate(translated_tokenized_sentences[sentence_idx]):
            # fastalign has found a token aligned with the translated one
            if token_idx in sentence_alignments.keys():
                # get the aligned token
                original_idx = sentence_alignments[token_idx]
                new_entry = original_tokenized_sentences_with_style[sentence_idx][original_idx].copy()
                new_entry["text"] = translated_token
                translated_sentence_with_style.append(new_entry)
            else:
                # WARNING this is a test
                # since fastalign doesn't know from which word to reference this token, copy the style of the previous word
                new_entry = translated_sentence_with_style[-1].copy()
                new_entry["text"] = translated_token
                translated_sentence_with_style.append(new_entry)

        translated_sentences_with_style.append(translated_sentence_with_style)

    return translated_sentences_with_style


def group_by_style(tokens: list[dict[str, str]], detokenizer) -> list[dict[str, str]]:
    """
    To avoid having issues in the future, we group the contiguous tokens that have the same style. Basically, we
    reconstruct the runs.

    Parameters:
    tokens: Tokens with style information
    detokenizer: Detokenizer object to merge tokens back together

    Returns:
    list[dict]: A list of translated runs with format and style
    """
    groups = []
    for key, group in groupby(tokens, key=lambda x: (x["id"], x["paragraph_index"])):
        text = detokenizer.detokenize([item['text'] for item in group])

        if groups and not text.startswith((",", ";", ":", ".", ")", "!", "?")):
            text = " " + text

        groups.append({"text": text,
                       "id": key[0],
                       "paragraph_index": key[1]})
    return groups


def runs_to_plain_text(paragraphs_with_style: dict[str, list[dict[str, str, str]]], out_file_path: str):
    """
    Generate a plain text file restoring the original tag structure like <g id=1> </g>

    Parameters:
    paragraphs_with_style: Dictionary where each key is the paragraph_index and its contents are a list of runs
    out_file_path: Path to the file where the plain text will be saved
    """
    with open(out_file_path, "w") as out_file:
        for key, paragraph in paragraphs_with_style.items():
            text_paragraph = ""
            for run in paragraph:
                if run["id"]:
                    text_paragraph += f'<g id="{run["id"]}">{run["text"]}</g>'
                else:
                    text_paragraph += run["text"]
            out_file.write(text_paragraph + "\n")


def translate_document(input_file: str, source_lang: str, target_lang: str,
                       aligner: Aligner,
                       detokenizer,
                       ip: str = "192.168.20.216",
                       temp_folder: str = "tmp",
                       port: str = "8000",
                       tikal_folder: str = "okapi-apps_gtk2-linux-x86_64_1.47.0") -> str:
    input_filename = input_file.split("/")[-1]
    # copy the original file to the temporal folder to avoid common issues with tikal
    temp_input_file = os.path.join(temp_folder, input_filename)
    shutil.copy(input_file, temp_input_file)

    original_xliff_file = os.path.join(temp_folder, input_filename + ".xlf")
    plain_text_file = doc_to_plain_text(temp_input_file, source_lang, target_lang, tikal_folder, original_xliff_file)

    # get paragraphs with runs
    paragraphs_with_runs = [get_runs_from_paragraph(line.strip(), idx) for idx, line in
                            enumerate(open(plain_text_file).readlines())]

    # translation = translate(open(original_moses_file).read(), ip, port)

    # translate using plaintext file
    # translated_paragraphs = []
    # for paragraph in tqdm.tqdm(paragraphs_with_runs, desc="Translating paragraphs..."):
    #     paragraph_text = detokenizer.detokenize([run["text"] for run in paragraph])
    #     translated_paragraphs.append(translate(paragraph_text, ip, port))

    translated_paragraphs = ["Catalan",
                             "Catalan (official name in Catalonia, the Balearic Islands, Andorra, the city of Alghero and traditional in Northern Catalonia) or Valencian (official name in the Valencian Community and traditional in Carxe) is a Romance language spoken in Catalonia, the Valencian Community (except for some regions and towns in the interior), the Balearic Islands (where it is also called Mallorcan, Menorcan, Ibizan or Formentera depending on the island), Andorra, the Franja de Ponent (in Aragon), the city of Alghero (on the island of Sardinia), Northern Catalonia, Carxe (a small territory of Murcia inhabited by Valencian settlers), and in communities around the world (among which Argentina stands out, with 200,000 speakers).",
                             "It has ten million speakers, of whom almost half are native speakers; Its linguistic domain, with an area of 68,730 km² and 13,992,625 inhabitants (2013-2015), includes 1,687 municipal districts. In 2023, it was spoken as a mother tongue by more than four million people (29% of the population of the linguistic territory), of whom 2,924,610 in Catalonia, 1,190,672 in the Valencian Community and 327,384 in the Balearic Islands. Like the other Romance languages, Catalan comes from Vulgar Latin spoken by the Romans who settled in Hispania during ancient times."]

    # time to align the translation with the original
    print("Generating alignments...")
    start_time = time.time()
    translated_sentences_with_style = generate_alignments(paragraphs_with_runs, translated_paragraphs, aligner,
                                                          temp_folder, detokenizer)
    print(f"Finished alignments in {time.time() - start_time} seconds")

    # flatten the sentences into a list of tokens
    translated_tokens_with_style = [item for sublist in translated_sentences_with_style for item in sublist]

    # group the tokens by style/run
    translated_runs_with_style = group_by_style(translated_tokens_with_style, detokenizer)

    # group the runs by original paragraph
    translated_paragraphs_with_style = dict()
    for item in translated_runs_with_style:
        if item['paragraph_index'] in translated_paragraphs_with_style:
            translated_paragraphs_with_style[item['paragraph_index']].append(item)
        else:
            # first item in the paragraph, remove starting blank space we introduced in group_by_style(), where we
            # didn't know where paragraphs started and ended
            first_item_in_paragraph = item.copy()
            first_item_in_paragraph["text"] = first_item_in_paragraph["text"].lstrip(" ")
            translated_paragraphs_with_style[item['paragraph_index']] = []
            translated_paragraphs_with_style[item['paragraph_index']].append(first_item_in_paragraph)

    # save to new plain text file
    translated_moses_file = os.path.join(original_xliff_file + f".{target_lang}")
    runs_to_plain_text(translated_paragraphs_with_style, translated_moses_file)

    # put the translations into the xlf
    tikal_moses_to_xliff_command = [os.path.join(tikal_folder, "tikal.sh"), "-lm", original_xliff_file, "-sl",
                                    source_lang, "-tl", target_lang, "-from", translated_moses_file, "-totrg",
                                    "-noalttrans", "-to", original_xliff_file]
    Popen(tikal_moses_to_xliff_command).wait()

    # merge into a docx again
    tikal_merge_doc_command = [os.path.join(tikal_folder, "tikal.sh"), "-m", original_xliff_file]
    final_process = Popen(tikal_merge_doc_command, stdout=PIPE, stderr=PIPE)
    stdout, stderr = final_process.communicate()
    final_process.wait()

    # get the path to the output file
    output = stdout.decode('utf-8')
    translated_file_path = re.search(r'(?<=Output:\s)(.*)', output)[0]

    print("Saved file")
    return translated_file_path