import whisper
import re
import sys
import os, random, copy
import numpy as np
import torch
import pandas as pd
import torchaudio
from tqdm.notebook import tqdm
import collections, json
import editdistance
from whisper.normalizers import EnglishTextNormalizer
from argparse import ArgumentParser
from num2words import num2words
sys.path.append('/home3/huyuchen/pytorch_workplace/jiwer')
from jiwer import wer_embdiff
import fasttext
from huggingface_hub import hf_hub_download
from pathlib import Path
from typing import Optional
from sentencepiece import SentencePieceProcessor, SentencePieceTrainer
from sentence_transformers import SentenceTransformer
from argparse import ArgumentParser
from evaluate import load
from lit_gpt.tokenizer import Tokenizer
eval_wer = load("wer")
normalizer = EnglishTextNormalizer()

checkpoint_dir = Path('/home3/huyuchen/pytorch_workplace/wgpt/checkpoints/Llama-2-7b-hf')
tokenizer = Tokenizer(checkpoint_dir)

sbert_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')


def calculate_wer(all_hypo, all_refer):
    return eval_wer.compute(predictions=all_hypo, references=all_refer)

def word_emb_diff(reference, hypothesis):
    output, edit_ops = wer_embdiff(reference, hypothesis)
    ref_words, hypo_words = output.references[0], output.hypotheses[0]

    emb_diffs = []
    for op in edit_ops:
        if op.tag == 'replace':
            ref_word, hypo_word = ref_words[op.src_pos], hypo_words[op.dest_pos]
        elif op.tag == 'delete':
            ref_word, hypo_word = ref_words[op.src_pos], None
        elif op.tag == 'insert':
            ref_word, hypo_word = None, hypo_words[op.dest_pos]
        else:
            continue

        ref_emb = torch.from_numpy(sbert_model.encode([ref_word])[0]) if ref_word else torch.zeros([384])
        hypo_emb = torch.from_numpy(sbert_model.encode([hypo_word])[0]) if hypo_word else torch.zeros([384])

        emb_diff = ref_emb - hypo_emb
        emb_diffs.append(emb_diff)

        # print('word', hypo_emb.mean(), ref_emb.mean(), emb_diff.mean())

    if len(emb_diffs) == 0:
        return torch.zeros([384])
    else:
        return torch.stack(emb_diffs, dim=0).mean(dim=0)

def sent_emb_diff(reference, hypothesis):
    embeddings = sbert_model.encode([reference, hypothesis])
    ref_emb, hypo_emb = torch.from_numpy(embeddings[0]), torch.from_numpy(embeddings[1])
    emb_diff = ref_emb - hypo_emb
    # print('sentence', hypo_emb.mean(), ref_emb.mean(), emb_diff.mean())

    return emb_diff

def generate_prompt(input1, input2):
    return (
        f"Below is the best-hypotheses transcribed from speech recognition system. Please try to revise it using the words which are only included into other-hypothesis, and write the response for the true transcription.\n\n### Best-hypothesis:\n{input1}\n\n### Other-hypothesis:\n{input2}\n\n### Response:\n"
    )


DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model('large-v2')

f_noisy_wav = open(f'noisy_wav.scp', 'r')
f_clean_wav = open(f'clean_wav.scp', 'r')
f_text = open(f'text', 'r')

id = 0
pt_file = []
all_hypo, all_refer = [], []
for line in f_noisy_wav.readlines():
    utt_id, audio_path = line.strip().split()[0], line.strip().split()[1]
    clean_line = f_clean_wav.readline()
    clean_utt_id, clean_audio_path = clean_line.strip().split()[0], clean_line.strip().split()[1]
    assert clean_utt_id == utt_id, (line, clean_line)
    gt = ' '.join(f_text.readline().strip().split()[1:])
    audio = whisper.load_audio(audio_path)
    audio = whisper.pad_or_trim(audio)
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    options = whisper.DecodingOptions(language='en', beam_size=50)
    texts, confidences = whisper.decode_score(model, mel, options)

    ## noisy audio feats
    audio_features = model.encoder(mel.unsqueeze(0))[0]
    
    ## clean audio feats
    clean_audio = whisper.load_audio(clean_audio_path)
    clean_audio = whisper.pad_or_trim(clean_audio)
    clean_mel = whisper.log_mel_spectrogram(clean_audio).to(model.device)
    clean_audio_features = model.encoder(clean_mel.unsqueeze(0))[0]

    input, score = [], []
    for text, confidence in zip(texts, confidences):
        if len(input) < 5 and len(text) > 0 and text not in input:
            input.append(text)
            score.append(confidence)

    # print('before', input, score, len(input))

    if len(input) < 5:
        options = whisper.DecodingOptions(language='en', temperature=1.2)
        for _ in range(5 - len(input)):
            result = whisper.decode(model, mel, options)
            text, condidence = result.text, result.avg_logprob
            if text in input:
                continue
            inserted = False
            for i in range(len(input)):
                if condidence > score[i]:
                    input.insert(i, text)
                    score.insert(i, condidence)
                    inserted = True
                    break
            if not inserted:
                input.append(text)
                score.append(condidence)

    # print('after ', input, score, len(input))

    if len(input) < 5:
        num_to_add = 5 - len(input)
        for _ in range(num_to_add):
            rand_id = random.randint(0, len(input) - 1)
            rep_input, rep_score = copy.deepcopy(input[rand_id]), copy.deepcopy(score[rand_id])
            input.insert(rand_id + 1, rep_input)
            score.insert(rand_id + 1, rep_score)

    for i in range(len(input)):
        try:
            text = normalizer(input[i])
            text = re.sub(r"[-+]?\d*\.?\d+|\d+%?", lambda m: num2words(m.group()), text).replace('%', ' percent')
        except Exception:
            text = normalizer(input[i])
            print(f'input exception: {text}')
        input[i] = text if len(text) > 0 else '<UNK>'

    try:
        output = normalizer(gt)
        output = re.sub(r"[-+]?\d*\.?\d+|\d+%?", lambda m: num2words(m.group()), output).replace('%', ' percent')
    except Exception:
        output = normalizer(gt)
        print(f'output exception: {output}')
    output = output if len(output) > 0 else '<UNK>'

    cur_wer = calculate_wer([input[0]], [output])

    # calculate emb diff
    we_diffs, se_diffs = [], []
    for i in range(5):
        for j in range(i + 1, 5):
            we_diffs.append(word_emb_diff(input[i], input[j]))
            se_diffs.append(sent_emb_diff(input[i], input[j]))

    we_diff = torch.stack(we_diffs, dim=0)      # [10, 384]
    se_diff = torch.stack(se_diffs, dim=0)      # [10, 384]
    emb_diff = torch.cat([we_diff, se_diff], dim=0)     # [20, 384]
    
    # generate ids
    input1 = input[0] + '.'
    input2 = '. '.join(input[1:]) + '.'

    full_prompt = generate_prompt(input1, input2)
    full_prompt_and_response = full_prompt + output
    encoded_full_prompt = tokenizer.encode(full_prompt, max_length=1024)
    encoded_full_prompt_and_response = tokenizer.encode(full_prompt_and_response, eos=True, max_length=1024)

    labels = encoded_full_prompt_and_response.clone()
    labels[: len(encoded_full_prompt)] = -1


    data = {"id": utt_id, "input_ids": encoded_full_prompt_and_response, "input_ids_no_response": encoded_full_prompt, "labels": labels,
             "input": input, 'ground_truth': output, "am_score": score, 'emb_diff': emb_diff, 'audio_features': noisy_audio_features, 
             'clean_audio_features': clean_audio_features}

    pt_file.append(data)

    # calculate wer
    id += 1
    print(f'utterance {id}: wer = {cur_wer}, confidence = {score[0]}')
    all_hypo.append(input[0])
    all_refer.append(output)


torch.save(pt_file, f'/home3/huyuchen/pytorch_workplace/wllama/hypo_paradise_v2/train_rats.pt')


f_noisy_wav.close()
f_clean_wav.close()
f_text.close()

all_wer = calculate_wer(all_hypo, all_refer)
print(f'all wer = {all_wer}')