from typing import Dict, List, Any, Union, Optional
from collections import Counter
from functools import cache
import contextlib
import tempfile
import shutil
import random
import subprocess
import json
import re
import io
import os

import torch
import requests
import transformers
import numpy as np
from datasets import load_dataset, Dataset
from PIL import Image

from multi_token.constants import IGNORE_INDEX


def encode_chat(
    item: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    modalities: List["Modality"],
) -> Dict:
    messages = list(item["messages"])
    chat_as_string = tokenizer.apply_chat_template(messages, tokenize=False)

    token_to_modality = {m.token: m for m in modalities}
    modality_token_counts = Counter()
    instruct_pattern = r"(\[INST\][\s\S]*?\[\/INST\])"
    pattern = "(" + "|".join(re.escape(m.token) for m in modalities) + ")"

    chat_part = re.split(instruct_pattern, chat_as_string)
    input_ids = []
    labels = []
    for part in chat_part:
        if "[INST]" in part:
            is_instruction = True
        else:
            is_instruction = False
        for subpart in re.split(pattern, part):
            if not subpart:
                continue
            if subpart in token_to_modality:
                assert (
                    is_instruction
                ), "There should be no modality tokens outside of instructions"
                m = token_to_modality[subpart]
                modality_token_counts[m.name] += 1
                input_ids.extend([m.token_idx] * m.token_width)
                labels.extend([IGNORE_INDEX] * m.token_width)
            elif is_instruction:
                part_ids = tokenizer(subpart, add_special_tokens=False).input_ids
                input_ids.extend(part_ids)
                labels.extend([IGNORE_INDEX] * len(part_ids))
            else:
                part_ids = tokenizer(subpart, add_special_tokens=False).input_ids
                input_ids.extend(part_ids)
                labels.extend(part_ids)

    input_ids = torch.tensor(input_ids, dtype=torch.long)
    labels = torch.tensor(labels, dtype=torch.long)

    data_dict = dict(
        input_ids=input_ids,
        labels=labels,
    )
    for m in modalities:
        data_dict[m.name] = m.preprocess_rows([item])[0]
    return data_dict

def encode_chat_multitask(
    item: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    modalities: List["Modality"],
) -> Dict:
    messages = list(item["messages"])
    chat_as_string = tokenizer.apply_chat_template(messages, tokenize=False)

    token_to_modality = {m.token: m for m in modalities}
    modality_token_counts = Counter()
    instruct_pattern = r"(\[INST\][\s\S]*?\[\/INST\])"
    pattern = "(" + "|".join(re.escape(m.token) for m in modalities) + ")"

    chat_part = re.split(instruct_pattern, chat_as_string)
    input_ids = []
    labels = []
    labels.append([])
    for part in chat_part:
        if "[INST]" in part:
            is_instruction = True
        else:
            is_instruction = False
        for subpart in re.split(pattern, part):
            if not subpart:
                continue
            if subpart in token_to_modality:
                assert (
                    is_instruction
                ), "There should be no modality tokens outside of instructions"
                m = token_to_modality[subpart]
                modality_token_counts[m.name] += 1
                input_ids.extend([m.token_idx] * m.token_width)
                labels[0].extend([IGNORE_INDEX] * m.token_width)
            elif is_instruction:
                part_ids = tokenizer(subpart, add_special_tokens=False).input_ids
                input_ids.extend(part_ids)
                labels[0].extend([IGNORE_INDEX] * len(part_ids))
            else:
                part_ids = tokenizer(subpart, add_special_tokens=False).input_ids
                input_ids.extend(part_ids)
                labels[0].extend(part_ids)

    input_ids = torch.tensor(input_ids, dtype=torch.long)
    labels[0] = torch.tensor(labels[0], dtype=torch.long)

    task_list = []
    for m in modalities:
        task_list += m.tasks["task_heads"].keys()
    # labels[task_specs["task_id"]] = load_tensor(item[task_name][0])

    for task_name in task_list:
        if task_name != "lmm_projector":
            labels.append(load_tensor(item[task_name][0]))

    # labels = torch.tensor(labels, dtype=torch.long)

    data_dict = dict(
        input_ids=input_ids,
        labels=labels,
    )
    for m in modalities:
        data_dict[m.name] = m.preprocess_rows([item])[0]
    return data_dict

def load_tensor(path: str) -> np.ndarray:
    return torch.tensor(np.load(path))


def load_image(value: Any) -> Image.Image:
    img = None
    if isinstance(value, str):
        if value.startswith("http://") or value.startswith("https://"):
            response = requests.get(value)
            img = Image.open(io.BytesIO(response.content))
        elif os.path.exists(value):
            img = Image.open(value)
    elif isinstance(value, Image.Image):
        img = value
    if img is None:
        raise ValueError(f"Could not load image from {value}")
    img = img.convert("RGB")
    return img


@contextlib.contextmanager
def with_local_files(fn_or_urls: List[Any]):
    local_fns = []
    fps = []
    for fn_or_url in fn_or_urls:
        if isinstance(fn_or_url, Image.Image):
            fp = tempfile.NamedTemporaryFile(suffix=".png", mode="wb")
            fn_or_url.convert("RGB").save(fp)
            fps.append(fp)
            local_fns.append(fp.name)
        elif fn_or_url.startswith("http://") or fn_or_url.startswith("https://"):
            suffix = os.path.splitext(fn_or_url)[-1]
            with requests.get(fn_or_url, stream=True) as r:
                fp = tempfile.NamedTemporaryFile(suffix=suffix, mode="wb")
                shutil.copyfileobj(r.raw, fp)
                fps.append(fp)
                local_fns.append(fp.name)
        else:
            local_fns.append(fn_or_url)
    try:
        yield local_fns
    finally:
        for fp in fps:
            fp.close()


@cache
def _get_dataset(dataset_args: str) -> Dataset:
    return load_dataset(**json.loads(dataset_args))


def get_dataset_cached(dataset_args: Dict) -> Dataset:
    return _get_dataset(json.dumps(dataset_args))


def load_audio_signal(input_: Union[Dict, str]) -> Dict:
    from audiotools import AudioSignal

    if isinstance(input_, dict) and "array" in input_:
        array = input_["array"]
    elif isinstance(input_, dict) and "dataset_args" in input_:
        item = get_dataset_cached(input_["dataset_args"])[input_["idx"]]
        array = item["audio"]["array"]
    elif isinstance(input_, dict) and "path" in input_:
        with with_local_files([input_["path"]]) as local_fns:
            array =  AudioSignal(local_fns[0])
    elif isinstance(input_, str):
        with with_local_files([input_]) as local_fns:
            array =  AudioSignal(local_fns[0])
    else:
        raise ValueError(f"Could not load audio from {input_}")

    return {"array": list(array)}


def load_audio(input_: Union[Dict, str], target_sampling_rate: int = None) -> Dict:
    import soundfile as sf
    import librosa

    if isinstance(input_, dict) and "array" in input_ and "sampling_rate" in input_:
        array = input_["array"]
        sampling_rate = input_["sampling_rate"]
    elif isinstance(input_, dict) and "dataset_args" in input_:
        item = get_dataset_cached(input_["dataset_args"])[input_["idx"]]
        array = item["audio"]["array"]
        sampling_rate = item["audio"]["sampling_rate"]
    elif isinstance(input_, dict) and "path" in input_:
        with with_local_files([input_["path"]]) as local_fns:
            array, sampling_rate = sf.read(local_fns[0])
    elif isinstance(input_, str):
        with with_local_files([input_]) as local_fns:
            array, sampling_rate = sf.read(local_fns[0])
    else:
        raise ValueError(f"Could not load audio from {input_}")

    if array.ndim == 2:
        array = array.mean(axis=1)

    if target_sampling_rate is not None and sampling_rate != target_sampling_rate:
        array = librosa.resample(
            array, orig_sr=sampling_rate, target_sr=target_sampling_rate
        )
        sampling_rate = target_sampling_rate

    return {"array": list(array), "sampling_rate": sampling_rate}


def _download_yt_video(url: str) -> str:
    from pytube import YouTube

    youtube = YouTube(url)
    video = youtube.streams.first()

    fn = "".join(random.choices("abcdefghijklmnopqrstuvwxyz", k=10))
    file_path = video.download(output_path=tempfile.gettempdir(), filename=fn)

    return file_path


def _read_video_pyav(container, indices):
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])


def _sample_frame_indices(clip_len, frame_sample_rate, seg_len):
    converted_len = int(clip_len * frame_sample_rate)
    end_idx = np.random.randint(converted_len, seg_len)
    start_idx = end_idx - converted_len
    indices = np.linspace(start_idx, end_idx, num=clip_len)
    indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
    return indices


def load_video(
    input_: str,
    frames: int = 8,
    frame_sample_rate: int = 1,
    start_time: Optional[int] = None,
    end_time: Optional[int] = None,
) -> np.ndarray:
    import av

    delete_file = False

    if isinstance(input_, dict) and "youtube.com" and input_.get("url", ""):
        file_path = _download_yt_video(input_["url"])
        delete_file = True
        # start_time = input_.get("start_time", None)
        # end_time = input_.get("end_time", None)
    elif isinstance(input_, str) and "youtube.com" in input_:
        file_path = _download_yt_video(input_)
        delete_file = True
    elif isinstance(input_, str):
        file_path = input_
    else:
        raise ValueError(f"Could not load video from {input_}")

    if start_time is not None or end_time is not None:
        start_time = start_time if start_time is not None else 0
        end_time = end_time if end_time is not None else "end"
        trim_file_path = f"{file_path.rsplit('.', 1)[0]}_trim.mp4"
        subprocess.run(
            [
                "ffmpeg",
                "-i",
                file_path,
                "-ss",
                str(start_time),
                "-to",
                str(end_time),
                "-c",
                "copy",
                trim_file_path,
            ]
        )
        file_path = trim_file_path

    container = av.open(file_path)
    indices = _sample_frame_indices(
        clip_len=frames,
        frame_sample_rate=frame_sample_rate,
        seg_len=container.streams.video[0].frames,
    )
    video = _read_video_pyav(container, indices)

    if delete_file:
        os.remove(file_path)

    return video