from torchvision.io import read_image, ImageReadMode
import torch
import numpy as np
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
from PIL import Image
import os
import requests
import streamlit as st
from transformers import MBart50TokenizerFast


def voicerss_tts(text, lang_id):
    lang_id_to_code_map = {
        "en": "en-us",
        "fr": "fr-fr",
        "de": "de-de",
        "es": "es-es"

    }
    url = "https://voicerss-text-to-speech.p.rapidapi.com/"

    querystring = {"key":st.secrets["voicerss_key"],"hl":lang_id_to_code_map[lang_id],"src":text,"f":"8khz_8bit_mono","c":"mp3","r":"0"}

    headers = {
        'x-rapidapi-key': "0493c0e0f4mshe80b4d8c8986e14p103af6jsna45ff41b76b1",
        'x-rapidapi-host': "voicerss-text-to-speech.p.rapidapi.com"
        }

    response = requests.request("GET", url, headers=headers, params=querystring)

    return response.content

class Toc:
    def __init__(self):
        self._items = []
        self._placeholder = None

    def title(self, text):
        self._markdown(text, "h1")

    def header(self, text):
        self._markdown(text, "h2", " " * 2)

    def subheader(self, text):
        self._markdown(text, "h3", " " * 4)

    def subsubheader(self, text):
        self._markdown(text, "h4", " " * 8)

    def placeholder(self, sidebar=False):
        self._placeholder = st.sidebar.empty() if sidebar else st.empty()

    def generate(self):
        if self._placeholder:
            self._placeholder.markdown("\n".join(self._items), unsafe_allow_html=True)

    def _markdown(self, text, level, space=""):
        key = "".join(filter(str.isalnum, text)).lower()

        st.markdown(f"<{level} id='{key}'>{text}</{level}>", unsafe_allow_html=True)
        self._items.append(f"{space}* <a href='#{key}'>{text}</a>")

tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50")

class Transform(torch.nn.Module):
    def __init__(self, image_size):
        super().__init__()
        self.transforms = torch.nn.Sequential(
            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
            CenterCrop(image_size),
            ConvertImageDtype(torch.float),
            Normalize(
                (0.48145466, 0.4578275, 0.40821073),
                (0.26862954, 0.26130258, 0.27577711),
            ),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        with torch.no_grad():
            x = self.transforms(x)
        return x


transform = Transform(224)

def get_transformed_image(image):
    if isinstance(image, np.ndarray) and image.shape[-1] == 3:
        image = image.transpose(2, 0, 1)
        image = torch.tensor(image)
    return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()

def read_markdown(path, parent="./sections/"):
    with open(os.path.join(parent, path)) as f:
        return f.read()

language_mapping = {
    "en": "en_XX",
    "de": "de_DE",
    "fr": "fr_XX",
    "es": "es_XX"
}

code_to_name = {
    "en": "English",
    "fr": "French",
    "de": "German",
    "es": "Spanish",
}