"""
Translation interface using the MADLAD-400 3B model.
Translates English text to 22 production-ready languages from the MADLAD-400 paper.
"""

import warnings
from functools import lru_cache

import gradio as gr
import spaces
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

from langmap.langid_mapping import langid_to_language

MODEL_NAME = "google/madlad400-3b-mt"


def _get_device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    warnings.warn("No GPU available. Running on CPU — translation will be slow.", stacklevel=2)
    return torch.device("cpu")


@lru_cache(maxsize=1)
def _load_tokenizer() -> AutoTokenizer:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
    if tokenizer is None:
        raise RuntimeError(f"Failed to load tokenizer for {MODEL_NAME}")
    return tokenizer


@lru_cache(maxsize=1)
def _load_model() -> AutoModelForSeq2SeqLM:
    device = _get_device()
    dtype = torch.float16 if device.type == "cuda" else torch.float32
    return AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME, dtype=dtype).to(device)


@lru_cache(maxsize=1)
def _build_language_mappings() -> tuple[dict[str, str], list[str]]:
    tokenizer = _load_tokenizer()
    vocab = tokenizer.get_vocab()
    name_to_code = {}
    for code, name in langid_to_language.items():
        if code in vocab:
            locale = code[2:-1]  # <2fr> → fr
            display_name = f"{name} ({locale})"
            name_to_code[display_name] = code
    return name_to_code, sorted(name_to_code.keys())


@spaces.GPU
def translate(
    text: str,
    target_language_name: str,
    max_new_tokens: int = 512,
    num_beams: int = 1,
    temperature: float = 1.0,
) -> str:
    tokenizer = _load_tokenizer()
    model = _load_model()
    device = model.device

    name_to_code, _ = _build_language_mappings()
    target_code = name_to_code.get(target_language_name)
    if target_code is None:
        raise ValueError(f"Unsupported language: {target_language_name}")

    if num_beams > 1 and temperature != 1.0:
        gr.Info("Temperature has no effect when beam search is enabled (num_beams > 1).")

    input_ids = tokenizer(target_code + " " + text, return_tensors="pt").input_ids.to(device)

    generate_kwargs: dict = {"input_ids": input_ids, "max_new_tokens": max_new_tokens, "num_beams": num_beams}
    if num_beams == 1:
        generate_kwargs["do_sample"] = True
        generate_kwargs["temperature"] = temperature

    outputs = model.generate(**generate_kwargs)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    if not isinstance(result, str):
        raise TypeError(f"Expected str from decode, got {type(result)}")
    return result


def _update_char_count(text: str) -> str:
    return f"{len(text)} characters"


def _build_demo() -> gr.Blocks:
    _, language_names = _build_language_mappings()

    with gr.Blocks(title="MADLAD-400 Translation") as demo:
        with gr.Row(equal_height=True):
            with gr.Column():
                gr.Markdown("**English**")
                input_text = gr.Textbox(
                    placeholder="Enter English text here",
                    lines=4,
                    show_label=False,
                )
                char_count = gr.Markdown("0 characters")
            with gr.Column():
                target_language = gr.Dropdown(
                    choices=language_names,
                    value="French (fr)",
                    show_label=False,
                    filterable=True,
                )
                output_text = gr.Textbox(
                    placeholder="Translation",
                    lines=4,
                    show_label=False,
                    interactive=False,
                    buttons=["copy"],
                )

        with gr.Row():
            clear_btn = gr.Button("Clear")
            translate_btn = gr.Button("Translate", variant="primary")

        translate_btn.click(
            fn=translate,
            inputs=[input_text, target_language],
            outputs=output_text,
        )
        input_text.submit(
            fn=translate,
            inputs=[input_text, target_language],
            outputs=output_text,
        )
        input_text.input(
            fn=_update_char_count,
            inputs=input_text,
            outputs=char_count,
        )
        clear_btn.click(
            fn=lambda: ("", "", "0 characters"),
            outputs=[input_text, output_text, char_count],
        )

    return demo


demo = _build_demo()


def main() -> None:
    demo.launch(theme=gr.themes.Soft())


if __name__ == "__main__":
    main()