import os
import streamlit as st
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
)

device = torch.cuda.device_count() - 1

TRANSLATION_NL_TO_EN = "translation_en_to_nl"


@st.cache(suppress_st_warning=True, allow_output_mutation=True)
def load_model(model_name, task):
    os.environ["TOKENIZERS_PARALLELISM"] = "false"
    try:
        if not os.path.exists(".streamlit/secrets.toml"):
            raise FileNotFoundError
        access_token = st.secrets.get("netherator")
    except FileNotFoundError:
        access_token = os.environ.get("HF_ACCESS_TOKEN", None)
    tokenizer = AutoTokenizer.from_pretrained(
        model_name, from_flax=True, use_auth_token=access_token
    )
    if tokenizer.pad_token is None:
        print("Adding pad_token to the tokenizer")
        tokenizer.pad_token = tokenizer.eos_token
    auto_model_class = (
        AutoModelForSeq2SeqLM if "translation" in task else AutoModelForCausalLM
    )
    model = auto_model_class.from_pretrained(
        model_name, from_flax=True, use_auth_token=access_token
    )
    if device != -1:
        model.to(f"cuda:{device}")
    return tokenizer, model


class Generator:
    def __init__(self, model_name, task, desc):
        self.model_name = model_name
        self.task = task
        self.desc = desc
        self.tokenizer = None
        self.model = None
        self.prefix = ""
        self.load()

    def load(self):
        if not self.model:
            print(f"Loading model {self.model_name}")
            self.tokenizer, self.model = load_model(self.model_name, self.task)

            try:
                if self.task in self.model.config.task_specific_params:
                    task_specific_params = self.model.config.task_specific_params[
                        self.task
                    ]
                    if "prefix" in task_specific_params:
                        self.prefix = task_specific_params["prefix"]
            except TypeError:
                pass

    def generate(self, text: str, **generate_kwargs) -> str:
        #
        # import pydevd_pycharm
        # pydevd_pycharm.settrace('10.1.0.144', port=12345, stdoutToServer=True, stderrToServer=True)
        #
        batch_encoded = self.tokenizer(
            self.prefix + text,
            max_length=generate_kwargs["max_length"],
            padding=False,
            truncation=False,
            return_tensors="pt",
        )
        if device != -1:
            batch_encoded.to(f"cuda:{device}")
        logits = self.model.generate(
            batch_encoded["input_ids"],
            attention_mask=batch_encoded["attention_mask"],
            **generate_kwargs,
        )
        decoded_preds = self.tokenizer.batch_decode(
            logits.cpu().numpy(), skip_special_tokens=False
        )
        decoded_preds = [
            pred.replace("<pad> ", "").replace("<pad>", "").replace("</s>", "")
            for pred in decoded_preds
        ]
        return decoded_preds

        # return self.pipeline(text, **generate_kwargs)

    def __str__(self):
        return self.desc


class GeneratorFactory:
    def __init__(self, generator_list):
        self.generators = []
        for g in generator_list:
            with st.spinner(text=f"Loading the model {g['desc']} ..."):
                self.add_generator(**g)

    def add_generator(self, model_name, task, desc):
        # If the generator is not yet present, add it
        if not self.get_generator(model_name=model_name, task=task, desc=desc):
            g = Generator(model_name, task, desc)
            g.load()
            self.generators.append(g)

    def get_generator(self, **kwargs):
        for g in self.generators:
            if all([g.__dict__.get(k) == v for k, v in kwargs.items()]):
                return g
        return None

    def __iter__(self):
        return iter(self.generators)

    def gpt_descs(self):
        return [g.desc for g in self.generators if g.task == TRANSLATION_NL_TO_EN]