import random
import os

import streamlit as st
import torch
from transformers import pipeline, set_seed
from transformers import AutoTokenizer, AutoModelForCausalLM


HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN", None)
DEVICE = os.environ.get("DEVICE", "cpu")  # cuda:0
DTYPE = torch.float32 if DEVICE == "cpu" else torch.float16
MODEL_NAME = os.environ.get("MODEL_NAME", "bertin-project/bertin-gpt-j-6B")
MAX_LENGTH = int(os.environ.get("MAX_LENGTH", 1024))
HEADER_INFO = """
# BERTIN-GPT-J-6B
Spanish BERTIN GPT-J-6B Model.
""".strip()
SIDEBAR_INFO = """
# Configuration
""".strip()
PROMPT_BOX = "Introduzca su texto..."
EXAMPLES = [
    "¿Cuál es la capital de Francia? Respuesta:",
]


def style():
    st.markdown("""
    <link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300&display=swap%22%20rel=%22stylesheet%22" rel="stylesheet">
    <style>
    .ltr,
    textarea {
        font-family: Roboto !important;
        text-align: left;
        direction: ltr !important;
    }
    .ltr-box {
        border-bottom: 1px solid #ddd;
        padding-bottom: 20px;
    }
    .rtl {
        text-align: left;
        direction: ltr !important;
    }
    span.result-text {
        padding: 3px 3px;
        line-height: 32px;
    }
    span.generated-text {
        background-color: rgb(118 200 147 / 13%);
    }
    </style>""", unsafe_allow_html=True)


class Normalizer:
    def remove_repetitions(self, text):
        """Remove repetitions"""
        first_ocurrences = []
        for sentence in text.split("."):
            if sentence not in first_ocurrences:
                first_ocurrences.append(sentence)
        return '.'.join(first_ocurrences)

    def trim_last_sentence(self, text):
        """Trim last sentence if incomplete"""
        return text[:text.rfind(".") + 1]

    def clean_txt(self, text):
        return self.trim_last_sentence(self.remove_repetitions(text))


class TextGeneration:
    def __init__(self):
        self.tokenizer = None
        self.generator = None
        self.task = "text-generation"
        self.model_name_or_path = MODEL_NAME
        set_seed(42)

    def load(self):
        self.tokenizer = AutoTokenizer.from_pretrained(
            self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
        )
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_name_or_path, use_auth_token=HF_AUTH_TOKEN if HF_AUTH_TOKEN else None,
            pad_token_id=self.tokenizer.eos_token_id, eos_token_id=self.tokenizer.eos_token_id,
            torch_dtype=DTYPE, low_cpu_mem_usage=False if DEVICE == "cpu" else True
        ).to(device=DEVICE, non_blocking=True)
        _ = self.model.eval()
        device_number = -1 if DEVICE == "cpu" else int(DEVICE.split(":")[-1])
        self.generator = pipeline(self.task, model=self.model, tokenizer=self.tokenizer, device=device_number)
        # with torch.no_grad():
        # tokens = tokenizer.encode(prompt, return_tensors='pt').to(device=device, non_blocking=True)
        # gen_tokens = self.model.generate(tokens, do_sample=True, temperature=0.8, max_length=128)
        # generated = tokenizer.batch_decode(gen_tokens)[0]

        # return generated


    def generate(self, prompt, generation_kwargs):
        max_length = len(self.tokenizer(prompt)["input_ids"]) + generation_kwargs["max_length"]
        generation_kwargs["max_length"] = min(max_length, self.model.config.n_positions)
        # generation_kwargs["num_return_sequences"] = 1
        # generation_kwargs["return_full_text"] = False
        return self.generator(
            prompt,
            **generation_kwargs,
        )[0]["generated_text"]


@st.cache(allow_output_mutation=True)
def load_text_generator():
    generator = TextGeneration()
    generator.load()
    return generator


def main():
    st.set_page_config(
        page_title="BERTIN-GPT-J-6B",
        page_icon="🇪🇸",
        layout="wide",
        initial_sidebar_state="expanded"
    )
    style()
    with st.spinner('Cargando el modelo. Por favor, espere...'):
        generator = load_text_generator()

    st.sidebar.markdown(SIDEBAR_INFO)

    max_length = st.sidebar.slider(
        label='Longitud máxima',
        help="Número máximo (aproximado) de palabras a generar.",
        min_value=1,
        max_value=MAX_LENGTH,
        value=50,
        step=1
    )
    top_k = st.sidebar.slider(
        label='Top-k',
        help="Número de palabras con alta probabilidad a mantener para el filtrado `top-k`",
        min_value=40,
        max_value=80,
        value=50,
        step=1
    )
    top_p = st.sidebar.slider(
        label='Top-p',
        help="Solo las palabras más probables con probabilidades que sumen `top_p` o más se mantienen para la generación.",
        min_value=0.0,
        max_value=1.0,
        value=0.95,
        step=0.01
    )
    temperature = st.sidebar.slider(
        label='Temperatura',
        help="Valor utilizado para modular las probabilidades de las siguientes palabras generadas.",
        min_value=0.1,
        max_value=10.0,
        value=0.8,
        step=0.05
    )
    do_sample = st.sidebar.selectbox(
        label='¿Muestrear?',
        options=(True, False),
        help="Si no se muestrea se usará una decodificación voraz (_greedy_).",
    )
    do_clean = st.sidebar.selectbox(
        label='¿Limpiar texto?',
        options=(True, False),
        help="Si eliminar o no las palabras repetidas y recortar las últimas frases sin terminar.",
    )
    generation_kwargs = {
        "max_length": max_length,
        "top_k": top_k,
        "top_p": top_p,
        "temperature": temperature,
        "do_sample": do_sample,
        "do_clean": do_clean,
    }
    st.markdown(HEADER_INFO)
    prompts = EXAMPLES + ["Personalizado"]
    prompt = st.selectbox('Ejemplos', prompts, index=len(prompts) - 1)

    if prompt == "Personalizado":
        prompt_box = PROMPT_BOX
    else:
        prompt_box = prompt

    text = st.text_area("Texto", prompt_box)
    generation_kwargs_ph = st.empty()
    cleaner = Normalizer()
    if st.button("¡Generar!"):
        with st.spinner(text="Generando..."):
            generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()]))
            if text:
                generated_text = generator.generate(text, generation_kwargs)
                if do_clean:
                    generated_text = cleaner.clean_txt(generated_text)
                if generated_text.strip().startswith(text):
                    generated_text = generated_text.replace(text, "", 1).strip()
                st.markdown(
                    f'<p class="ltr ltr-box">'
                    f'<span class="result-text">{text} <span>'
                    f'<span class="result-text generated-text">{generated_text}</span>'
                    f'</p>',
                    unsafe_allow_html=True
                )

if __name__ == '__main__':
    main()