from transformers import AutoTokenizer, AutoModelForCausalLM
from string import Template
import torch

FINETUNE_INST = "You are evaluating errors in a model-generated output for a(an) ${task} task."
FINETUNE_INPUT = """\
Task instruction: ${generation_instruction}
Source: ${input_context}
Model-generated Output: ${hypothesis_output}

Based on the given task instruction and source, identify errors in this model-generated output.
For each error you give in the response, please also elaborate the following information:
- error location (the words that are wrong in the output)
- error aspect it belongs to.
- explanation why it's an error, and the correction suggestions.
- severity of the error ("Major" or "Minor"). 
- reduction of score (between 0.5 and 5 given the severity of the error)

Your evaluation output:
"""

TIGERScore_model_map = {
    "7b": "TIGER-Lab/TIGERScore-7B-V1.0",
    "13b": "TIGER-Lab/TIGERScore-13B-V1.0",
}
tigerscore_model = None
tigerscore_tokenizer = None

tasks = [
    "translation",
    "summarization",
    "data2text",
    "mathQA",
    "long-form QA",
    "instruction-following",
]

def load_tigerscore(model_size):
    assert model_size in TIGERScore_model_map
    model_name = TIGERScore_model_map[model_size]
    global tigerscore_model, tigerscore_tokenizer
    tigerscore_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.bfloat16,
        device_map="auto"
    )
    tigerscore_tokenizer = AutoTokenizer.from_pretrained(
        model_name,
        use_fast=True
    )

def generate(task, input_context, generation_instruction, hypo_output, **generate_kwargs):
    inst_part = Template(FINETUNE_INST)
    inst_part = inst_part.substitute(task=task)
    input_part = Template(FINETUNE_INPUT)
    input_part = input_part.substitute(
        generation_instruction=generation_instruction,
        input_context=input_context,
        hypothesis_output=hypo_output
    )
    prompt = (inst_part + "\n" + input_part).strip("\n ") + "\n"
    encodings = tigerscore_tokenizer(prompt, return_tensors="pt")
    input_ids = encodings["input_ids"].to(tigerscore_model.device)
    attention_mask = encodings["attention_mask"].to(tigerscore_model.device)
    gen_params = {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "max_new_tokens": 512,
        "do_sample": True,
        "top_k": 1,
        "num_return_sequences": 1,
    }
    gen_params.update(generate_kwargs)
    output = tigerscore_model.generate(**gen_params)
    output = tigerscore_tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True)
    return output

if __name__ == "__main__":
    task = "translation"
    input_context = "Der künftige EM-Cheforganisator Philipp Lahm soll laut Grindel im DFB-Präsidium mitarbeiten."
    generation_instruction = "Translate the following text from German to English."
    hypo_output = "According to Grindel, the future head of the European Championships, Philipp Lahm, is to participate in the DFB Presidency."
    output = generate(task, input_context, generation_instruction, hypo_output)
    print(output)