import os
import json
from random import shuffle, seed
from itertools import permutations
import pandas as pd
from datasets import load_dataset
from lmppl import EncoderDecoderLM, LM, OpenAI

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", None)
runs = 3
shots_num = [1, 3]
prompt_dict = {
    "friend/ally of": "entities that are friends or allies",
    "competitor/rival of": "entities that are competitors or rivals",
    "known for": "examples of what entities are known for",
    "influenced by": "what has influenced different entities",
    "similar to": "examples of entities that are similar"
}
data = load_dataset("cardiffnlp/relentless", split="test")
shots_ref = {}
for shots in shots_num:
    all_perms = list(permutations(range(5), shots))
    seed(42)
    shuffle(all_perms)
    shots_ref[shots] = all_perms

full_result = []
for lm, ppl_class, batch, pretty_name in [
            ("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"),
            ("google/flan-t5-xxl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XXL}"),
            ("facebook/opt-13b", LM, 1, "OPT\textsubscript{13B}"),
            ("davinci", OpenAI, None, "GPT-3\textsubscript{davinci}")
        ]:
    scorer = None
    for shots in shots_num:
        for s in range(runs):
            os.makedirs(f"results/lm_lc_{shots}shots_{s}seed/{os.path.basename(lm)}", exist_ok=True)

            for d in data:
                ppl_file = f"results/lm_lc_{shots}shots_{s}seed/{os.path.basename(lm)}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"

                if not os.path.exists(ppl_file):

                    if scorer is None:
                        if ppl_class is OpenAI:
                            scorer = ppl_class(OPENAI_API_KEY, model=lm)
                        else:
                            scorer = ppl_class(lm, device_map='auto', low_cpu_mem_usage=True, offload_folder=f"./offload_folder/{os.path.basename(lm)}")

                    demo = [d['prototypical_examples'][h] for h in list(shots_ref[shots][s])]
                    # proto = ",".join([f'["{a}", "{b}"]' for a, b in demo])
                    content = "\n".join([f'* ["{a}", "{b}"]' for a, b in demo])
                    prompt_input = f"{prompt_dict[d['relation_type']]}:\n{content}"
                    if ppl_class is LM:
                        prompt_input = [f'{prompt_input}\n* ["{x}", "{y}"]' for x, y in d['pairs']]
                        ppl = scorer.get_perplexity(input_texts=prompt_input, batch=batch)
                        output = [{"perplexity": p, "input": i, "output": ""} for p, i in zip(ppl, prompt_input)]
                    elif ppl_class is EncoderDecoderLM:
                        prompt_output = [f'* ["{x}", "{y}"]' for x, y in d['pairs']]
                        ppl = scorer.get_perplexity(input_texts=[prompt_input] * len(prompt_output), output_texts=prompt_output, batch=batch)
                        output = [{"perplexity": p, "input": prompt_input, "output": o} for p, o in zip(ppl, prompt_output)]
                    else:
                        prompt_input = [f'{prompt_input}\n* ["{x}", "{y}"]' for x, y in d['pairs']]
                        ppl = scorer.get_perplexity(input_texts=prompt_input)
                        output = [{"perplexity": p, "input": i, "output": ""} for p, i in zip(ppl, prompt_input)]

                    with open(ppl_file, "w") as f:
                        f.write("\n".join([json.dumps(i) for i in output]))

                with open(ppl_file) as f:
                    ppl = [json.loads(i)['perplexity'] for i in f.read().split("\n") if len(i) > 0]
                true_rank = d['ranks']
                assert len(true_rank) == len(ppl), f"Mismatch in number of examples: {len(true_rank)} vs {len(ppl)}"
                rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)}
                prediction = [rank_map[p] for p in ppl]
                tmp = pd.DataFrame([true_rank, prediction], index=['true', 'pred']).T
                cor = tmp.corr("spearman").values[0, 1]
                full_result.append({"model": pretty_name, "shot": shots, "seed": s, "relation_type": d['relation_type'], "correlation": cor})

df = pd.DataFrame(full_result)
models = df['model'].unique()
df = df.pivot(columns="relation_type", index=["model", "shot",  "seed"], values="correlation")
df = df.T[models].T
df['average'] = df.mean(1)
df.to_csv(f"results/lm_lc_fewshots.csv")
df = (100 * df).round()
print(df)
print(df.to_markdown())
print(df.to_latex(escape=False))