# This file is taken from the original DPST implementation by Meisenbacher et al.
# Paper: https://aclanthology.org/2025.emnlp-main.455/
# Source: https://github.com/sjmeis/DPST

import os
import weaviate
from weaviate.classes.query import MetadataQuery, Filter

from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, pipeline
from sentence_transformers import util

os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
from openie import StanfordOpenIE
from collections import defaultdict
from tqdm.auto import tqdm
import multiprocessing as mp
import numpy as np
from functools import partial
import nltk
import json
import importlib_resources as impresources

import spacy
spacy.prefer_gpu()

import torch
from torch.nn import CrossEntropyLoss

from datasketch import MinHash, MinHashLSH
from nltk import ngrams

# globals
properties = {
            "openie.affinity_probability_cap": 2 / 3,
            "openie.triple.strict": False,
        }
IEclient = StanfordOpenIE(properties=properties)

class DPST():
    def __init__(self, mode, hf_token=None, model_checkpoint="meta-llama/Llama-3.2-3B-Instruct"):
        print("Initializing...", flush=True)

        mode_map = {
            "50k": "fiftyk",
            "100k": "hundredk",
            "200k": "twohundredk"
        }

        if mode not in mode_map:
            print("Error: [MODE] must be one of [50k, 100k, 200k].")
            return
        else:
            self.mode = mode_map[mode]

        # db connection
        self.client = weaviate.connect_to_local()
        self.collection = self.client.collections.get("Triples")

        if torch.cuda.is_available() == True:
            self.device = "cuda"
        else:
            self.device = "cpu"

        with open(impresources.files("data") / "clusters" / "{}.json".format(mode), 'r') as f:
            self.centroids = torch.tensor(json.load(f)).to(self.device)

        with open(impresources.files("data") / "clusters" / "{}_counts.json".format(mode), 'r') as f:
            self.cluster_counts = json.load(f)

        self.model_checkpoint = model_checkpoint

        #self.pool = mp.Pool(mp.cpu_count(), initargs=(nlp,))
        self.model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True).to(self.device)

        self.gen_model = AutoModelForCausalLM.from_pretrained(self.model_checkpoint, token=hf_token, device_map=self.device)
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_checkpoint, token=hf_token)
        
        self.pipe = pipeline(
            "text-generation",
            model=self.gen_model,
            tokenizer=self.tokenizer,
            torch_dtype=torch.bfloat16,
            device_map="auto",
        )

        self.ppl_model = AutoModelForCausalLM.from_pretrained("gpt2").to(self.device)
        self.ppl_tokenizer = AutoTokenizer.from_pretrained("gpt2")

        print("Finished.", flush=True)   

    def cleanup(self):
        self.client.close()
        #IEclient.client.stop()

    def exponential(self, candidates, epsilon, sensitivity=1):
        probabilities = [np.exp(epsilon * x[1] / (2 * sensitivity)) for x in candidates]
        probabilities = probabilities / np.linalg.norm(probabilities, ord=1)
        return np.random.choice([x[0] for x in candidates], 1, p=probabilities)[0]

    def query_db(self, vector, cluster):
        # Convert numpy array to list if needed
        vector_list = vector.tolist() if isinstance(vector, np.ndarray) else vector
        response = self.collection.query.near_vector(near_vector=vector_list, limit=self.cluster_counts[cluster], filters=Filter.by_property(self.mode).equal(cluster), return_metadata=MetadataQuery(distance=True))
        candidates = [(x.properties["text"], max(1 - x.metadata.distance, 0)) for x in response.objects]
        return candidates

    def get_prompt(self, triples, messages=True):
        PROMPT = [
            {"role": "system", "content": "Generate a concise text for the given set of triples. Ensure that the generated output only includes the provided information from the triples, but feel free to fill in the gaps where sensible. If necessary, ignore triples that do not fit into the larger context. It is very important that the output is grammatically correct, natural, and logical. Provide a text that captures the semantic meaning of the triples, without being too verbose or lengthy. Do not provide any further explanation, only provide the output text."},
            {"role": "user", "content": "Input triples: [{’object’: ’Mike_Mularkey’,’property’: ’coach’,’subject’: ’Tennessee_Titans’}]"},
            {"role": "assistant", "content": "Output text: Mike Mularkey is the coach of the Tennessee Titans."},
            {"role": "user", "content": "Input triples: [{’object’: ’Albert_E._Austin’, ’property’: ’successor’, ’subject’: ’Alfred_N._Phillips’}, {’object’: ’Connecticut’, ’property’: ’birthPlace’, ’subject’: ’Alfred_N._Phillips’}, {’object’: ’United_States_House_of_Representatives’, ’property’: ’office’, ’subject’: ’Alfred_N._Phillips’}]"},
            {"role": "assistant", "content": "Output text: Albert E. Austin succeeded Alfred N. Phillips who was born in Connecticut and worked at the United States House of Representatives."},
            {"role": "user", "content": "Input triples: [{’object’: ’College_of_William_&_Mary’, ’property’: ’owner’, ’subject’: ’Alan_B._Miller_Hall’}, {’object’: ’2009-06-01’, ’property’: ’completionDate’, ’subject’: ’Alan_B._Miller_Hall’}, {’object’: ’101 Ukrop Way’, ’property’: ’address’, ’subject’: ’Alan_B._Miller_Hall’}, {’object’: ’Williamsburg,_Virginia’, ’property’: ’location’, ’subject’: ’Alan_B._Miller_Hall’}, {’object’: ’Robert_A._M._Stern’, ’property’: ’architect’, ’subject’: ’Alan_B._Miller_Hall’}]"},
            {"role": "assistant", "content": "Output text: The Alan B Miller Hall’s location is 101 Ukrop Way, Williamsburg, Virginia. It was designed by Robert A.M. Stern and was completed on 1 June 2009. Its owner is the College of William and Mary."},
            {"role": "user", "content": "Input Triples: {}".format(str(triples))}
        ]
        return PROMPT
    
    def compute_ppl(self, predictions, batch_size: int = 16, add_start_token: bool = True, max_length=32):
        # if batch_size > 1 (which generally leads to padding being required), and
        # if there is not an already assigned pad_token, assign an existing
        # special token to also be the padding token
        if self.ppl_tokenizer.pad_token is None and batch_size > 1:
            existing_special_tokens = list(self.ppl_tokenizer.special_tokens_map_extended.values())
            # check that the model already has at least one special token defined
            assert (
                len(existing_special_tokens) > 0
            ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
            # assign one of the special tokens to also be the pad token
            self.ppl_tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]})

        if add_start_token and max_length:
            # leave room for <BOS> token to be added:
            assert (
                self.ppl_tokenizer.bos_token is not None
            ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
            max_tokenized_len = max_length - 1
        else:
            max_tokenized_len = max_length

        encodings = self.ppl_tokenizer(
            predictions,
            add_special_tokens=False,
            padding=True,
            truncation=True if max_tokenized_len else False,
            max_length=max_tokenized_len,
            return_tensors="pt",
            return_attention_mask=True,
        ).to(self.device)

        encoded_texts = encodings["input_ids"]
        attn_masks = encodings["attention_mask"]

        # check that each input is long enough:
        if add_start_token:
            assert torch.all(torch.ge(attn_masks.sum(1), 1)), "Each input text must be at least one token long."
        else:
            assert torch.all(
                torch.ge(attn_masks.sum(1), 2)
            ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

        ppls = []
        loss_fct = CrossEntropyLoss(reduction="none")

        for start_index in range(0, len(encoded_texts), batch_size):
            end_index = min(start_index + batch_size, len(encoded_texts))
            encoded_batch = encoded_texts[start_index:end_index]
            attn_mask = attn_masks[start_index:end_index]

            if add_start_token:
                bos_tokens_tensor = torch.tensor([[self.ppl_tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(self.device)
                encoded_batch = torch.cat([bos_tokens_tensor, encoded_batch], dim=1)
                attn_mask = torch.cat(
                    [torch.ones(bos_tokens_tensor.size(), dtype=torch.int64).to(self.device), attn_mask], dim=1
                )

            labels = encoded_batch

            with torch.no_grad():
                out_logits = self.ppl_model(encoded_batch, attention_mask=attn_mask).logits

            shift_logits = out_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            shift_attention_mask_batch = attn_mask[..., 1:].contiguous()

            perplexity_batch = torch.exp(
                (loss_fct(shift_logits.transpose(1, 2), shift_labels) * shift_attention_mask_batch).sum(1)
                / shift_attention_mask_batch.sum(1)
            )

            ppls += perplexity_batch.tolist()

        return {"perplexities": ppls, "mean_perplexity": np.mean(ppls)}

    def get_triples_ie(self, text):
        res = [x for x in IEclient.annotate(text)]
        temp = [tuple(x.values()) for x in res]

        current = defaultdict(list)
        for t in temp:
            current[(t[0], t[1])].append(t)

        final = []
        for t in temp:
            s = "{} | {} | {}".format(t[0], t[1], t[2])
            if s not in final:
                final.append(s.replace("_", " "))
        
        lsh = MinHashLSH(threshold=0.4, num_perm=128)
        minhashes = {}
        for i, f in enumerate(final):
            minhash = MinHash(num_perm=128)
            for d in ngrams(f, 3):
                minhash.update("".join(d).encode('utf-8'))
            lsh.insert(i, minhash)
            minhashes[i] = minhash

        matches = {}
        for x, y in zip(final, minhashes):
            matches[x] = [final[z] for z in lsh.query(minhashes[y]) if z != y] 

        clusters = []
        covered = []
        for m in sorted(matches, key=lambda x: len(matches[x]), reverse=True):
            if m not in covered and len(matches[m]) > 0:
                clusters.append(matches[m])
                covered.extend(matches[m])

        clean = [x.replace(" | ", " ") for x in covered]
        if len(clean) == 0:
            return []
        ppls = dict(zip(covered, self.compute_ppl(predictions=clean, batch_size=64)["perplexities"]))

        best = []
        for c in clusters:
            scores = [ppls[x] for x in c]
            imin = np.argmin(scores)
            best.append(c[imin])

        ordered = []
        for f in final:
            if f in best:
                ordered.append(f)
        return ordered

    def privatize(self, texts, epsilon=10, DP=True, verbose=False):
        results = []
        for i, t in tqdm(enumerate(texts), total=len(texts)):
            triples = self.get_triples_ie(t)
            if len(triples) == 0:
                results.append(t)
                continue

            if verbose:
                print(f"\n  Extracted Triples : {triples}")

            if DP == True:
                eps = epsilon / len(triples)
                query_vectors = self.model.encode(triples, task="text-matching", truncate_dim=32, max_length=64)

                res = util.semantic_search(query_embeddings=torch.tensor(query_vectors).to(self.device), corpus_embeddings=self.centroids, top_k=1)
                clusters = [r[0]["corpus_id"] for r in res]

                candidates = []
                for q, c in zip(query_vectors, clusters):
                    near = self.query_db(q, c)
                    if len(near) > 0:
                        candidates.append(near)
                private_triples = [self.exponential(c, eps) for c in candidates]

                if verbose:
                    print(f"  Private Triples   : {private_triples}")

                final = []
                for p in private_triples:
                    m = p.split(" | ")
                    final.append({"object": m[0], "property": m[1], "subject": m[2]})
                prompt = self.get_prompt(final)
            else:
                final = []
                for x in triples:
                    m = x.split(" | ")
                    final.append({"object": m[0], "property": m[1], "subject": m[2]})
                prompt = self.get_prompt(final)

            outputs = self.pipe(
                prompt,
                pad_token_id=self.tokenizer.eos_token_id,
                max_new_tokens=int(len(self.tokenizer.encode(texts[i], return_tensors="pt")[0]))
            )
            generated = outputs[0]["generated_text"][-1]["content"]

            generated = generated.split("Output text: ")[-1].strip().replace("\n", "")
            generated = generated.split("USER:")[0].strip().replace("\n", "")
            generated = generated.split("\t")[0].split("ASSISTANT")[0].split("USER")[0].split("###")[0].split("Note:")[0].split("Explanation:")[0].split("```")[0].split("EXPECTED_OUTPUT")[0]
            results.append(generated.strip())
        return results