import logging
import os
import pathlib
import random

from beir import LoggingHandler, util
from beir.datasets.data_loader import GenericDataLoader
from beir.reranking import Rerank
from beir.reranking.models import CrossEncoder
from beir.retrieval.evaluation import EvaluateRetrieval
from beir.retrieval.search.lexical import BM25Search as BM25

#### Just some code to print debug information to stdout
logging.basicConfig(
    format="%(asctime)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=logging.INFO,
    handlers=[LoggingHandler()],
)
#### /print debug information to stdout

#### Download trec-covid.zip dataset and unzip the dataset
dataset = "trec-covid"
url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{dataset}.zip"
out_dir = os.path.join(pathlib.Path(__file__).parent.absolute(), "datasets")
data_path = util.download_and_unzip(url, out_dir)

#### Provide the data path where trec-covid has been downloaded and unzipped to the data loader
# data folder would contain these files:
# (1) trec-covid/corpus.jsonl  (format: jsonlines)
# (2) trec-covid/queries.jsonl (format: jsonlines)
# (3) trec-covid/qrels/test.tsv (format: tsv ("\t"))

corpus, queries, qrels = GenericDataLoader(data_path).load(split="test")

#########################################
#### (1) RETRIEVE Top-100 docs using BM25
#########################################

#### Provide parameters for Elasticsearch
hostname = "your-hostname"  # localhost
index_name = "your-index-name"  # trec-covid
initialize = True  # False

model = BM25(index_name=index_name, hostname=hostname, initialize=initialize)
retriever = EvaluateRetrieval(model)

#### Retrieve dense results (format of results is identical to qrels)
results = retriever.retrieve(corpus, queries)

################################################
#### (2) RERANK Top-100 docs using Cross-Encoder
################################################

#### Reranking using Cross-Encoder models #####
#### https://www.sbert.net/docs/pretrained_cross-encoders.html
cross_encoder_model = CrossEncoder("cross-encoder/ms-marco-electra-base")

#### Or use MiniLM, TinyBERT etc. CE models (https://www.sbert.net/docs/pretrained-models/ce-msmarco.html)
# cross_encoder_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# cross_encoder_model = CrossEncoder('cross-encoder/ms-marco-TinyBERT-L-6')

reranker = Rerank(cross_encoder_model, batch_size=128)

# Rerank top-100 results using the reranker provided
rerank_results = reranker.rerank(corpus, queries, results, top_k=100)

#### Evaluate your retrieval using NDCG@k, MAP@K ...
ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(qrels, rerank_results, retriever.k_values)

#### Print top-k documents retrieved ####
top_k = 10

query_id, ranking_scores = random.choice(list(rerank_results.items()))
scores_sorted = sorted(ranking_scores.items(), key=lambda item: item[1], reverse=True)
logging.info(f"Query : {queries[query_id]}\n")

for rank in range(top_k):
    doc_id = scores_sorted[rank][0]
    # Format: Rank x: ID [Title] Body
    logging.info(f"Rank {rank + 1}: {doc_id} [{corpus[doc_id].get('title')}] - {corpus[doc_id].get('text')}\n")
