from itertools import chain
from typing import List, Tuple

import pandas as pd
import torch
import torch.nn.functional as F
from tqdm import tqdm

from autorag.nodes.passagereranker.base import passage_reranker_node
from autorag.nodes.passagereranker.tart.modeling_enc_t5 import (
	EncT5ForSequenceClassification,
)
from autorag.nodes.passagereranker.tart.tokenization_enc_t5 import EncT5Tokenizer
from autorag.utils.util import make_batch, sort_by_scores, flatten_apply, select_top_k


@passage_reranker_node
def tart(
	queries: List[str],
	contents_list: List[List[str]],
	scores_list: List[List[float]],
	ids_list: List[List[str]],
	top_k: int,
	instruction: str = "Find passage to answer given question",
	batch: int = 64,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
	"""
	Rerank a list of contents based on their relevance to a query using Tart.
	TART is a reranker based on TART (https://github.com/facebookresearch/tart).
	You can rerank the passages with the instruction using TARTReranker.
	The default model is facebook/tart-full-flan-t5-xl.

	:param queries: The list of queries to use for reranking
	:param contents_list: The list of lists of contents to rerank
	:param scores_list: The list of lists of scores retrieved from the initial ranking
	:param ids_list: The list of lists of ids retrieved from the initial ranking
	:param top_k: The number of passages to be retrieved
	:param instruction: The instruction for reranking.
	    Note: default instruction is "Find passage to answer given question"
	        The default instruction from the TART paper is being used.
	        If you want to use a different instruction, you can change the instruction through this parameter
	:param batch: The number of queries to be processed in a batch
	:return: tuple of lists containing the reranked contents, ids, and scores
	"""
	model_name = "facebook/tart-full-flan-t5-xl"
	model = EncT5ForSequenceClassification.from_pretrained(model_name)
	tokenizer = EncT5Tokenizer.from_pretrained(model_name)
	device = "cuda" if torch.cuda.is_available() else "cpu"
	model = model.to(device)

	nested_list = [
		[["{} [SEP] {}".format(instruction, query)] for _ in contents]
		for query, contents in zip(queries, contents_list)
	]

	rerank_scores = flatten_apply(
		tart_run_model,
		nested_list,
		model=model,
		batch_size=batch,
		tokenizer=tokenizer,
		device=device,
		contents_list=contents_list,
	)

	df = pd.DataFrame(
		{
			"contents": contents_list,
			"ids": ids_list,
			"scores": rerank_scores,
		}
	)
	df[["contents", "ids", "scores"]] = df.apply(
		sort_by_scores, axis=1, result_type="expand"
	)
	results = select_top_k(df, ["contents", "ids", "scores"], top_k)

	del model
	del tokenizer
	if torch.cuda.is_available():
		torch.cuda.empty_cache()

	return (
		results["contents"].tolist(),
		results["ids"].tolist(),
		results["scores"].tolist(),
	)


def tart_run_model(
	input_texts, contents_list, model, batch_size: int, tokenizer, device
):
	flattened_texts = list(chain.from_iterable(input_texts))
	flattened_contents = list(chain.from_iterable(contents_list))
	batch_input_texts = make_batch(flattened_texts, batch_size)
	batch_contents_list = make_batch(flattened_contents, batch_size)
	results = []
	for batch_texts, batch_contents in tqdm(
		zip(batch_input_texts, batch_contents_list)
	):
		feature = tokenizer(
			batch_texts,
			batch_contents,
			padding=True,
			truncation=True,
			return_tensors="pt",
		).to(device)
		with torch.no_grad():
			pred_scores = model(**feature).logits
			normalized_scores = [
				float(score[1]) for score in F.softmax(pred_scores, dim=1)
			]
		results.extend(normalized_scores)
	return results
