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"""
Tests that the pretrained models produce the correct scores on the STSbenchmark dataset
"""

import csv
import gzip
import os
from functools import partial
from typing import Optional

import pytest

from sentence_transformers import InputExample, SentenceTransformer, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator


def pretrained_model_score(
    model_name, expected_score: float, max_test_samples: int = 100, cache_dir: Optional[str] = None
) -> None:
    model = SentenceTransformer(model_name, cache_folder=cache_dir)
    sts_dataset_path = "datasets/stsbenchmark.tsv.gz"

    if not os.path.exists(sts_dataset_path):
        util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)

    test_samples = []
    with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
        reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
        for row in reader:
            score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
            inp_example = InputExample(texts=[row["sentence1"], row["sentence2"]], label=score)

            if row["split"] == "test":
                test_samples.append(inp_example)
            if max_test_samples != -1 and len(test_samples) >= max_test_samples:
                break

    evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name="sts-test")

    score = model.evaluate(evaluator) * 100
    print(model_name, "{:.2f} vs. exp: {:.2f}".format(score, expected_score))
    assert score > expected_score or abs(score - expected_score) < 0.1


pretrained_model_score = partial(pretrained_model_score, max_test_samples=100)
pretrained_model_score_slow = partial(pretrained_model_score, max_test_samples=-1)


@pytest.mark.slow
def test_bert_base_slow() -> None:
    pretrained_model_score_slow("bert-base-nli-mean-tokens", 77.12)
    pretrained_model_score_slow("bert-base-nli-max-tokens", 77.21)
    pretrained_model_score_slow("bert-base-nli-cls-token", 76.30)
    pretrained_model_score_slow("bert-base-nli-stsb-mean-tokens", 85.14)


@pytest.mark.slow
def test_bert_large_slow() -> None:
    pretrained_model_score_slow("bert-large-nli-mean-tokens", 79.19)
    pretrained_model_score_slow("bert-large-nli-max-tokens", 78.41)
    pretrained_model_score_slow("bert-large-nli-cls-token", 78.29)
    pretrained_model_score_slow("bert-large-nli-stsb-mean-tokens", 85.29)


@pytest.mark.slow
def test_roberta_slow() -> None:
    pretrained_model_score_slow("roberta-base-nli-mean-tokens", 77.49)
    pretrained_model_score_slow("roberta-large-nli-mean-tokens", 78.69)
    pretrained_model_score_slow("roberta-base-nli-stsb-mean-tokens", 85.30)
    pretrained_model_score_slow("roberta-large-nli-stsb-mean-tokens", 86.39)


@pytest.mark.slow
def test_distilbert_slow() -> None:
    pretrained_model_score_slow("distilbert-base-nli-mean-tokens", 78.69)
    pretrained_model_score_slow("distilbert-base-nli-stsb-mean-tokens", 85.16)
    pretrained_model_score_slow("paraphrase-distilroberta-base-v1", 81.81)


@pytest.mark.slow
def test_multiling_slow() -> None:
    pretrained_model_score_slow("distiluse-base-multilingual-cased", 80.75)
    pretrained_model_score_slow("paraphrase-xlm-r-multilingual-v1", 83.50)
    pretrained_model_score_slow("paraphrase-multilingual-MiniLM-L12-v2", 84.42)


@pytest.mark.slow
def test_mpnet_slow() -> None:
    pretrained_model_score_slow("paraphrase-mpnet-base-v2", 86.99)


@pytest.mark.slow
def test_other_models_slow() -> None:
    pretrained_model_score_slow("average_word_embeddings_komninos", 61.56)


@pytest.mark.slow
def test_msmarco_slow() -> None:
    pretrained_model_score_slow("msmarco-roberta-base-ance-firstp", 77.0)
    pretrained_model_score_slow("msmarco-distilbert-base-v3", 78.85)


@pytest.mark.slow
def test_sentence_t5_slow() -> None:
    pretrained_model_score_slow("sentence-t5-base", 85.52)


def test_bert_base(cache_dir) -> None:
    pretrained_model_score("bert-base-nli-mean-tokens", 86.53, cache_dir=cache_dir)
    pretrained_model_score("bert-base-nli-max-tokens", 87.00, cache_dir=cache_dir)
    pretrained_model_score("bert-base-nli-cls-token", 85.93, cache_dir=cache_dir)
    pretrained_model_score("bert-base-nli-stsb-mean-tokens", 89.26, cache_dir=cache_dir)


def test_bert_large(cache_dir) -> None:
    pretrained_model_score("bert-large-nli-mean-tokens", 90.06, cache_dir=cache_dir)
    pretrained_model_score("bert-large-nli-max-tokens", 90.15, cache_dir=cache_dir)
    pretrained_model_score("bert-large-nli-cls-token", 89.51, cache_dir=cache_dir)
    pretrained_model_score("bert-large-nli-stsb-mean-tokens", 92.27, cache_dir=cache_dir)


def test_roberta(cache_dir) -> None:
    pretrained_model_score("roberta-base-nli-mean-tokens", 87.91, cache_dir=cache_dir)
    pretrained_model_score("roberta-large-nli-mean-tokens", 89.41, cache_dir=cache_dir)
    pretrained_model_score("roberta-base-nli-stsb-mean-tokens", 93.39, cache_dir=cache_dir)
    pretrained_model_score("roberta-large-nli-stsb-mean-tokens", 91.26, cache_dir=cache_dir)


def test_distilbert(cache_dir) -> None:
    pretrained_model_score("distilbert-base-nli-mean-tokens", 88.83, cache_dir=cache_dir)
    pretrained_model_score("distilbert-base-nli-stsb-mean-tokens", 91.01, cache_dir=cache_dir)
    pretrained_model_score("paraphrase-distilroberta-base-v1", 90.89, cache_dir=cache_dir)


def test_multiling(cache_dir) -> None:
    pretrained_model_score("distiluse-base-multilingual-cased", 88.79, cache_dir=cache_dir)
    pretrained_model_score("paraphrase-xlm-r-multilingual-v1", 92.76, cache_dir=cache_dir)
    pretrained_model_score("paraphrase-multilingual-MiniLM-L12-v2", 92.64, cache_dir=cache_dir)


def test_mpnet(cache_dir) -> None:
    pretrained_model_score("paraphrase-mpnet-base-v2", 92.83, cache_dir=cache_dir)


def test_other_models(cache_dir) -> None:
    pretrained_model_score("average_word_embeddings_komninos", 68.97, cache_dir=cache_dir)


def test_msmarco(cache_dir) -> None:
    pretrained_model_score("msmarco-roberta-base-ance-firstp", 83.61, cache_dir=cache_dir)
    pretrained_model_score("msmarco-distilbert-base-v3", 87.96, cache_dir=cache_dir)


def test_sentence_t5(cache_dir) -> None:
    pretrained_model_score("sentence-t5-base", 92.75, cache_dir=cache_dir)