import csv
import os

import pytest

output_path = 'regression_result'
model = 'internlm-chat-7b-hf'
dataset = 'siqa'


@pytest.fixture()
def result_scores():
    file = find_csv_files(output_path)
    if file is None:
        return None
    return read_csv_file(file)


@pytest.mark.usefixtures('result_scores')
class TestChatScore:
    """Test cases for chat model."""

    def test_model_dataset_score(self, result_scores):
        result_score = result_scores.get(model).get(dataset)
        assert_score(result_score, 73.59)


def assert_score(score, baseline):
    if score is None or score == '-':
        assert False, 'value is none'
    if float(score) < (baseline * 1.03) and float(score) > (baseline * 0.97):
        print(score + ' between ' + str(baseline * 0.97) + ' and ' +
              str(baseline * 1.03))
        assert True
    else:
        assert False, score + ' not between ' + str(
            baseline * 0.97) + ' and ' + str(baseline * 1.03)


def find_csv_files(directory):
    csv_files = []
    for root, dirs, files in os.walk(directory):
        for file in files:
            if file.endswith('.csv'):
                csv_files.append(os.path.join(root, file))
    if len(csv_files) > 1:
        raise 'have more than 1 result file, please check the result manually'
    if len(csv_files) == 0:
        return None
    return csv_files[0]


def read_csv_file(file_path):
    with open(file_path, 'r') as csvfile:
        reader = csv.DictReader(csvfile)
        filtered_data = []

        for row in reader:
            filtered_row = {
                k: v
                for k, v in row.items()
                if k not in ['version', 'metric', 'mode']
            }
            filtered_data.append(filtered_row)

    result = {}
    for data in filtered_data:
        dataset = data.get('dataset')
        for key in data.keys():
            if key == 'dataset':
                continue
            else:
                if key in result.keys():
                    result.get(key)[dataset] = data.get(key)
                else:
                    result[key] = {dataset: data.get(key)}
    return result
