import numpy as np
import pandas as pd
import pytest

from cleanlab.datalab.internal.issue_manager.outlier import OutlierIssueManager
from cleanlab.datalab.internal.issue_manager.data_valuation import DataValuationIssueManager

SEED = 42


class TestDataValuationIssueManager:
    @pytest.fixture
    def issue_manager(self, lab):
        return DataValuationIssueManager(datalab=lab)

    @pytest.fixture
    def outlier_issue_manager(self, lab):
        return OutlierIssueManager(datalab=lab, k=3)

    @pytest.fixture
    def embeddings(self, lab):
        np.random.seed(SEED)
        embeddings_array = 0.5 + 0.1 * np.random.rand(lab.get_info("statistics")["num_examples"], 2)
        embeddings_array[4, :] = -1
        return {"embedding": embeddings_array}

    def test_find_issues_with_input(self, issue_manager, embeddings, outlier_issue_manager):
        outlier_issue_manager.find_issues(features=embeddings["embedding"])
        knn_graph = outlier_issue_manager._process_knn_graph_from_features({})
        issue_manager.find_issues(knn_graph=knn_graph)
        issues, summary, info = issue_manager.issues, issue_manager.summary, issue_manager.info
        assert isinstance(issues, pd.DataFrame), "Issues should be a dataframe"

        assert isinstance(summary, pd.DataFrame), "Summary should be a dataframe"
        assert summary["issue_type"].values[0] == "data_valuation"

        assert isinstance(info, dict), "Info should be a dict"
        assert isinstance(issues, pd.DataFrame), "Issues should be a dataframe"
        info_keys = info.keys()
        expected_keys = [
            "num_low_valuation_issues",
            "average_data_valuation",
        ]
        assert all(
            [key in info_keys for key in expected_keys]
        ), f"Info should have the right keys, but is missing {set(expected_keys) - set(info_keys)}"

    def test_find_issues_with_stats(self, issue_manager, embeddings):
        issue_manager.datalab.find_issues(
            features=embeddings["embedding"], issue_types={"outlier": {"k": 3}}
        )
        issue_manager.find_issues()
        issues, summary, info = issue_manager.issues, issue_manager.summary, issue_manager.info
        assert isinstance(issues, pd.DataFrame), "Issues should be a dataframe"

        assert isinstance(summary, pd.DataFrame), "Summary should be a dataframe"
        assert summary["issue_type"].values[0] == "data_valuation"

        assert isinstance(info, dict), "Info should be a dict"
        assert isinstance(issues, pd.DataFrame), "Issues should be a dataframe"
        info_keys = info.keys()
        expected_keys = [
            "num_low_valuation_issues",
            "average_data_valuation",
        ]
        assert all(
            [key in info_keys for key in expected_keys]
        ), f"Info should have the right keys, but is missing {set(expected_keys) - set(info_keys)}"

    def test_get_larger_k_than_knn_graph(self, issue_manager, embeddings, outlier_issue_manager):
        outlier_issue_manager.find_issues(features=embeddings["embedding"])
        knn_graph = outlier_issue_manager._process_knn_graph_from_features({})
        issue_manager.k = 4
        issue_manager.find_issues(knn_graph=knn_graph)
        assert issue_manager.k == 3