import datetime

import pytest
from freezegun import freeze_time

from great_expectations.core import (
    ExpectationConfiguration,
    ExpectationSuiteValidationResult,
    ExpectationValidationResult,
    RunIdentifier,
)
from great_expectations.core.metric import ValidationMetricIdentifier
from great_expectations.data_context.util import instantiate_class_from_config


@pytest.fixture(
    params=[
        {
            "class_name": "EvaluationParameterStore",
            "store_backend": {
                "class_name": "DatabaseStoreBackend",
                "credentials": {
                    "drivername": "postgresql",
                    "username": "postgres",
                    "password": "",
                    "host": "localhost",
                    "port": "5432",
                    "database": "test_ci",
                },
            },
        },
        {
            "class_name": "EvaluationParameterStore",
            "module_name": "great_expectations.data_context.store",
        },
    ]
)
def param_store(request, test_backends):
    if "postgresql" not in test_backends:
        pytest.skip("skipping fixture because postgresql not selected")

    return instantiate_class_from_config(
        config=request.param,
        config_defaults={"module_name": "great_expectations.data_context.store",},
        runtime_environment={},
    )


def test_evaluation_parameter_store_methods(
    data_context_parameterized_expectation_suite,
):
    run_id = RunIdentifier(run_name="20191125T000000.000000Z")
    source_patient_data_results = ExpectationSuiteValidationResult(
        meta={
            "expectation_suite_name": "source_patient_data.default",
            "run_id": run_id,
        },
        results=[
            ExpectationValidationResult(
                expectation_config=ExpectationConfiguration(
                    expectation_type="expect_table_row_count_to_equal",
                    kwargs={"value": 1024,},
                ),
                success=True,
                exception_info={
                    "exception_message": None,
                    "exception_traceback": None,
                    "raised_exception": False,
                },
                result={
                    "observed_value": 1024,
                    "element_count": 1024,
                    "missing_percent": 0.0,
                    "missing_count": 0,
                },
            )
        ],
        success=True,
    )

    data_context_parameterized_expectation_suite.store_evaluation_parameters(
        source_patient_data_results
    )

    bound_parameters = data_context_parameterized_expectation_suite.evaluation_parameter_store.get_bind_params(
        run_id
    )
    assert bound_parameters == {
        "urn:great_expectations:validations:source_patient_data.default:expect_table_row_count_to_equal.result"
        ".observed_value": 1024
    }
    source_diabetes_data_results = ExpectationSuiteValidationResult(
        meta={
            "expectation_suite_name": "source_diabetes_data.default",
            "run_id": run_id,
        },
        results=[
            ExpectationValidationResult(
                expectation_config=ExpectationConfiguration(
                    expectation_type="expect_column_unique_value_count_to_be_between",
                    kwargs={"column": "patient_nbr", "min": 2048, "max": 2048},
                ),
                success=True,
                exception_info={
                    "exception_message": None,
                    "exception_traceback": None,
                    "raised_exception": False,
                },
                result={
                    "observed_value": 2048,
                    "element_count": 5000,
                    "missing_percent": 0.0,
                    "missing_count": 0,
                },
            )
        ],
        success=True,
    )

    data_context_parameterized_expectation_suite.store_evaluation_parameters(
        source_diabetes_data_results
    )
    bound_parameters = data_context_parameterized_expectation_suite.evaluation_parameter_store.get_bind_params(
        run_id
    )
    assert bound_parameters == {
        "urn:great_expectations:validations:source_patient_data.default:expect_table_row_count_to_equal.result"
        ".observed_value": 1024,
        "urn:great_expectations:validations:source_diabetes_data.default"
        ":expect_column_unique_value_count_to_be_between.result.observed_value:column=patient_nbr": 2048,
    }


def test_database_evaluation_parameter_store_basics(param_store):
    run_id = RunIdentifier(
        run_name=datetime.datetime.now(datetime.timezone.utc).strftime(
            "%Y%m%dT%H%M%S.%fZ"
        )
    )
    metric_identifier = ValidationMetricIdentifier(
        run_id=run_id,
        data_asset_name=None,
        expectation_suite_identifier="asset.warning",
        metric_name="expect_column_values_to_match_regex.result.unexpected_percent",
        metric_kwargs_id="column=mycol",
    )
    metric_value = 12.3456789

    param_store.set(metric_identifier, metric_value)
    value = param_store.get(metric_identifier)
    assert value == metric_value


@freeze_time("09/26/2019 13:42:41")
def test_database_evaluation_parameter_store_get_bind_params(param_store):
    # Bind params must be expressed as a string-keyed dictionary.
    # Verify that the param_store supports that
    run_id = RunIdentifier(
        run_name=datetime.datetime.now(datetime.timezone.utc).strftime(
            "%Y%m%dT%H%M%S.%fZ"
        )
    )
    metric_identifier = ValidationMetricIdentifier(
        run_id=run_id,
        data_asset_name=None,
        expectation_suite_identifier="asset.warning",
        metric_name="expect_column_values_to_match_regex.result.unexpected_percent",
        metric_kwargs_id="column=mycol",
    )

    metric_value = 12.3456789
    param_store.set(metric_identifier, metric_value)

    metric_identifier = ValidationMetricIdentifier(
        run_id=run_id,
        data_asset_name=None,
        expectation_suite_identifier="asset.warning",
        metric_name="expect_table_row_count_to_be_between.result.observed_value",
        metric_kwargs_id=None,
    )

    metric_value = 512
    param_store.set(metric_identifier, metric_value)

    metric_identifier = ValidationMetricIdentifier(
        run_id=run_id,
        data_asset_name=None,
        expectation_suite_identifier="asset2.warning",
        metric_name="expect_column_values_to_match_regex.result.unexpected_percent",
        metric_kwargs_id="column=mycol",
    )

    metric_value = 12.3456789
    param_store.set(metric_identifier, metric_value)

    params = param_store.get_bind_params(run_id)
    assert params == {
        "urn:great_expectations:validations:asset.warning:"
        "expect_column_values_to_match_regex.result.unexpected_percent:column=mycol": 12.3456789,
        "urn:great_expectations:validations:asset.warning:"
        "expect_table_row_count_to_be_between.result.observed_value": 512,
        "urn:great_expectations:validations:asset2.warning:"
        "expect_column_values_to_match_regex.result.unexpected_percent:column=mycol": 12.3456789,
    }
