import logging
from typing import Dict
from unittest import mock

import pytest

from great_expectations import DataContext
from great_expectations.core.usage_statistics.schemas import (
    anonymized_usage_statistics_record_schema,
)
from great_expectations.core.usage_statistics.usage_statistics import (
    UsageStatisticsHandler,
    get_profiler_run_usage_statistics,
)
from great_expectations.data_context import BaseDataContext
from great_expectations.data_context.types.base import DataContextConfig
from great_expectations.rule_based_profiler.rule_based_profiler import RuleBasedProfiler
from tests.core.usage_statistics.util import usage_stats_invalid_messages_exist
from tests.integration.usage_statistics.test_integration_usage_statistics import (
    USAGE_STATISTICS_QA_URL,
)


@pytest.fixture
def in_memory_data_context_config_usage_stats_enabled():

    return DataContextConfig(
        **{
            "commented_map": {},
            "config_version": 2,
            "plugins_directory": None,
            "evaluation_parameter_store_name": "evaluation_parameter_store",
            "validations_store_name": "validations_store",
            "expectations_store_name": "expectations_store",
            "config_variables_file_path": None,
            "datasources": {},
            "stores": {
                "expectations_store": {
                    "class_name": "ExpectationsStore",
                },
                "validations_store": {
                    "class_name": "ValidationsStore",
                },
                "evaluation_parameter_store": {
                    "class_name": "EvaluationParameterStore",
                },
            },
            "data_docs_sites": {},
            "validation_operators": {
                "default": {
                    "class_name": "ActionListValidationOperator",
                    "action_list": [],
                }
            },
            "anonymous_usage_statistics": {
                "enabled": True,
                "data_context_id": "00000000-0000-0000-0000-000000000001",
                "usage_statistics_url": USAGE_STATISTICS_QA_URL,
            },
        }
    )


@pytest.fixture
def sample_partial_message():
    return {
        "event": "checkpoint.run",
        "event_payload": {
            "anonymized_name": "f563d9aa1604e16099bb7dff7b203319",
            "config_version": 1.0,
            "anonymized_expectation_suite_name": "6a04fc37da0d43a4c21429f6788d2cff",
            "anonymized_action_list": [
                {
                    "anonymized_name": "8e3e134cd0402c3970a02f40d2edfc26",
                    "parent_class": "StoreValidationResultAction",
                },
                {
                    "anonymized_name": "40e24f0c6b04b6d4657147990d6f39bd",
                    "parent_class": "StoreEvaluationParametersAction",
                },
                {
                    "anonymized_name": "2b99b6b280b8a6ad1176f37580a16411",
                    "parent_class": "UpdateDataDocsAction",
                },
            ],
            "anonymized_validations": [
                {
                    "anonymized_batch_request": {
                        "anonymized_batch_request_required_top_level_properties": {
                            "anonymized_datasource_name": "a732a247720783a5931fa7c4606403c2",
                            "anonymized_data_connector_name": "d52d7bff3226a7f94dd3510c1040de78",
                            "anonymized_data_asset_name": "556e8c06239d09fc66f424eabb9ca491",
                        },
                        "batch_request_optional_top_level_keys": [
                            "batch_identifiers",
                            "runtime_parameters",
                        ],
                        "runtime_parameters_keys": ["batch_data"],
                    },
                    "anonymized_expectation_suite_name": "6a04fc37da0d43a4c21429f6788d2cff",
                    "anonymized_action_list": [
                        {
                            "anonymized_name": "8e3e134cd0402c3970a02f40d2edfc26",
                            "parent_class": "StoreValidationResultAction",
                        },
                        {
                            "anonymized_name": "40e24f0c6b04b6d4657147990d6f39bd",
                            "parent_class": "StoreEvaluationParametersAction",
                        },
                        {
                            "anonymized_name": "2b99b6b280b8a6ad1176f37580a16411",
                            "parent_class": "UpdateDataDocsAction",
                        },
                    ],
                },
            ],
        },
        "success": True,
        # "version": "1.0.0",
        # "event_time": "2020-06-25T16:08:28.070Z",
        # "event_duration": 123,
        # "data_context_id": "00000000-0000-0000-0000-000000000002",
        # "data_context_instance_id": "10000000-0000-0000-0000-000000000002",
        # "ge_version": "0.13.45.manual_testing",
        "x-forwarded-for": "00.000.00.000, 00.000.000.000",
    }


def test_usage_statistics_handler_build_envelope(
    in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):
    """This test is for a happy path only but will fail if there is an exception thrown in build_envelope"""

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    envelope = usage_statistics_handler.build_envelope(sample_partial_message)
    required_keys = [
        "event",
        "event_payload",
        "version",
        "ge_version",
        "data_context_id",
        "data_context_instance_id",
        "event_time",
    ]
    assert all([key in envelope.keys() for key in required_keys])

    assert envelope["version"] == "1.0.0"
    assert envelope["data_context_id"] == "00000000-0000-0000-0000-000000000001"


def test_usage_statistics_handler_validate_message_failure(
    caplog, in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):

    # caplog default is WARNING and above, we want to see DEBUG level messages for this test
    caplog.set_level(
        level=logging.DEBUG,
        logger="great_expectations.core.usage_statistics.usage_statistics",
    )

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    validated_message = usage_statistics_handler.validate_message(
        sample_partial_message, anonymized_usage_statistics_record_schema
    )
    assert not validated_message
    assert usage_stats_invalid_messages_exist(caplog.messages)


def test_usage_statistics_handler_validate_message_success(
    caplog, in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):

    # caplog default is WARNING and above, we want to see DEBUG level messages for this test
    caplog.set_level(
        level=logging.DEBUG,
        logger="great_expectations.core.usage_statistics.usage_statistics",
    )

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    envelope = usage_statistics_handler.build_envelope(sample_partial_message)
    validated_message = usage_statistics_handler.validate_message(
        envelope, anonymized_usage_statistics_record_schema
    )

    assert validated_message
    assert not usage_stats_invalid_messages_exist(caplog.messages)


def test_build_init_payload(
    titanic_pandas_data_context_with_v013_datasource_with_checkpoints_v1_with_empty_store_stats_enabled,
):
    """This test is for a happy path only but will fail if there is an exception thrown in init_payload"""

    context: DataContext = titanic_pandas_data_context_with_v013_datasource_with_checkpoints_v1_with_empty_store_stats_enabled
    usage_statistics_handler = context._usage_statistics_handler
    init_payload = usage_statistics_handler.build_init_payload()
    assert list(init_payload.keys()) == [
        "platform.system",
        "platform.release",
        "version_info",
        "anonymized_datasources",
        "anonymized_stores",
        "anonymized_validation_operators",
        "anonymized_data_docs_sites",
        "anonymized_expectation_suites",
    ]
    assert init_payload["anonymized_datasources"] == [
        {
            "anonymized_data_connectors": [
                {
                    "anonymized_name": "af09acd176f54642635a8a2975305437",
                    "parent_class": "InferredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "e475f70ca0bcbaf2748b93da5e9867ec",
                    "parent_class": "ConfiguredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "2030a96b1eaa8579087d31709fb6ec1b",
                    "parent_class": "ConfiguredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "d52d7bff3226a7f94dd3510c1040de78",
                    "parent_class": "RuntimeDataConnector",
                },
            ],
            "anonymized_execution_engine": {
                "anonymized_name": "212039ff9860a796a32c75c7d5c2fac0",
                "parent_class": "PandasExecutionEngine",
            },
            "anonymized_name": "a732a247720783a5931fa7c4606403c2",
            "parent_class": "Datasource",
        }
    ]
    assert init_payload["anonymized_expectation_suites"] == []


@mock.patch("great_expectations.data_context.data_context.DataContext")
def test_get_profiler_run_usage_statistics_with_handler_valid_payload(
    mock_data_context: mock.MagicMock,
):
    # Ensure that real handler gets passed down by the context
    handler: UsageStatisticsHandler = UsageStatisticsHandler(
        mock_data_context, "my_id", "my_url"
    )
    mock_data_context.usage_statistics_handler = handler

    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler", config_version=1.0, data_context=mock_data_context
    )

    override_rules: Dict[str, dict] = {
        "my_override_rule": {
            "domain_builder": {
                "class_name": "ColumnDomainBuilder",
                "module_name": "great_expectations.rule_based_profiler.domain_builder",
            },
            "parameter_builders": [
                {
                    "class_name": "MetricMultiBatchParameterBuilder",
                    "module_name": "great_expectations.rule_based_profiler.parameter_builder",
                    "name": "my_parameter",
                    "metric_name": "my_metric",
                },
                {
                    "class_name": "NumericMetricRangeMultiBatchParameterBuilder",
                    "module_name": "great_expectations.rule_based_profiler.parameter_builder",
                    "name": "my_other_parameter",
                    "metric_name": "my_other_metric",
                },
            ],
            "expectation_configuration_builders": [
                {
                    "class_name": "DefaultExpectationConfigurationBuilder",
                    "module_name": "great_expectations.rule_based_profiler.expectation_configuration_builder",
                    "expectation_type": "expect_column_pair_values_A_to_be_greater_than_B",
                    "column_A": "$domain.domain_kwargs.column_A",
                    "column_B": "$domain.domain_kwargs.column_B",
                    "my_one_arg": "$parameter.my_parameter.value[0]",
                    "meta": {
                        "details": {
                            "my_parameter_estimator": "$parameter.my_parameter.details",
                            "note": "Important remarks about estimation algorithm.",
                        },
                    },
                },
                {
                    "class_name": "DefaultExpectationConfigurationBuilder",
                    "module_name": "great_expectations.rule_based_profiler.expectation_configuration_builder",
                    "expectation_type": "expect_column_min_to_be_between",
                    "column": "$domain.domain_kwargs.column",
                    "my_another_arg": "$parameter.my_other_parameter.value[0]",
                    "meta": {
                        "details": {
                            "my_other_parameter_estimator": "$parameter.my_other_parameter.details",
                            "note": "Important remarks about estimation algorithm.",
                        },
                    },
                },
            ],
        },
    }

    payload: dict = get_profiler_run_usage_statistics(
        profiler=profiler, rules=override_rules
    )

    assert payload == {
        "anonymized_name": "a0061ec021855cd2b3a994dd8d90fe5d",
        "anonymized_rules": [
            {
                "anonymized_domain_builder": {"parent_class": "ColumnDomainBuilder"},
                "anonymized_expectation_configuration_builders": [
                    {
                        "expectation_type": "expect_column_pair_values_A_to_be_greater_than_B",
                        "parent_class": "DefaultExpectationConfigurationBuilder",
                    },
                    {
                        "expectation_type": "expect_column_min_to_be_between",
                        "parent_class": "DefaultExpectationConfigurationBuilder",
                    },
                ],
                "anonymized_name": "bd8a8b4465a94b363caf2b307c080547",
                "anonymized_parameter_builders": [
                    {
                        "anonymized_name": "25dac9e56a1969727bc0f90db6eaa833",
                        "parent_class": "MetricMultiBatchParameterBuilder",
                    },
                    {
                        "anonymized_name": "be5baa3f1064e6e19356f2168968cbeb",
                        "parent_class": "NumericMetricRangeMultiBatchParameterBuilder",
                    },
                ],
            }
        ],
        "config_version": 1.0,
        "rule_count": 1,
        "variable_count": 0,
    }


@mock.patch("great_expectations.data_context.data_context.DataContext")
def test_get_profiler_run_usage_statistics_with_handler_invalid_payload(
    mock_data_context: mock.MagicMock,
):
    # Ensure that real handler gets passed down by the context
    handler: UsageStatisticsHandler = UsageStatisticsHandler(
        mock_data_context, "my_id", "my_url"
    )
    mock_data_context.usage_statistics_handler = handler

    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler", config_version=1.0, data_context=mock_data_context
    )

    payload: dict = get_profiler_run_usage_statistics(profiler=profiler)

    # Payload won't pass schema validation due to a lack of rules but we can confirm that it is anonymized
    assert payload == {
        "anonymized_name": "a<PASSWORD>",
        "config_version": 1.0,
        "rule_count": 0,
        "variable_count": 0,
    }


def test_get_profiler_run_usage_statistics_without_handler():
    # Without a DataContext, the usage stats handler is not propogated down to the RBP
    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler",
        config_version=1.0,
    )
    payload: dict = get_profiler_run_usage_statistics(profiler=profiler)
    assert payload == {}

import logging
from typing import Dict
from unittest import mock

import pytest

from great_expectations import DataContext
from great_expectations.core.usage_statistics.schemas import (
    anonymized_usage_statistics_record_schema,
)
from great_expectations.core.usage_statistics.usage_statistics import (
    UsageStatisticsHandler,
    get_profiler_run_usage_statistics,
)
from great_expectations.data_context import BaseDataContext
from great_expectations.data_context.types.base import DataContextConfig
from great_expectations.rule_based_profiler.rule_based_profiler import RuleBasedProfiler
from tests.core.usage_statistics.util import usage_stats_invalid_messages_exist
from tests.integration.usage_statistics.test_integration_usage_statistics import (
    USAGE_STATISTICS_QA_URL,
)


@pytest.fixture
def in_memory_data_context_config_usage_stats_enabled():

    return DataContextConfig(
        **{
            "commented_map": {},
            "config_version": 2,
            "plugins_directory": None,
            "evaluation_parameter_store_name": "evaluation_parameter_store",
            "validations_store_name": "validations_store",
            "expectations_store_name": "expectations_store",
            "config_variables_file_path": None,
            "datasources": {},
            "stores": {
                "expectations_store": {
                    "class_name": "ExpectationsStore",
                },
                "validations_store": {
                    "class_name": "ValidationsStore",
                },
                "evaluation_parameter_store": {
                    "class_name": "EvaluationParameterStore",
                },
            },
            "data_docs_sites": {},
            "validation_operators": {
                "default": {
                    "class_name": "ActionListValidationOperator",
                    "action_list": [],
                }
            },
            "anonymous_usage_statistics": {
                "enabled": True,
                "data_context_id": "00000000-0000-0000-0000-000000000001",
                "usage_statistics_url": USAGE_STATISTICS_QA_URL,
            },
        }
    )


@pytest.fixture
def sample_partial_message():
    return {
        "event": "checkpoint.run",
        "event_payload": {
            "anonymized_name": "f563d9aa1604e16099bb7dff7b203319",
            "config_version": 1.0,
            "anonymized_expectation_suite_name": "6a04fc37da0d43a4c21429f6788d2cff",
            "anonymized_action_list": [
                {
                    "anonymized_name": "8e3e134cd0402c3970a02f40d2edfc26",
                    "parent_class": "StoreValidationResultAction",
                },
                {
                    "anonymized_name": "40e24f0c6b04b6d4657147990d6f39bd",
                    "parent_class": "StoreEvaluationParametersAction",
                },
                {
                    "anonymized_name": "2b99b6b280b8a6ad1176f37580a16411",
                    "parent_class": "UpdateDataDocsAction",
                },
            ],
            "anonymized_validations": [
                {
                    "anonymized_batch_request": {
                        "anonymized_batch_request_required_top_level_properties": {
                            "anonymized_datasource_name": "a732a247720783a5931fa7c4606403c2",
                            "anonymized_data_connector_name": "d52d7bff3226a7f94dd3510c1040de78",
                            "anonymized_data_asset_name": "556e8c06239d09fc66f424eabb9ca491",
                        },
                        "batch_request_optional_top_level_keys": [
                            "batch_identifiers",
                            "runtime_parameters",
                        ],
                        "runtime_parameters_keys": ["batch_data"],
                    },
                    "anonymized_expectation_suite_name": "6a04fc37da0d43a4c21429f6788d2cff",
                    "anonymized_action_list": [
                        {
                            "anonymized_name": "8e3e134cd0402c3970a02f40d2edfc26",
                            "parent_class": "StoreValidationResultAction",
                        },
                        {
                            "anonymized_name": "40e24f0c6b04b6d4657147990d6f39bd",
                            "parent_class": "StoreEvaluationParametersAction",
                        },
                        {
                            "anonymized_name": "2b99b6b280b8a6ad1176f37580a16411",
                            "parent_class": "UpdateDataDocsAction",
                        },
                    ],
                },
            ],
        },
        "success": True,
        # "version": "1.0.0",
        # "event_time": "2020-06-25T16:08:28.070Z",
        # "event_duration": 123,
        # "data_context_id": "00000000-0000-0000-0000-000000000002",
        # "data_context_instance_id": "10000000-0000-0000-0000-000000000002",
        # "ge_version": "0.13.45.manual_testing",
        "x-forwarded-for": "00.000.00.000, 00.000.000.000",
    }


def test_usage_statistics_handler_build_envelope(
    in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):
    """This test is for a happy path only but will fail if there is an exception thrown in build_envelope"""

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    envelope = usage_statistics_handler.build_envelope(sample_partial_message)
    required_keys = [
        "event",
        "event_payload",
        "version",
        "ge_version",
        "data_context_id",
        "data_context_instance_id",
        "event_time",
    ]
    assert all([key in envelope.keys() for key in required_keys])

    assert envelope["version"] == "1.0.0"
    assert envelope["data_context_id"] == "00000000-0000-0000-0000-000000000001"


def test_usage_statistics_handler_validate_message_failure(
    caplog, in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):

    # caplog default is WARNING and above, we want to see DEBUG level messages for this test
    caplog.set_level(
        level=logging.DEBUG,
        logger="great_expectations.core.usage_statistics.usage_statistics",
    )

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    validated_message = usage_statistics_handler.validate_message(
        sample_partial_message, anonymized_usage_statistics_record_schema
    )
    assert not validated_message
    assert usage_stats_invalid_messages_exist(caplog.messages)


def test_usage_statistics_handler_validate_message_success(
    caplog, in_memory_data_context_config_usage_stats_enabled, sample_partial_message
):

    # caplog default is WARNING and above, we want to see DEBUG level messages for this test
    caplog.set_level(
        level=logging.DEBUG,
        logger="great_expectations.core.usage_statistics.usage_statistics",
    )

    context: BaseDataContext = BaseDataContext(
        in_memory_data_context_config_usage_stats_enabled
    )

    usage_statistics_handler = UsageStatisticsHandler(
        data_context=context,
        data_context_id=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.data_context_id,
        usage_statistics_url=in_memory_data_context_config_usage_stats_enabled.anonymous_usage_statistics.usage_statistics_url,
    )

    assert (
        usage_statistics_handler._data_context_id
        == "00000000-0000-0000-0000-000000000001"
    )

    envelope = usage_statistics_handler.build_envelope(sample_partial_message)
    validated_message = usage_statistics_handler.validate_message(
        envelope, anonymized_usage_statistics_record_schema
    )

    assert validated_message
    assert not usage_stats_invalid_messages_exist(caplog.messages)


def test_build_init_payload(
    titanic_pandas_data_context_with_v013_datasource_with_checkpoints_v1_with_empty_store_stats_enabled,
):
    """This test is for a happy path only but will fail if there is an exception thrown in init_payload"""

    context: DataContext = titanic_pandas_data_context_with_v013_datasource_with_checkpoints_v1_with_empty_store_stats_enabled
    usage_statistics_handler = context._usage_statistics_handler
    init_payload = usage_statistics_handler.build_init_payload()
    assert list(init_payload.keys()) == [
        "platform.system",
        "platform.release",
        "version_info",
        "anonymized_datasources",
        "anonymized_stores",
        "anonymized_validation_operators",
        "anonymized_data_docs_sites",
        "anonymized_expectation_suites",
    ]
    assert init_payload["anonymized_datasources"] == [
        {
            "anonymized_data_connectors": [
                {
                    "anonymized_name": "af09acd176f54642635a8a2975305437",
                    "parent_class": "InferredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "e475f70ca0bcbaf2748b93da5e9867ec",
                    "parent_class": "ConfiguredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "2030a96b1eaa8579087d31709fb6ec1b",
                    "parent_class": "ConfiguredAssetFilesystemDataConnector",
                },
                {
                    "anonymized_name": "d52d7bff3226a7f94dd3510c1040de78",
                    "parent_class": "RuntimeDataConnector",
                },
            ],
            "anonymized_execution_engine": {
                "anonymized_name": "212039ff9860a796a32c75c7d5c2fac0",
                "parent_class": "PandasExecutionEngine",
            },
            "anonymized_name": "a732a247720783a5931fa7c4606403c2",
            "parent_class": "Datasource",
        }
    ]
    assert init_payload["anonymized_expectation_suites"] == []


@mock.patch("great_expectations.data_context.data_context.DataContext")
def test_get_profiler_run_usage_statistics_with_handler_valid_payload(
    mock_data_context: mock.MagicMock,
):
    # Ensure that real handler gets passed down by the context
    handler: UsageStatisticsHandler = UsageStatisticsHandler(
        mock_data_context, "my_id", "my_url"
    )
    mock_data_context.usage_statistics_handler = handler

    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler", config_version=1.0, data_context=mock_data_context
    )

    override_rules: Dict[str, dict] = {
        "my_override_rule": {
            "domain_builder": {
                "class_name": "ColumnDomainBuilder",
                "module_name": "great_expectations.rule_based_profiler.domain_builder",
            },
            "parameter_builders": [
                {
                    "class_name": "MetricMultiBatchParameterBuilder",
                    "module_name": "great_expectations.rule_based_profiler.parameter_builder",
                    "name": "my_parameter",
                    "metric_name": "my_metric",
                },
                {
                    "class_name": "NumericMetricRangeMultiBatchParameterBuilder",
                    "module_name": "great_expectations.rule_based_profiler.parameter_builder",
                    "name": "my_other_parameter",
                    "metric_name": "my_other_metric",
                },
            ],
            "expectation_configuration_builders": [
                {
                    "class_name": "DefaultExpectationConfigurationBuilder",
                    "module_name": "great_expectations.rule_based_profiler.expectation_configuration_builder",
                    "expectation_type": "expect_column_pair_values_A_to_be_greater_than_B",
                    "column_A": "$domain.domain_kwargs.column_A",
                    "column_B": "$domain.domain_kwargs.column_B",
                    "my_one_arg": "$parameter.my_parameter.value[0]",
                    "meta": {
                        "details": {
                            "my_parameter_estimator": "$parameter.my_parameter.details",
                            "note": "Important remarks about estimation algorithm.",
                        },
                    },
                },
                {
                    "class_name": "DefaultExpectationConfigurationBuilder",
                    "module_name": "great_expectations.rule_based_profiler.expectation_configuration_builder",
                    "expectation_type": "expect_column_min_to_be_between",
                    "column": "$domain.domain_kwargs.column",
                    "my_another_arg": "$parameter.my_other_parameter.value[0]",
                    "meta": {
                        "details": {
                            "my_other_parameter_estimator": "$parameter.my_other_parameter.details",
                            "note": "Important remarks about estimation algorithm.",
                        },
                    },
                },
            ],
        },
    }

    payload: dict = get_profiler_run_usage_statistics(
        profiler=profiler, rules=override_rules
    )

    assert payload == {
        "anonymized_name": "a0061ec021855cd2b3a994dd8d90fe5d",
        "anonymized_rules": [
            {
                "anonymized_domain_builder": {"parent_class": "ColumnDomainBuilder"},
                "anonymized_expectation_configuration_builders": [
                    {
                        "expectation_type": "expect_column_pair_values_A_to_be_greater_than_B",
                        "parent_class": "DefaultExpectationConfigurationBuilder",
                    },
                    {
                        "expectation_type": "expect_column_min_to_be_between",
                        "parent_class": "DefaultExpectationConfigurationBuilder",
                    },
                ],
                "anonymized_name": "bd8a8b4465a94b363caf2b307c080547",
                "anonymized_parameter_builders": [
                    {
                        "anonymized_name": "25dac9e56a1969727bc0f90db6eaa833",
                        "parent_class": "MetricMultiBatchParameterBuilder",
                    },
                    {
                        "anonymized_name": "be5baa3f1064e6e19356f2168968cbeb",
                        "parent_class": "NumericMetricRangeMultiBatchParameterBuilder",
                    },
                ],
            }
        ],
        "config_version": 1.0,
        "rule_count": 1,
        "variable_count": 0,
    }


@mock.patch("great_expectations.data_context.data_context.DataContext")
def test_get_profiler_run_usage_statistics_with_handler_invalid_payload(
    mock_data_context: mock.MagicMock,
):
    # Ensure that real handler gets passed down by the context
    handler: UsageStatisticsHandler = UsageStatisticsHandler(
        mock_data_context, "my_id", "my_url"
    )
    mock_data_context.usage_statistics_handler = handler

    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler", config_version=1.0, data_context=mock_data_context
    )

    payload: dict = get_profiler_run_usage_statistics(profiler=profiler)

    # Payload won't pass schema validation due to a lack of rules but we can confirm that it is anonymized
    assert payload == {
        "anonymized_name": "a<PASSWORD>",
        "config_version": 1.0,
        "rule_count": 0,
        "variable_count": 0,
    }


def test_get_profiler_run_usage_statistics_without_handler():
    # Without a DataContext, the usage stats handler is not propogated down to the RBP
    profiler: RuleBasedProfiler = RuleBasedProfiler(
        name="my_profiler",
        config_version=1.0,
    )
    payload: dict = get_profiler_run_usage_statistics(profiler=profiler)
    assert payload == {}

# "version": "1.0.0", # "event_time": "2020-06-25T16:08:28.070Z", # "event_duration": 123, # "data_context_id": "00000000-0000-0000-0000-000000000002", # "data_context_instance_id": "10000000-0000-0000-0000-000000000002", # "ge_version": "0.13.45.manual_testing", This test is for a happy path only but will fail if there is an exception thrown in build_envelope # caplog default is WARNING and above, we want to see DEBUG level messages for this test # caplog default is WARNING and above, we want to see DEBUG level messages for this test This test is for a happy path only but will fail if there is an exception thrown in init_payload # Ensure that real handler gets passed down by the context # Ensure that real handler gets passed down by the context # Payload won't pass schema validation due to a lack of rules but we can confirm that it is anonymized # Without a DataContext, the usage stats handler is not propogated down to the RBP