# Copyright The OpenTelemetry Authors
# SPDX-License-Identifier: Apache-2.0

import logging
import unittest
from collections.abc import Sequence
from contextlib import contextmanager

from opentelemetry import metrics as metrics_api
from opentelemetry import trace as trace_api
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics._internal.aggregation import (
    _DEFAULT_EXPLICIT_BUCKET_HISTOGRAM_AGGREGATION_BOUNDARIES,
)
from opentelemetry.sdk.metrics._internal.point import Metric
from opentelemetry.sdk.metrics.export import (
    DataPointT,
    HistogramDataPoint,
    InMemoryMetricReader,
    MetricReader,
    NumberDataPoint,
)
from opentelemetry.sdk.trace import TracerProvider, export
from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
    InMemorySpanExporter,
)
from opentelemetry.test.globals_test import (
    reset_metrics_globals,
    reset_trace_globals,
)


class TestBase(unittest.TestCase):
    # pylint: disable=C0103

    def setUp(self):
        super().setUp()
        result = self.create_tracer_provider()
        self.tracer_provider, self.memory_exporter = result
        # This is done because set_tracer_provider cannot override the
        # current tracer provider.
        reset_trace_globals()
        trace_api.set_tracer_provider(self.tracer_provider)

        self.memory_exporter.clear()
        # This is done because set_meter_provider cannot override the
        # current meter provider.
        reset_metrics_globals()
        (
            self.meter_provider,
            self.memory_metrics_reader,
        ) = self.create_meter_provider()
        metrics_api.set_meter_provider(self.meter_provider)

    def tearDown(self):
        super().tearDown()
        reset_trace_globals()
        reset_metrics_globals()

    def get_finished_spans(self):
        return FinishedTestSpans(self, self.memory_exporter.get_finished_spans())

    def assertEqualSpanInstrumentationScope(self, span, module):
        self.assertEqual(span.instrumentation_scope.name, module.__name__)
        self.assertEqual(span.instrumentation_scope.version, module.__version__)

    def assertSpanHasAttributes(self, span, attributes):
        for key, val in attributes.items():
            self.assertIn(key, span.attributes)
            self.assertEqual(val, span.attributes[key])

    def sorted_spans(self, spans):  # pylint: disable=R0201
        """
        Sorts spans by span creation time.

        Note: This method should not be used to sort spans in a deterministic way as the
        order depends on timing precision provided by the platform.
        """
        return sorted(
            spans,
            key=lambda s: s._start_time,  # pylint: disable=W0212
            reverse=True,
        )

    @staticmethod
    def create_tracer_provider(**kwargs):
        """Helper to create a configured tracer provider.

        Creates and configures a `TracerProvider` with a
        `SimpleSpanProcessor` and a `InMemorySpanExporter`.
        All the parameters passed are forwarded to the TracerProvider
        constructor.

        Returns:
            A list with the tracer provider in the first element and the
            in-memory span exporter in the second.
        """
        tracer_provider = TracerProvider(**kwargs)
        memory_exporter = InMemorySpanExporter()
        span_processor = export.SimpleSpanProcessor(memory_exporter)
        tracer_provider.add_span_processor(span_processor)

        return tracer_provider, memory_exporter

    @staticmethod
    def create_meter_provider(**kwargs) -> tuple[MeterProvider, MetricReader]:
        """Helper to create a configured meter provider
        Creates a `MeterProvider` and an `InMemoryMetricReader`.
        Returns:
            A tuple with the meter provider in the first element and the
            in-memory metrics exporter in the second
        """
        memory_reader = InMemoryMetricReader()
        metric_readers = kwargs.get("metric_readers", [])
        metric_readers.append(memory_reader)
        kwargs["metric_readers"] = metric_readers
        meter_provider = MeterProvider(**kwargs)
        return meter_provider, memory_reader

    @staticmethod
    @contextmanager
    def disable_logging(highest_level=logging.CRITICAL):
        logging.disable(highest_level)

        try:
            yield
        finally:
            logging.disable(logging.NOTSET)

    def get_sorted_metrics(self, scope: str | None = None):
        """Returns recorded metrics sorted by name.

        Args:
            scope: Optional scope name to filter metrics by. If unset,
                   all metrics are returned.
        """
        metrics_data = self.memory_metrics_reader.get_metrics_data()
        resource_metrics = metrics_data.resource_metrics if metrics_data else []

        all_metrics = []
        for metrics in resource_metrics:
            for scope_metrics in metrics.scope_metrics:
                if scope is not None and scope_metrics.scope.name != scope:
                    continue
                all_metrics.extend(scope_metrics.metrics)

        return self.sorted_metrics(all_metrics)

    @staticmethod
    def sorted_metrics(metrics):
        """
        Sorts metrics by metric name.
        """
        return sorted(
            metrics,
            key=lambda m: m.name,
        )

    def assert_metric_expected(
        self,
        metric: Metric,
        expected_data_points: Sequence[DataPointT],
        est_value_delta: float | None = 0,
    ):
        self.assertEqual(len(expected_data_points), len(metric.data.data_points))
        for expected_data_point in expected_data_points:
            self.assert_data_point_expected(expected_data_point, metric.data.data_points, est_value_delta)

    # pylint: disable=unidiomatic-typecheck
    @staticmethod
    def is_data_points_equal(
        expected_data_point: DataPointT,
        data_point: DataPointT,
        est_value_delta: float | None = 0,
    ):
        if type(expected_data_point) != type(  # noqa: E721
            data_point
        ) or not isinstance(expected_data_point, (HistogramDataPoint, NumberDataPoint)):
            return False

        values_diff = None
        if isinstance(data_point, NumberDataPoint):
            values_diff = abs(expected_data_point.value - data_point.value)
        elif isinstance(data_point, HistogramDataPoint):
            values_diff = abs(expected_data_point.sum - data_point.sum)
            if expected_data_point.count != data_point.count or (
                est_value_delta == 0
                and (expected_data_point.min != data_point.min or expected_data_point.max != data_point.max)
            ):
                return False

            if expected_data_point.explicit_bounds != data_point.explicit_bounds:
                return False

        return values_diff <= est_value_delta and expected_data_point.attributes == dict(data_point.attributes)

    def assert_data_point_expected(
        self,
        expected_data_point: DataPointT,
        data_points: Sequence[DataPointT],
        est_value_delta: float | None = 0,
    ):
        is_data_point_exist = False
        for data_point in data_points:
            if self.is_data_points_equal(expected_data_point, data_point, est_value_delta):
                is_data_point_exist = True
                break

        self.assertTrue(
            is_data_point_exist,
            msg=f"Data point {expected_data_point} does not exist",
        )

    @staticmethod
    def create_number_data_point(value, attributes):
        return NumberDataPoint(
            value=value,
            attributes=attributes,
            start_time_unix_nano=0,
            time_unix_nano=0,
        )

    @staticmethod
    def create_histogram_data_point(
        sum_data_point,
        count,
        max_data_point,
        min_data_point,
        attributes,
        explicit_bounds=None,
    ):
        return HistogramDataPoint(
            count=count,
            sum=sum_data_point,
            min=min_data_point,
            max=max_data_point,
            attributes=attributes,
            start_time_unix_nano=0,
            time_unix_nano=0,
            bucket_counts=[],
            explicit_bounds=explicit_bounds
            if explicit_bounds is not None
            else _DEFAULT_EXPLICIT_BUCKET_HISTOGRAM_AGGREGATION_BOUNDARIES,
        )


class FinishedTestSpans(list):
    def __init__(self, test, spans):
        super().__init__(spans)
        self.test = test

    def by_name(self, name):
        for span in self:
            if span.name == name:
                return span
        self.test.fail(f"Did not find span with name {name}")
        return None

    def by_attr(self, key, value):
        for span in self:
            if span.attributes.get(key) == value:
                return span
        self.test.fail(f"Did not find span with attrs {key}={value}")
        return None
