import pytest
from pydantic import ValidationError as PydanticValidationError

from src.models.get_metric_metadata import MAX_METADATA_METRICS, MetricMetadataRequest


class TestMetricMetadataRequestValidation:
    def test_valid_minimal_request(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "prod",
                "deploy_type": "premium",
            }
        )
        assert req.cluster_id == "123"
        assert req.metric is None
        assert req.match is None
        assert req.limit is None

    def test_single_metric_lookup(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "prod",
                "deploy_type": "premium",
                "metric": "topsql_cpu_time_ms",
            }
        )
        assert req.metric == "topsql_cpu_time_ms"

    def test_metric_strips_whitespace(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "prod",
                "deploy_type": "premium",
                "metric": "  topsql_cpu_time_ms  ",
            }
        )
        assert req.metric == "topsql_cpu_time_ms"

    def test_empty_metric_becomes_none(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "prod",
                "deploy_type": "premium",
                "metric": "  ",
            }
        )
        assert req.metric is None

    def test_valid_match_regex(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "prod",
                "deploy_type": "premium",
                "match": "^topsql_",
            }
        )
        assert req.match == "^topsql_"

    def test_invalid_match_regex_rejected(self):
        with pytest.raises(PydanticValidationError):
            MetricMetadataRequest.model_validate(
                {
                    "cluster_id": "123",
                    "env": "prod",
                    "deploy_type": "premium",
                    "match": "[invalid",
                }
            )

    def test_limit_upper_bound(self):
        with pytest.raises(PydanticValidationError):
            MetricMetadataRequest.model_validate(
                {
                    "cluster_id": "123",
                    "env": "prod",
                    "deploy_type": "premium",
                    "limit": MAX_METADATA_METRICS + 1,
                }
            )

    def test_limit_lower_bound(self):
        with pytest.raises(PydanticValidationError):
            MetricMetadataRequest.model_validate(
                {
                    "cluster_id": "123",
                    "env": "prod",
                    "deploy_type": "premium",
                    "limit": 0,
                }
            )

    def test_normalizes_env_and_deploy_type(self):
        req = MetricMetadataRequest.model_validate(
            {
                "cluster_id": "123",
                "env": "  Prod  ",
                "deploy_type": "  Premium  ",
            }
        )
        assert req.env == "prod"
        assert req.deploy_type == "premium"

    def test_rejects_non_string_cluster_id(self):
        with pytest.raises(PydanticValidationError):
            MetricMetadataRequest.model_validate(
                {
                    "cluster_id": 123,
                    "env": "prod",
                    "deploy_type": "premium",
                }
            )