
"""
NeuralBridge Adapter for Google Cloud Storage (GCS).

This adapter provides a unified interface for interacting with Google Cloud Storage.
It allows for listing buckets and objects, uploading, downloading, and deleting objects,
and retrieving object metadata.

**Configuration (YAML):**

.. code-block:: yaml

    adapters:
      my_gcs_storage:
        adapter: gcs
        config:
          project_id: "your-gcp-project-id"
          credentials_path: "/path/to/your/gcs_credentials.json" # Optional
          default_bucket: "your-default-bucket-name" # Optional
          location: "US-CENTRAL1" # Optional, for bucket creation

**Supported Operations:**

- ``list_buckets``: List all buckets in the project.
- ``list_objects``: List objects within a specified bucket.
- ``get_object``: Download an object from a bucket.
- ``upload_object``: Upload an object to a bucket.
- ``delete_object``: Delete an object from a bucket.
- ``get_metadata``: Retrieve metadata for a specific object.

"""
from __future__ import annotations

import asyncio
import datetime
from typing import Any

import structlog

from neuralbridge.adapters.base import AdapterResponse, BaseAdapter

logger = structlog.get_logger(__name__)


class GCSAdapter(BaseAdapter):
    """
    Google Cloud Storage adapter for NeuralBridge.
    """

    adapter_type = "gcs"
    category = "cloud"
    description = "Interface with Google Cloud Storage for object storage operations."
    version = "0.1.0"

    supported_operations = [
        "list_buckets",
        "list_objects",
        "get_object",
        "upload_object",
        "delete_object",
        "get_metadata",
    ]

    def __init__(self, config: dict[str, Any] | None = None) -> None:
        """
        Initialise the GCS adapter.

        Args:
            config: Adapter configuration dictionary.
        """
        super().__init__(config)
        self.gcs_client: Any = None
        logger.info("gcs_adapter_initialised", config=self.config)

    def _get_config_schema(self) -> dict[str, Any]:
        """
        Return the JSON schema for the GCS adapter configuration.

        Returns:
            A dictionary representing the JSON schema.
        """
        return {
            "type": "object",
            "properties": {
                "project_id": {
                    "type": "string",
                    "description": "Google Cloud project ID.",
                },
                "credentials_path": {
                    "type": "string",
                    "description": "Path to the GCP service account JSON file.",
                },
                "default_bucket": {
                    "type": "string",
                    "description": "Default bucket to use for operations.",
                },
                "location": {
                    "type": "string",
                    "description": "Default location for creating new buckets.",
                    "default": "US-CENTRAL1",
                },
            },
            "required": ["project_id"],
        }

    async def _do_connect(self) -> None:
        """
        Establish a connection to Google Cloud Storage.

        This method would typically initialize the GCS client using the provided
        credentials. For this mock implementation, we simulate a successful connection.
        """
        project_id = self.config.get("project_id")
        if not project_id:
            raise ValueError("`project_id` is a required configuration field.")

        logger.info(
            "gcs_connecting",
            project_id=project_id,
            creds_path=self.config.get("credentials_path"),
        )

        # MOCK: In production, this would initialize the real GCS client.
        # from google.cloud import storage
        # from google.oauth2 import service_account
        # if self.config.get('credentials_path'):
        #     creds = service_account.Credentials.from_service_account_file(
        #         self.config['credentials_path']
        #     )
        #     self.gcs_client = storage.Client(project=project_id, credentials=creds)
        # else:
        #     self.gcs_client = storage.Client(project=project_id)

        await asyncio.sleep(0.1)  # Simulate network latency
        self.gcs_client = object()  # Mock client object
        logger.info("gcs_connection_successful")

    async def _do_disconnect(self) -> None:
        """
        Disconnect from Google Cloud Storage.

        In a real implementation, this might close client sessions. Here, we just log.
        """
        logger.info("gcs_disconnecting")
        self.gcs_client = None
        await asyncio.sleep(0.05)
        logger.info("gcs_disconnected")

    async def _do_validate_credentials(self) -> dict[str, Any]:
        """
        Validate the configured GCS credentials.

        MOCK: Simulates a check against a GCS API endpoint to verify credentials.
        """
        logger.info("gcs_validating_credentials")
        # MOCK: In production, this would make a lightweight API call, e.g., list_buckets with a limit of 1.
        await asyncio.sleep(0.2)
        return {
            "status": "ok",
            "message": "Mock credentials validation successful.",
            "project_id": self.config.get("project_id"),
        }

    async def _do_execute(
        self, operation: str, params: dict[str, Any]
    ) -> AdapterResponse:
        """
        Execute a given operation on Google Cloud Storage.

        Args:
            operation: The operation to perform (e.g., 'list_objects').
            params: A dictionary of parameters for the operation.

        Returns:
            An AdapterResponse containing the result of the operation.
        """
        if not self.gcs_client:
            raise ConnectionError("Not connected to Google Cloud Storage.")

        handler = getattr(self, f"_op_{operation}", None)
        if not handler:
            raise ValueError(f"Unsupported GCS operation: {operation}")

        result = await handler(params)
        return result  # type: ignore[no-any-return]
        return result

    async def _op_list_buckets(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: List GCS buckets."""
        # MOCK: In production, this would call self.gcs_client.list_buckets()
        await asyncio.sleep(0.1)
        mock_buckets = [
            {"name": "neuralbridge-data", "id": "1", "location": "US-CENTRAL1"},
            {"name": "neuralbridge-logs", "id": "2", "location": "US-EAST1"},
            {"name": self.config.get("default_bucket", "my-default-bucket"), "id": "3", "location": "US-WEST1"},
        ]
        return AdapterResponse(success=True, data=mock_buckets)

    async def _op_list_objects(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: List objects in a GCS bucket."""
        bucket_name = params.get("bucket_name") or self.config.get("default_bucket")
        if not bucket_name:
            return AdapterResponse(
                success=False,
                error="Missing required parameter: bucket_name or default_bucket config",
            )

        # MOCK: In production, this would call self.gcs_client.list_blobs(bucket_name)
        await asyncio.sleep(0.15)
        mock_objects = [
            {
                "name": "documents/report-2024-q1.pdf",
                "size": 1024 * 768,
                "updated": datetime.datetime.now(datetime.UTC).isoformat(),
            },
            {
                "name": "images/logo.png",
                "size": 1024 * 55,
                "updated": datetime.datetime.now(datetime.UTC).isoformat(),
            },
        ]
        return AdapterResponse(success=True, data=mock_objects)

    async def _op_get_object(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: Download an object from a GCS bucket."""
        bucket_name = params.get("bucket_name") or self.config.get("default_bucket")
        object_name = params.get("object_name")
        if not bucket_name or not object_name:
            return AdapterResponse(
                success=False, error="Missing required parameters: bucket_name and object_name"
            )

        # MOCK: In production, this would download the object content.
        await asyncio.sleep(0.2)
        mock_content = b"This is the mock content of the downloaded file."
        return AdapterResponse(
            success=True,
            data=mock_content,
            metadata={"content_type": "application/octet-stream", "size": len(mock_content)},
        )

    async def _op_upload_object(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: Upload an object to a GCS bucket."""
        bucket_name = params.get("bucket_name") or self.config.get("default_bucket")
        object_name = params.get("object_name")
        content = params.get("content")

        if not all([bucket_name, object_name, content]):
            return AdapterResponse(
                success=False,
                error="Missing required parameters: bucket_name, object_name, and content",
            )

        assert content is not None
        # MOCK: In production, this would upload the content to the specified blob.
        await asyncio.sleep(0.3)
        return AdapterResponse(
            success=True,
            data={
                "status": "uploaded",
                "bucket": bucket_name,
                "object": object_name,
                "size": len(content),
            },
        )

    async def _op_delete_object(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: Delete an object from a GCS bucket."""
        bucket_name = params.get("bucket_name") or self.config.get("default_bucket")
        object_name = params.get("object_name")
        if not bucket_name or not object_name:
            return AdapterResponse(
                success=False, error="Missing required parameters: bucket_name and object_name"
            )

        # MOCK: In production, this would delete the specified blob.
        await asyncio.sleep(0.1)
        return AdapterResponse(
            success=True, data={"status": "deleted", "object": object_name}
        )

    async def _op_get_metadata(self, params: dict[str, Any]) -> AdapterResponse:
        """MOCK: Get metadata for a GCS object."""
        bucket_name = params.get("bucket_name") or self.config.get("default_bucket")
        object_name = params.get("object_name")
        if not bucket_name or not object_name:
            return AdapterResponse(
                success=False, error="Missing required parameters: bucket_name and object_name"
            )

        # MOCK: In production, this would get the blob metadata.
        await asyncio.sleep(0.1)
        mock_metadata = {
            "name": object_name,
            "bucket": bucket_name,
            "size": 12345,
            "content_type": "image/png",
            "created": (datetime.datetime.now(datetime.UTC) - datetime.timedelta(days=1)).isoformat(),
            "updated": datetime.datetime.now(datetime.UTC).isoformat(),
            "storage_class": "STANDARD",
        }
        return AdapterResponse(success=True, data=mock_metadata)

