"""LookML adapter for importing Looker semantic models.

Note: This is a simplified LookML parser. Full LookML parsing would require
a complete LookML grammar parser (LookML uses a custom DSL).
"""

from pathlib import Path

from sidemantic.adapters.base import BaseAdapter
from sidemantic.core.dimension import Dimension
from sidemantic.core.metric import Metric
from sidemantic.core.model import Model
from sidemantic.core.relationship import Relationship
from sidemantic.core.semantic_graph import SemanticGraph


class LookMLAdapter(BaseAdapter):
    """Adapter for importing LookML view definitions.

    Transforms LookML definitions into Sidemantic format:
    - Views → Models
    - Dimensions → Dimensions
    - Measures → Measures
    - dimension_group (time) → Time dimensions

    Note: This is a simplified parser. Full LookML support would require
    a complete grammar parser and handling of explores/joins.
    """

    def parse(self, source: str | Path) -> SemanticGraph:
        """Parse LookML files into semantic graph.

        Args:
            source: Path to .lkml file or directory

        Returns:
            Semantic graph with imported models

        Raises:
            NotImplementedError: LookML parsing requires a full grammar parser
        """
        raise NotImplementedError(
            "LookML adapter requires a full grammar parser. "
            "Consider using lkml library or contribute a parser implementation."
        )

    def _parse_file(self, file_path: Path, graph: SemanticGraph) -> None:
        """Parse a single LookML file.

        This is a placeholder for full LookML parsing logic.
        """
        # Full implementation would use lkml library or custom parser
        # to parse LookML's custom syntax
        pass

    def _parse_view(self, view_def: dict) -> Model | None:
        """Parse LookML view into Sidemantic model.

        Args:
            view_def: View definition dictionary (after parsing)

        Returns:
            Model instance or None
        """
        name = view_def.get("name")
        if not name:
            return None

        # Get table name
        table = view_def.get("sql_table_name")

        # Parse derived table SQL
        sql = None
        derived_table = view_def.get("derived_table")
        if derived_table:
            sql = derived_table.get("sql")

        # Parse dimensions and find primary key
        dimensions = []
        primary_key = "id"  # default

        for dim_def in view_def.get("dimensions", []):
            dim = self._parse_dimension(dim_def)
            if dim:
                dimensions.append(dim)

                # Check for primary key
                if dim_def.get("primary_key"):
                    primary_key = dim.name

        # Parse dimension_group (time dimensions)
        for dim_group_def in view_def.get("dimension_groups", []):
            dims = self._parse_dimension_group(dim_group_def)
            dimensions.extend(dims)

        # Parse measures
        measures = []
        for measure_def in view_def.get("measures", []):
            measure = self._parse_measure(measure_def)
            if measure:
                measures.append(measure)

        return Model(
            name=name,
            table=table,
            sql=sql,
            primary_key=primary_key,
            dimensions=dimensions,
            metrics=measures,
        )

    def _parse_dimension(self, dim_def: dict) -> Dimension | None:
        """Parse LookML dimension.

        Args:
            dim_def: Dimension definition

        Returns:
            Dimension instance or None
        """
        name = dim_def.get("name")
        if not name:
            return None

        dim_type = dim_def.get("type", "string")

        # Map LookML types to Sidemantic types
        type_mapping = {
            "string": "categorical",
            "number": "numeric",
            "yesno": "boolean",
            "tier": "categorical",
        }

        sidemantic_type = type_mapping.get(dim_type, "categorical")

        return Dimension(
            name=name,
            type=sidemantic_type,
            sql=dim_def.get("sql"),
            description=dim_def.get("description"),
        )

    def _parse_dimension_group(self, dim_group_def: dict) -> list[Dimension]:
        """Parse LookML dimension_group (time dimensions).

        Args:
            dim_group_def: Dimension group definition

        Returns:
            List of time dimensions with different granularities
        """
        name = dim_group_def.get("name")
        if not name:
            return []

        group_type = dim_group_def.get("type", "time")
        if group_type != "time":
            return []

        timeframes = dim_group_def.get("timeframes", ["date"])

        # Create a dimension for each timeframe
        dimensions = []
        for timeframe in timeframes:
            if timeframe == "raw":
                continue  # Skip raw timeframe

            # Map LookML timeframe to granularity
            granularity_mapping = {
                "time": "hour",
                "date": "day",
                "week": "week",
                "month": "month",
                "quarter": "quarter",
                "year": "year",
            }

            granularity = granularity_mapping.get(timeframe, "day")

            dimensions.append(
                Dimension(
                    name=f"{name}_{timeframe}",
                    type="time",
                    sql=dim_group_def.get("sql"),
                    granularity=granularity,
                )
            )

        return dimensions

    def _parse_measure(self, measure_def: dict) -> Metric | None:
        """Parse LookML measure.

        Args:
            measure_def: Metric definition

        Returns:
            Measure instance or None
        """
        name = measure_def.get("name")
        if not name:
            return None

        measure_type = measure_def.get("type", "count")

        # Map LookML measure types
        type_mapping = {
            "count": "count",
            "count_distinct": "count_distinct",
            "sum": "sum",
            "average": "avg",
            "min": "min",
            "max": "max",
        }

        agg_type = type_mapping.get(measure_type, "count")

        # Parse filters
        filters = []
        for filter_def in measure_def.get("filters", []):
            # LookML filters are more complex, this is simplified
            if isinstance(filter_def, dict):
                for field, value in filter_def.items():
                    filters.append(f"{field} = '{value}'")

        return Metric(
            name=name,
            agg=agg_type,
            sql=measure_def.get("sql"),
            filters=filters if filters else None,
            description=measure_def.get("description"),
        )
