"""
Code generator for dbt to Feast imports.

This module generates Python code files containing Feast object definitions
(Entity, DataSource, FeatureView) from dbt model metadata.
"""

import logging
from typing import Any, List, Optional, Set, Union

from jinja2 import BaseLoader, Environment

from feast.dbt.mapper import map_dbt_type_to_feast_type
from feast.dbt.parser import DbtModel
from feast.types import (
    Array,
    Bool,
    Bytes,
    Float32,
    Float64,
    Int32,
    Int64,
    String,
    UnixTimestamp,
)

logger = logging.getLogger(__name__)

# Template for generating a complete Feast definitions file
FEAST_FILE_TEMPLATE = '''"""
Feast feature definitions generated from dbt models.

Source: {{ manifest_path }}
Project: {{ project_name }}
Generated by: feast dbt import
"""

from datetime import timedelta

from feast import Entity, FeatureView, Field
{% if type_imports %}
from feast.types import {{ type_imports | join(', ') }}
{% endif %}
{% if data_source_type == 'bigquery' %}
from feast.infra.offline_stores.bigquery_source import BigQuerySource
{% elif data_source_type == 'snowflake' %}
from feast.infra.offline_stores.snowflake_source import SnowflakeSource
{% elif data_source_type == 'file' %}
from feast.infra.offline_stores.file_source import FileSource
{% endif %}


# =============================================================================
# Entities
# =============================================================================

{% for entity in entities %}
{{ entity.var_name }} = Entity(
    name="{{ entity.name }}",
    join_keys=["{{ entity.join_key }}"],
    description="{{ entity.description }}",
    tags={{ entity.tags }},
)

{% endfor %}

# =============================================================================
# Data Sources
# =============================================================================

{% for source in data_sources %}
{% if data_source_type == 'bigquery' %}
{{ source.var_name }} = BigQuerySource(
    name="{{ source.name }}",
    table="{{ source.table }}",
    timestamp_field="{{ source.timestamp_field }}",
    description="{{ source.description }}",
    tags={{ source.tags }},
)
{% elif data_source_type == 'snowflake' %}
{{ source.var_name }} = SnowflakeSource(
    name="{{ source.name }}",
    database="{{ source.database }}",
    schema="{{ source.schema }}",
    table="{{ source.table }}",
    timestamp_field="{{ source.timestamp_field }}",
    description="{{ source.description }}",
    tags={{ source.tags }},
)
{% elif data_source_type == 'file' %}
{{ source.var_name }} = FileSource(
    name="{{ source.name }}",
    path="{{ source.path }}",
    timestamp_field="{{ source.timestamp_field }}",
    description="{{ source.description }}",
    tags={{ source.tags }},
)
{% endif %}

{% endfor %}

# =============================================================================
# Feature Views
# =============================================================================

{% for fv in feature_views %}
{{ fv.var_name }} = FeatureView(
    name="{{ fv.name }}",
    entities=[{{ fv.entity_vars | join(', ') }}],
    ttl=timedelta(days={{ fv.ttl_days }}),
    schema=[
{% for field in fv.fields %}
        Field(name="{{ field.name }}", dtype={{ field.dtype }}{% if field.description %}, description="{{ field.description }}"{% endif %}),
{% endfor %}
    ],
    online={{ fv.online }},
    source={{ fv.source_var }},
    description="{{ fv.description }}",
    tags={{ fv.tags }},
)

{% endfor %}
'''


def _get_feast_type_name(feast_type: Any) -> str:
    """Get the string name of a Feast type for code generation."""
    if isinstance(feast_type, Array):
        # Safely get base_type. Should always exist since Array.__init__ sets it.
        # Example: Array(String) -> base_type = String
        base_type = getattr(feast_type, "base_type", None)

        if base_type is None:
            logger.warning(
                "Array type missing 'base_type' attribute. "
                "This indicates a bug in Array initialization. Falling back to String."
            )
            base_type = String

        base_type_name = _get_feast_type_name(base_type)
        return f"Array({base_type_name})"

    # Map type objects to their names.
    # Note: ImageBytes and PdfBytes are excluded since dbt manifests only expose
    # generic BYTES type without semantic information about binary content.
    type_map = {
        String: "String",
        Int32: "Int32",
        Int64: "Int64",
        Float32: "Float32",
        Float64: "Float64",
        Bool: "Bool",
        UnixTimestamp: "UnixTimestamp",
        Bytes: "Bytes",
    }

    return type_map.get(feast_type, "String")


def _make_var_name(name: str) -> str:
    """Convert a name to a valid Python variable name."""
    # Replace hyphens and spaces with underscores
    var_name = name.replace("-", "_").replace(" ", "_")
    # Ensure it starts with a letter or underscore
    if var_name and var_name[0].isdigit():
        var_name = f"_{var_name}"
    return var_name


def _escape_description(desc: Optional[str]) -> str:
    """Escape a description string for use in Python code."""
    if not desc:
        return ""
    # Escape quotes and newlines
    return desc.replace("\\", "\\\\").replace('"', '\\"').replace("\n", " ")


class DbtCodeGenerator:
    """
    Generates Python code for Feast objects from dbt models.

    This class creates complete, importable Python files containing
    Entity, DataSource, and FeatureView definitions.

    Example::

        generator = DbtCodeGenerator(
            data_source_type="bigquery",
            timestamp_field="event_timestamp",
            ttl_days=7
        )
        code = generator.generate(
            models=models,
            entity_column="user_id",
            manifest_path="target/manifest.json",
            project_name="my_project"
        )
        with open("features.py", "w") as f:
            f.write(code)
    """

    def __init__(
        self,
        data_source_type: str = "bigquery",
        timestamp_field: str = "event_timestamp",
        ttl_days: int = 1,
    ):
        self.data_source_type = data_source_type.lower()
        self.timestamp_field = timestamp_field
        self.ttl_days = ttl_days

        # Set up Jinja2 environment
        self.env = Environment(
            loader=BaseLoader(),
            trim_blocks=True,
            lstrip_blocks=True,
        )
        self.template = self.env.from_string(FEAST_FILE_TEMPLATE)

    def generate(
        self,
        models: List[DbtModel],
        entity_columns: Union[str, List[str]],
        manifest_path: str = "",
        project_name: str = "",
        exclude_columns: Optional[List[str]] = None,
        online: bool = True,
    ) -> str:
        """
        Generate Python code for Feast objects from dbt models.

        Args:
            models: List of DbtModel objects to generate code for
            entity_columns: Entity column name(s) - single string or list of strings
            manifest_path: Path to the dbt manifest (for documentation)
            project_name: dbt project name (for documentation)
            exclude_columns: Columns to exclude from features
            online: Whether to enable online serving

        Returns:
            Generated Python code as a string
        """
        # Normalize entity_columns to list
        entity_cols: List[str] = (
            [entity_columns] if isinstance(entity_columns, str) else entity_columns
        )

        if not entity_cols:
            raise ValueError("At least one entity column must be specified")

        # Note: entity columns should NOT be excluded - FeatureView.__init__
        # expects entity columns to be in the schema and will extract them
        excluded = {self.timestamp_field}
        if exclude_columns:
            excluded.update(exclude_columns)

        # Collect all Feast types used for imports
        type_imports: Set[str] = set()

        # Prepare entity data - create one entity per entity column
        entities = []
        entity_vars = []  # Track variable names for feature views
        for entity_col in entity_cols:
            entity_var = _make_var_name(entity_col)
            entity_vars.append(entity_var)
            entities.append(
                {
                    "var_name": entity_var,
                    "name": entity_col,
                    "join_key": entity_col,
                    "description": "Entity key for dbt models",
                    "tags": {"source": "dbt"},
                }
            )

        # Prepare data sources and feature views
        data_sources = []
        feature_views = []

        for model in models:
            # Check required columns exist
            column_names = [c.name for c in model.columns]
            if self.timestamp_field not in column_names:
                continue

            # Skip if ANY entity column is missing
            if not all(e in column_names for e in entity_cols):
                continue

            # Build tags
            tags = {"dbt.model": model.name}
            for tag in model.tags:
                tags[f"dbt.tag.{tag}"] = "true"

            # Data source
            source_var = _make_var_name(f"{model.name}_source")
            source_data = {
                "var_name": source_var,
                "name": f"{model.name}_source",
                "timestamp_field": self.timestamp_field,
                "description": _escape_description(model.description),
                "tags": tags,
            }

            if self.data_source_type == "bigquery":
                source_data["table"] = model.full_table_name
            elif self.data_source_type == "snowflake":
                source_data["database"] = model.database
                source_data["schema"] = model.schema
                source_data["table"] = model.alias
            elif self.data_source_type == "file":
                source_data["path"] = f"/data/{model.name}.parquet"

            data_sources.append(source_data)

            # Feature view fields
            fields = []
            for column in model.columns:
                if column.name in excluded:
                    continue

                feast_type = map_dbt_type_to_feast_type(column.data_type)
                type_name = _get_feast_type_name(feast_type)

                # Track base type for imports. For Array types, import both Array and base type.
                # Example: Array(Int64) requires imports: Array, Int64
                if isinstance(feast_type, Array):
                    type_imports.add("Array")

                    base_type = getattr(feast_type, "base_type", None)
                    if base_type is None:
                        logger.warning(
                            "Array type missing 'base_type' attribute while generating imports. "
                            "This indicates a bug in Array initialization. Falling back to String."
                        )
                        base_type = String

                    base_type_name = _get_feast_type_name(base_type)
                    type_imports.add(base_type_name)
                else:
                    type_imports.add(type_name)

                fields.append(
                    {
                        "name": column.name,
                        "dtype": type_name,
                        "description": _escape_description(column.description),
                    }
                )

            # Feature view
            fv_var = _make_var_name(f"{model.name}_fv")
            feature_views.append(
                {
                    "var_name": fv_var,
                    "name": model.name,
                    "entity_vars": entity_vars,
                    "source_var": source_var,
                    "ttl_days": self.ttl_days,
                    "fields": fields,
                    "online": online,
                    "description": _escape_description(model.description),
                    "tags": tags,
                }
            )

        # Sort type imports for consistent output
        sorted_types = sorted(type_imports)

        # Render template
        return self.template.render(
            manifest_path=manifest_path,
            project_name=project_name,
            data_source_type=self.data_source_type,
            type_imports=sorted_types,
            entities=entities,
            data_sources=data_sources,
            feature_views=feature_views,
        )


def generate_feast_code(
    models: List[DbtModel],
    entity_columns: Union[str, List[str]],
    data_source_type: str = "bigquery",
    timestamp_field: str = "event_timestamp",
    ttl_days: int = 1,
    manifest_path: str = "",
    project_name: str = "",
    exclude_columns: Optional[List[str]] = None,
    online: bool = True,
) -> str:
    """
    Convenience function to generate Feast code from dbt models.

    Args:
        models: List of DbtModel objects
        entity_columns: Entity column name(s) - single string or list of strings
        data_source_type: Type of data source (bigquery, snowflake, file)
        timestamp_field: Timestamp column name
        ttl_days: TTL in days for feature views
        manifest_path: Path to manifest for documentation
        project_name: Project name for documentation
        exclude_columns: Columns to exclude from features
        online: Whether to enable online serving

    Returns:
        Generated Python code as a string
    """
    generator = DbtCodeGenerator(
        data_source_type=data_source_type,
        timestamp_field=timestamp_field,
        ttl_days=ttl_days,
    )

    return generator.generate(
        models=models,
        entity_columns=entity_columns,
        manifest_path=manifest_path,
        project_name=project_name,
        exclude_columns=exclude_columns,
        online=online,
    )