"""
Schema Loader - Extract JSON schemas from markdown files.

This module provides functionality to load schemas from markdown files that
contain embedded JSON schemas in code blocks, along with YAML frontmatter
metadata and rich documentation.

Markdown Schema Format:
    ---
    schema-id: "https://markitect.dev/schemas/domain/v1"
    version: "1.0.0"
    status: "stable|draft|deprecated"
    ---

    # Schema Title v1.0

    ## Documentation sections...

    ## Schema Definition

    ```json
    {
      "$schema": "http://json-schema.org/draft-07/schema#",
      ...
    }
    ```

This enables:
- Rich documentation alongside schemas
- Version history in same file
- Human-readable schema files
- Markdown-first approach aligned with MarkiTect philosophy
"""

import re
import json
import yaml
from pathlib import Path
from typing import Dict, Any, Optional, List, Tuple


class SchemaLoaderError(Exception):
    """Base exception for schema loading errors."""
    pass


class InvalidSchemaFormatError(SchemaLoaderError):
    """Schema file format is invalid."""
    pass


class SchemaNotFoundError(SchemaLoaderError):
    """No JSON schema found in markdown file."""
    pass


class MarkdownSchemaLoader:
    """
    Load and parse markdown schema files.

    Supports:
    - YAML frontmatter for metadata
    - JSON code blocks for schema definition
    - Validation of schema structure
    - Metadata merging

    Example:
        >>> loader = MarkdownSchemaLoader()
        >>> schema_data = loader.load_schema(Path("manpage-schema-v1.0.md"))
        >>> schema = schema_data['schema']
        >>> metadata = schema_data['metadata']
    """

    def __init__(self):
        """Initialize the schema loader with regex patterns."""
        # Pattern to match YAML frontmatter
        # Matches: --- ... --- at start of file
        self.frontmatter_pattern = re.compile(
            r'^---\s*\n(.*?)\n---\s*\n',
            re.DOTALL | re.MULTILINE
        )

        # Pattern to match JSON code blocks
        # Matches: ```json ... ```
        self.json_code_block_pattern = re.compile(
            r'```json\s*\n(.*?)\n```',
            re.DOTALL | re.MULTILINE
        )

        # Pattern to find Schema Definition section
        # This helps us find the right JSON block if there are multiple
        self.schema_section_pattern = re.compile(
            r'##\s+Schema Definition\s*\n',
            re.MULTILINE
        )

    def load_schema(self, md_path: Path) -> Dict[str, Any]:
        """
        Load schema from markdown file.

        Args:
            md_path: Path to markdown schema file

        Returns:
            Dictionary containing:
                - schema: Extracted JSON schema (dict)
                - metadata: Frontmatter metadata (dict)
                - documentation: Full markdown content (str)
                - source_file: Source file path (str)

        Raises:
            FileNotFoundError: If schema file doesn't exist
            InvalidSchemaFormatError: If file format is invalid
            SchemaNotFoundError: If no JSON schema found

        Example:
            >>> loader = MarkdownSchemaLoader()
            >>> data = loader.load_schema(Path("manpage-schema-v1.0.md"))
            >>> print(data['schema']['title'])
            'Unix Manual Page Schema'
        """
        if not md_path.exists():
            raise FileNotFoundError(f"Schema file not found: {md_path}")

        # Read file content
        try:
            content = md_path.read_text(encoding='utf-8')
        except Exception as e:
            raise InvalidSchemaFormatError(f"Failed to read schema file: {e}")

        # Extract frontmatter
        metadata = self._extract_frontmatter(content)

        # Extract JSON schema
        schema = self._extract_json_schema(content)

        if not schema:
            raise SchemaNotFoundError(
                f"No JSON schema found in {md_path}. "
                f"Expected a ```json code block with schema definition."
            )

        # Merge metadata into schema
        schema = self._merge_metadata(schema, metadata, md_path)

        return {
            'schema': schema,
            'metadata': metadata,
            'documentation': content,
            'source_file': str(md_path)
        }

    def _extract_frontmatter(self, content: str) -> Dict[str, Any]:
        """
        Extract YAML frontmatter from markdown content.

        Args:
            content: Markdown file content

        Returns:
            Dictionary of frontmatter metadata (empty if none found)

        Raises:
            InvalidSchemaFormatError: If YAML is malformed
        """
        match = self.frontmatter_pattern.search(content)
        if not match:
            return {}

        yaml_content = match.group(1)
        try:
            metadata = yaml.safe_load(yaml_content) or {}
            if not isinstance(metadata, dict):
                raise InvalidSchemaFormatError(
                    f"Frontmatter must be a YAML dictionary, got {type(metadata)}"
                )
            return metadata
        except yaml.YAMLError as e:
            raise InvalidSchemaFormatError(f"Invalid YAML frontmatter: {e}")

    def _extract_json_schema(self, content: str) -> Optional[Dict[str, Any]]:
        """
        Extract JSON schema from markdown code blocks.

        Prefers JSON blocks under "## Schema Definition" section,
        but will use first JSON block if no Schema Definition section found.

        Args:
            content: Markdown file content

        Returns:
            JSON schema dictionary or None if not found

        Raises:
            InvalidSchemaFormatError: If JSON is malformed
        """
        # Find all JSON code blocks
        json_blocks = self.json_code_block_pattern.findall(content)

        if not json_blocks:
            return None

        # Try to find the Schema Definition section
        schema_section_match = self.schema_section_pattern.search(content)

        if schema_section_match:
            # Find JSON block that comes after Schema Definition section
            section_pos = schema_section_match.end()

            # Re-search for JSON blocks starting from section position
            remaining_content = content[section_pos:]
            section_json_blocks = self.json_code_block_pattern.findall(remaining_content)

            if section_json_blocks:
                json_text = section_json_blocks[0]
            else:
                # Fallback to first JSON block in entire document
                json_text = json_blocks[0]
        else:
            # No Schema Definition section, use first JSON block
            json_text = json_blocks[0]

        # Parse JSON
        try:
            schema = json.loads(json_text)
            if not isinstance(schema, dict):
                raise InvalidSchemaFormatError(
                    f"Schema must be a JSON object, got {type(schema)}"
                )
            return schema
        except json.JSONDecodeError as e:
            raise InvalidSchemaFormatError(f"Invalid JSON schema: {e}")

    def _merge_metadata(
        self,
        schema: Dict[str, Any],
        metadata: Dict[str, Any],
        source_file: Path
    ) -> Dict[str, Any]:
        """
        Merge frontmatter metadata into schema.

        Adds x-markitect-source extension with file info and metadata.
        Optionally overrides schema fields with frontmatter values.

        Args:
            schema: JSON schema dictionary
            metadata: Frontmatter metadata dictionary
            source_file: Path to source file

        Returns:
            Schema with merged metadata
        """
        # Create a copy to avoid modifying original
        merged_schema = schema.copy()

        # Add MarkiTect-specific source metadata
        merged_schema['x-markitect-source'] = {
            'file': str(source_file),
            'filename': source_file.name,
            'format': 'markdown',
            'frontmatter': metadata
        }

        # Override schema fields with frontmatter if present
        # This allows frontmatter to be the source of truth for metadata
        if 'version' in metadata:
            merged_schema['version'] = metadata['version']

        if 'schema-id' in metadata:
            merged_schema['$id'] = metadata['schema-id']

        if 'status' in metadata:
            if 'x-markitect-metadata' not in merged_schema:
                merged_schema['x-markitect-metadata'] = {}
            merged_schema['x-markitect-metadata']['status'] = metadata['status']

        return merged_schema

    def save_schema(
        self,
        schema: Dict[str, Any],
        md_path: Path,
        template: Optional[str] = None,
        frontmatter: Optional[Dict[str, Any]] = None
    ):
        """
        Save schema as markdown file.

        Args:
            schema: JSON schema dictionary to save
            md_path: Output path for markdown file
            template: Optional markdown template string
            frontmatter: Optional frontmatter metadata (extracted from schema if not provided)

        Raises:
            InvalidSchemaFormatError: If schema is invalid

        Example:
            >>> loader = MarkdownSchemaLoader()
            >>> loader.save_schema(
            ...     schema={'title': 'My Schema', ...},
            ...     md_path=Path('my-schema-v1.0.md')
            ... )
        """
        if template:
            # Use provided template
            content = self._render_template(template, schema, frontmatter)
        else:
            # Generate basic markdown
            content = self._generate_markdown(schema, frontmatter)

        # Create parent directory if needed
        md_path.parent.mkdir(parents=True, exist_ok=True)

        # Write file
        try:
            md_path.write_text(content, encoding='utf-8')
        except Exception as e:
            raise InvalidSchemaFormatError(f"Failed to write schema file: {e}")

    def _generate_markdown(
        self,
        schema: Dict[str, Any],
        frontmatter: Optional[Dict[str, Any]] = None
    ) -> str:
        """
        Generate markdown from schema.

        Args:
            schema: JSON schema dictionary
            frontmatter: Optional frontmatter metadata

        Returns:
            Markdown content as string
        """
        # Extract metadata from schema
        title = schema.get('title', 'Untitled Schema')
        version = schema.get('version', '1.0.0')
        description = schema.get('description', '')
        schema_id = schema.get('$id', '')

        # Build frontmatter
        if frontmatter is None:
            frontmatter = {}

        # Set defaults
        if 'schema-id' not in frontmatter and schema_id:
            frontmatter['schema-id'] = schema_id
        if 'version' not in frontmatter:
            frontmatter['version'] = version
        if 'status' not in frontmatter:
            frontmatter['status'] = 'draft'

        # Generate frontmatter YAML
        frontmatter_yaml = yaml.dump(
            frontmatter,
            default_flow_style=False,
            allow_unicode=True
        ).strip()

        # Generate JSON (pretty-printed)
        schema_json = json.dumps(schema, indent=2, ensure_ascii=False)

        # Build markdown content
        md_content = f"""---
{frontmatter_yaml}
---

# {title} v{version}

## Overview

{description}

## Usage

```bash
markitect validate document.md --schema {Path(frontmatter.get('schema-id', 'schema')).name}
```

## Schema Definition

```json
{schema_json}
```

## Version History

### v{version}
- Initial version
"""

        return md_content

    def _render_template(
        self,
        template: str,
        schema: Dict[str, Any],
        frontmatter: Optional[Dict[str, Any]] = None
    ) -> str:
        """
        Render markdown from template.

        Simple template rendering using string formatting.
        For complex templates, consider using Jinja2 or similar.

        Args:
            template: Template string
            schema: JSON schema dictionary
            frontmatter: Optional frontmatter metadata

        Returns:
            Rendered markdown content
        """
        # Build context for template
        context = {
            'title': schema.get('title', 'Untitled'),
            'version': schema.get('version', '1.0.0'),
            'description': schema.get('description', ''),
            'schema_id': schema.get('$id', ''),
            'schema_json': json.dumps(schema, indent=2, ensure_ascii=False),
            'frontmatter': frontmatter or {},
        }

        # Simple template rendering
        try:
            return template.format(**context)
        except KeyError as e:
            raise InvalidSchemaFormatError(f"Template missing key: {e}")

    def list_json_blocks(self, content: str) -> List[Tuple[int, str]]:
        """
        List all JSON code blocks in markdown content.

        Useful for debugging or when multiple JSON blocks exist.

        Args:
            content: Markdown file content

        Returns:
            List of (position, json_content) tuples

        Example:
            >>> loader = MarkdownSchemaLoader()
            >>> content = Path('schema.md').read_text()
            >>> blocks = loader.list_json_blocks(content)
            >>> print(f"Found {len(blocks)} JSON blocks")
        """
        blocks = []
        for match in self.json_code_block_pattern.finditer(content):
            blocks.append((match.start(), match.group(1)))
        return blocks

    def validate_schema_structure(self, schema: Dict[str, Any]) -> List[str]:
        """
        Validate basic schema structure.

        Checks for required JSON Schema fields and MarkiTect conventions.

        Args:
            schema: JSON schema dictionary

        Returns:
            List of warning/error messages (empty if valid)

        Example:
            >>> loader = MarkdownSchemaLoader()
            >>> issues = loader.validate_schema_structure(schema)
            >>> if issues:
            ...     print("Schema issues:", issues)
        """
        issues = []

        # Check required JSON Schema fields
        if '$schema' not in schema:
            issues.append("Missing required field: $schema")

        if 'type' not in schema:
            issues.append("Missing recommended field: type")

        if 'title' not in schema:
            issues.append("Missing recommended field: title")

        if 'description' not in schema:
            issues.append("Missing recommended field: description")

        # Check MarkiTect conventions
        if 'version' not in schema:
            issues.append("Missing MarkiTect convention: version field")

        if '$id' not in schema:
            issues.append("Missing recommended field: $id")

        # Check $id format if present
        if '$id' in schema:
            schema_id = schema['$id']
            if not isinstance(schema_id, str):
                issues.append("$id must be a string")
            elif not schema_id.startswith('https://'):
                issues.append("$id should be a full HTTPS URL")

        return issues


def auto_ingest_schemas(db_manager=None, schema_dir: Optional[Path] = None, verbose: bool = False) -> Dict[str, Any]:
    """Automatically ingest schemas from markitect/schemas/ directory.

    This function scans the schemas directory for .md schema files and ingests
    any that are not already in the database. Useful for post-install setup
    or automatic schema registration.

    Args:
        db_manager: DatabaseManager instance (optional, will create if not provided)
        schema_dir: Directory containing schemas (defaults to markitect/schemas/)
        verbose: If True, print detailed progress messages

    Returns:
        Dictionary with ingestion results:
        {
            'ingested': [list of schema names that were ingested],
            'skipped': [list of schema names that were already present],
            'failed': [list of (schema_name, error) tuples for failures]
        }

    Example:
        >>> from markitect.schema_loader import auto_ingest_schemas
        >>> results = auto_ingest_schemas(verbose=True)
        >>> print(f"Ingested {len(results['ingested'])} schemas")
    """
    # Determine schema directory
    if schema_dir is None:
        schema_dir = Path(__file__).parent / "schemas"

    if not schema_dir.exists():
        if verbose:
            print(f"⚠️  Schema directory not found: {schema_dir}")
        return {'ingested': [], 'skipped': [], 'failed': []}

    # Initialize database manager if not provided
    if db_manager is None:
        from .database import DatabaseManager
        db_path = Path.home() / '.markitect' / 'markitect.db'
        db_manager = DatabaseManager(str(db_path))
        db_manager.initialize_database()

    # Get list of already ingested schemas
    try:
        existing_schemas = {schema['name'] for schema in db_manager.list_schemas()}
    except Exception as e:
        if verbose:
            print(f"❌ Error listing existing schemas: {e}")
        return {'ingested': [], 'skipped': [], 'failed': []}

    results = {
        'ingested': [],
        'skipped': [],
        'failed': []
    }

    # Find all schema files
    schema_files = list(schema_dir.glob("*-schema-v*.md"))

    if verbose and schema_files:
        print(f"🔍 Found {len(schema_files)} schema file(s) in {schema_dir}")

    loader = MarkdownSchemaLoader()

    for schema_file in sorted(schema_files):
        schema_name = schema_file.name

        # Skip if already ingested
        if schema_name in existing_schemas:
            results['skipped'].append(schema_name)
            if verbose:
                print(f"⏭️  Skipping {schema_name} (already ingested)")
            continue

        # Try to ingest
        try:
            # Load schema
            schema_data_full = loader.load_schema(schema_file)
            schema_data = schema_data_full['schema']

            # Store in database
            schema_content = json.dumps(schema_data, indent=2)
            record_id = db_manager.store_schema_file(schema_name, schema_content)

            if record_id:
                results['ingested'].append(schema_name)
                if verbose:
                    title = schema_data.get('title', schema_name)
                    print(f"✅ Ingested {schema_name} (title: {title})")
            else:
                results['failed'].append((schema_name, "Failed to store in database"))
                if verbose:
                    print(f"❌ Failed to store {schema_name} in database")

        except Exception as e:
            results['failed'].append((schema_name, str(e)))
            if verbose:
                print(f"❌ Failed to ingest {schema_name}: {e}")

    if verbose:
        print(f"\n📊 Auto-ingestion complete:")
        print(f"   Ingested: {len(results['ingested'])}")
        print(f"   Skipped: {len(results['skipped'])}")
        print(f"   Failed: {len(results['failed'])}")

    return results