"""
Extraction Schemas for Document Intelligence

Pydantic models for schema-based field extraction, tables, and charts.
"""

from enum import Enum
from typing import List, Dict, Any, Optional, Union
from pydantic import BaseModel, Field

from .core import BoundingBox, EvidenceRef


class FieldType(str, Enum):
    """Supported field types for extraction."""
    STRING = "string"
    INTEGER = "integer"
    FLOAT = "float"
    BOOLEAN = "boolean"
    DATE = "date"
    CURRENCY = "currency"
    PERCENTAGE = "percentage"
    EMAIL = "email"
    PHONE = "phone"
    ADDRESS = "address"
    LIST = "list"
    OBJECT = "object"


class FieldDefinition(BaseModel):
    """
    Definition of a field to extract from a document.
    Used to build extraction schemas.
    """
    name: str = Field(..., description="Field name/key")
    type: FieldType = Field(..., description="Expected data type")
    description: str = Field(..., description="Human-readable description")
    required: bool = Field(default=False, description="Whether field is required")

    # Validation constraints
    pattern: Optional[str] = Field(default=None, description="Regex pattern for validation")
    min_value: Optional[float] = Field(default=None, description="Minimum numeric value")
    max_value: Optional[float] = Field(default=None, description="Maximum numeric value")
    enum_values: Optional[List[str]] = Field(default=None, description="Allowed values")

    # Extraction hints
    aliases: List[str] = Field(
        default_factory=list,
        description="Alternative names/labels for the field"
    )
    search_context: Optional[str] = Field(
        default=None,
        description="Context hint for where to find this field"
    )

    # Nested fields (for object/list types)
    nested_fields: Optional[List["FieldDefinition"]] = Field(
        default=None,
        description="Nested field definitions for complex types"
    )


class ExtractionSchema(BaseModel):
    """
    Schema defining fields to extract from a document.
    Supports document-type-specific extraction rules.
    """
    schema_id: str = Field(..., description="Unique schema identifier")
    name: str = Field(..., description="Human-readable schema name")
    description: str = Field(..., description="Schema description")
    version: str = Field(default="1.0", description="Schema version")

    # Field definitions
    fields: List[FieldDefinition] = Field(
        default_factory=list,
        description="Fields to extract"
    )

    # Document type association
    document_types: List[str] = Field(
        default_factory=list,
        description="Applicable document types"
    )

    # Validation rules
    cross_field_validations: List[str] = Field(
        default_factory=list,
        description="Cross-field validation expressions"
    )

    # Extraction configuration
    require_evidence: bool = Field(
        default=True,
        description="Require evidence for all extracted fields"
    )
    min_confidence: float = Field(
        default=0.7,
        ge=0.0,
        le=1.0,
        description="Minimum confidence threshold"
    )
    abstain_on_low_confidence: bool = Field(
        default=True,
        description="Abstain rather than guess when confidence is low"
    )

    def get_field(self, name: str) -> Optional[FieldDefinition]:
        """Get field definition by name."""
        for field in self.fields:
            if field.name == name or name in field.aliases:
                return field
        return None

    def get_required_fields(self) -> List[FieldDefinition]:
        """Get all required field definitions."""
        return [f for f in self.fields if f.required]


class TableCell(BaseModel):
    """
    Single cell in a table structure.
    """
    cell_id: str = Field(..., description="Unique cell identifier")
    row: int = Field(..., ge=0, description="Row index (0-based)")
    col: int = Field(..., ge=0, description="Column index (0-based)")
    text: str = Field(..., description="Cell text content")
    bbox: BoundingBox = Field(..., description="Cell bounding box")

    # Span information
    row_span: int = Field(default=1, ge=1, description="Number of rows spanned")
    col_span: int = Field(default=1, ge=1, description="Number of columns spanned")

    # Cell type
    is_header: bool = Field(default=False, description="Whether cell is a header")
    is_empty: bool = Field(default=False, description="Whether cell is empty")

    # Confidence
    confidence: float = Field(default=1.0, ge=0.0, le=1.0)


class TableData(BaseModel):
    """
    Structured table data extracted from a document.
    """
    table_id: str = Field(..., description="Unique table identifier")
    page: int = Field(..., ge=0, description="Page number")
    bbox: BoundingBox = Field(..., description="Table bounding box")

    # Structure
    num_rows: int = Field(..., ge=1, description="Number of rows")
    num_cols: int = Field(..., ge=1, description="Number of columns")
    cells: List[TableCell] = Field(default_factory=list, description="All cells")

    # Headers
    header_rows: List[int] = Field(
        default_factory=list,
        description="Row indices that are headers"
    )
    header_cols: List[int] = Field(
        default_factory=list,
        description="Column indices that are headers"
    )

    # Caption
    caption: Optional[str] = Field(default=None, description="Table caption")
    caption_bbox: Optional[BoundingBox] = Field(default=None)

    # Confidence
    confidence: float = Field(default=1.0, ge=0.0, le=1.0)

    # Evidence
    evidence: Optional[EvidenceRef] = Field(default=None)

    def to_markdown(self) -> str:
        """Convert table to markdown format."""
        if not self.cells:
            return ""

        # Build grid
        grid = [[None for _ in range(self.num_cols)] for _ in range(self.num_rows)]
        for cell in self.cells:
            if cell.row < self.num_rows and cell.col < self.num_cols:
                grid[cell.row][cell.col] = cell.text

        # Generate markdown
        lines = []
        for i, row in enumerate(grid):
            line = "| " + " | ".join(str(c) if c else "" for c in row) + " |"
            lines.append(line)
            if i == 0 or i in self.header_rows:
                lines.append("|" + "|".join(["---"] * self.num_cols) + "|")

        return "\n".join(lines)

    def to_dict_list(self) -> List[Dict[str, str]]:
        """Convert table to list of dictionaries (using first row as keys)."""
        if not self.cells or self.num_rows < 2:
            return []

        # Build grid
        grid = [[None for _ in range(self.num_cols)] for _ in range(self.num_rows)]
        for cell in self.cells:
            if cell.row < self.num_rows and cell.col < self.num_cols:
                grid[cell.row][cell.col] = cell.text

        # Use first row as headers
        headers = [str(h) if h else f"col_{i}" for i, h in enumerate(grid[0])]

        # Build list of dicts
        result = []
        for row in grid[1:]:
            row_dict = {headers[i]: str(v) if v else "" for i, v in enumerate(row)}
            result.append(row_dict)

        return result


class ChartType(str, Enum):
    """Types of charts/graphs."""
    BAR = "bar"
    LINE = "line"
    PIE = "pie"
    SCATTER = "scatter"
    AREA = "area"
    HISTOGRAM = "histogram"
    BOX = "box"
    HEATMAP = "heatmap"
    TREEMAP = "treemap"
    FLOWCHART = "flowchart"
    DIAGRAM = "diagram"
    OTHER = "other"


class ChartData(BaseModel):
    """
    Structured chart/graph data extracted from a document.
    """
    chart_id: str = Field(..., description="Unique chart identifier")
    page: int = Field(..., ge=0, description="Page number")
    bbox: BoundingBox = Field(..., description="Chart bounding box")
    chart_type: ChartType = Field(..., description="Type of chart")

    # Chart content
    title: Optional[str] = Field(default=None, description="Chart title")
    x_axis_label: Optional[str] = Field(default=None, description="X-axis label")
    y_axis_label: Optional[str] = Field(default=None, description="Y-axis label")

    # Data series
    series: List[Dict[str, Any]] = Field(
        default_factory=list,
        description="Data series extracted from chart"
    )

    # Trends and insights
    trends: List[str] = Field(
        default_factory=list,
        description="Identified trends or patterns"
    )

    # Caption
    caption: Optional[str] = Field(default=None, description="Chart caption")

    # Confidence and evidence
    confidence: float = Field(default=1.0, ge=0.0, le=1.0)
    evidence: Optional[EvidenceRef] = Field(default=None)

    # Raw description (for LLM extraction)
    description: Optional[str] = Field(
        default=None,
        description="Natural language description of the chart"
    )


class ExtractedField(BaseModel):
    """
    A single extracted field value with evidence.
    """
    field_name: str = Field(..., description="Field name from schema")
    value: Any = Field(..., description="Extracted value")
    confidence: float = Field(..., ge=0.0, le=1.0, description="Extraction confidence")
    evidence: List[EvidenceRef] = Field(
        default_factory=list,
        description="Evidence supporting the extraction"
    )

    # Validation status
    is_valid: bool = Field(default=True, description="Whether value passed validation")
    validation_errors: List[str] = Field(
        default_factory=list,
        description="Validation error messages"
    )

    # Abstention
    abstained: bool = Field(
        default=False,
        description="Whether extraction was abstained"
    )
    abstain_reason: Optional[str] = Field(
        default=None,
        description="Reason for abstention"
    )

    @property
    def is_grounded(self) -> bool:
        """Check if extraction has evidence."""
        return len(self.evidence) > 0 and not self.abstained