import re
from collections import Counter
from typing import Dict, List, Set

from api.services.workflow.dto import EdgeDataDTO, NodeType, ReactFlowDTO
from api.services.workflow.errors import ItemKind, WorkflowError
from api.services.workflow.node_data import BaseNodeData
from api.services.workflow.node_specs import all_specs, get_spec

# Regex for matching {{ variable }} template placeholders.
# Captures: group(1) = variable path, group(2) = filter name, group(3) = filter value.
# Shared with api.utils.template_renderer via import.
TEMPLATE_VAR_PATTERN = r"\{\{\s*([^|\s}]+)(?:\s*\|\s*([^:}]+)(?::([^}]+))?)?\s*\}\}"

# Variables injected by the system at runtime, not from source data.
_SYSTEM_VARIABLES = {"campaign_id", "provider", "source_uuid"}


def extract_template_variables(text: str) -> Set[str]:
    """Extract template variable names from a string, excluding nested paths,
    variables with a fallback filter, and system-injected variables."""
    variables: Set[str] = set()
    for match in re.finditer(TEMPLATE_VAR_PATTERN, text):
        var_name = match.group(1).strip()
        filter_name = match.group(2).strip() if match.group(2) else None

        # Skip nested paths (runtime-resolved, e.g. gathered_context.city)
        if "." in var_name:
            continue
        # Skip variables with a fallback (they have a default value)
        # Supports both {{var | default}} and legacy {{var | fallback:default}}
        if filter_name is not None:
            continue
        # Skip system-injected variables
        if var_name in _SYSTEM_VARIABLES:
            continue

        variables.add(var_name)
    return variables


class Edge:
    def __init__(self, source: str, target: str, data: EdgeDataDTO):
        self.source = source
        self.target = target

        self.label = data.label
        self.condition = data.condition
        self.transition_speech = data.transition_speech

        self.data = data

    def get_function_name(self):
        return re.sub(r"[^a-z0-9]", "_", self.label.lower())

    def __eq__(self, other):
        if not isinstance(other, Edge):
            return False
        return self.source == other.source and self.target == other.target

    def __hash__(self):
        return hash((self.source, self.target))


class Node:
    def __init__(self, id: str, node_type: str, data: BaseNodeData):
        self.id, self.node_type, self.data = id, node_type, data
        self.out: Dict[str, "Node"] = {}  # forward nodes
        self.out_edges: List[Edge] = []  # forward edges with properties

        # Start/end semantics are defined by node type. The persisted
        # data flags are legacy UI/runtime state and may be stale.
        self.name = data.name
        self.is_start = node_type == NodeType.startNode.value
        self.is_end = node_type == NodeType.endNode.value

        # Type-specific fields — read with getattr so this works for every
        # node variant in the discriminated union.
        self.prompt = getattr(data, "prompt", None)
        self.allow_interrupt = getattr(data, "allow_interrupt", False)
        self.extraction_enabled = getattr(data, "extraction_enabled", False)
        self.extraction_prompt = getattr(data, "extraction_prompt", None)
        self.extraction_variables = getattr(data, "extraction_variables", None)
        self.add_global_prompt = getattr(data, "add_global_prompt", True)
        self.greeting = getattr(data, "greeting", None)
        self.greeting_type = getattr(data, "greeting_type", None)
        self.greeting_recording_id = getattr(data, "greeting_recording_id", None)
        self.delayed_start = getattr(data, "delayed_start", False)
        self.delayed_start_duration = getattr(data, "delayed_start_duration", None)
        self.tool_uuids = getattr(data, "tool_uuids", None)
        self.document_uuids = getattr(data, "document_uuids", None)
        self.mcp_tool_filters = getattr(data, "mcp_tool_filters", None)
        self.pre_call_fetch_enabled = getattr(data, "pre_call_fetch_enabled", False)
        self.pre_call_fetch_url = getattr(data, "pre_call_fetch_url", None)
        self.pre_call_fetch_credential_uuid = getattr(
            data, "pre_call_fetch_credential_uuid", None
        )

        self.data = data


def _instance_constraint_message(
    label: str,
    count: int,
    *,
    min_count: int | None = None,
    max_count: int | None = None,
) -> str:
    if max_count is not None and count > max_count:
        if max_count == 1:
            return f"Workflow can have at most one {label}"
        return f"Workflow can have at most {max_count} {label} nodes"
    if min_count is not None and count < min_count:
        if min_count == 1:
            return f"Workflow must have at least one {label}"
        return f"Workflow must have at least {min_count} {label} nodes"
    return ""


def validate_node_instance_constraints(
    node_types: list[str],
    *,
    enforce_min_instances: bool = True,
    skip_types: Set[str] | None = None,
) -> list[WorkflowError]:
    """Validate workflow-level node type counts from NodeSpec.graph_constraints."""
    errors: list[WorkflowError] = []
    skip_types = skip_types or set()
    counts = Counter(node_types)

    for spec in all_specs():
        if spec.name in skip_types:
            continue
        gc = spec.graph_constraints
        if gc is None:
            continue

        count = counts.get(spec.name, 0)
        if gc.max_instances is not None and count > gc.max_instances:
            errors.append(
                WorkflowError(
                    kind=ItemKind.workflow,
                    id=None,
                    field=None,
                    message=_instance_constraint_message(
                        spec.display_name,
                        count,
                        max_count=gc.max_instances,
                    ),
                )
            )
        if (
            enforce_min_instances
            and gc.min_instances is not None
            and count < gc.min_instances
        ):
            errors.append(
                WorkflowError(
                    kind=ItemKind.workflow,
                    id=None,
                    field=None,
                    message=_instance_constraint_message(
                        spec.display_name,
                        count,
                        min_count=gc.min_instances,
                    ),
                )
            )

    return errors


class WorkflowGraph:
    """
    *All* business invariants (acyclic, cardinality, etc.) are verified here.
    The constructor accepts a validated ReactFlowDTO.
    """

    def __init__(
        self,
        dto: ReactFlowDTO,
        *,
        skip_instance_constraints_for: Set[str] | None = None,
    ):
        # Build adjacency list from validated DTO nodes. Core node comparisons
        # still use NodeType string enums; integration nodes remain plain
        # strings and resolve constraints through node specs.
        self.nodes: Dict[str, Node] = {
            n.id: Node(n.id, n.type, n.data) for n in dto.nodes
        }

        # Store all edges
        self.edges: List[Edge] = []

        for e in dto.edges:
            source_node = self.nodes[e.source]
            target_node = self.nodes[e.target]

            # Create the edge with properties from dto
            edge = Edge(source=e.source, target=e.target, data=e.data)

            # Add to the edge list
            self.edges.append(edge)

            # Add to the source node's outgoing edges
            source_node.out_edges.append(edge)

            # Set up the node references for backward compatibility
            source_node.out[target_node.id] = target_node

        self._validate_graph(skip_instance_constraints_for or set())

        # Get a reference to the start node
        self.start_node_id = [
            n.id for n in dto.nodes if n.type == NodeType.startNode.value
        ][0]

        # Get a reference to the global node
        try:
            self.global_node_id = [
                n.id for n in dto.nodes if n.type == NodeType.globalNode.value
            ][0]
        except IndexError:
            self.global_node_id = None

    # -----------------------------------------------------------
    # template variable extraction
    # -----------------------------------------------------------
    def get_required_template_variables(self) -> Set[str]:
        """Extract all template variables referenced in node prompts/greetings
        and edge transition speeches.

        Scans:
          - Start node: prompt, greeting
          - Agent / End / Global nodes: prompt
          - All edges: transition_speech

        Returns a set of top-level variable names that the workflow expects
        from the source data (excluding nested paths, fallback vars, and
        system-injected vars).
        """
        variables: Set[str] = set()

        for node in self.nodes.values():
            if node.node_type in (
                NodeType.startNode,
                NodeType.agentNode,
                NodeType.endNode,
                NodeType.globalNode,
            ):
                if node.prompt:
                    variables |= extract_template_variables(node.prompt)

            # greeting is only relevant on the start node
            if node.node_type == NodeType.startNode and node.greeting:
                variables |= extract_template_variables(node.greeting)

        for edge in self.edges:
            if edge.transition_speech:
                variables |= extract_template_variables(edge.transition_speech)

        return variables

    # -----------------------------------------------------------
    # validators
    # -----------------------------------------------------------
    def _validate_graph(self, skip_instance_constraints_for: Set[str]) -> None:
        errors: list[WorkflowError] = []

        # TODO: Figure out what kind of cyclic contraints can be applied, since there can be a cycle in the graph
        # try:
        #     self._assert_acyclic()
        # except ValueError as e:
        #     errors.append(
        #         WorkflowError(
        #             kind=ItemKind.workflow, id=None, field=None, message=str(e)
        #         )
        #     )

        errors.extend(
            validate_node_instance_constraints(
                [n.node_type for n in self.nodes.values()],
                skip_types=skip_instance_constraints_for,
            )
        )
        errors.extend(self._assert_connection_counts())
        errors.extend(self._assert_node_configs())
        if errors:
            raise ValueError(errors)

    def _assert_acyclic(self):
        color: Dict[str, str] = {}  # white / gray / black

        def dfs(n: Node):
            if color.get(n.id) == "gray":  # back-edge
                raise ValueError("workflow contains a cycle")
            if color.get(n.id) != "black":
                color[n.id] = "gray"
                for m in n.out.values():
                    dfs(m)
                color[n.id] = "black"

        for n in self.nodes.values():
            dfs(n)

    def _assert_connection_counts(self):
        """Enforce per-type incoming/outgoing edge constraints.

        Driven by `NodeSpec.graph_constraints` so a single source of truth
        in the spec dictates what's legal. Types without a `graph_constraints`
        block are unconstrained (e.g. agentNode on the outgoing side).
        """
        errors: list[WorkflowError] = []

        out_deg = Counter()
        in_deg = Counter()
        for n in self.nodes.values():
            out_deg[n.id] = in_deg[n.id] = 0
        for src, n in self.nodes.items():
            for m in n.out.values():
                out_deg[src] += 1
                in_deg[m.id] += 1

        for n in self.nodes.values():
            spec = get_spec(n.node_type)
            if spec is None or spec.graph_constraints is None:
                continue
            gc = spec.graph_constraints
            in_d, out_d = in_deg[n.id], out_deg[n.id]
            label = spec.display_name

            if gc.max_incoming is not None and in_d > gc.max_incoming:
                msg = (
                    f"{label} cannot have incoming edges"
                    if gc.max_incoming == 0
                    else f"{label} can have at most {gc.max_incoming} incoming edge(s)"
                )
                errors.append(
                    WorkflowError(kind=ItemKind.node, id=n.id, field=None, message=msg)
                )
            if gc.min_incoming is not None and in_d < gc.min_incoming:
                errors.append(
                    WorkflowError(
                        kind=ItemKind.node,
                        id=n.id,
                        field=None,
                        message=f"{label} must have at least {gc.min_incoming} incoming edge(s)",
                    )
                )
            if gc.max_outgoing is not None and out_d > gc.max_outgoing:
                msg = (
                    f"{label} cannot have outgoing edges"
                    if gc.max_outgoing == 0
                    else f"{label} can have at most {gc.max_outgoing} outgoing edge(s)"
                )
                errors.append(
                    WorkflowError(kind=ItemKind.node, id=n.id, field=None, message=msg)
                )
            if gc.min_outgoing is not None and out_d < gc.min_outgoing:
                errors.append(
                    WorkflowError(
                        kind=ItemKind.node,
                        id=n.id,
                        field=None,
                        message=f"{label} must have at least {gc.min_outgoing} outgoing edge(s)",
                    )
                )

        return errors

    def _assert_node_configs(self):
        """Validate node-specific configuration constraints."""
        errors: list[WorkflowError] = []

        for node in self.nodes.values():
            # Validate StartNode constraints
            if node.node_type == NodeType.startNode:
                # No specific validations for start node at this time
                pass

        return errors