"""
Constraint Parallel Strategy for search optimization.

Key features:
1. Runs separate searches for each constraint in parallel
2. Uses entity type detection to focus all searches
3. Collects candidates from all constraint-specific searches
4. Evaluates candidates using the existing evaluation system
5. Early rejection of poor candidates for efficiency
"""

import concurrent.futures
import threading
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple

from loguru import logger

from ..candidates.base_candidate import Candidate
from ..constraints.base_constraint import Constraint
from .dual_confidence_with_rejection import DualConfidenceWithRejectionStrategy


@dataclass
class ConstraintSearchState:
    """Tracks the state of parallel constraint-specific searches."""

    all_candidates: List[Candidate] = field(default_factory=list)
    evaluated_candidates: List[Tuple[Candidate, float]] = field(
        default_factory=list
    )
    total_evaluated: int = 0
    start_time: float = field(default_factory=time.time)
    constraint_searches: Dict[str, List[Candidate]] = field(
        default_factory=dict
    )
    candidates_lock: threading.Lock = field(default_factory=threading.Lock)
    stop_search: threading.Event = field(default_factory=threading.Event)
    evaluation_futures: List[concurrent.futures.Future] = field(
        default_factory=list
    )
    entity_type: str = "unknown entity"


class ConstraintParallelStrategy(DualConfidenceWithRejectionStrategy):
    """
    Strategy that runs parallel searches for each constraint independently.

    Rather than combining constraints in queries, this approach:
    1. Runs a separate search for each constraint in parallel
    2. Uses entity type to focus all searches
    3. Collects candidates from all constraint searches
    4. Evaluates candidates as they're found
    5. Identifies candidates that match multiple constraints
    """

    def __init__(
        self,
        *args,
        # Concurrent execution settings
        max_workers: int = 10,
        # Candidate targets
        min_good_candidates: int = 3,
        target_candidates: int = 5,
        max_candidates: int = 10,
        # Quality thresholds
        min_score_threshold: float = 0.65,
        exceptional_score: float = 0.95,
        quality_plateau_threshold: float = 0.1,
        # Time and resource limits
        max_search_time: float = 30.0,
        max_evaluations: int = 30,
        # Search behavior
        initial_search_timeout: float = 5.0,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)

        # Thread pool for concurrent operations
        self.search_executor = concurrent.futures.ThreadPoolExecutor(
            max_workers=max_workers
        )
        self.evaluation_executor = concurrent.futures.ThreadPoolExecutor(
            max_workers=max_workers
        )

        # Candidate thresholds
        self.min_good_candidates = min_good_candidates
        self.target_candidates = target_candidates
        self.max_candidates = max_candidates

        # Quality settings
        self.min_score_threshold = min_score_threshold
        self.exceptional_score = exceptional_score
        self.quality_plateau_threshold = quality_plateau_threshold

        # Resource limits
        self.max_search_time = max_search_time
        self.max_evaluations = max_evaluations
        self.initial_search_timeout = initial_search_timeout

        # Search state
        self.state: Optional[ConstraintSearchState] = None

    def find_relevant_information(self):
        """Override to use parallel constraint-specific searches."""
        # Initialize state
        self.state = ConstraintSearchState(start_time=time.time())

        # Detect entity type first to guide all searches
        self.state.entity_type = self._detect_entity_type()
        logger.info(f"Detected entity type: {self.state.entity_type}")

        if self.progress_callback:
            self.progress_callback(
                f"Starting parallel searches for {self.state.entity_type}",
                10,
                {
                    "phase": "entity_detection",
                    "entity_type": self.state.entity_type,
                },
            )

        # Start parallel constraint searches with concurrent evaluation
        try:
            self._run_parallel_constraint_searches()
        finally:
            # Clean up thread pools
            self.search_executor.shutdown(wait=False)
            self.evaluation_executor.shutdown(wait=False)

        # Return best candidates
        self.candidates = [
            c
            for c, _ in sorted(
                self.state.evaluated_candidates,
                key=lambda x: x[1],
                reverse=True,
            )[: self.max_candidates]
        ]

        logger.info(
            f"Found {len(self.candidates)} candidates after parallel constraint searches"
        )

    def _detect_entity_type(self) -> str:
        """Use LLM to detect what type of entity we're searching for."""
        # Build context from constraints
        constraint_text = "\n".join(
            [f"- {c.value}" for c in self.constraint_ranking]
        )

        prompt = f"""
        Analyze these search constraints and determine what type of entity is being searched for:

        Constraints:
        {constraint_text}

        What is the primary entity type being searched for? Be specific.

        Examples of entity types (but you can choose any appropriate type):
        - fictional character
        - TV show
        - movie
        - actor/actress
        - historical figure
        - company
        - product
        - location
        - event

        Respond with just the entity type.
        """

        try:
            entity_type = self.model.invoke(prompt).content.strip()
            logger.info(f"LLM determined entity type: {entity_type}")
            return entity_type
        except Exception:
            logger.exception("Failed to detect entity type")
            return "unknown entity"

    def _run_parallel_constraint_searches(self):
        """Run separate searches for each constraint in parallel."""
        # Submit a search for each constraint
        search_futures = {}

        for i, constraint in enumerate(self.constraint_ranking):
            if self.state.stop_search.is_set():
                break

            logger.info(
                f"Scheduling search for constraint {i + 1}/{len(self.constraint_ranking)}: {constraint.value}"
            )

            # Submit search task
            future = self.search_executor.submit(
                self._run_constraint_search,
                constraint,
                i,
                len(self.constraint_ranking),
            )
            search_futures[future] = constraint

        # Process results as they complete
        for future in concurrent.futures.as_completed(search_futures):
            if self.state.stop_search.is_set():
                break

            constraint = search_futures[future]
            try:
                candidates = future.result()

                # Store results by constraint
                with self.state.candidates_lock:
                    self.state.constraint_searches[constraint.id] = candidates
                    # Add to overall candidate pool
                    self.state.all_candidates.extend(candidates)

                logger.info(
                    f"Constraint '{constraint.value[:30]}...' found {len(candidates)} candidates"
                )

                # Submit candidates for evaluation
                self._submit_candidates_for_evaluation(candidates)

            except Exception:
                logger.exception(
                    f"Search failed for constraint {constraint.value}"
                )

        # Wait for evaluations to complete
        self._finalize_evaluations()

    def _run_constraint_search(
        self, constraint: Constraint, index: int, total: int
    ) -> List[Candidate]:
        """Execute search for a specific constraint."""
        try:
            # Build a query combining entity type and constraint
            query = self._build_constraint_query(constraint)

            if self.progress_callback:
                self.progress_callback(
                    f"Searching for constraint {index + 1}/{total}: {constraint.value[:30]}...",
                    20 + int(30 * (index / total)),
                    {
                        "phase": "constraint_search",
                        "constraint_index": index,
                        "constraint_total": total,
                        "constraint_value": constraint.value,
                    },
                )

            # Execute search
            search_results = self._execute_search(query)

            # Extract candidates
            candidates = self._extract_relevant_candidates(
                search_results, constraint
            )

            logger.info(
                f"Found {len(candidates)} candidates for constraint: {constraint.value[:30]}..."
            )
            return candidates

        except Exception:
            logger.exception("Error in constraint search")
            return []

    def _build_constraint_query(self, constraint: Constraint) -> str:
        """Build a query combining entity type and constraint."""
        # Get entity type
        entity_type = self.state.entity_type
        query_parts = []

        # Always include entity type if known
        if entity_type and entity_type != "unknown entity":
            # Add entity type as a search term
            if " " in entity_type and not entity_type.startswith('"'):
                query_parts.append(f'"{entity_type}"')
            else:
                query_parts.append(entity_type)

        # Add constraint
        value = constraint.value
        if " " in value and not value.startswith('"'):
            query_parts.append(f'"{value}"')
        else:
            query_parts.append(value)

        # Convert constraint to search-friendly terms based on type
        search_terms = constraint.to_search_terms()
        if search_terms and search_terms != value:
            query_parts.append(search_terms)

        return " ".join(query_parts)

    def _submit_candidates_for_evaluation(self, candidates: List[Candidate]):
        """Submit candidates for concurrent evaluation."""
        for candidate in candidates:
            if self.state.stop_search.is_set():
                break

            if self.state.total_evaluated >= self.max_evaluations:
                logger.info("Reached maximum evaluations limit")
                self.state.stop_search.set()
                break

            # Check if we already evaluated this candidate
            if self._is_candidate_evaluated(candidate):
                continue

            # Submit for evaluation
            future = self.evaluation_executor.submit(
                self._evaluate_candidate_thread, candidate
            )
            self.state.evaluation_futures.append(future)
            self.state.total_evaluated += 1

    def _evaluate_candidate_thread(
        self, candidate: Candidate
    ) -> Tuple[Candidate, float]:
        """Evaluate a candidate in a separate thread."""
        try:
            thread_name = threading.current_thread().name
            logger.info(
                f"[{thread_name}] Starting evaluation of {candidate.name}"
            )

            # FIRST CHECK: Verify that candidate matches the expected entity type
            entity_match_score = self._verify_entity_type_match(candidate)
            if entity_match_score < 0.5:  # Threshold for entity type match
                logger.info(
                    f"[{thread_name}] ❌ {candidate.name} rejected - Not a {self.state.entity_type} (score: {entity_match_score:.3f})"
                )
                return (candidate, 0.0)

            # Continue with parent's evaluation with early rejection
            score = self._evaluate_candidate_immediately(candidate)

            # Log result
            if score >= self.min_score_threshold:
                logger.info(
                    f"[{thread_name}] ✓ {candidate.name} passed (score: {score:.3f})"
                )

                # Add to good candidates
                with self.state.candidates_lock:
                    self.state.evaluated_candidates.append((candidate, score))

                    # Check if we should stop
                    if self._should_stop_search():
                        logger.info("Stopping criteria met after evaluation")
                        self.state.stop_search.set()
            else:
                logger.info(
                    f"[{thread_name}] ❌ {candidate.name} rejected (score: {score:.3f})"
                )

            return (candidate, score)

        except Exception:
            logger.exception(f"Error evaluating {candidate.name}")
            return (candidate, 0.0)

    def _verify_entity_type_match(self, candidate: Candidate) -> float:
        """Verify that the candidate matches the expected entity type.

        Returns:
            float: Score between 0.0 and 1.0 indicating confidence that candidate matches the entity type
        """
        entity_type = self.state.entity_type
        candidate_name = candidate.name

        # Skip check if entity type is unknown
        if not entity_type or entity_type == "unknown entity":
            return 1.0

        # Use LLM to verify entity type match
        try:
            prompt = f"""
            Determine whether "{candidate_name}" is a specific {entity_type} or a general category/collection.

            Rules:
            1. A specific {entity_type} refers to a single, identifiable instance (e.g., "Mount Rainier" is a specific mountain)
            2. A general category refers to a group or collection (e.g., "U.S. national parks" is a category, not a specific location)
            3. Be strict - answer must be a single, concrete {entity_type}

            Return ONLY a score from 0.0 to 1.0 where:
            - 1.0 = Definitely a specific {entity_type}
            - 0.5 = Unclear or partially matches
            - 0.0 = Definitely NOT a specific {entity_type} (too general or wrong type)

            Score:
            """

            response = self.model.invoke(prompt).content.strip()

            # Extract numeric score from response
            try:
                score = float(response.split()[0].strip())
                # Ensure score is in valid range
                score = max(0.0, min(score, 1.0))
                logger.info(
                    f"Entity type check for {candidate_name}: {score:.2f} (entity type: {entity_type})"
                )
                return score
            except (ValueError, IndexError):
                logger.warning(
                    f"Could not parse entity type score from: {response}"
                )
                return 0.5  # Default to middle value on parsing error

        except Exception:
            logger.exception(
                f"Error verifying entity type for {candidate_name}"
            )
            return 0.5  # Default to middle value on error

    def _is_candidate_evaluated(self, candidate: Candidate) -> bool:
        """Check if we already evaluated this candidate."""
        with self.state.candidates_lock:
            return any(
                c.name == candidate.name
                for c, _ in self.state.evaluated_candidates
            )

    def _should_stop_search(self) -> bool:
        """Determine if we should stop searching based on multiple criteria."""
        # Always respect the stop flag
        if self.state.stop_search.is_set():
            return True

        num_good = len(self.state.evaluated_candidates)

        # 1. Maximum candidates reached
        if num_good >= self.max_candidates:
            logger.info(f"Maximum candidates reached ({self.max_candidates})")
            return True

        # 2. Target reached with good quality
        if num_good >= self.target_candidates:
            avg_score = (
                sum(s for _, s in self.state.evaluated_candidates) / num_good
            )
            if avg_score >= 0.8:
                logger.info(
                    f"Target reached with high quality (avg: {avg_score:.3f})"
                )
                return True

        # 3. Minimum satisfied with exceptional candidates
        if num_good >= self.min_good_candidates:
            top_score = max(s for _, s in self.state.evaluated_candidates)
            if top_score >= self.exceptional_score:
                logger.info(
                    f"Exceptional candidate found (score: {top_score:.3f})"
                )
                return True

        # 4. Time limit reached
        elapsed = time.time() - self.state.start_time
        if elapsed > self.max_search_time:
            logger.info(f"Time limit reached ({elapsed:.1f}s)")
            return True

        # 5. Too many evaluations
        if self.state.total_evaluated >= self.max_evaluations:
            logger.info(f"Evaluation limit reached ({self.max_evaluations})")
            return True

        # 6. Quality plateau detection
        if num_good >= 5:
            recent_scores = [s for _, s in self.state.evaluated_candidates[-5:]]
            score_range = max(recent_scores) - min(recent_scores)
            if score_range < self.quality_plateau_threshold:
                logger.info(
                    f"Quality plateau detected (range: {score_range:.3f})"
                )
                return True

        return False

    def _finalize_evaluations(self):
        """Wait for or cancel remaining evaluations."""
        if self.state.evaluation_futures:
            logger.info(
                f"Finalizing {len(self.state.evaluation_futures)} remaining evaluations"
            )

            # Give them a short time to complete
            wait_time = min(
                5.0,
                self.max_search_time - (time.time() - self.state.start_time),
            )
            if wait_time > 0:
                concurrent.futures.wait(
                    self.state.evaluation_futures,
                    timeout=wait_time,
                    return_when=concurrent.futures.FIRST_COMPLETED,
                )

            # Cancel any still running
            for future in self.state.evaluation_futures:
                if not future.done():
                    future.cancel()

        # Final report
        logger.info(
            f"""
Search completed:
- Total evaluated: {self.state.total_evaluated}
- Good candidates found: {len(self.state.evaluated_candidates)}
- Time taken: {time.time() - self.state.start_time:.1f}s
- Constraint searches: {len(self.state.constraint_searches)}
"""
        )
