"""
LoComo-specific benchmark implementations.

Provides dataset, answer generator, and evaluator for the LoComo benchmark.
"""

import asyncio
import json
import os
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import pydantic
from hindsight_api.engine.llm_wrapper import LLMConfig
from openai import AsyncOpenAI

from benchmarks.common.benchmark_runner import (
    BenchmarkDataset,
    BenchmarkRunner,
    LLMAnswerEvaluator,
    LLMAnswerGenerator,
)


class LoComoDataset(BenchmarkDataset):
    """LoComo dataset implementation."""

    def load(self, path: Path, max_items: Optional[int] = None) -> List[Dict[str, Any]]:
        """Load LoComo dataset from JSON file."""
        with open(path, "r") as f:
            dataset = json.load(f)

        if max_items:
            dataset = dataset[:max_items]

        return dataset

    def get_item_id(self, item: Dict) -> str:
        """Get sample ID from LoComo item."""
        return item["sample_id"]

    def prepare_sessions_for_ingestion(self, item: Dict) -> List[Dict[str, Any]]:
        """
        Prepare LoComo conversation for batch ingestion.

        Each session is ingested as a separate item with its own date.

        Returns:
            List of session dicts, each containing 'content', 'context', 'event_date', 'document_id'
        """
        conv = item["conversation"]
        speaker_a = conv["speaker_a"]
        speaker_b = conv["speaker_b"]

        # Get all session keys sorted
        session_keys = sorted([k for k in conv.keys() if k.startswith("session_") and not k.endswith("_date_time")])

        session_items = []

        for session_key in session_keys:
            if session_key not in conv or not isinstance(conv[session_key], list):
                continue

            session_data = conv[session_key]

            # Get session date
            date_key = f"{session_key}_date_time"
            session_date = self._parse_date(conv.get(date_key))
            session_content = json.dumps(session_data)
            document_id = f"{item['sample_id']}_{session_key}"
            session_items.append(
                {
                    "content": session_content,
                    "context": f"Conversation between {speaker_a} and {speaker_b} ({session_key} of {item['sample_id']})",
                    "event_date": session_date,
                    "document_id": document_id,
                }
            )

        return session_items

    def get_qa_pairs(self, item: Dict) -> List[Dict[str, Any]]:
        """
        Extract QA pairs from LoComo item.

        Returns:
            List of QA dicts with 'question', 'answer', 'category'
        """
        return item["qa"]

    def _parse_date(self, date_string: str) -> datetime:
        """Parse LoComo date format to datetime."""
        # Format: "1:56 pm on 8 May, 2023"
        try:
            dt = datetime.strptime(date_string, "%I:%M %p on %d %B, %Y")
            return dt.replace(tzinfo=timezone.utc)
        except ValueError:
            raise


class QuestionAnswer(pydantic.BaseModel):
    """Answer format for LoComo questions."""

    answer: str
    reasoning: str


class LoComoAnswerGenerator(LLMAnswerGenerator):
    """LoComo-specific answer generator using configurable LLM provider."""

    def __init__(self):
        """Initialize with LLM configuration for answer generation.

        Uses HINDSIGHT_API_ANSWER_LLM_* env vars with fallback to HINDSIGHT_API_LLM_* for
        benchmark-specific LLM configuration (separate from the API config system).
        """
        self.llm_config = LLMConfig(
            provider=os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "openai")),
            api_key=os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", "")),
            base_url=os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")),
            model=os.getenv("HINDSIGHT_API_ANSWER_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "gpt-4o-mini")),
            reasoning_effort="high",
        )
        self.client = self.llm_config._client
        self.model = self.llm_config.model

    async def generate_answer(
        self,
        question: str,
        recall_result: Dict[str, Any],
        question_date: Optional[datetime] = None,
        question_type: Optional[str] = None,
        bank_id: Optional[str] = None,
    ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
        """
        Generate answer from retrieved memories using Groq.

        Args:
            question: The question text
            recall_result: Full RecallResult dict containing results, entities, chunks, and trace
            question_date: Date when the question was asked (for temporal context)
            question_type: Question category (unused in Locomo)

        Returns:
            Tuple of (answer, reasoning, None)
            - None indicates to use the memories from recall_result
        """
        context = json.dumps(recall_result)

        # Format question date if provided
        question_date_str = ""
        if question_date:
            question_date_str = f"\n# CURRENT DATE:\nThe question is being asked on: {question_date.strftime('%Y-%m-%d %H:%M:%S')} UTC\n"

        # Use LLM to generate answer
        try:
            answer_obj = await self.llm_config.call(
                messages=[
                    {
                        "role": "system",
                        "content": "You are a helpful expert assistant answering questions from lme_experiment users based on the provided context.",
                    },
                    {
                        "role": "user",
                        "content": f"""
# CONTEXT:
You have access to facts and entities from a conversation.
{question_date_str}
# INSTRUCTIONS:
1. Carefully analyze all provided memories
2. Pay special attention to the timestamps to determine the answer
3. If the question asks about a specific event or fact, look for direct evidence in the memories
4. If the memories contain contradictory information or multiple instances of an event, say them all
5. Always convert relative time references to specific dates, months, or years.
6. Be as specific as possible when talking about people, places, and events
7. If the answer is not explicitly stated in the memories, use logical reasoning based on the information available to answer (e.g. calculate duration of an event from different memories).

Context:

{context}

Question: {question}
Answer:

""",
                    },
                ],
                response_format=QuestionAnswer,
                scope="memory",
            )
            return answer_obj.answer, answer_obj.reasoning, None
        except Exception as e:
            return f"Error generating answer: {str(e)}", "Error occurred during answer generation.", None


class LoComoReflectAnswerGenerator(LLMAnswerGenerator):
    """LoComo answer generator using the reflect API instead of search + LLM.

    This generator performs its own retrieval internally via the reflect API,
    so it doesn't need external search to be performed by the benchmark runner.
    """

    def __init__(self, memory: "MemoryEngine"):
        """Initialize with memory instance.

        Args:
            memory: MemoryEngine instance
        """
        self.memory = memory

    def needs_external_search(self) -> bool:
        """Reflect API does its own retrieval, so no external search needed."""
        return False

    async def generate_answer(
        self,
        question: str,
        recall_result: Dict[str, Any],
        question_date: Optional[datetime] = None,
        question_type: Optional[str] = None,
        bank_id: Optional[str] = None,
    ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
        """
        Generate answer using the integrated reflect API.

        The reflect API performs both search and answer generation in a single call,
        combining world facts, experience facts, and mental models to formulate a response.

        Args:
            question: Question to answer
            recall_result: Not used (empty dict), as reflect does its own retrieval
            question_date: Date when the question was asked (currently not used by reflect API)
            question_type: Question category (unused in reflect API)
            bank_id: Bank ID to query

        Returns:
            Tuple of (answer, reasoning, retrieved_memories)
            - retrieved_memories: Combined list of all facts from based_on
        """
        from hindsight_api.models import RequestContext

        try:
            question_date_str = ""
            if question_date:
                question_date_str = f"\n# CURRENT DATE:\nThe question is being asked on: {question_date.strftime('%Y-%m-%d %H:%M:%S')} UTC\n"

            query = f"""
# CONTEXT:
You have access to facts and entities from a conversation.
{question_date_str}
# INSTRUCTIONS:
1. Search thoroughly across all available memories before answering - do not stop at the first result
2. Keep searching with different queries until you have a comprehensive answer
3. Carefully analyze all provided memories
4. Pay special attention to the timestamps to determine the answer
5. If the question asks about a specific event or fact, look for direct evidence in the memories
6. If the memories contain contradictory information or multiple instances of an event, say them all
7. Always convert relative time references to specific dates, months, or years.
8. Be as specific as possible when talking about people, places, and events
9. If the answer is not explicitly stated in the memories, use logical reasoning based on the information available to answer (e.g. calculate duration of an event from different memories).

Question: {question}
"""

            from hindsight_api.engine.memory_engine import Budget

            result = await self.memory.reflect_async(
                bank_id=bank_id,
                query=query,
                budget=Budget.HIGH,
                request_context=RequestContext(),
            )

            answer = result.text

            # Flatten all facts from based_on into retrieved_memories
            based_on = result.based_on
            retrieved_memories = []
            for facts in based_on.values():
                if isinstance(facts, list):
                    for fact in facts:
                        if hasattr(fact, "model_dump"):
                            retrieved_memories.append(fact.model_dump())
                        elif isinstance(fact, dict):
                            retrieved_memories.append(fact)

            counts = {k: len(v) for k, v in based_on.items() if isinstance(v, list)}
            reasoning = "Reflect API: " + ", ".join(f"{v} {k}" for k, v in counts.items())

            return answer, reasoning, retrieved_memories
        except Exception as e:
            return f"Error generating answer: {str(e)}", "Error occurred during reflect API call.", []


async def run_benchmark(
    max_conversations: int = None,
    max_questions_per_conv: int = None,
    skip_ingestion: bool = False,
    use_reflect: bool = False,
    conversation: str = None,
    api_url: str = None,
    max_concurrent_questions_override: int = None,
    only_failed: bool = False,
    only_invalid: bool = False,
    question_index: int = None,
    wait_consolidation: bool = False,
    template_path: str = None,
):
    """
    Run the LoComo benchmark.

    Args:
        max_conversations: Maximum number of conversations to evaluate (None for all)
        max_questions_per_conv: Maximum questions per conversation (None for all)
        skip_ingestion: Whether to skip ingestion and use existing data
        use_reflect: Whether to use the reflect API instead of search + LLM
        conversation: Specific conversation ID to run (e.g., "conv-26")
        api_url: Optional API URL to connect to (default: use local memory)
        only_failed: If True, only run conversations that have failed questions (is_correct=False)
        only_invalid: If True, only run conversations that have invalid questions (is_invalid=True)
        question_index: Run only the question at this index (0-based) within each conversation
    """
    from rich.console import Console

    console = Console()

    # Load previous results if filtering for failed/invalid conversations
    failed_conversation_ids = set()
    invalid_conversation_ids = set()
    if only_failed or only_invalid:
        suffix = "_reflect" if use_reflect else ""
        results_filename = f"benchmark_results{suffix}.json"
        results_path = Path(__file__).parent / "results" / results_filename

        if not results_path.exists():
            console.print("[red]Error: Cannot use --only-failed or --only-invalid without existing results file[/red]")
            console.print(f"[yellow]Results file not found: {results_path}[/yellow]")
            return

        with open(results_path, "r") as f:
            previous_results = json.load(f)

        # Extract conversation IDs that have failed or invalid questions
        for item_result in previous_results.get("item_results", []):
            item_id = item_result["item_id"]
            for detail in item_result["metrics"].get("detailed_results", []):
                if only_failed and detail.get("is_correct") == False and not detail.get("is_invalid", False):
                    failed_conversation_ids.add(item_id)
                if only_invalid and detail.get("is_invalid", False):
                    invalid_conversation_ids.add(item_id)

        if only_failed:
            console.print(
                f"[cyan]Filtering to {len(failed_conversation_ids)} conversations with failed questions (is_correct=False)[/cyan]"
            )
        if only_invalid:
            console.print(
                f"[cyan]Filtering to {len(invalid_conversation_ids)} conversations with invalid questions (is_invalid=True)[/cyan]"
            )

        target_ids = failed_conversation_ids if only_failed else invalid_conversation_ids
        if not target_ids:
            filter_type = "failed" if only_failed else "invalid"
            console.print(
                f"[yellow]No conversations with {filter_type} questions found in previous results. Nothing to run.[/yellow]"
            )
            return

    # Initialize components
    dataset = LoComoDataset()

    # Use remote API client if api_url is provided, otherwise use local memory
    if api_url:
        from benchmarks.common.benchmark_runner import HindsightClientAdapter

        memory = HindsightClientAdapter(base_url=api_url)
        await memory.initialize()
    else:
        from benchmarks.common.benchmark_runner import create_memory_engine

        memory = await create_memory_engine()

    # Select answer generator based on mode
    if use_reflect:
        console.print("[blue]Mode: reflect (using reflect API)[/blue]")
        answer_generator = LoComoReflectAnswerGenerator(memory=memory)
        max_concurrent_questions = max_concurrent_questions_override or 4
        eval_semaphore_size = 4
    else:
        console.print("[blue]Mode: recall+LLM (traditional)[/blue]")
        answer_generator = LoComoAnswerGenerator()
        # Reduced from 32 to 10 to match search semaphore limit
        # Prevents "too many connections" errors
        max_concurrent_questions = max_concurrent_questions_override or 10
        eval_semaphore_size = 8

    answer_evaluator = LLMAnswerEvaluator()

    # Create benchmark runner
    runner = BenchmarkRunner(
        dataset=dataset, answer_generator=answer_generator, answer_evaluator=answer_evaluator, memory=memory
    )

    # Filter dataset if using --only-failed or --only-invalid
    dataset_path = Path(__file__).parent / "datasets" / "locomo10.json"

    if only_failed or only_invalid:
        # Load and filter dataset
        target_ids = failed_conversation_ids if only_failed else invalid_conversation_ids
        original_items = dataset.load(dataset_path, max_conversations)
        filtered_items = [item for item in original_items if dataset.get_item_id(item) in target_ids]
        console.print(f"[green]Found {len(filtered_items)} conversations to re-evaluate[/green]")

        # Temporarily replace dataset's load method
        original_load = dataset.load

        def filtered_load(path: Path, max_items: Optional[int] = None):
            return filtered_items[:max_items] if max_items else filtered_items

        dataset.load = filtered_load

    # Filter to a single question by index if requested
    if question_index is not None:
        original_get_qa_pairs = dataset.get_qa_pairs

        def filtered_get_qa_pairs(item: Dict) -> List[Dict[str, Any]]:
            pairs = original_get_qa_pairs(item)
            if question_index >= len(pairs):
                console.print(
                    f"[red]Error: question index {question_index} out of range (conversation has {len(pairs)} questions)[/red]"
                )
                return []
            selected = pairs[question_index]
            console.print(f"[cyan]Running single question [{question_index}]: {selected['question']}[/cyan]")
            return [selected]

        dataset.get_qa_pairs = filtered_get_qa_pairs

    # Determine output filename based on mode
    suffix = "_reflect" if use_reflect else ""
    results_filename = f"benchmark_results{suffix}.json"
    output_path = Path(__file__).parent / "results" / results_filename

    # Create results directory if it doesn't exist
    output_path.parent.mkdir(parents=True, exist_ok=True)

    # Merge with existing results if running a specific conversation or using filters
    merge_with_existing = conversation is not None or only_failed or only_invalid

    # Each conversation gets its own isolated bank
    separate_ingestion = False
    clear_per_item = True  # Use unique agent ID per conversation
    concurrent_items = 3  # Process up to 3 conversations in parallel

    # Run benchmark with parallel conversation processing
    # Each conversation gets its own agent ID (locomo_conv-26, locomo_conv-30, etc.)
    # This allows conversations to run in parallel (up to max_concurrent_items at a time)
    results = await runner.run(
        dataset_path=dataset_path,
        agent_id="locomo",
        max_items=max_conversations,
        max_questions_per_item=max_questions_per_conv,
        thinking_budget=500,
        max_tokens=4096,
        skip_ingestion=skip_ingestion,
        max_concurrent_questions=max_concurrent_questions,
        eval_semaphore_size=eval_semaphore_size,
        specific_item=conversation,
        separate_ingestion_phase=separate_ingestion,
        clear_agent_per_item=clear_per_item,
        max_concurrent_items=concurrent_items,
        output_path=output_path,  # Save results incrementally
        merge_with_existing=merge_with_existing,
        wait_consolidation=wait_consolidation,
        template_path=template_path,
    )

    # Display results (final save already happened incrementally)
    runner.display_results(results)
    console.print(f"\n[green]✓[/green] Results saved incrementally to {output_path}")

    # Generate markdown table
    generate_markdown_table(results, use_reflect=use_reflect)

    return results


def generate_markdown_table(results: dict, use_reflect: bool = False):
    """
    Generate a markdown table with benchmark results.

    Category mapping:
    1 = Multi-hop
    2 = Single-hop
    3 = Temporal
    4 = Open-domain
    """
    from rich.console import Console

    console = Console()

    category_names = {"1": "Multi-hop", "2": "Single-hop", "3": "Temporal", "4": "Open-domain"}

    # Build markdown content
    lines = []
    mode_str = " (Reflect Mode)" if use_reflect else ""
    lines.append(f"# LoComo Benchmark Results{mode_str}")
    lines.append("")

    # Add model configuration
    if "model_config" in results:
        config = results["model_config"]
        lines.append("## Model Configuration")
        lines.append("")
        lines.append(f"- **Hindsight**: {config['hindsight']['provider']}/{config['hindsight']['model']}")
        lines.append(
            f"- **Answer Generation**: {config['answer_generation']['provider']}/{config['answer_generation']['model']}"
        )
        lines.append(f"- **LLM Judge**: {config['judge']['provider']}/{config['judge']['model']}")
        lines.append("")

    lines.append(
        f"**Overall Accuracy**: {results['overall_accuracy']:.2f}% ({results['total_correct']}/{results['total_questions']})"
    )
    lines.append("")
    lines.append(
        "| Sample ID | Sessions | Questions | Correct | Accuracy | Multi-hop | Single-hop | Temporal | Open-domain |"
    )
    lines.append(
        "|-----------|----------|-----------|---------|----------|-----------|------------|----------|-------------|"
    )

    for item_result in results["item_results"]:
        item_id = item_result["item_id"]
        num_sessions = item_result["num_sessions"]
        metrics = item_result["metrics"]

        # Calculate category accuracies
        cat_stats = metrics.get("category_stats", {})
        cat_accuracies = {}

        for cat_id in ["1", "2", "3", "4"]:
            if cat_id in cat_stats:
                stats = cat_stats[cat_id]
                acc = (stats["correct"] / stats["total"] * 100) if stats["total"] > 0 else 0
                cat_accuracies[cat_id] = f"{acc:.1f}% ({stats['correct']}/{stats['total']})"
            else:
                cat_accuracies[cat_id] = "N/A"

        lines.append(
            f"| {item_id} | {num_sessions} | {metrics['total']} | {metrics['correct']} | "
            f"{metrics['accuracy']:.2f}% | {cat_accuracies['1']} | {cat_accuracies['2']} | "
            f"{cat_accuracies['3']} | {cat_accuracies['4']} |"
        )

    # Write to file with suffix
    suffix = "_reflect" if use_reflect else ""
    output_file = Path(__file__).parent / "results" / f"results_table{suffix}.md"
    output_file.parent.mkdir(parents=True, exist_ok=True)
    output_file.write_text("\n".join(lines))
    console.print(f"\n[green]✓[/green] Results table saved to {output_file}")


if __name__ == "__main__":
    import argparse
    import logging

    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

    parser = argparse.ArgumentParser(description="Run LoComo benchmark")
    parser.add_argument("--max-conversations", type=int, default=None, help="Maximum conversations to evaluate")
    parser.add_argument("--max-questions", type=int, default=None, help="Maximum questions per conversation")
    parser.add_argument("--skip-ingestion", action="store_true", help="Skip ingestion and use existing data")
    parser.add_argument("--use-reflect", action="store_true", help="Use reflect API instead of search + LLM")
    parser.add_argument(
        "--conversation", type=str, default=None, help='Run only specific conversation (e.g., "conv-26")'
    )
    parser.add_argument(
        "--api-url",
        type=str,
        default=None,
        help="Hindsight API URL (default: use local memory, example: http://localhost:8888)",
    )
    parser.add_argument(
        "--max-concurrent-questions",
        type=int,
        default=None,
        help="Max concurrent questions per conversation (default: 4 for think, 10 for search)",
    )
    parser.add_argument(
        "--only-failed",
        action="store_true",
        help="Only run conversations that have failed questions (is_correct=False). Requires existing results file.",
    )
    parser.add_argument(
        "--only-invalid",
        action="store_true",
        help="Only run conversations that have invalid questions (is_invalid=True). Requires existing results file.",
    )
    parser.add_argument(
        "--question-index",
        type=int,
        default=None,
        help="Run only the question at this 0-based index within each conversation (e.g., 11)",
    )
    parser.add_argument(
        "--wait-consolidation",
        action="store_true",
        help="Wait for consolidation to complete after ingestion (or immediately when using --skip-ingestion) before evaluating QA.",
    )
    parser.add_argument(
        "--template",
        type=str,
        default=None,
        help="Path to a bank template manifest JSON to apply before ingestion (sets config, mental models, directives)",
    )

    args = parser.parse_args()

    # Validate that only one of --only-failed or --only-invalid is set
    if args.only_failed and args.only_invalid:
        parser.error("Cannot use both --only-failed and --only-invalid at the same time")

    results = asyncio.run(
        run_benchmark(
            max_conversations=args.max_conversations,
            max_questions_per_conv=args.max_questions,
            skip_ingestion=args.skip_ingestion,
            use_reflect=args.use_reflect,
            conversation=args.conversation,
            api_url=args.api_url,
            max_concurrent_questions_override=args.max_concurrent_questions,
            only_failed=args.only_failed,
            only_invalid=args.only_invalid,
            question_index=args.question_index,
            wait_consolidation=args.wait_consolidation,
            template_path=args.template,
        )
    )
