use std::{
    collections::{HashMap, HashSet},
    fs,
    io::BufReader,
    path::PathBuf,
};

use anyhow::{Context, Result, anyhow};
use chrono::{DateTime, Utc};
use common::storage::{
    db::SurrealDbClient,
    types::{
        StoredObject, knowledge_entity::KnowledgeEntity,
        knowledge_relationship::KnowledgeRelationship, text_chunk::TextChunk,
        text_content::TextContent,
    },
};
use ingestion_pipeline::{IngestionTuning, PipelineArtifacts, persist_artifacts};
use serde::Deserialize;
use tracing::{debug, warn};

use crate::datasets::{ConvertedParagraph, ConvertedQuestion};

pub const MANIFEST_VERSION: u32 = 3;
pub const PARAGRAPH_SHARD_VERSION: u32 = 3;
fn current_manifest_version() -> u32 {
    MANIFEST_VERSION
}

fn current_paragraph_shard_version() -> u32 {
    PARAGRAPH_SHARD_VERSION
}

fn default_chunk_min_tokens() -> usize {
    500
}

fn default_chunk_max_tokens() -> usize {
    2_000
}

fn default_chunk_only() -> bool {
    true
}

// Reuse the pipeline's canonical embedded-artifact types so the on-disk corpus
// format and the ingestion output never drift apart.
pub use ingestion_pipeline::{EmbeddedKnowledgeEntity, EmbeddedTextChunk};

#[derive(Debug, Clone, serde::Deserialize)]
struct LegacyKnowledgeEntity {
    #[serde(flatten)]
    pub entity: KnowledgeEntity,
    #[serde(default)]
    pub embedding: Vec<f32>,
}

#[derive(Debug, Clone, serde::Deserialize)]
struct LegacyTextChunk {
    #[serde(flatten)]
    pub chunk: TextChunk,
    #[serde(default)]
    pub embedding: Vec<f32>,
}

fn deserialize_embedded_entities<'de, D>(
    deserializer: D,
) -> Result<Vec<EmbeddedKnowledgeEntity>, D::Error>
where
    D: serde::Deserializer<'de>,
{
    #[derive(serde::Deserialize)]
    #[serde(untagged)]
    enum EntityInput {
        Embedded(Vec<EmbeddedKnowledgeEntity>),
        Legacy(Vec<LegacyKnowledgeEntity>),
    }

    match EntityInput::deserialize(deserializer)? {
        EntityInput::Embedded(items) => Ok(items),
        EntityInput::Legacy(items) => Ok(items
            .into_iter()
            .map(|legacy| EmbeddedKnowledgeEntity {
                entity: legacy.entity,
                embedding: legacy.embedding,
            })
            .collect()),
    }
}

fn deserialize_embedded_chunks<'de, D>(deserializer: D) -> Result<Vec<EmbeddedTextChunk>, D::Error>
where
    D: serde::Deserializer<'de>,
{
    #[derive(serde::Deserialize)]
    #[serde(untagged)]
    enum ChunkInput {
        Embedded(Vec<EmbeddedTextChunk>),
        Legacy(Vec<LegacyTextChunk>),
    }

    match ChunkInput::deserialize(deserializer)? {
        ChunkInput::Embedded(items) => Ok(items),
        ChunkInput::Legacy(items) => Ok(items
            .into_iter()
            .map(|legacy| EmbeddedTextChunk {
                chunk: legacy.chunk,
                embedding: legacy.embedding,
            })
            .collect()),
    }
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct CorpusManifest {
    #[serde(default = "current_manifest_version")]
    pub version: u32,
    pub metadata: CorpusMetadata,
    pub paragraphs: Vec<CorpusParagraph>,
    pub questions: Vec<CorpusQuestion>,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct NamespaceSeedRecord {
    pub namespace: String,
    pub database: String,
    pub slice_case_count: usize,
    pub seeded_at: DateTime<Utc>,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct CorpusMetadata {
    pub dataset_id: String,
    pub dataset_label: String,
    pub slice_id: String,
    pub include_unanswerable: bool,
    #[serde(default)]
    pub require_verified_chunks: bool,
    pub ingestion_fingerprint: String,
    pub embedding_backend: String,
    pub embedding_model: Option<String>,
    pub embedding_dimension: usize,
    pub converted_checksum: String,
    pub generated_at: DateTime<Utc>,
    pub paragraph_count: usize,
    pub question_count: usize,
    #[serde(default = "default_chunk_min_tokens")]
    pub chunk_min_tokens: usize,
    #[serde(default = "default_chunk_max_tokens")]
    pub chunk_max_tokens: usize,
    #[serde(default = "default_chunk_only")]
    pub chunk_only: bool,
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub namespace_seed: Option<NamespaceSeedRecord>,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct CorpusParagraph {
    pub paragraph_id: String,
    pub title: String,
    pub text_content: TextContent,
    #[serde(deserialize_with = "deserialize_embedded_entities")]
    pub entities: Vec<EmbeddedKnowledgeEntity>,
    pub relationships: Vec<KnowledgeRelationship>,
    #[serde(deserialize_with = "deserialize_embedded_chunks")]
    pub chunks: Vec<EmbeddedTextChunk>,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct CorpusQuestion {
    pub question_id: String,
    pub paragraph_id: String,
    pub text_content_id: String,
    pub question_text: String,
    pub answers: Vec<String>,
    pub is_impossible: bool,
    pub matching_chunk_ids: Vec<String>,
}

pub struct CorpusHandle {
    pub manifest: CorpusManifest,
    pub path: PathBuf,
    pub reused_ingestion: bool,
    pub reused_embeddings: bool,
    pub positive_reused: usize,
    pub positive_ingested: usize,
    pub negative_reused: usize,
    pub negative_ingested: usize,
}

#[allow(
    clippy::arithmetic_side_effects,
    clippy::cast_possible_truncation,
    clippy::cast_precision_loss,
    clippy::cast_sign_loss,
    clippy::indexing_slicing
)]
pub fn window_manifest(
    manifest: &CorpusManifest,
    offset: usize,
    length: usize,
    negative_multiplier: f32,
) -> Result<CorpusManifest> {
    let total = manifest.questions.len();
    if total == 0 {
        return Err(anyhow!(
            "manifest contains no questions; cannot select a window"
        ));
    }
    if offset >= total {
        return Err(anyhow!(
            "window offset {offset} exceeds manifest questions ({total})"
        ));
    }
    let end = (offset + length).min(total);
    let questions = manifest.questions[offset..end].to_vec();

    let selected_positive_ids: HashSet<_> =
        questions.iter().map(|q| q.paragraph_id.clone()).collect();
    let positives_all: HashSet<_> = manifest
        .questions
        .iter()
        .map(|q| q.paragraph_id.as_str())
        .collect();
    let available_negatives = manifest
        .paragraphs
        .len()
        .saturating_sub(positives_all.len());
    let desired_negatives =
        ((selected_positive_ids.len() as f32) * negative_multiplier).ceil() as usize;
    let desired_negatives = desired_negatives.min(available_negatives);

    let mut paragraphs = Vec::new();
    let mut negative_count = 0usize;
    for paragraph in &manifest.paragraphs {
        if selected_positive_ids.contains(&paragraph.paragraph_id) {
            paragraphs.push(paragraph.clone());
        } else if negative_count < desired_negatives {
            paragraphs.push(paragraph.clone());
            negative_count += 1;
        }
    }

    let mut narrowed = manifest.clone();
    narrowed.questions = questions;
    narrowed.paragraphs = paragraphs;
    narrowed.metadata.paragraph_count = narrowed.paragraphs.len();
    narrowed.metadata.question_count = narrowed.questions.len();

    Ok(narrowed)
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct ParagraphShard {
    #[serde(default = "current_paragraph_shard_version")]
    pub version: u32,
    pub paragraph_id: String,
    pub shard_path: String,
    pub ingestion_fingerprint: String,
    pub ingested_at: DateTime<Utc>,
    pub title: String,
    pub text_content: TextContent,
    #[serde(deserialize_with = "deserialize_embedded_entities")]
    pub entities: Vec<EmbeddedKnowledgeEntity>,
    pub relationships: Vec<KnowledgeRelationship>,
    #[serde(deserialize_with = "deserialize_embedded_chunks")]
    pub chunks: Vec<EmbeddedTextChunk>,
    #[serde(default)]
    pub question_bindings: HashMap<String, Vec<String>>,
    #[serde(default)]
    pub embedding_backend: String,
    #[serde(default)]
    pub embedding_model: Option<String>,
    #[serde(default)]
    pub embedding_dimension: usize,
    #[serde(default = "default_chunk_min_tokens")]
    pub chunk_min_tokens: usize,
    #[serde(default = "default_chunk_max_tokens")]
    pub chunk_max_tokens: usize,
    #[serde(default = "default_chunk_only")]
    pub chunk_only: bool,
}

pub struct ParagraphShardStore {
    base_dir: PathBuf,
}

impl ParagraphShardStore {
    pub fn new(base_dir: PathBuf) -> Self {
        Self { base_dir }
    }

    pub fn ensure_base_dir(&self) -> Result<()> {
        fs::create_dir_all(&self.base_dir)
            .with_context(|| format!("creating shard base dir {}", self.base_dir.display()))
    }

    fn resolve(&self, relative: &str) -> PathBuf {
        self.base_dir.join(relative)
    }

    pub fn load(&self, relative: &str, fingerprint: &str) -> Result<Option<ParagraphShard>> {
        let path = self.resolve(relative);
        let file = match fs::File::open(&path) {
            Ok(file) => file,
            Err(err) if err.kind() == std::io::ErrorKind::NotFound => return Ok(None),
            Err(err) => {
                return Err(err).with_context(|| format!("opening shard {}", path.display()));
            }
        };
        let reader = BufReader::new(file);
        let mut shard: ParagraphShard = serde_json::from_reader(reader)
            .with_context(|| format!("parsing shard {}", path.display()))?;

        if shard.ingestion_fingerprint != fingerprint {
            debug!(
                path = %path.display(),
                expected = fingerprint,
                found = shard.ingestion_fingerprint,
                "Shard fingerprint mismatch; will rebuild"
            );
            return Ok(None);
        }
        if shard.version != PARAGRAPH_SHARD_VERSION {
            warn!(
                path = %path.display(),
                version = shard.version,
                expected = PARAGRAPH_SHARD_VERSION,
                "Upgrading shard to current version"
            );
            shard.version = PARAGRAPH_SHARD_VERSION;
        }
        shard.shard_path = relative.to_string();
        Ok(Some(shard))
    }

    pub fn persist(&self, shard: &ParagraphShard) -> Result<()> {
        let mut shard = shard.clone();
        shard.version = PARAGRAPH_SHARD_VERSION;

        let path = self.resolve(&shard.shard_path);
        if let Some(parent) = path.parent() {
            fs::create_dir_all(parent)
                .with_context(|| format!("creating shard dir {}", parent.display()))?;
        }
        let tmp_path = path.with_extension("json.tmp");
        let body = serde_json::to_vec_pretty(&shard).context("serialising paragraph shard")?;
        fs::write(&tmp_path, &body)
            .with_context(|| format!("writing shard tmp {}", tmp_path.display()))?;
        fs::rename(&tmp_path, &path)
            .with_context(|| format!("renaming shard tmp {}", path.display()))?;
        Ok(())
    }
}

impl ParagraphShard {
    #[allow(clippy::too_many_arguments)]
    pub fn new(
        paragraph: &ConvertedParagraph,
        shard_path: String,
        ingestion_fingerprint: &str,
        text_content: TextContent,
        entities: Vec<EmbeddedKnowledgeEntity>,
        relationships: Vec<KnowledgeRelationship>,
        chunks: Vec<EmbeddedTextChunk>,
        embedding_backend: &str,
        embedding_model: Option<String>,
        embedding_dimension: usize,
        chunk_min_tokens: usize,
        chunk_max_tokens: usize,
        chunk_only: bool,
    ) -> Self {
        Self {
            version: PARAGRAPH_SHARD_VERSION,
            paragraph_id: paragraph.id.clone(),
            shard_path,
            ingestion_fingerprint: ingestion_fingerprint.to_string(),
            ingested_at: Utc::now(),
            title: paragraph.title.clone(),
            text_content,
            entities,
            relationships,
            chunks,
            question_bindings: HashMap::new(),
            embedding_backend: embedding_backend.to_string(),
            embedding_model,
            embedding_dimension,
            chunk_min_tokens,
            chunk_max_tokens,
            chunk_only,
        }
    }

    pub fn to_corpus_paragraph(&self) -> CorpusParagraph {
        CorpusParagraph {
            paragraph_id: self.paragraph_id.clone(),
            title: self.title.clone(),
            text_content: self.text_content.clone(),
            entities: self.entities.clone(),
            relationships: self.relationships.clone(),
            chunks: self.chunks.clone(),
        }
    }

    pub fn ensure_question_binding(
        &mut self,
        question: &ConvertedQuestion,
    ) -> Result<(Vec<String>, bool)> {
        if let Some(existing) = self.question_bindings.get(&question.id) {
            return Ok((existing.clone(), false));
        }
        let chunk_ids = validate_answers(&self.text_content, &self.chunks, question)?;
        self.question_bindings
            .insert(question.id.clone(), chunk_ids.clone());
        Ok((chunk_ids, true))
    }
}

fn validate_answers(
    content: &TextContent,
    chunks: &[EmbeddedTextChunk],
    question: &ConvertedQuestion,
) -> Result<Vec<String>> {
    if question.is_impossible || question.answers.is_empty() {
        return Ok(Vec::new());
    }

    let mut matches = std::collections::BTreeSet::new();
    let mut found_any = false;
    let haystack = content.text.to_ascii_lowercase();
    let haystack_norm = normalize_answer_text(&haystack);
    for answer in &question.answers {
        let needle: String = answer.to_ascii_lowercase();
        let needle_norm = normalize_answer_text(&needle);
        let text_match = haystack.contains(&needle)
            || (!needle_norm.is_empty() && haystack_norm.contains(&needle_norm));
        if text_match {
            found_any = true;
        }
        for chunk in chunks {
            let chunk_text = chunk.chunk.chunk.to_ascii_lowercase();
            let chunk_norm = normalize_answer_text(&chunk_text);
            if chunk_text.contains(&needle)
                || (!needle_norm.is_empty() && chunk_norm.contains(&needle_norm))
            {
                matches.insert(chunk.chunk.id().to_string());
                found_any = true;
            }
        }
    }

    if found_any {
        Ok(matches.into_iter().collect())
    } else {
        Err(anyhow!(
            "expected answer for question '{}' was not found in ingested content",
            question.id
        ))
    }
}

fn normalize_answer_text(text: &str) -> String {
    text.chars()
        .map(|ch| {
            if ch.is_alphanumeric() || ch.is_whitespace() {
                ch.to_ascii_lowercase()
            } else {
                ' '
            }
        })
        .collect::<String>()
        .split_whitespace()
        .collect::<Vec<_>>()
        .join(" ")
}

pub async fn seed_manifest_into_db(db: &SurrealDbClient, manifest: &CorpusManifest) -> Result<()> {
    let tuning = IngestionTuning::default();
    let embedding_dimensions = manifest.metadata.embedding_dimension;
    let mut seen_text_content = HashSet::new();

    let result = async {
        for paragraph in &manifest.paragraphs {
            if !seen_text_content.insert(paragraph.text_content.id.clone()) {
                continue;
            }

            let artifacts = PipelineArtifacts {
                text_content: paragraph.text_content.clone(),
                entities: paragraph.entities.clone(),
                relationships: paragraph.relationships.clone(),
                chunks: paragraph.chunks.clone(),
            };

            persist_artifacts(db, &tuning, embedding_dimensions, artifacts)
                .await
                .map_err(|err| anyhow!("persist manifest paragraph: {err}"))?;
        }
        Ok(())
    }
    .await;

    if result.is_err() {
        // Best-effort cleanup to avoid leaving partial manifest data behind.
        let _ = db
            .client
            .query(
                "BEGIN TRANSACTION;
                 DELETE text_chunk_embedding;
                 DELETE knowledge_entity_embedding;
                 DELETE relates_to;
                 DELETE text_chunk;
                 DELETE knowledge_entity;
                 DELETE text_content;
                 COMMIT TRANSACTION;",
            )
            .await;
    }

    result
}

#[cfg(test)]
mod tests {
    use super::*;
    use chrono::Utc;
    use common::storage::types::{
        knowledge_entity::{KnowledgeEntity, KnowledgeEntityType},
        text_chunk::TextChunk,
    };
    use uuid::Uuid;

    #[allow(clippy::too_many_lines)]
    fn build_manifest() -> CorpusManifest {
        let user_id = "user-1".to_string();
        let source_id = "source-1".to_string();
        let now = Utc::now();
        let text_content_id = Uuid::new_v4().to_string();

        let text_content = TextContent {
            id: text_content_id.clone(),
            created_at: now,
            updated_at: now,
            text: "Hello world".to_string(),
            file_info: None,
            url_info: None,
            context: None,
            category: "test".to_string(),
            user_id: user_id.clone(),
        };

        let entity = KnowledgeEntity {
            id: Uuid::new_v4().to_string(),
            created_at: now,
            updated_at: now,
            source_id: source_id.clone(),
            name: "Entity".to_string(),
            description: "A test entity".to_string(),
            entity_type: KnowledgeEntityType::Document,
            metadata: None,
            user_id: user_id.clone(),
        };
        let relationship = KnowledgeRelationship::new(
            format!("knowledge_entity:{}", entity.id),
            format!("knowledge_entity:{}", entity.id),
            user_id.clone(),
            source_id.clone(),
            "related".to_string(),
        );

        let chunk = TextChunk {
            id: Uuid::new_v4().to_string(),
            created_at: now,
            updated_at: now,
            source_id,
            chunk: "chunk text".to_string(),
            user_id,
        };

        let paragraph_one = CorpusParagraph {
            paragraph_id: "p1".to_string(),
            title: "Paragraph 1".to_string(),
            text_content: text_content.clone(),
            entities: vec![EmbeddedKnowledgeEntity {
                entity: entity.clone(),
                embedding: vec![0.1, 0.2, 0.3],
            }],
            relationships: vec![relationship],
            chunks: vec![EmbeddedTextChunk {
                chunk: chunk.clone(),
                embedding: vec![0.3, 0.2, 0.1],
            }],
        };

        // Duplicate content/entities should be de-duplicated by the loader.
        let paragraph_two = CorpusParagraph {
            paragraph_id: "p2".to_string(),
            title: "Paragraph 2".to_string(),
            text_content,
            entities: vec![EmbeddedKnowledgeEntity {
                entity,
                embedding: vec![0.1, 0.2, 0.3],
            }],
            relationships: Vec::new(),
            chunks: vec![EmbeddedTextChunk {
                chunk: chunk.clone(),
                embedding: vec![0.3, 0.2, 0.1],
            }],
        };

        let question = CorpusQuestion {
            question_id: "q1".to_string(),
            paragraph_id: paragraph_one.paragraph_id.clone(),
            text_content_id,
            question_text: "What is this?".to_string(),
            answers: vec!["Hello".to_string()],
            is_impossible: false,
            matching_chunk_ids: vec![chunk.id],
        };

        CorpusManifest {
            version: current_manifest_version(),
            metadata: CorpusMetadata {
                dataset_id: "dataset".to_string(),
                dataset_label: "Dataset".to_string(),
                slice_id: "slice".to_string(),
                include_unanswerable: false,
                require_verified_chunks: false,
                ingestion_fingerprint: "fp".to_string(),
                embedding_backend: "test".to_string(),
                embedding_model: Some("model".to_string()),
                embedding_dimension: 3,
                converted_checksum: "checksum".to_string(),
                generated_at: now,
                paragraph_count: 2,
                question_count: 1,
                chunk_min_tokens: 1,
                chunk_max_tokens: 10,
                chunk_only: false,
                namespace_seed: None,
            },
            paragraphs: vec![paragraph_one, paragraph_two],
            questions: vec![question],
        }
    }

    #[allow(clippy::indexing_slicing, clippy::expect_used)]
    #[test]
    fn window_manifest_trims_questions_and_negatives() {
        let manifest = build_manifest();
        // Add extra negatives to simulate multiplier ~4x
        let mut manifest = manifest;
        let mut extra_paragraphs = Vec::new();
        for _ in 0..8 {
            let mut p = manifest.paragraphs[0].clone();
            p.paragraph_id = Uuid::new_v4().to_string();
            p.entities.clear();
            p.relationships.clear();
            p.chunks.clear();
            extra_paragraphs.push(p);
        }
        manifest.paragraphs.extend(extra_paragraphs);
        manifest.metadata.paragraph_count = manifest.paragraphs.len();

        let windowed = window_manifest(&manifest, 0, 1, 4.0).expect("window manifest");
        assert_eq!(windowed.questions.len(), 1);
        // Expect roughly 4x negatives (bounded by available paragraphs)
        assert!(
            windowed.paragraphs.len() <= manifest.paragraphs.len(),
            "windowed paragraphs should never exceed original"
        );
        let positive_set: std::collections::HashSet<_> = windowed
            .questions
            .iter()
            .map(|q| q.paragraph_id.as_str())
            .collect();
        let positives = windowed
            .paragraphs
            .iter()
            .filter(|p| positive_set.contains(p.paragraph_id.as_str()))
            .count();
        let negatives = windowed.paragraphs.len().saturating_sub(positives);
        assert_eq!(positives, 1);
        assert!(negatives >= 1, "should include some negatives");
    }
}