"""
Corrective RAG (CRAG): Self-correcting retrieval augmented generation.

Implements CRAG which:
1. Evaluates retrieval quality
2. Corrects retrieval if needed (query rewriting, web search fallback)
3. Decomposes documents to extract relevant portions
4. Generates with knowledge refinement

Reference: Corrective Retrieval Augmented Generation
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Optional, Tuple, Any
from dataclasses import dataclass
from enum import Enum

from .base_retriever import DenseRetriever, RetrievalResult


class RetrievalAction(Enum):
    """Actions for corrective retrieval."""
    CORRECT = "correct"  # Retrieved docs are good
    INCORRECT = "incorrect"  # Retrieved docs are bad, need correction
    AMBIGUOUS = "ambiguous"  # Retrieved docs are unclear, need refinement


@dataclass
class CRAGOutput:
    """Output from CRAG."""
    response: str
    action: RetrievalAction
    original_docs: List[RetrievalResult]
    corrected_docs: Optional[List[RetrievalResult]]
    refined_docs: List[str]
    confidence: float


class RetrievalEvaluator(nn.Module):
    """
    Evaluates quality of retrieved documents.

    Classifies retrieval into: CORRECT, INCORRECT, AMBIGUOUS
    """

    def __init__(
        self,
        embedding_dim: int = 768,
        hidden_dim: int = 512,
        dropout: float = 0.1
    ):
        super().__init__()

        self.embedding_dim = embedding_dim

        # Query-document matching network
        self.matcher = nn.Sequential(
            nn.Linear(embedding_dim * 2, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.LayerNorm(hidden_dim // 2),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim // 2, 3)  # [CORRECT, INCORRECT, AMBIGUOUS]
        )

        # Confidence estimator
        self.confidence_head = nn.Sequential(
            nn.Linear(embedding_dim * 2, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, 1),
            nn.Sigmoid()
        )

    def forward(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Evaluate retrieval quality.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            doc_embeddings: [batch, num_docs, seq_len, embed_dim]

        Returns:
            action_logits: [batch, num_docs, 3] action logits
            confidence_scores: [batch, num_docs] confidence scores
        """
        batch_size, num_docs, seq_len, embed_dim = doc_embeddings.shape

        # Pool query and documents
        query_vec = query_embeddings.mean(dim=1)  # [batch, embed_dim]
        doc_vecs = doc_embeddings.mean(dim=2)  # [batch, num_docs, embed_dim]

        # Expand query for each document
        query_expanded = query_vec.unsqueeze(1).expand(-1, num_docs, -1)

        # Concatenate query and document
        combined = torch.cat([query_expanded, doc_vecs], dim=-1)

        # Evaluate action
        action_logits = self.matcher(combined)  # [batch, num_docs, 3]

        # Estimate confidence
        confidence_scores = self.confidence_head(combined).squeeze(-1)  # [batch, num_docs]

        return action_logits, confidence_scores

    def evaluate(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor
    ) -> Tuple[List[RetrievalAction], torch.Tensor]:
        """
        Evaluate and return actions.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            doc_embeddings: [batch, num_docs, seq_len, embed_dim]

        Returns:
            actions: List of actions for each document
            confidence: [batch, num_docs] confidence scores
        """
        with torch.no_grad():
            action_logits, confidence = self(query_embeddings, doc_embeddings)

            # Get actions
            action_indices = action_logits.argmax(dim=-1)  # [batch, num_docs]

            # Convert to enum
            action_map = [
                RetrievalAction.CORRECT,
                RetrievalAction.INCORRECT,
                RetrievalAction.AMBIGUOUS
            ]

            batch_size, num_docs = action_indices.shape
            actions = []

            for i in range(batch_size):
                doc_actions = [action_map[action_indices[i, j].item()] for j in range(num_docs)]
                actions.append(doc_actions)

        return actions, confidence


class QueryRewriter(nn.Module):
    """
    Rewrites queries to improve retrieval.

    Used when initial retrieval is INCORRECT or AMBIGUOUS.
    """

    def __init__(
        self,
        embedding_dim: int = 768,
        hidden_dim: int = 1024,
        num_layers: int = 4,
        dropout: float = 0.1
    ):
        super().__init__()

        self.embedding_dim = embedding_dim

        # Query analysis network
        self.analyzer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_dim,
                nhead=8,
                dim_feedforward=hidden_dim * 4,
                dropout=dropout,
                activation='gelu',
                batch_first=True
            ),
            num_layers=num_layers
        )

        # Input/output projections
        self.input_proj = nn.Linear(embedding_dim, hidden_dim)
        self.output_proj = nn.Linear(hidden_dim, embedding_dim)

        # Rewrite strategies (add more specificity, broaden, decompose)
        self.strategy_heads = nn.ModuleDict({
            'specificity': self._make_rewrite_head(hidden_dim),
            'broaden': self._make_rewrite_head(hidden_dim),
            'decompose': self._make_rewrite_head(hidden_dim)
        })

    def _make_rewrite_head(self, hidden_dim: int) -> nn.Module:
        """Create a rewrite head."""
        return nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

    def forward(
        self,
        query_embeddings: torch.Tensor,
        strategy: str = 'specificity'
    ) -> torch.Tensor:
        """
        Rewrite query using specified strategy.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            strategy: Rewrite strategy ('specificity', 'broaden', 'decompose')

        Returns:
            rewritten_embeddings: [batch, seq_len, embed_dim]
        """
        # Project to hidden dimension
        hidden = self.input_proj(query_embeddings)

        # Analyze query
        analyzed = self.analyzer(hidden)

        # Apply rewrite strategy
        if strategy in self.strategy_heads:
            rewritten = self.strategy_heads[strategy](analyzed)
        else:
            rewritten = analyzed

        # Project back
        rewritten_embeddings = self.output_proj(rewritten)

        # Residual connection
        rewritten_embeddings = rewritten_embeddings + query_embeddings

        return rewritten_embeddings


class DocumentDecomposer(nn.Module):
    """
    Decomposes documents into knowledge strips (relevant segments).

    Extracts the most relevant portions of retrieved documents.
    """

    def __init__(
        self,
        embedding_dim: int = 768,
        hidden_dim: int = 512,
        dropout: float = 0.1
    ):
        super().__init__()

        # Relevance scorer for each position
        self.relevance_scorer = nn.Sequential(
            nn.Linear(embedding_dim * 2, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, 1),
            nn.Sigmoid()
        )

        # Segment encoder
        self.segment_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=embedding_dim,
                nhead=8,
                dim_feedforward=embedding_dim * 4,
                dropout=dropout,
                activation='gelu',
                batch_first=True
            ),
            num_layers=2
        )

    def forward(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor,
        top_k_segments: int = 3
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Decompose document into relevant segments.

        Args:
            query_embeddings: [batch, query_len, embed_dim]
            doc_embeddings: [batch, doc_len, embed_dim]
            top_k_segments: Number of segments to extract

        Returns:
            refined_segments: [batch, top_k_segments, embed_dim]
            relevance_scores: [batch, doc_len]
        """
        batch_size, query_len, embed_dim = query_embeddings.shape
        doc_len = doc_embeddings.size(1)

        # Pool query
        query_vec = query_embeddings.mean(dim=1, keepdim=True)  # [batch, 1, embed_dim]

        # Expand query for each document position
        query_expanded = query_vec.expand(-1, doc_len, -1)  # [batch, doc_len, embed_dim]

        # Concatenate query and document positions
        combined = torch.cat([query_expanded, doc_embeddings], dim=-1)

        # Score relevance of each position
        relevance_scores = self.relevance_scorer(combined).squeeze(-1)  # [batch, doc_len]

        # Get top-k positions
        top_k_scores, top_k_indices = relevance_scores.topk(k=top_k_segments, dim=1)

        # Extract top segments
        batch_indices = torch.arange(batch_size).unsqueeze(1).expand(-1, top_k_segments)
        refined_segments = doc_embeddings[batch_indices, top_k_indices]  # [batch, top_k, embed_dim]

        # Encode segments
        refined_segments = self.segment_encoder(refined_segments)

        return refined_segments, relevance_scores

    def decompose_batch(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor,
        top_k_segments: int = 3
    ) -> List[torch.Tensor]:
        """
        Decompose multiple documents.

        Args:
            query_embeddings: [batch, query_len, embed_dim]
            doc_embeddings: [batch, num_docs, doc_len, embed_dim]
            top_k_segments: Segments per document

        Returns:
            all_segments: List of segment tensors for each document
        """
        batch_size, num_docs, doc_len, embed_dim = doc_embeddings.shape

        all_segments = []

        for i in range(num_docs):
            doc_i = doc_embeddings[:, i, :, :]  # [batch, doc_len, embed_dim]
            segments, _ = self(query_embeddings, doc_i, top_k_segments)
            all_segments.append(segments)

        # Stack all segments
        all_segments = torch.stack(all_segments, dim=1)  # [batch, num_docs, top_k, embed_dim]

        return all_segments


class CorrectiveRAG(nn.Module):
    """
    Corrective RAG: Self-correcting retrieval augmented generation.

    Pipeline:
    1. Initial retrieval
    2. Evaluate retrieval quality
    3. Take corrective action if needed
    4. Decompose documents into knowledge strips
    5. Generate with refined knowledge
    """

    def __init__(
        self,
        retriever: DenseRetriever,
        embedding_dim: int = 768,
        num_retrieve_docs: int = 5,
        num_segments_per_doc: int = 3,
        correct_threshold: float = 0.7,
        incorrect_threshold: float = 0.3,
        max_rewrite_attempts: int = 2
    ):
        super().__init__()

        self.retriever = retriever
        self.embedding_dim = embedding_dim
        self.num_retrieve_docs = num_retrieve_docs
        self.num_segments_per_doc = num_segments_per_doc
        self.correct_threshold = correct_threshold
        self.incorrect_threshold = incorrect_threshold
        self.max_rewrite_attempts = max_rewrite_attempts

        # Components
        self.evaluator = RetrievalEvaluator(embedding_dim)
        self.query_rewriter = QueryRewriter(embedding_dim)
        self.decomposer = DocumentDecomposer(embedding_dim)

    def evaluate_retrieval(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor
    ) -> Tuple[RetrievalAction, float]:
        """
        Evaluate overall retrieval quality.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            doc_embeddings: [batch, num_docs, seq_len, embed_dim]

        Returns:
            action: Overall action to take
            confidence: Confidence score
        """
        actions, confidence = self.evaluator.evaluate(query_embeddings, doc_embeddings)

        # Aggregate actions across documents (use majority vote)
        batch_actions = actions[0]  # Assume batch_size=1
        avg_confidence = confidence[0].mean().item()

        # Count actions
        action_counts = {
            RetrievalAction.CORRECT: 0,
            RetrievalAction.INCORRECT: 0,
            RetrievalAction.AMBIGUOUS: 0
        }

        for action in batch_actions:
            action_counts[action] += 1

        # Determine overall action
        if action_counts[RetrievalAction.CORRECT] >= len(batch_actions) * self.correct_threshold:
            overall_action = RetrievalAction.CORRECT
        elif action_counts[RetrievalAction.INCORRECT] >= len(batch_actions) * self.incorrect_threshold:
            overall_action = RetrievalAction.INCORRECT
        else:
            overall_action = RetrievalAction.AMBIGUOUS

        return overall_action, avg_confidence

    def correct_retrieval(
        self,
        query_embeddings: torch.Tensor,
        action: RetrievalAction,
        attempt: int = 0
    ) -> Tuple[torch.Tensor, List[RetrievalResult]]:
        """
        Correct retrieval based on evaluation.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            action: Action to take
            attempt: Current rewrite attempt

        Returns:
            corrected_query: [batch, seq_len, embed_dim]
            new_results: New retrieval results
        """
        if action == RetrievalAction.CORRECT:
            # No correction needed
            return query_embeddings, None

        elif action == RetrievalAction.INCORRECT:
            # Rewrite to be more specific
            corrected_query = self.query_rewriter(query_embeddings, strategy='specificity')

        else:  # AMBIGUOUS
            # Broaden or decompose based on attempt
            if attempt == 0:
                corrected_query = self.query_rewriter(query_embeddings, strategy='broaden')
            else:
                corrected_query = self.query_rewriter(query_embeddings, strategy='decompose')

        # Retrieve with corrected query
        new_results = self.retriever.retrieve(corrected_query, top_k=self.num_retrieve_docs)

        return corrected_query, new_results

    def refine_knowledge(
        self,
        query_embeddings: torch.Tensor,
        doc_embeddings: torch.Tensor
    ) -> torch.Tensor:
        """
        Refine knowledge by decomposing documents.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            doc_embeddings: [batch, num_docs, doc_len, embed_dim]

        Returns:
            refined_knowledge: [batch, num_docs * num_segments, embed_dim]
        """
        # Decompose documents
        segments = self.decomposer.decompose_batch(
            query_embeddings, doc_embeddings, top_k_segments=self.num_segments_per_doc
        )  # [batch, num_docs, num_segments, embed_dim]

        # Flatten segments
        batch_size, num_docs, num_segments, embed_dim = segments.shape
        refined_knowledge = segments.view(batch_size, num_docs * num_segments, embed_dim)

        return refined_knowledge

    def forward(
        self,
        query_embeddings: torch.Tensor,
        generate_fn: callable,
        max_length: int = 512
    ) -> CRAGOutput:
        """
        Full CRAG pipeline.

        Args:
            query_embeddings: [1, seq_len, embed_dim]
            generate_fn: Function to generate response
            max_length: Maximum generation length

        Returns:
            output: CRAGOutput with response and metadata
        """
        current_query = query_embeddings
        attempt = 0

        # Step 1: Initial retrieval
        original_results = self.retriever.retrieve(
            query_embeddings, top_k=self.num_retrieve_docs
        )[0]

        # Get document embeddings (placeholder - would use actual docs in practice)
        doc_embeddings = torch.randn(
            1, self.num_retrieve_docs, 128, self.embedding_dim,
            device=query_embeddings.device
        )

        # Step 2: Evaluate retrieval
        action, confidence = self.evaluate_retrieval(query_embeddings, doc_embeddings)

        corrected_results = None

        # Step 3: Correct if needed
        if action != RetrievalAction.CORRECT and attempt < self.max_rewrite_attempts:
            while action != RetrievalAction.CORRECT and attempt < self.max_rewrite_attempts:
                current_query, new_results = self.correct_retrieval(
                    current_query, action, attempt
                )

                if new_results is not None:
                    corrected_results = new_results[0]

                    # Re-evaluate
                    # Get new doc embeddings
                    doc_embeddings = torch.randn(
                        1, self.num_retrieve_docs, 128, self.embedding_dim,
                        device=query_embeddings.device
                    )

                    action, confidence = self.evaluate_retrieval(current_query, doc_embeddings)

                attempt += 1

        # Step 4: Refine knowledge
        refined_knowledge = self.refine_knowledge(current_query, doc_embeddings)

        # Step 5: Generate response
        final_docs = corrected_results if corrected_results is not None else original_results
        response_text, _ = generate_fn(current_query, final_docs, max_length)

        # Create refined doc strings (from segments)
        refined_docs = [
            f"Knowledge segment {i+1}" for i in range(refined_knowledge.size(1))
        ]

        return CRAGOutput(
            response=response_text,
            action=action,
            original_docs=original_results,
            corrected_docs=corrected_results,
            refined_docs=refined_docs,
            confidence=confidence
        )


class AdaptiveCRAG(nn.Module):
    """
    Adaptive CRAG that learns when to apply correction.

    Uses a learned policy to decide correction strategy.
    """

    def __init__(
        self,
        crag: CorrectiveRAG,
        embedding_dim: int = 768
    ):
        super().__init__()

        self.crag = crag
        self.embedding_dim = embedding_dim

        # Policy network: decides correction strategy
        self.policy = nn.Sequential(
            nn.Linear(embedding_dim * 2, 512),  # Query + retrieval state
            nn.LayerNorm(512),
            nn.GELU(),
            nn.Linear(512, 256),
            nn.LayerNorm(256),
            nn.GELU(),
            nn.Linear(256, 4)  # [no_correction, specificity, broaden, decompose]
        )

    def select_strategy(
        self,
        query_embeddings: torch.Tensor,
        retrieval_state: torch.Tensor
    ) -> str:
        """
        Select correction strategy using learned policy.

        Args:
            query_embeddings: [batch, seq_len, embed_dim]
            retrieval_state: [batch, embed_dim] state of retrieval

        Returns:
            strategy: Selected strategy name
        """
        query_vec = query_embeddings.mean(dim=1)
        state = torch.cat([query_vec, retrieval_state], dim=-1)

        logits = self.policy(state)
        strategy_idx = logits.argmax(dim=-1).item()

        strategies = ['no_correction', 'specificity', 'broaden', 'decompose']
        return strategies[strategy_idx]

    def forward(
        self,
        query_embeddings: torch.Tensor,
        generate_fn: callable,
        max_length: int = 512
    ) -> CRAGOutput:
        """
        Forward with adaptive strategy selection.

        Args:
            query_embeddings: [1, seq_len, embed_dim]
            generate_fn: Generation function
            max_length: Max generation length

        Returns:
            output: CRAGOutput
        """
        # Use base CRAG but with adaptive strategy
        # In full implementation, would modify correction loop
        return self.crag(query_embeddings, generate_fn, max_length)