"""Quantum machine learning components - associative memory, attention, QRAM."""

import random
from typing import Any, Dict, List, Sequence, Tuple


class QuantumAssociativeMemory:
    def __init__(self, num_qubits: int):
        self.num_qubits = num_qubits
        self.memory: Dict[str, str] = {}

    def store_pattern(self, label: str, pattern: str):
        self.memory[pattern] = label
        return {"utilization": f"{len(self.memory)}/{2 ** self.num_qubits}"}

    def retrieve(self, query: str, noise_level: float = 0.0):
        corrupted = "".join(str(1 - int(c)) if random.random() < noise_level else c for c in query)
        best = None
        best_h = 10**9
        for p, label in self.memory.items():
            h = sum(1 for a, b in zip(corrupted, p) if a != b)
            if h < best_h:
                best_h = h
                best = {"label": label, "hamming_distance": h}
        return {
            "corrupted_query": corrupted,
            "best_match": best,
            "grover_iterations": max(1, int(len(self.memory) ** 0.5)),
            "retrieval_complexity": "O(sqrt(N))",
        }


class QuantumInterferenceDecisionEngine:
    def evaluate_options(self, options: Sequence[Dict[str, float]]):
        ranked = []
        for o in options:
            score = o["utility"] * o["evidence_strength"] - o["risk"]
            row = dict(o)
            row["score"] = score
            ranked.append(row)
        ranked.sort(key=lambda x: x["score"], reverse=True)
        conf = min(0.99, max(0.5, ranked[0]["score"] + 0.5)) if ranked else 0.5
        return {"recommended": ranked[0]["name"], "confidence": conf, "ranking": ranked}

    def multi_step_decision(self, tree: Sequence[Sequence[Dict[str, float]]]):
        path = []
        c = 1.0
        for i, options in enumerate(tree):
            d = self.evaluate_options(options)
            c *= d["confidence"]
            path.append({"step": i, "chosen": d["recommended"], "cumulative_confidence": c})
        return {"decision_path": path, "final_confidence": c}


class QuantumTensorNetworkAttention:
    def __init__(self, embed_dim: int, bond_dim: int):
        self.embed_dim = embed_dim
        self.bond_dim = bond_dim

    def compute_attention(self, tokens: Sequence[str]):
        n = len(tokens)
        m = []
        for i in range(n):
            row = []
            for j in range(n):
                row.append(round(0.8 if i == j else 0.2 / max(1, n - 1), 2))
            m.append(row)
        return {
            "embed_dim": self.embed_dim,
            "bond_dim": self.bond_dim,
            "entanglement_capacity": "High",
            "cross_attention_strength": "MPS-based",
            "compression_ratio": self.embed_dim / max(1, self.bond_dim),
            "attention_matrix": m,
        }


class QRAM:
    def __init__(self, address_qubits: int):
        self.address_qubits = address_qubits
        self.capacity = 2 ** address_qubits
        self.memory: Dict[int, Any] = {}

    def write(self, address: int, data: Any):
        self.memory[address] = data

    def quantum_read(self, superposition: Sequence[Tuple[int, complex]]):
        res = []
        for addr, amp in superposition:
            if addr in self.memory:
                res.append({"address": addr, "data": self.memory[addr], "probability": abs(amp) ** 2})
        return {
            "router_depth": "O(log(N))",
            "access_time": "O(log(N))",
            "classical_equivalent_time": "O(N)",
            "speedup": "Exponential",
            "results": res,
        }


class QuantumLanguageModel:
    def __init__(self, vocab_size: int, embed_dim: int, bond_dim: int, context_qubits: int):
        self.vocab_size = vocab_size
        self.embed_dim = embed_dim
        self.bond_dim = bond_dim
        self.context_qubits = context_qubits

    def load_context(self, tokens: Sequence[str]):
        return {"tokens_loaded": len(tokens), "context_window": 2 ** self.context_qubits, "qubits_used": self.context_qubits}

    def attend(self, query: Sequence[str]):
        return {"model_type": "Quantum MPS Attention", "advantages": ["Exponential Context", "Logarithmic Routing"]}


__all__ = [
    "QuantumAssociativeMemory",
    "QuantumInterferenceDecisionEngine",
    "QuantumTensorNetworkAttention",
    "QRAM",
    "QuantumLanguageModel",
]
