from __future__ import annotations

import math
import re
from collections import Counter
from dataclasses import dataclass
from typing import Protocol


@dataclass(frozen=True)
class ClassificationResult:
    style: str
    confidence: float
    scores: dict[str, float]


class StyleClassifier(Protocol):
    def classify(
        self,
        text: str,
        available_styles: list[str],
    ) -> ClassificationResult: ...


# ---------------------------------------------------------------------------
# Signal detection helpers
# ---------------------------------------------------------------------------

_TECHNICAL_VOCAB: frozenset[str] = frozenset(
    [
        "api",
        "latency",
        "vector",
        "embedding",
        "model",
        "pipeline",
        "inference",
        "query",
        "database",
        "schema",
        "endpoint",
        "migration",
        "deployment",
        "cache",
        "index",
        "algorithm",
        "runtime",
        "throughput",
        "p99",
        "gpu",
        "cpu",
        "tokenizer",
        "transformer",
        "tensor",
        "gradient",
        "epoch",
        "batch",
        "parameter",
        "checkpoint",
        "quantization",
        "precision",
        "recall",
        "accuracy",
        "loss",
        "optimizer",
        "learning",
        "architecture",
        "layer",
        "neuron",
        "weights",
        "bias",
        "activation",
        "function",
        "module",
        "service",
        "namespace",
        "kubernetes",
        "container",
        "docker",
        "cluster",
        "replica",
        "shard",
        "partition",
        "replication",
        "concurrency",
        "async",
        "coroutine",
        "serialization",
        "deserialization",
        "protocol",
        "socket",
        "bandwidth",
        "packet",
        "buffer",
        "heap",
        "stack",
        "pointer",
        "memory",
        "register",
        "interrupt",
        "syscall",
        "kernel",
        "thread",
        "process",
        "mutex",
        "semaphore",
        "deadlock",
        "benchmark",
        "profiling",
        "instrumentation",
        "observability",
        "metric",
        "telemetry",
        "tracing",
        "logging",
        "monitoring",
        "alerting",
        "dashboard",
        "visualization",
        "regression",
        "classification",
        "clustering",
        "dimensionality",
        "feature",
        "preprocessing",
        "normalization",
        "augmentation",
        "validation",
        "tokenization",
        "vocabulary",
        "corpus",
        "dataset",
        "annotation",
        "fine-tuning",
        "pretraining",
        "rlhf",
        "reward",
        "policy",
        "sampling",
        "temperature",
        "logits",
        "softmax",
        "attention",
        "cross-attention",
        "positional",
        "encoding",
        "decoding",
        "generation",
        "completion",
        "prompt",
        "context",
        "window",
        "retrieval",
        "reranking",
    ]
)

_CASUAL_MARKERS: frozenset[str] = frozenset(
    [
        "honestly",
        "basically",
        "actually",
        "kind of",
        "sort of",
        "you know",
        "i think",
        "i mean",
        "like",
        "pretty much",
        "just",
        "really",
        "so",
        "well",
        "anyway",
        "right",
    ]
)

_EMPHATIC_SUPERLATIVES: frozenset[str] = frozenset(
    [
        "best",
        "worst",
        "fastest",
        "incredible",
        "unbelievable",
        "amazing",
        "game-changer",
        "revolutionary",
        "everything",
        "nothing",
        "insane",
        "mind-blowing",
        "unreal",
        "phenomenal",
        "extraordinary",
        "epic",
        "massive",
        "huge",
        "groundbreaking",
        "transformative",
    ]
)

_EMPHATIC_INTENSIFIERS: frozenset[str] = frozenset(
    [
        "absolutely",
        "completely",
        "totally",
        "extremely",
        "incredibly",
        "fundamentally",
        "literally",
        "undeniably",
        "definitively",
        "unquestionably",
        "profoundly",
        "radically",
    ]
)

_EMPHATIC_IMPERATIVES: frozenset[str] = frozenset(
    [
        "look",
        "see",
        "check",
        "notice",
        "imagine",
        "try",
        "watch",
        "listen",
        "stop",
        "go",
        "read",
        "remember",
        "consider",
    ]
)

_TRANSITIONAL_PHRASES: list[str] = [
    "however",
    "moreover",
    "furthermore",
    "in addition",
    "consequently",
    "nevertheless",
    "as a result",
    "on the other hand",
    "in fact",
    "indeed",
    "therefore",
    "thus",
    "hence",
    "nonetheless",
    "meanwhile",
    "subsequently",
    "in contrast",
    "by contrast",
    "in conclusion",
    "to summarize",
]

_THIRD_PERSON_PRONOUNS: frozenset[str] = frozenset(
    ["he", "she", "they", "it", "his", "her", "their", "its", "him", "them"]
)

_FIRST_PERSON_PRONOUNS: frozenset[str] = frozenset(
    ["i", "we", "my", "me", "our", "mine", "ours", "myself", "ourselves"]
)

_CONTRACTIONS: frozenset[str] = frozenset(
    [
        "don't",
        "can't",
        "won't",
        "it's",
        "i'm",
        "i've",
        "that's",
        "there's",
        "we're",
        "isn't",
        "aren't",
        "wasn't",
        "weren't",
        "hasn't",
        "haven't",
        "hadn't",
        "doesn't",
        "didn't",
        "couldn't",
        "shouldn't",
        "wouldn't",
        "you're",
        "you've",
        "you'll",
        "they're",
        "they've",
        "he's",
        "she's",
        "i'll",
        "we'll",
        "i'd",
        "we'd",
        "you'd",
        "they'd",
    ]
)

_UNIT_PATTERN = re.compile(
    r"\b\d+(?:\.\d+)?\s*(?:ms|GB|MB|KB|Hz|kHz|MHz|GHz|TB|ns|µs|px|fps|"
    r"tokens|rpm|qps|rps|wps|ops|req|s|m|k|M|B)\b",
    re.IGNORECASE,
)
_CAMEL_SNAKE_PATTERN = re.compile(r"\b[a-z]+[A-Z][a-zA-Z]+\b|\b[a-z]+_[a-z_]+\b")
_DOTTED_CALL_PATTERN = re.compile(r"\b\w+\.\w+\(")
_ACRONYM_PATTERN = re.compile(r"\b[A-Z]{2,}\b")


def _tokenize(text: str) -> list[str]:
    return re.findall(r"[a-zA-Z']+", text.lower())


def _sentences(text: str) -> list[str]:
    parts = re.split(r"[.!?]+", text)
    return [p.strip() for p in parts if p.strip()]


def _technical_score(text: str) -> float:
    if not text.strip():
        return 0.0
    tokens = _tokenize(text)
    if not tokens:
        return 0.0
    total = len(tokens)
    vocab_hits = sum(1 for t in tokens if t in _TECHNICAL_VOCAB)
    vocab_ratio = vocab_hits / total

    unit_count = len(_UNIT_PATTERN.findall(text))
    camel_snake_count = len(_CAMEL_SNAKE_PATTERN.findall(text))
    dotted_count = len(_DOTTED_CALL_PATTERN.findall(text))
    acronym_count = len(_ACRONYM_PATTERN.findall(text))

    boost = (
        min(unit_count * 0.05, 0.2)
        + min(camel_snake_count * 0.03, 0.15)
        + min(dotted_count * 0.05, 0.15)
        + min(acronym_count * 0.02, 0.1)
    )
    return min(1.0, vocab_ratio * 2.0 + boost)


def _conversational_score(text: str) -> float:
    if not text.strip():
        return 0.0
    lower = text.lower()
    tokens = _tokenize(text)
    if not tokens:
        return 0.0
    total = len(tokens)

    casual_hits = sum(1 for marker in _CASUAL_MARKERS if marker in lower)
    first_person_hits = sum(1 for t in tokens if t in _FIRST_PERSON_PRONOUNS)
    contraction_hits = sum(1 for c in _CONTRACTIONS if c in lower)

    question_marks = text.count("?")
    sents = _sentences(text)
    avg_len = total / max(len(sents), 1)
    short_sentence_boost = 0.1 if avg_len < 12 else 0.0

    score = (
        min(casual_hits * 0.05, 0.25)
        + min(first_person_hits / total * 1.5, 0.3)
        + min(contraction_hits * 0.04, 0.2)
        + min(question_marks * 0.05, 0.15)
        + short_sentence_boost
    )
    return min(1.0, score)


def _emphatic_score(text: str) -> float:
    if not text.strip():
        return 0.0
    lower = text.lower()
    tokens = _tokenize(text)
    if not tokens:
        return 0.0

    exclamation_count = text.count("!")
    superlative_hits = sum(1 for w in _EMPHATIC_SUPERLATIVES if w in lower)
    intensifier_hits = sum(1 for w in _EMPHATIC_INTENSIFIERS if w in lower)

    sents = _sentences(text)
    imperative_hits = 0
    for sent in sents:
        first_word = sent.split()[0].lower().rstrip(".,!?") if sent.split() else ""
        if first_word in _EMPHATIC_IMPERATIVES:
            imperative_hits += 1

    score = (
        min(exclamation_count * 0.1, 0.3)
        + min(superlative_hits * 0.07, 0.25)
        + min(intensifier_hits * 0.06, 0.2)
        + min(imperative_hits * 0.1, 0.2)
    )
    return min(1.0, score)


def _narration_score(text: str) -> float:
    if not text.strip():
        return 0.0
    tokens = _tokenize(text)
    if not tokens:
        return 0.0
    total = len(tokens)
    lower = text.lower()

    sents = _sentences(text)
    avg_sent_len = total / max(len(sents), 1)
    long_sentence_boost = (
        min((avg_sent_len - 10) * 0.015, 0.25) if avg_sent_len > 10 else 0.0
    )

    unique_ratio = len(set(tokens)) / total
    low_density_boost = max(0.0, (0.7 - unique_ratio) * 0.3)

    no_questions = 0.1 if "?" not in text else 0.0
    no_exclamations = 0.1 if "!" not in text else 0.0

    transition_hits = sum(1 for ph in _TRANSITIONAL_PHRASES if ph in lower)
    third_person_hits = sum(1 for t in tokens if t in _THIRD_PERSON_PRONOUNS)

    score = (
        long_sentence_boost
        + low_density_boost
        + no_questions
        + no_exclamations
        + min(transition_hits * 0.08, 0.25)
        + min(third_person_hits / total * 0.8, 0.2)
    )
    return min(1.0, score)


# ---------------------------------------------------------------------------
# RuleBasedClassifier
# ---------------------------------------------------------------------------

_STYLE_SCORERS = {
    "conversational": _conversational_score,
    "technical": _technical_score,
    "narration": _narration_score,
    "emphatic": _emphatic_score,
}


class RuleBasedClassifier:
    """Fast heuristic classifier (~0.1 ms). Deterministic for identical input."""

    def classify(
        self,
        text: str,
        available_styles: list[str],
    ) -> ClassificationResult:
        if not available_styles:
            return ClassificationResult(
                style="conversational",
                confidence=0.5,
                scores={},
            )

        if len(available_styles) == 1:
            style = available_styles[0]
            return ClassificationResult(
                style=style,
                confidence=1.0,
                scores={style: 1.0},
            )

        stripped = text.strip()
        tokens = _tokenize(stripped)

        if not stripped or len(tokens) < 5:
            default = available_styles[0]
            scores = {s: 0.0 for s in available_styles}
            scores[default] = 1.0
            return ClassificationResult(
                style=default,
                confidence=0.5,
                scores=scores,
            )

        raw: dict[str, float] = {}
        for style in available_styles:
            scorer = _STYLE_SCORERS.get(style)
            raw[style] = scorer(stripped) if scorer is not None else 0.0

        total = sum(raw.values())
        if total == 0.0:
            default = available_styles[0]
            uniform = 1.0 / len(available_styles)
            scores = {s: uniform for s in available_styles}
            return ClassificationResult(
                style=default,
                confidence=0.5,
                scores=scores,
            )

        normalized = {s: v / total for s, v in raw.items()}

        best_style = max(normalized, key=lambda s: normalized[s])
        best_raw = raw[best_style]

        if best_raw < 0.3:
            return ClassificationResult(
                style=best_style,
                confidence=0.5,
                scores=normalized,
            )

        confidence = min(0.95, 0.5 + best_raw)
        return ClassificationResult(
            style=best_style,
            confidence=confidence,
            scores=normalized,
        )


# ---------------------------------------------------------------------------
# CentroidClassifier
# ---------------------------------------------------------------------------


def _tf(tokens: list[str]) -> dict[str, float]:
    if not tokens:
        return {}
    counts = Counter(tokens)
    total = len(tokens)
    return {word: count / total for word, count in counts.items()}


def _cosine_similarity(a: dict[str, float], b: dict[str, float]) -> float:
    dot = sum(a.get(w, 0.0) * v for w, v in b.items())
    norm_a = math.sqrt(sum(v * v for v in a.values()))
    norm_b = math.sqrt(sum(v * v for v in b.values()))
    if norm_a == 0.0 or norm_b == 0.0:
        return 0.0
    return dot / (norm_a * norm_b)


class CentroidClassifier:
    """Bag-of-words TF-IDF centroid classifier (~1 ms). No external dependencies."""

    def __init__(self) -> None:
        self._centroids: dict[str, dict[str, float]] = {}
        self._idf: dict[str, float] = {}
        self._fitted = False

    def fit(self, examples: list[tuple[str, str]]) -> None:
        """Fit on (text, style_label) pairs. Computes TF-IDF centroids per style."""
        if not examples:
            return

        tokenized: list[tuple[list[str], str]] = [
            (_tokenize(text), label) for text, label in examples
        ]
        n_docs = len(tokenized)

        # Document frequency per word
        df: Counter[str] = Counter()
        for tokens, _ in tokenized:
            for word in set(tokens):
                df[word] += 1

        self._idf = {
            word: math.log(n_docs / (1 + count))
            for word, count in df.items()
        }

        # Accumulate TF-IDF vectors per style
        style_vectors: dict[str, list[dict[str, float]]] = {}
        for tokens, label in tokenized:
            tf_map = _tf(tokens)
            tfidf_vec = {w: tf_map[w] * self._idf.get(w, 0.0) for w in tf_map}
            style_vectors.setdefault(label, []).append(tfidf_vec)

        # Average vectors → centroids
        self._centroids = {}
        for label, vecs in style_vectors.items():
            merged: dict[str, float] = {}
            for vec in vecs:
                for word, val in vec.items():
                    merged[word] = merged.get(word, 0.0) + val
            count = len(vecs)
            self._centroids[label] = {w: v / count for w, v in merged.items()}

        self._fitted = True

    def classify(
        self,
        text: str,
        available_styles: list[str],
    ) -> ClassificationResult:
        if not self._fitted:
            raise RuntimeError("CentroidClassifier not fitted")

        if not available_styles:
            return ClassificationResult(
                style="conversational",
                confidence=0.5,
                scores={},
            )

        if len(available_styles) == 1:
            style = available_styles[0]
            return ClassificationResult(
                style=style,
                confidence=1.0,
                scores={style: 1.0},
            )

        tokens = _tokenize(text.strip())
        tf_map = _tf(tokens)
        tfidf_vec = {w: tf_map[w] * self._idf.get(w, 0.0) for w in tf_map}

        similarities: dict[str, float] = {}
        for style in available_styles:
            centroid = self._centroids.get(style, {})
            similarities[style] = _cosine_similarity(tfidf_vec, centroid)

        total = sum(similarities.values())
        if total == 0.0:
            default = available_styles[0]
            uniform = 1.0 / len(available_styles)
            return ClassificationResult(
                style=default,
                confidence=0.5,
                scores={s: uniform for s in available_styles},
            )

        normalized = {s: v / total for s, v in similarities.items()}
        best_style = max(normalized, key=lambda s: normalized[s])
        confidence = min(0.95, 0.5 + similarities[best_style])

        return ClassificationResult(
            style=best_style,
            confidence=confidence,
            scores=normalized,
        )


# ---------------------------------------------------------------------------
# Embedded training data
# ---------------------------------------------------------------------------


def _labeled(texts: list[str], label: str) -> list[tuple[str, str]]:
    return [(t, label) for t in texts]


def get_training_data() -> list[tuple[str, str]]:
    """Return labeled (text, style) pairs for classifier training."""
    conv = _labeled(
        [
            (
                "So I've been tinkering with this side project,"
                " and it's actually kind of fun."
            ),
            "Honestly, the whole thing took way longer than I expected.",
            (
                "I think the thing that surprised me most was how simple"
                " the fix turned out to be."
            ),
            (
                "You know what? I just gave up and rewrote the whole"
                " thing from scratch."
            ),
            (
                "I mean, at some point you just have to ship it"
                " and see what happens."
            ),
            (
                "We've been going back and forth on this for weeks"
                " and I'm honestly tired."
            ),
            (
                "Basically I tried like three different approaches"
                " and none of them really worked."
            ),
            (
                "It's kind of weird how the simplest problems are"
                " sometimes the hardest to debug."
            ),
            (
                "I'm pretty sure I spent more time reading docs"
                " than actually writing code."
            ),
            (
                "So we finally launched it yesterday and people seem"
                " to like it, which is nice."
            ),
            "Can I just say that retries saved my life on this one?",
            (
                "I've never been so relieved to see a green test suite"
                " in my entire life."
            ),
            (
                "My whole weekend basically disappeared into this rabbit"
                " hole and I regret nothing."
            ),
            "We were supposed to finish this in two days — it took three weeks.",
            (
                "Honestly I don't know how people built software"
                " before good logging existed."
            ),
            (
                "I kind of stumbled into this solution by accident"
                " but I'll take it."
            ),
            (
                "Me and my coworker basically argued about this for an hour"
                " and then realized we were both wrong."
            ),
            "It's funny how the most annoying bugs are usually typos.",
            (
                "I keep meaning to refactor this but it works"
                " so I'm not touching it."
            ),
            (
                "Pretty sure I've opened and closed this PR"
                " like six times at this point."
            ),
            "What even is sleep at this point, honestly.",
            (
                "I think my favorite part of this job is when things"
                " actually work the first time. Which is rare."
            ),
            (
                "We ended up just hardcoding it for now"
                " and I feel a little bad about it."
            ),
            (
                "So the demo is tomorrow and I just realized a critical"
                " piece is broken. Fun times."
            ),
            (
                "I actually learned more from reading other people's code"
                " than any tutorial."
            ),
            (
                "You know that feeling when you delete code and everything"
                " still works? Chef's kiss."
            ),
            (
                "I've been meaning to learn Rust for like two years"
                " and I still haven't started."
            ),
            "We shipped it at 2am and just kind of hoped for the best.",
            "I wasn't expecting it to be that complicated but here we are.",
            (
                "My approach was basically to throw things at the wall"
                " and see what sticks."
            ),
            (
                "I finally got around to reading that book everyone"
                " recommends and it's actually good."
            ),
            "So I broke production today. That was an adventure.",
            (
                "I think we've been overthinking this"
                " — it's really just a CRUD app."
            ),
            (
                "Honestly the onboarding docs were so bad I had to"
                " figure everything out by reading the source."
            ),
            (
                "Can we talk about how annoying timezone handling is?"
                " Because it's really annoying."
            ),
            (
                "I wrote a script to automate the boring part and now"
                " I spend my time maintaining the script."
            ),
            (
                "The code review took longer than writing the code,"
                " but I think it's better for it."
            ),
            "We have a test for that, I swear. I just have to find it.",
            (
                "I didn't expect to care this much about a naming"
                " convention but here I am."
            ),
            (
                "My first instinct was wrong, my second instinct was also"
                " wrong, but the third one worked."
            ),
            (
                "I've been using this library for years and just discovered"
                " a feature I needed the whole time."
            ),
            (
                "We probably should have defined the API contract before"
                " building both ends simultaneously."
            ),
            "I took a break, came back, and immediately saw the bug. Classic.",
            (
                "My manager asked for an ETA and I gave the honest answer:"
                " I have no idea."
            ),
            (
                "So someone deleted the wrong table in prod and we spent"
                " six hours recovering it."
            ),
            (
                "I wrote it in Python first, rewrote it in Go for"
                " performance, and now I'm writing it in Python again."
            ),
            (
                "Just discovered that the feature I spent two days on"
                " was already built by someone else."
            ),
            (
                "I think the real lesson here is to read the existing"
                " codebase before writing new code."
            ),
            (
                "We tried to parallelize it and somehow made it slower."
                " Still not sure why."
            ),
            "Honestly the hardest part of the job is deciding what NOT to build.",
            "I miss the days when my whole codebase fit in my head.",
            (
                "The bug was in a part of the code I wrote six months ago"
                " and barely remember."
            ),
            "I genuinely thought this would take an hour. It took a week.",
        ],
        "conversational",
    )

    tech = _labeled(
        [
            (
                "The embedding model uses a 768-dimensional dense vector"
                " space with cosine similarity."
            ),
            (
                "Latency dropped from 340 milliseconds to 89 milliseconds"
                " after the migration."
            ),
            (
                "The pipeline processes queries in three stages:"
                " embedding lookup, context reranking, and completion."
            ),
            (
                "TF-IDF vectorization computes term frequency normalized"
                " by inverse document frequency across the corpus."
            ),
            (
                "The API endpoint accepts a JSON payload with a required"
                " `text` field and optional `style` parameter."
            ),
            (
                "Cache eviction uses an LRU policy with a configurable"
                " TTL of 3600 seconds by default."
            ),
            (
                "The transformer architecture uses multi-head self-attention"
                " with 12 heads and a hidden size of 768."
            ),
            (
                "GPU utilization peaked at 94% during the training run"
                " with a batch size of 32."
            ),
            (
                "The database schema uses a composite index on"
                " (identity_id, label) for O(log n) lookup."
            ),
            (
                "Cosine similarity between the query vector and the"
                " centroid vector determines the routing decision."
            ),
            (
                "The inference pipeline achieves p99 latency of 120 ms"
                " with a throughput of 800 requests per second."
            ),
            "WAL mode in SQLite allows concurrent reads without blocking writes.",
            (
                "The tokenizer uses byte-pair encoding with a vocabulary"
                " size of 50,257 tokens."
            ),
            (
                "Gradient clipping is applied at a max norm of 1.0 to"
                " prevent exploding gradients during fine-tuning."
            ),
            (
                "The model checkpoint is serialized using safetensors format"
                " for safe, zero-copy deserialization."
            ),
            (
                "The container image is built FROM python:3.12-slim"
                " and exposes port 8080."
            ),
            (
                "The migration adds a NOT NULL column with a default value"
                " to avoid locking the entire table."
            ),
            (
                "Each API request is traced with a unique X-Request-ID"
                " header propagated through the call chain."
            ),
            (
                "The embedding lookup uses approximate nearest-neighbor"
                " search with an HNSW index."
            ),
            (
                "Quantization reduces model size from 14 GB to 3.5 GB"
                " with less than 1% accuracy degradation."
            ),
            (
                "The service mesh handles retries with exponential backoff"
                " capped at 30 seconds."
            ),
            (
                "The schema validation runs at startup and raises a"
                " ValueError if required environment variables are missing."
            ),
            (
                "The endpoint returns HTTP 429 with a Retry-After header"
                " when rate limits are exceeded."
            ),
            (
                "Distributed tracing is implemented with OpenTelemetry"
                " and exported to a Jaeger collector."
            ),
            (
                "The IDF formula is log(N / (1 + df)) where N is total"
                " document count and df is the term's document frequency."
            ),
            (
                "Memory-mapped files reduce I/O overhead when loading"
                " large embedding matrices at startup."
            ),
            (
                "The message queue processes events in FIFO order with"
                " at-least-once delivery guarantees."
            ),
            (
                "Replication lag on the read replica is monitored"
                " with a 500 ms threshold alert."
            ),
            (
                "The feature extractor uses a sliding window of 512 tokens"
                " with 50% overlap."
            ),
            (
                "CPU affinity is pinned to isolate the inference thread"
                " from OS scheduler interference."
            ),
            (
                "The pipeline uses asyncio.gather to fan out embedding"
                " requests concurrently."
            ),
            (
                "The index is rebuilt nightly using a cron job that"
                " triggers model.generate_index() at 02:00 UTC."
            ),
            (
                "Authorization is enforced via signed JWT tokens validated"
                " against the RS256 public key."
            ),
            (
                "The build artifact is pushed to the registry with the"
                " git commit SHA as the image tag."
            ),
            (
                "Rate limiting uses a token bucket algorithm with a"
                " refill rate of 100 requests per minute."
            ),
            (
                "The health check endpoint returns HTTP 200 with a JSON"
                " body containing the service version."
            ),
            (
                "Query planning shows a sequential scan on the embeddings"
                " table due to missing vector index."
            ),
            (
                "Each shard handles 1/8 of the key space with consistent"
                " hashing for balanced distribution."
            ),
            (
                "The fine-tuning run uses LoRA adapters with rank 16"
                " to reduce trainable parameter count."
            ),
            (
                "Sampling temperature is set to 0.7 to balance diversity"
                " and coherence in generation."
            ),
            (
                "The deserialization step validates field types using"
                " pydantic with strict mode enabled."
            ),
            (
                "Context window overflow is handled by truncating from the"
                " left to preserve the most recent tokens."
            ),
            "The test suite achieves 91% branch coverage across the router module.",
            (
                "Pod autoscaling is configured with a minimum of 2 replicas"
                " and a CPU target of 70%."
            ),
            (
                "The HTTP client uses connection pooling with a maximum"
                " of 100 keep-alive connections."
            ),
            "Mutual TLS is enforced between all internal services in the mesh.",
            (
                "The checkpoint is loaded with map_location='cpu' to"
                " support machines without CUDA."
            ),
            (
                "The vector database index supports filtered ANN search"
                " using pre-filtering on metadata fields."
            ),
            (
                "Input sanitization strips null bytes and truncates input"
                " at 8192 characters before processing."
            ),
            (
                "The epoch count is set to 3 with early stopping triggered"
                " when validation loss does not improve for 2 epochs."
            ),
            (
                "P50 latency is 45 ms and P95 latency is 210 ms"
                " under the current load profile."
            ),
            (
                "The service uses a circuit breaker pattern to prevent"
                " cascading failures across dependencies."
            ),
        ],
        "technical",
    )

    narr = _labeled(
        [
            (
                "Building reliable systems requires patience, discipline,"
                " and a willingness to throw things away."
            ),
            (
                "There's a certain clarity that comes from working on"
                " a problem long enough."
            ),
            (
                "The history of computing is littered with ideas"
                " that were ahead of their time."
            ),
            (
                "In the early days of the internet, bandwidth was so scarce"
                " that every byte was a negotiation."
            ),
            (
                "Software does not age gracefully; it accumulates the"
                " decisions of everyone who touched it."
            ),
            (
                "The transition from prototype to production is where"
                " most of the real engineering happens."
            ),
            (
                "There is a particular kind of satisfaction in deleting"
                " code that has served its purpose."
            ),
            "Good abstractions hide complexity without hiding understanding.",
            (
                "The most valuable engineers are not those who write the"
                " most code, but those who write the least necessary."
            ),
            (
                "Over time, every system tends toward the architecture"
                " of the organization that built it."
            ),
            (
                "Consistency, more than brilliance, is what separates"
                " maintainable systems from unmaintainable ones."
            ),
            (
                "The gap between a working prototype and a production"
                " system is where careers are made."
            ),
            (
                "In retrospect, the warning signs were always there"
                " — they simply went unheeded."
            ),
            (
                "Every abstraction is a bet that the underlying complexity"
                " won't need to surface."
            ),
            "The tools we use shape the problems we think are worth solving.",
            (
                "A codebase, like a city, is never finished — it merely"
                " evolves from one incomplete state to another."
            ),
            (
                "The engineers who built these systems rarely imagined"
                " they would still be running decades later."
            ),
            (
                "Distributed systems force a reckoning with assumptions"
                " that centralized systems allow us to ignore."
            ),
            (
                "The hardest part of writing software is not the code"
                " itself, but the decisions that precede it."
            ),
            (
                "In the end, every engineering organization is a product"
                " of the incentives it operates under."
            ),
            "The failure was not sudden; it had been accumulating silently for months.",
            (
                "There is a long tradition in computing of solving the"
                " wrong problem with exceptional precision."
            ),
            (
                "The shift to machine learning did not replace software"
                " engineering — it expanded its surface area."
            ),
            (
                "Performance optimization is, at its core,"
                " the art of measuring before assuming."
            ),
            (
                "What looks like technical debt is often a record of"
                " tradeoffs made under real constraints."
            ),
            (
                "The design patterns that seem obvious in hindsight were"
                " revolutionary when first articulated."
            ),
            (
                "Reliability is not a feature that can be bolted on after"
                " the fact; it must be designed in."
            ),
            (
                "The documentation, if it existed at all, had not been"
                " updated since the original deployment."
            ),
            (
                "In distributed computing, the network is always"
                " the most honest adversary."
            ),
            (
                "The model performed well on benchmarks and poorly in"
                " production, as models often do."
            ),
            (
                "Every incident is an opportunity to discover assumptions"
                " that were previously invisible."
            ),
            (
                "The history of programming languages is also a history"
                " of what programmers found difficult."
            ),
            (
                "As systems grow in complexity, the number of people who"
                " understand them in full approaches zero."
            ),
            (
                "The abstraction leak is subtle at first"
                " — a workaround here, an exception there."
            ),
            (
                "Open source software is built on a kind of distributed"
                " trust that defies easy accounting."
            ),
            (
                "The engineers who inherited the system were not the ones"
                " who had made the original decisions."
            ),
            (
                "Security is not a property of a system; it is a property"
                " of a system in a particular context."
            ),
            "The migration took longer than planned, as migrations always do.",
            (
                "In the beginning, the architecture was simple enough"
                " that one person could hold it in their head."
            ),
            (
                "The project was declared finished three times"
                " before it was actually finished."
            ),
            (
                "The irony of automation is that it often increases"
                " the demand for human judgment."
            ),
            (
                "Naming things well is one of the few forms of documentation"
                " that cannot become outdated."
            ),
            (
                "The codebase had grown to a size where no single person"
                " understood all of it anymore."
            ),
            "Every deprecation notice is a small act of optimism about the future.",
            (
                "The latency problem had been known for two years before"
                " anyone had the time to address it."
            ),
            (
                "Moreover, the organizational boundaries shaped the service"
                " boundaries in ways no architecture diagram captured."
            ),
            (
                "Indeed, the simplest systems are often the hardest to"
                " build, because simplicity requires restraint."
            ),
            (
                "The refactor proceeded carefully, preserving behavior"
                " at each step before moving to the next."
            ),
            (
                "Consequently, the team adopted a convention that would"
                " outlast its original rationale by several years."
            ),
            (
                "Furthermore, the decision to decouple the components"
                " proved prescient when requirements changed."
            ),
            (
                "Nevertheless, the old system remained in production"
                " long after the new one was ready."
            ),
            (
                "On the other hand, premature optimization had caused more"
                " harm than the performance problems it was meant to solve."
            ),
        ],
        "narration",
    )

    emph = _labeled(
        [
            "This changes everything. The numbers are unreal.",
            "You have to see this — it's the fastest inference I've ever measured.",
            "Stop what you're doing and look at these results!",
            "Absolutely incredible. We just hit a new benchmark record.",
            "This is the best model we have ever shipped. Full stop.",
            "Watch this — 50 milliseconds. Fifty. That used to take two seconds.",
            "Nothing will ever be the same after this release. Nothing.",
            "Look at that latency curve. I have never seen anything like it.",
            "We did it. Completely rewrote the pipeline and it's 10x faster.",
            "This is the most important thing we have shipped this year.",
            "Unbelievable. The model generalizes better than anything we've tested.",
            "Check this benchmark — it literally defies what I thought was possible.",
            (
                "Listen: this is not an incremental improvement."
                " It's a total transformation."
            ),
            "The results are in and they are extraordinary. Every metric improved.",
            (
                "I have never been more confident in a product launch"
                " than I am right now."
            ),
            "This is fundamentally different from everything else in the space.",
            "Stop the meeting. Read this. We need to talk about what this means.",
            "Completely unprecedented. The team has outdone themselves.",
            "This is the game-changer we have been building toward for two years!",
            (
                "Revolutionary. There is no other word for what"
                " this architecture achieves."
            ),
            (
                "See these numbers? This is what happens when you get"
                " the fundamentals right."
            ),
            (
                "We just shipped something incredible and I need everyone"
                " to understand how big this is."
            ),
            (
                "Try the new version and tell me that is not the fastest"
                " thing you have ever run."
            ),
            (
                "Imagine: sub-10ms response times across every endpoint."
                " That is our new baseline."
            ),
            "This is not a demo. This is production. And it is absolutely flying.",
            (
                "Every single metric is up. No regressions."
                " This is what perfect execution looks like."
            ),
            "The best part? We did it without adding a single dependency.",
            (
                "Totally unreal. I ran the benchmark three times because"
                " I didn't believe the first result."
            ),
            (
                "This is the fastest, cleanest, most reliable version"
                " we have ever deployed."
            ),
            (
                "Look at the memory usage! Completely flat."
                " This is what we have been striving for."
            ),
            (
                "We just eliminated an entire class of bugs that has"
                " been plaguing us for months!"
            ),
            (
                "Incredible work. The team delivered something that"
                " completely exceeded expectations."
            ),
            (
                "This is the moment everything we've been working on"
                " comes together. Don't miss it."
            ),
            (
                "Extremely proud of what we built here."
                " This is the best work we've ever done."
            ),
            "Notice the throughput on that graph — it's vertical. Straight up.",
            (
                "We went from worst-in-class to best-in-class latency"
                " in a single quarter. Unbelievable."
            ),
            (
                "This is exactly the kind of breakthrough that redefines"
                " what people think is possible."
            ),
            "The numbers don't lie. This is transformative.",
            "Go look at the dashboard right now. I'll wait. Amazing, right?",
            "We just made something incredibly difficult look completely effortless.",
            "This is not an improvement. This is a reinvention.",
            (
                "Zero downtime migration, zero data loss, zero customer impact."
                " Absolutely flawless."
            ),
            "The results speak for themselves. This is groundbreaking work.",
            (
                "Incredibly, we shipped on time, under budget, and with"
                " better performance than projected."
            ),
            (
                "This is the most complete, polished, battle-tested release"
                " we have ever put out."
            ),
            (
                "Listen to me: what this team built in six weeks would"
                " have taken others two years."
            ),
            (
                "See this diff? We deleted 40,000 lines of code and made"
                " everything faster. Incredible."
            ),
            (
                "The response has been overwhelming. Developers everywhere"
                " are calling this a game-changer."
            ),
            (
                "Nothing in this space comes close."
                " This is the best-in-class solution, period."
            ),
            (
                "Totally mind-blowing. I've been in this industry twenty years"
                " and have never seen this."
            ),
            "Watch: one click, instant deploy, zero config. That is the future.",
            (
                "Absolutely everything works. Tests pass, benchmarks pass,"
                " users are happy. This is perfect."
            ),
        ],
        "emphatic",
    )

    return conv + tech + narr + emph
