"""Tests para la logica de context/compactor.py — estimacion de tokens,
extraccion de facts, construccion de summaries y compactacion de secciones.

Se replica la logica pura sin importar src/ (evita dependencias pesadas).
"""

import hashlib
import re
from dataclasses import dataclass, field
from typing import List

import pytest


# =====================================================================
# Replicas de la logica del compactor (funciones puras)
# =====================================================================


def estimate_tokens_fallback(text: str) -> int:
    """Replica del fallback de estimate_tokens (sin tiktoken)."""
    if not text:
        return 0
    return max(1, len(text) // 4)


def extract_facts(raw_output: str) -> list:
    """Replica exacta de ContextCompactor._extract_facts."""
    facts = []
    lines = raw_output.strip().splitlines()

    for line in lines[:100]:
        line = line.strip()
        if not line or len(line) < 10:
            continue
        if re.match(r"^[\w\s]+:\s+.+", line) and len(line) < 200:
            facts.append(line)
        elif re.match(r"^(✓|✗|PASS|FAIL|ERROR|OK|INFO|WARNING)", line):
            facts.append(line)
        elif re.match(r"^[\w/\\.]+\s*[:\-]\s*.+", line) and len(line) < 200:
            facts.append(line)

    seen = set()
    unique = []
    for f in facts:
        if f not in seen:
            seen.add(f)
            unique.append(f)
    return unique[:15]


def build_summary(tool_name: str, raw_output: str, facts: list) -> str:
    """Replica exacta de ContextCompactor._build_summary."""
    lines = raw_output.strip().splitlines()
    total_lines = len(lines)
    char_count = len(raw_output)

    parts = [f"Tool '{tool_name}' returned {total_lines} lines ({char_count} chars)."]

    if facts:
        parts.append(f"Key findings: {'; '.join(facts[:5])}")

    meaningful = [l.strip() for l in lines if l.strip()]
    if meaningful:
        parts.append(f"First: {meaningful[0][:120]}")
        if len(meaningful) > 1:
            parts.append(f"Last: {meaningful[-1][:120]}")

    return " ".join(parts)


def infer_artifact_type(tool_name: str) -> str:
    """Replica de ContextCompactor._infer_artifact_type."""
    tool_lower = tool_name.lower()
    if any(k in tool_lower for k in ("read", "file", "code", "write", "edit")):
        return "code"
    if any(k in tool_lower for k in ("test", "check", "lint", "validate")):
        return "test_result"
    if any(k in tool_lower for k in ("search", "find", "grep", "glob")):
        return "analysis"
    if any(k in tool_lower for k in ("plan", "design", "architect")):
        return "plan"
    return "general"


def summarize_tool_output(tool_name: str, raw_output: str, session_id: str, task_id: str) -> dict:
    """Replica simplificada de ContextCompactor.summarize_tool_output.
    Devuelve un dict con los mismos campos que ArtifactSummary.
    """
    facts = extract_facts(raw_output)
    summary = build_summary(tool_name, raw_output, facts)
    artifact_type = infer_artifact_type(tool_name)
    artifact_id = hashlib.sha256(
        f"{session_id}:{task_id}:{tool_name}:{raw_output[:200]}".encode()
    ).hexdigest()[:16]

    return {
        "artifact_id": artifact_id,
        "session_id": session_id,
        "task_id": task_id,
        "artifact_type": artifact_type,
        "title": f"Output of {tool_name}",
        "summary": summary,
        "facts": facts,
        "source_tool": tool_name,
        "char_count": len(raw_output),
    }


# --- Modelo simplificado de ContextSection para test de compactacion ---


@dataclass
class Section:
    section_type: str  # "immutable_rules", "working_context", "task_state", etc.
    content: str
    priority: int = 0
    token_estimate: int = 0


def compact_sections(sections: list, max_tokens: int) -> list:
    """Replica de ContextCompactor.compact_sections (logica pura)."""
    # 1. Deduplicar
    seen = set()
    unique = []
    for s in sections:
        h = hashlib.md5(s.content.encode()).hexdigest()
        if h not in seen:
            seen.add(h)
            unique.append(s)
    sections = unique

    # 2. Estimar tokens
    for s in sections:
        s.token_estimate = estimate_tokens_fallback(s.content)

    total = sum(s.token_estimate for s in sections)
    if total <= max_tokens:
        return sections

    # 3. Ordenar por prioridad (mayor primero)
    sections.sort(key=lambda s: s.priority, reverse=True)

    # 4. Trim de menor prioridad
    while total > max_tokens and sections:
        lowest = sections[-1]
        if lowest.section_type == "immutable_rules":
            break
        # Compactacion simple: eliminar lineas vacias
        compacted_lines = [l.rstrip() for l in lowest.content.splitlines() if l.strip()]
        compacted = "\n".join(compacted_lines)
        new_est = estimate_tokens_fallback(compacted)
        saved = lowest.token_estimate - new_est
        if saved > 0:
            lowest.content = compacted
            lowest.token_estimate = new_est
            total -= saved
        else:
            total -= lowest.token_estimate
            sections.pop()

    return sections


# =====================================================================
# Tests: estimate_tokens
# =====================================================================


class TestEstimateTokens:
    def test_positive_for_nonempty_text(self):
        result = estimate_tokens_fallback("Hello world, this is a test string.")
        assert isinstance(result, int)
        assert result > 0

    def test_zero_for_empty_string(self):
        assert estimate_tokens_fallback("") == 0

    def test_longer_text_more_tokens(self):
        short = estimate_tokens_fallback("hi")
        long = estimate_tokens_fallback("hi " * 500)
        assert long > short

    def test_returns_int_type(self):
        assert isinstance(estimate_tokens_fallback("cualquier texto"), int)

    def test_minimum_is_one_for_short_text(self):
        # "ab" -> len 2 // 4 = 0, pero max(1, 0) = 1
        assert estimate_tokens_fallback("ab") == 1


# =====================================================================
# Tests: _extract_facts
# =====================================================================


class TestExtractFacts:
    def test_extracts_key_value_lines(self):
        raw = "Status: running\nVersion: 3.2.1\nIgnored short\nName: my-module"
        facts = extract_facts(raw)
        assert any("Status: running" in f for f in facts)
        assert any("Version: 3.2.1" in f for f in facts)
        assert any("Name: my-module" in f for f in facts)

    def test_extracts_status_indicators(self):
        raw = "PASS test_login completed\nFAIL test_logout broken\nOK everything fine"
        facts = extract_facts(raw)
        assert any("PASS" in f for f in facts)
        assert any("FAIL" in f for f in facts)

    def test_ignores_short_lines(self):
        raw = "ok\nhi\nyes\nStatus: this is long enough to be a fact"
        facts = extract_facts(raw)
        assert not any(f in ("ok", "hi", "yes") for f in facts)

    def test_deduplicates(self):
        raw = "Status: running value\nStatus: running value\nStatus: running value"
        facts = extract_facts(raw)
        assert facts.count("Status: running value") == 1

    def test_limits_to_15(self):
        lines = [f"Key{i}: value number {i} with enough length" for i in range(30)]
        raw = "\n".join(lines)
        facts = extract_facts(raw)
        assert len(facts) <= 15

    def test_empty_input(self):
        facts = extract_facts("")
        assert facts == []


# =====================================================================
# Tests: _build_summary
# =====================================================================


class TestBuildSummary:
    def test_includes_tool_name(self):
        summary = build_summary("read_file", "line1\nline2\nline3", [])
        assert "read_file" in summary

    def test_includes_line_count(self):
        raw = "line1\nline2\nline3"
        summary = build_summary("my_tool", raw, [])
        assert "3 lines" in summary

    def test_includes_char_count(self):
        raw = "some content here"
        summary = build_summary("my_tool", raw, [])
        assert str(len(raw)) in summary

    def test_includes_facts_when_present(self):
        facts = ["Status: ok", "Count: 42"]
        summary = build_summary("my_tool", "data", facts)
        assert "Status: ok" in summary

    def test_includes_first_line(self):
        raw = "primera linea importante\nsegunda\ntercera"
        summary = build_summary("tool", raw, [])
        assert "primera linea importante" in summary


# =====================================================================
# Tests: summarize_tool_output
# =====================================================================


class TestSummarizeToolOutput:
    def test_returns_dict_with_correct_fields(self):
        result = summarize_tool_output(
            tool_name="read_file",
            raw_output="Status: ok\nContent: hello world here",
            session_id="sess-001",
            task_id="task-001",
        )
        assert isinstance(result, dict)
        assert result["session_id"] == "sess-001"
        assert result["task_id"] == "task-001"
        assert result["source_tool"] == "read_file"
        assert result["title"] == "Output of read_file"
        assert result["artifact_id"]  # no vacio
        assert result["summary"]  # no vacio
        assert result["char_count"] > 0

    def test_artifact_type_inference(self):
        assert summarize_tool_output("read_file", "x", "s", "t")["artifact_type"] == "code"
        assert summarize_tool_output("test_run", "x", "s", "t")["artifact_type"] == "test_result"
        assert summarize_tool_output("search_records", "x", "s", "t")["artifact_type"] == "analysis"
        assert summarize_tool_output("deploy_app", "x", "s", "t")["artifact_type"] == "general"

    def test_artifact_id_is_deterministic(self):
        r1 = summarize_tool_output("tool", "output", "s", "t")
        r2 = summarize_tool_output("tool", "output", "s", "t")
        assert r1["artifact_id"] == r2["artifact_id"]

    def test_artifact_id_length(self):
        result = summarize_tool_output("tool", "output", "s", "t")
        assert len(result["artifact_id"]) == 16


# =====================================================================
# Tests: compact_sections
# =====================================================================


class TestCompactSections:
    def test_never_removes_immutable_rules(self):
        sections = [
            Section(
                section_type="immutable_rules",
                content="You must always follow these rules " * 20,
                priority=100,
            ),
            Section(
                section_type="working_context",
                content="Some working context data " * 50,
                priority=1,
            ),
        ]
        result = compact_sections(sections, max_tokens=50)
        types = [s.section_type for s in result]
        assert "immutable_rules" in types

    def test_respects_priority_order(self):
        """Secciones de mayor prioridad sobreviven a la compactacion.
        Usamos un budget que cabe la seccion alta pero no ambas."""
        high = Section(
            section_type="task_state",
            content="Important task data here",  # ~6 tokens
            priority=90,
        )
        low = Section(
            section_type="working_context",
            content="Low priority stuff " * 50,  # ~250 tokens
            priority=1,
        )
        # Budget suficiente para high (~6) pero no para high+low (~256)
        result = compact_sections([high, low], max_tokens=20)
        types = [s.section_type for s in result]
        assert "task_state" in types
        # La de baja prioridad deberia haberse eliminado o compactado
        assert len(result) <= 2

    def test_no_compaction_when_within_budget(self):
        sections = [
            Section(
                section_type="task_state",
                content="Short content",
                priority=50,
            ),
        ]
        result = compact_sections(sections, max_tokens=999_999)
        assert len(result) == 1
        assert result[0].content == "Short content"

    def test_deduplicates_identical_sections(self):
        sections = [
            Section(section_type="working_context", content="duplicated content", priority=10),
            Section(section_type="working_context", content="duplicated content", priority=10),
        ]
        result = compact_sections(sections, max_tokens=999_999)
        assert len(result) == 1