from __future__ import annotations

import json

import httpx
import pytest

from replicalab.agents.scientist_policy import (
    RetryMetadata,
    ScientistCallResult,
    ScientistOutputParseError,
    build_anthropic_scientist_policy,
    build_baseline_scientist_action,
    build_ollama_scientist_policy,
    build_scientist_system_prompt,
    call_scientist_with_retry,
    format_scientist_observation,
    parse_scientist_output,
)
from replicalab.models import (
    ConversationEntry,
    Protocol,
    ScientistActionType,
    ScientistObservation,
)
from replicalab.scenarios import generate_scenario


# ---------------------------------------------------------------------------
# Shared valid JSON payloads for retry tests
# ---------------------------------------------------------------------------

_VALID_REQUEST_INFO_JSON = """{
  "action_type": "request_info",
  "sample_size": 0,
  "controls": [],
  "technique": "",
  "duration_days": 0,
  "required_equipment": [],
  "required_reagents": [],
  "questions": ["What compute budget is available?"],
  "rationale": ""
}"""


def test_parse_scientist_output_accepts_plain_json() -> None:
    raw_text = """
    {
      "action_type": "request_info",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": ["What compute budget is available?"],
      "rationale": ""
    }
    """

    action = parse_scientist_output(raw_text)

    assert action.action_type is ScientistActionType.REQUEST_INFO
    assert action.questions == ["What compute budget is available?"]


def test_parse_scientist_output_accepts_fenced_json_with_prose() -> None:
    raw_text = """
    I would revise the plan as follows:

    ```json
    {
      "action_type": "revise_protocol",
      "sample_size": 24,
      "controls": ["baseline", "ablation"],
      "technique": "small_scale_backtest",
      "duration_days": 3,
      "required_equipment": ["gpu_node"],
      "required_reagents": [],
      "questions": [],
      "rationale": "Shrink the trial to fit the available compute window."
    }
    ```
    """

    action = parse_scientist_output(raw_text)

    assert action.action_type is ScientistActionType.REVISE_PROTOCOL
    assert action.technique == "small_scale_backtest"


def test_parse_scientist_output_raises_explicit_error_when_json_is_missing() -> None:
    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output("I need more context before I can answer.")

    assert exc_info.value.code == "no_json"
    assert "does not contain a JSON object" in exc_info.value.message


def test_parse_scientist_output_raises_explicit_error_when_json_is_invalid() -> None:
    raw_text = """
    ```json
    {
      "action_type": "request_info",
      "questions": ["What budget do we have?"],
    }
    ```
    """

    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output(raw_text)

    assert exc_info.value.code == "invalid_json"
    assert "could not be decoded" in exc_info.value.message


def test_parse_scientist_output_raises_explicit_error_when_schema_is_invalid() -> None:
    raw_text = """
    {
      "action_type": "request_info",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": ""
    }
    """

    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output(raw_text)

    assert exc_info.value.code == "invalid_action"
    assert "ScientistAction validation" in exc_info.value.message


def test_build_scientist_system_prompt_uses_normalized_scenario_data() -> None:
    scenario = generate_scenario(seed=202, template="ml_benchmark", difficulty="medium")

    prompt = build_scientist_system_prompt(scenario)

    assert "You are the Scientist agent in ReplicaLab." in prompt
    assert scenario.task_summary in prompt
    assert scenario.success_criteria[0] in prompt
    assert scenario.resources[0].label in prompt
    assert "action_type values" in prompt
    assert "propose_protocol" in prompt
    assert "request_info" in prompt


# ---------------------------------------------------------------------------
# AGT 02 — format_scientist_observation
# ---------------------------------------------------------------------------


def _base_observation(**overrides) -> ScientistObservation:
    defaults = {
        "paper_title": "Test Paper Title",
        "paper_hypothesis": "X improves Y.",
        "paper_method": "Run benchmark Z.",
        "paper_key_finding": "10% improvement.",
        "experiment_goal": "Replicate the 10% improvement.",
        "conversation_history": [],
        "current_protocol": None,
        "round_number": 0,
        "max_rounds": 6,
    }
    defaults.update(overrides)
    return ScientistObservation(**defaults)


def test_format_observation_empty_history_no_protocol() -> None:
    obs = _base_observation()
    result = format_scientist_observation(obs)

    assert "Round 0 of 6" in result
    assert "Test Paper Title" in result
    assert "X improves Y." in result
    assert "Run benchmark Z." in result
    assert "10% improvement." in result
    assert "Replicate the 10% improvement." in result
    assert "No conversation history yet" in result
    assert "No protocol has been proposed yet" in result
    assert "ScientistAction schema reminder:" in result
    assert (
        "allowed action_type values: propose_protocol, revise_protocol, "
        "request_info, accept"
    ) in result
    assert "include all ScientistAction fields in every response" in result
    assert "Respond with exactly one JSON object" in result


def test_format_observation_with_history_and_protocol() -> None:
    history = [
        ConversationEntry(
            role="scientist",
            message="I propose a grid search protocol.",
            round_number=1,
            action_type="propose_protocol",
        ),
        ConversationEntry(
            role="lab_manager",
            message="The compute cluster is booked. Consider a smaller run.",
            round_number=1,
            action_type="suggest_alternative",
        ),
    ]
    protocol = Protocol(
        sample_size=24,
        controls=["baseline", "ablation"],
        technique="grid_search",
        duration_days=3,
        required_equipment=["gpu_node"],
        required_reagents=["benchmark_dataset"],
        rationale="Standard hyperparameter sweep.",
    )
    obs = _base_observation(
        conversation_history=history,
        current_protocol=protocol,
        round_number=2,
    )
    result = format_scientist_observation(obs)

    assert "Round 2 of 6" in result
    assert "Conversation so far:" in result
    assert "[SCIENTIST r1 [propose_protocol]]" in result
    assert "I propose a grid search protocol." in result
    assert "[LAB_MANAGER r1 [suggest_alternative]]" in result
    assert "compute cluster is booked" in result
    assert "Current protocol:" in result
    assert "technique: grid_search" in result
    assert "sample_size: 24" in result
    assert "controls: baseline, ablation" in result
    assert "duration_days: 3" in result
    assert "gpu_node" in result
    assert "benchmark_dataset" in result
    assert "Standard hyperparameter sweep." in result


def test_format_observation_stable_section_order() -> None:
    obs = _base_observation(
        conversation_history=[
            ConversationEntry(
                role="scientist",
                message="First proposal.",
                round_number=1,
                action_type="propose_protocol",
            ),
        ],
        current_protocol=Protocol(
            sample_size=10,
            controls=["ctrl"],
            technique="method_a",
            duration_days=2,
            required_equipment=["tool_a"],
            required_reagents=["reagent_a"],
            rationale="Simple test.",
        ),
        round_number=1,
    )
    result = format_scientist_observation(obs)

    # Sections must appear in this fixed order
    round_pos = result.index("Round 1 of 6")
    paper_pos = result.index("Paper:")
    history_pos = result.index("Conversation so far:")
    protocol_pos = result.index("Current protocol:")
    action_pos = result.index("Respond with exactly one JSON")

    assert round_pos < paper_pos < history_pos < protocol_pos < action_pos


def test_format_observation_history_entry_without_action_type() -> None:
    obs = _base_observation(
        conversation_history=[
            ConversationEntry(
                role="system",
                message="Episode started.",
                round_number=0,
                action_type=None,
            ),
        ],
    )
    result = format_scientist_observation(obs)

    assert "[SYSTEM r0]:" in result
    assert "Episode started." in result


def test_format_observation_from_generated_scenario() -> None:
    scenario = generate_scenario(seed=77, template="finance_trading", difficulty="easy")
    obs = scenario.scientist_observation
    result = format_scientist_observation(obs)

    assert obs.paper_title in result
    assert obs.experiment_goal in result
    assert "Round 0" in result
    assert "No conversation history yet" in result
    assert "Respond with exactly one JSON" in result


# ---------------------------------------------------------------------------
# AGT 03 — call_scientist_with_retry
# ---------------------------------------------------------------------------


def _make_system_prompt() -> str:
    scenario = generate_scenario(seed=1, template="math_reasoning", difficulty="easy")
    return build_scientist_system_prompt(scenario)


def test_retry_success_on_first_try() -> None:
    def gen_fn(messages: list[dict[str, str]]) -> str:
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    result = call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    assert isinstance(result, ScientistCallResult)
    assert result.action.action_type is ScientistActionType.REQUEST_INFO
    assert result.metadata.attempt_count == 1
    assert result.metadata.retry_count == 0
    assert result.metadata.last_error_code is None
    assert result.metadata.last_error_message is None


def test_retry_malformed_json_then_valid() -> None:
    call_count = 0

    def gen_fn(messages: list[dict[str, str]]) -> str:
        nonlocal call_count
        call_count += 1
        if call_count == 1:
            return '{"action_type": "request_info", trailing garbage'
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    result = call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    assert result.action.action_type is ScientistActionType.REQUEST_INFO
    assert result.metadata.attempt_count == 2
    assert result.metadata.retry_count == 1
    assert result.metadata.last_error_code == "invalid_json"


def test_retry_invalid_action_then_valid() -> None:
    # First attempt: valid JSON but questions is empty for request_info
    invalid_json = """{
      "action_type": "request_info",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": ""
    }"""
    call_count = 0

    def gen_fn(messages: list[dict[str, str]]) -> str:
        nonlocal call_count
        call_count += 1
        if call_count == 1:
            return invalid_json
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    result = call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    assert result.action.action_type is ScientistActionType.REQUEST_INFO
    assert result.metadata.attempt_count == 2
    assert result.metadata.retry_count == 1
    assert result.metadata.last_error_code == "invalid_action"
    assert "ScientistAction validation" in result.metadata.last_error_message


def test_retry_exhausted_raises_last_error() -> None:
    def gen_fn(messages: list[dict[str, str]]) -> str:
        return "I cannot produce JSON right now."

    obs = _base_observation()
    with pytest.raises(ScientistOutputParseError) as exc_info:
        call_scientist_with_retry(gen_fn, _make_system_prompt(), obs, max_retries=2)

    assert exc_info.value.code == "no_json"


def test_retry_correction_message_includes_parser_error() -> None:
    """The correction prompt sent to the model must include the specific error."""
    captured_messages: list[list[dict[str, str]]] = []

    call_count = 0

    def gen_fn(messages: list[dict[str, str]]) -> str:
        nonlocal call_count
        call_count += 1
        captured_messages.append(list(messages))
        if call_count == 1:
            return "Just some prose, no JSON here."
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    # Second call should have 4 messages: system, user, assistant (bad), user (correction)
    assert len(captured_messages) == 2
    retry_messages = captured_messages[1]
    assert len(retry_messages) == 4
    assert retry_messages[2]["role"] == "assistant"
    assert retry_messages[2]["content"] == "Just some prose, no JSON here."
    assert retry_messages[3]["role"] == "user"
    correction = retry_messages[3]["content"]
    assert "did not contain a JSON object" in correction
    assert "No markdown fences, no prose" in correction


def test_retry_correction_for_invalid_action_includes_validation_detail() -> None:
    """invalid_action correction must include the schema validation message."""
    captured_messages: list[list[dict[str, str]]] = []

    invalid_json = """{
      "action_type": "request_info",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": ""
    }"""
    call_count = 0

    def gen_fn(messages: list[dict[str, str]]) -> str:
        nonlocal call_count
        call_count += 1
        captured_messages.append(list(messages))
        if call_count == 1:
            return invalid_json
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    retry_messages = captured_messages[1]
    correction = retry_messages[3]["content"]
    assert "failed ScientistAction validation" in correction
    assert "Fix the validation error" in correction


def test_retry_metadata_serializable() -> None:
    def gen_fn(messages: list[dict[str, str]]) -> str:
        return _VALID_REQUEST_INFO_JSON

    obs = _base_observation()
    result = call_scientist_with_retry(gen_fn, _make_system_prompt(), obs)

    dumped = result.metadata.model_dump_json()
    restored = RetryMetadata.model_validate_json(dumped)
    assert restored.attempt_count == 1
    assert restored.retry_count == 0


# ---------------------------------------------------------------------------
# AGT 04 - build_baseline_scientist_action
# ---------------------------------------------------------------------------


def test_baseline_scientist_proposes_protocol_for_fresh_observation() -> None:
    action = build_baseline_scientist_action(_base_observation())

    assert action.action_type is ScientistActionType.PROPOSE_PROTOCOL
    assert action.sample_size >= 1
    assert action.duration_days >= 1
    assert action.questions == []
    assert action.rationale


def test_baseline_scientist_accepts_existing_protocol_without_blocker() -> None:
    obs = _base_observation(
        current_protocol=Protocol(
            sample_size=10,
            controls=["baseline_check"],
            technique="published_split_replication",
            duration_days=2,
            required_equipment=[],
            required_reagents=[],
            rationale="Initial protocol is already in place.",
        ),
        conversation_history=[
            ConversationEntry(
                role="lab_manager",
                message="The current plan remains feasible.",
                round_number=1,
                action_type="report_feasibility",
            )
        ],
        round_number=1,
    )

    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.ACCEPT
    assert action.sample_size == 0
    assert action.controls == []


def test_baseline_scientist_revises_when_latest_feedback_has_blocker() -> None:
    obs = _base_observation(
        current_protocol=Protocol(
            sample_size=12,
            controls=["published_split_check", "heldout_evaluation"],
            technique="published_split_replication",
            duration_days=3,
            required_equipment=[],
            required_reagents=[],
            rationale="Original scope is full-size.",
        ),
        conversation_history=[
            ConversationEntry(
                role="lab_manager",
                message="The current GPU plan is booked, so the schedule is too tight.",
                round_number=1,
                action_type="suggest_alternative",
            )
        ],
        round_number=1,
    )

    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.REVISE_PROTOCOL
    assert action.sample_size == 6
    assert action.duration_days == 2
    assert "latest Lab Manager concern" in action.rationale


def test_baseline_scientist_finishes_stub_episode_without_crashing() -> None:
    from server.app import _StubEnv

    env = _StubEnv()

    first_observation = env.reset(
        seed=14,
        scenario="ml_benchmark",
        difficulty="easy",
    ).scientist
    assert first_observation is not None

    first_action = build_baseline_scientist_action(first_observation)
    first_step = env.step(first_action)
    assert first_step.done is False
    assert first_step.observation is not None
    assert first_step.observation.scientist is not None

    second_action = build_baseline_scientist_action(first_step.observation.scientist)
    second_step = env.step(second_action)

    assert second_step.done is True
    assert second_step.info.agreement_reached is True


# ---------------------------------------------------------------------------
# AGT 08 — Extended prompt, parser, formatter, and baseline coverage
# ---------------------------------------------------------------------------


# --- Parser happy paths ---


def test_parse_scientist_output_accepts_propose_protocol() -> None:
    raw_text = """{
      "action_type": "propose_protocol",
      "sample_size": 48,
      "controls": ["vehicle_control", "positive_control"],
      "technique": "wst1_assay",
      "duration_days": 5,
      "required_equipment": ["plate_reader"],
      "required_reagents": ["wst1", "dmso"],
      "questions": [],
      "rationale": "Standard viability assay with two controls."
    }"""

    action = parse_scientist_output(raw_text)

    assert action.action_type is ScientistActionType.PROPOSE_PROTOCOL
    assert action.sample_size == 48
    assert action.technique == "wst1_assay"
    assert action.controls == ["vehicle_control", "positive_control"]
    assert action.questions == []


def test_parse_scientist_output_accepts_accept_action() -> None:
    raw_text = """{
      "action_type": "accept",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": ""
    }"""

    action = parse_scientist_output(raw_text)

    assert action.action_type is ScientistActionType.ACCEPT
    assert action.sample_size == 0
    assert action.rationale == ""


def test_parse_scientist_output_accepts_prose_wrapped_json() -> None:
    raw_text = (
        "After reviewing the constraints I think a request is in order.\n\n"
        '{"action_type": "request_info", "sample_size": 0, '
        '"controls": [], "technique": "", "duration_days": 0, '
        '"required_equipment": [], "required_reagents": [], '
        '"questions": ["Is the GPU available?"], "rationale": ""}\n\n'
        "That should clarify the compute situation."
    )

    action = parse_scientist_output(raw_text)

    assert action.action_type is ScientistActionType.REQUEST_INFO
    assert action.questions == ["Is the GPU available?"]


# --- Parser edge cases ---


def test_parse_scientist_output_raises_on_empty_string() -> None:
    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output("")

    assert exc_info.value.code == "no_json"


def test_parse_scientist_output_raises_on_whitespace_only() -> None:
    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output("   \n\t  ")

    assert exc_info.value.code == "no_json"


def test_parse_scientist_output_raises_on_json_list() -> None:
    # The parser's brace extractor finds the inner object from the list,
    # so this surfaces as an invalid_action (missing required fields)
    # rather than an invalid_json error.
    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output('[{"action_type": "accept"}]')

    assert exc_info.value.code == "invalid_action"


def test_parse_scientist_output_raises_on_extra_forbidden_keys() -> None:
    raw_text = """{
      "action_type": "accept",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": "",
      "secret_field": "should not be here"
    }"""

    with pytest.raises(ScientistOutputParseError) as exc_info:
        parse_scientist_output(raw_text)

    assert exc_info.value.code == "invalid_action"
    assert exc_info.value.parsed_payload is not None
    assert "secret_field" in exc_info.value.parsed_payload


def test_parse_error_to_dict_serialization() -> None:
    try:
        parse_scientist_output("no json here")
    except ScientistOutputParseError as exc:
        result = exc.to_dict()
        assert result["code"] == "no_json"
        assert result["raw_text"] == "no json here"
        assert result["parsed_payload"] is None
        assert "message" in result
    else:
        pytest.fail("Expected ScientistOutputParseError")


def test_parse_error_to_dict_with_parsed_payload() -> None:
    raw_text = """{
      "action_type": "request_info",
      "sample_size": 0,
      "controls": [],
      "technique": "",
      "duration_days": 0,
      "required_equipment": [],
      "required_reagents": [],
      "questions": [],
      "rationale": ""
    }"""
    try:
        parse_scientist_output(raw_text)
    except ScientistOutputParseError as exc:
        result = exc.to_dict()
        assert result["code"] == "invalid_action"
        assert result["parsed_payload"] is not None
        assert result["parsed_payload"]["action_type"] == "request_info"
    else:
        pytest.fail("Expected ScientistOutputParseError")


# --- System prompt: domain coverage ---


def test_system_prompt_math_domain() -> None:
    scenario = generate_scenario(seed=10, template="math_reasoning", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "Domain: mathematics" in prompt
    assert scenario.task_summary in prompt
    assert "You are the Scientist agent" in prompt


def test_system_prompt_finance_domain() -> None:
    scenario = generate_scenario(seed=10, template="finance_trading", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "Domain: finance_trading" in prompt
    assert scenario.task_summary in prompt


def test_system_prompt_ml_domain() -> None:
    scenario = generate_scenario(seed=10, template="ml_benchmark", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "Domain: machine_learning" in prompt
    assert scenario.task_summary in prompt


def test_system_prompt_accepts_dict_input() -> None:
    scenario = generate_scenario(seed=5, template="math_reasoning", difficulty="easy")
    pack_dict = scenario.model_dump()

    prompt = build_scientist_system_prompt(pack_dict)

    assert "You are the Scientist agent" in prompt
    assert scenario.task_summary in prompt
    assert "Domain: mathematics" in prompt


# --- System prompt: bounded-tool policy assertions ---


def test_system_prompt_contains_bounded_tool_policy() -> None:
    scenario = generate_scenario(seed=1, template="math_reasoning", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "search_evidence" in prompt
    assert "run_code_check" in prompt
    assert "inspect_image" in prompt


def test_system_prompt_bounded_tool_policy_rules() -> None:
    scenario = generate_scenario(seed=1, template="ml_benchmark", difficulty="medium")
    prompt = build_scientist_system_prompt(scenario)

    assert "No unrestricted web browsing" in prompt
    assert "No audio" in prompt
    assert "do not override constraints" in prompt or "Tools do not override constraints" in prompt


def test_system_prompt_bounded_tool_policy_present_in_all_domains() -> None:
    for template in ("math_reasoning", "ml_benchmark", "finance_trading"):
        scenario = generate_scenario(seed=42, template=template, difficulty="easy")
        prompt = build_scientist_system_prompt(scenario)

        assert "Bounded tool policy" in prompt, f"Missing in {template}"
        assert "search_evidence" in prompt, f"Missing search_evidence in {template}"
        assert "run_code_check" in prompt, f"Missing run_code_check in {template}"
        assert "inspect_image" in prompt, f"Missing inspect_image in {template}"


# --- System prompt: role-boundary assertions ---


def test_system_prompt_contains_role_boundaries() -> None:
    scenario = generate_scenario(seed=1, template="math_reasoning", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "do not invent resources" in prompt
    assert "do not assume access to hidden ground truth" in prompt.lower() or \
           "hidden ground truth" in prompt


def test_system_prompt_contains_output_contract() -> None:
    scenario = generate_scenario(seed=1, template="math_reasoning", difficulty="easy")
    prompt = build_scientist_system_prompt(scenario)

    assert "Output contract" in prompt
    assert "exactly one JSON object" in prompt
    assert "no extra keys" in prompt


# --- Observation formatter edge cases ---


def test_format_observation_final_round() -> None:
    obs = _base_observation(round_number=5, max_rounds=6)
    result = format_scientist_observation(obs)

    assert "Round 5 of 6" in result
    assert "Respond with exactly one JSON" in result


def test_format_observation_protocol_with_empty_lists() -> None:
    protocol = Protocol(
        sample_size=1,
        controls=[],
        technique="minimal_check",
        duration_days=1,
        required_equipment=[],
        required_reagents=[],
        rationale="Minimal protocol.",
    )
    obs = _base_observation(current_protocol=protocol, round_number=1)
    result = format_scientist_observation(obs)

    assert "Current protocol:" in result
    assert "technique: minimal_check" in result
    assert "controls: (none)" in result
    assert "required_equipment: (none)" in result
    assert "required_reagents: (none)" in result


# --- Baseline: domain inference ---


def test_baseline_scientist_infers_ml_domain() -> None:
    obs = _base_observation(
        paper_title="Reproducing CIFAR-10 accuracy with ResNet",
        paper_method="Train on CIFAR dataset with GPU",
        experiment_goal="Match the published benchmark accuracy.",
    )
    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.PROPOSE_PROTOCOL
    assert action.technique == "published_split_replication"


def test_baseline_scientist_infers_finance_domain() -> None:
    obs = _base_observation(
        paper_title="Offline backtest of SPY mean-reversion",
        paper_method="Daily bar backtest with slippage modeling",
        experiment_goal="Evaluate Sharpe ratio under drawdown limits.",
    )
    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.PROPOSE_PROTOCOL
    assert action.technique == "offline_backtest_workflow"


def test_baseline_scientist_infers_math_domain() -> None:
    obs = _base_observation(
        paper_title="Planning a proof of AM-GM inequality",
        paper_method="Algebraic manipulation with induction.",
        experiment_goal="Verify the proof outline.",
    )
    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.PROPOSE_PROTOCOL
    assert action.technique == "structured_proof_outline"


# --- Baseline: forced accept at final round ---


def test_baseline_scientist_accepts_at_final_round_even_with_blocker() -> None:
    obs = _base_observation(
        current_protocol=Protocol(
            sample_size=20,
            controls=["ctrl"],
            technique="method_a",
            duration_days=5,
            required_equipment=[],
            required_reagents=[],
            rationale="Full scope plan.",
        ),
        conversation_history=[
            ConversationEntry(
                role="lab_manager",
                message="Budget is tight and equipment is booked.",
                round_number=4,
                action_type="suggest_alternative",
            ),
        ],
        round_number=5,
        max_rounds=6,
    )

    action = build_baseline_scientist_action(obs)

    assert action.action_type is ScientistActionType.ACCEPT


def test_build_anthropic_scientist_policy_calls_messages_api() -> None:
    captured: dict[str, object] = {}

    def handler(request: httpx.Request) -> httpx.Response:
        captured["headers"] = dict(request.headers)
        captured["payload"] = json.loads(request.content.decode("utf-8"))
        return httpx.Response(
            200,
            json={
                "content": [
                    {
                        "type": "text",
                        "text": _VALID_REQUEST_INFO_JSON,
                    }
                ]
            },
        )

    client = httpx.Client(transport=httpx.MockTransport(handler))
    policy = build_anthropic_scientist_policy(
        api_key="test-key",
        model="claude-test",
        client=client,
        base_url="https://api.anthropic.com/v1/messages",
    )

    action = policy(
        _base_observation(),
        scenario="ml_benchmark",
        difficulty="medium",
    )

    assert action.action_type is ScientistActionType.REQUEST_INFO
    payload = captured["payload"]
    assert isinstance(payload, dict)
    assert payload["model"] == "claude-test"
    assert payload["temperature"] == 0.0
    assert payload["messages"][0]["role"] == "user"
    headers = captured["headers"]
    assert isinstance(headers, dict)
    assert headers["x-api-key"] == "test-key"


def test_build_ollama_scientist_policy_calls_local_chat_api() -> None:
    captured: dict[str, object] = {}

    def handler(request: httpx.Request) -> httpx.Response:
        captured["payload"] = json.loads(request.content.decode("utf-8"))
        return httpx.Response(
            200,
            json={
                "message": {
                    "role": "assistant",
                    "content": _VALID_REQUEST_INFO_JSON,
                }
            },
        )

    client = httpx.Client(transport=httpx.MockTransport(handler))
    policy = build_ollama_scientist_policy(
        model="glm-5:cloud",
        client=client,
        base_url="http://127.0.0.1:11434/api/chat",
    )

    action = policy(
        _base_observation(),
        scenario="finance_trading",
        difficulty="medium",
    )

    assert action.action_type is ScientistActionType.REQUEST_INFO
    payload = captured["payload"]
    assert isinstance(payload, dict)
    assert payload["model"] == "glm-5:cloud"
    assert payload["stream"] is False
    assert payload["format"] == "json"
    assert payload["messages"][0]["role"] == "system"