conftest.py

from copy import deepcopy
import datetime
import json
import os
import pathlib
from typing import Any, Dict, List, Text, Tuple

from anchorpoint.textselectors import TextQuoteSelector
from dotenv import load_dotenv
from legislice.download import Client
from nettlesome.terms import ContextRegister
from nettlesome.entities import Entity
from nettlesome.predicates import Predicate
from nettlesome.quantities import Comparison, Q_
import pytest

from authorityspoke.evidence import Evidence, Exhibit
from nettlesome.factors import Factor
from authorityspoke.facts import Fact, build_fact
from authorityspoke.holdings import Holding
from authorityspoke.opinions import Opinion
from authorityspoke.pleadings import Pleading, Allegation
from authorityspoke.rules import Procedure, Rule

from authorityspoke.io import loaders, readers
from authorityspoke.io.downloads import FakeClient
from authorityspoke.io.schemas_json import EnactmentSchema, RawFactor, RawHolding


load_dotenv()

TOKEN = os.getenv("LEGISLICE_API_TOKEN")
API_ROOT = "https://authorityspoke.com/api/v1"
legislice_client = Client(api_token=TOKEN)


@pytest.fixture(scope="module")
def vcr_config():
    return {
        # Replace the Authorization request header with "DUMMY" in cassettes
        "filter_headers": [("authorization", "DUMMY")],
    }


@pytest.fixture(scope="class")
def test_client() -> Client:
    client = Client(api_token=TOKEN, api_root=API_ROOT)
    client.coverage["/us/usc"] = {
        "latest_heading": "United States Code (USC)",
        "first_published": datetime.date(1926, 6, 30),
        "earliest_in_db": datetime.date(2013, 7, 18),
        "latest_in_db": datetime.date(2020, 8, 8),
    }
    client.coverage["/test/acts"] = {
        "latest_heading": "Test Acts",
        "first_published": datetime.date(1935, 4, 1),
        "earliest_in_db": datetime.date(1935, 4, 1),
        "latest_in_db": datetime.date(2013, 7, 18),
    }
    return client


@pytest.fixture(scope="class")
def section6d():
    return {
        "heading": "Waiver of beard tax in special circumstances",
        "content": "",
        "children": [
            {
                "heading": "",
                "content": "The Department of Beards shall waive the collection of beard tax upon issuance of beardcoin under Section 6C where the reason the maintainer wears a beard is due to bona fide religious, cultural, or medical reasons.",
                "children": [],
                "end_date": None,
                "node": "/test/acts/47/6D/1",
                "start_date": "2013-07-18",
                "url": "http://127.0.0.1:8000/api/v1/test/acts/47/6D/1@2018-03-11/",
            },
            {
                "heading": "",
                "content": "The determination of the Department of Beards as to what constitutes bona fide religious or cultural reasons shall be final and no right of appeal shall exist.",
                "children": [],
                "end_date": None,
                "node": "/test/acts/47/6D/2",
                "start_date": "1935-04-01",
                "url": "http://127.0.0.1:8000/api/v1/test/acts/47/6D/2@2018-03-11/",
            },
        ],
        "end_date": None,
        "node": "/test/acts/47/6D",
        "start_date": "1935-04-01",
        "url": "http://127.0.0.1:8000/api/v1/test/acts/47/6D@2018-03-11/",
        "parent": "http://127.0.0.1:8000/api/v1/test/acts/47@2018-03-11/",
    }


@pytest.fixture(scope="function")
def section_11_subdivided():
    return {
        "heading": "Licensed repurchasers of beardcoin",
        "start_date": "2013-07-18",
        "node": "/test/acts/47/11",
        "text_version": {
            "id": 1142710,
            "url": "https://authorityspoke.com/api/v1/textversions/1142710/",
            "content": "The Department of Beards may issue licenses to such",
        },
        "url": "https://authorityspoke.com/api/v1/test/acts/47/11/",
        "end_date": None,
        "children": [
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/i",
                "text_version": {
                    "id": 1142704,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142704/",
                    "content": "barbers,",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/i/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/ii",
                "text_version": {
                    "id": 1142705,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142705/",
                    "content": "hairdressers, or",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/ii/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/iii",
                "text_version": {
                    "id": 1142706,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142706/",
                    "content": "other male grooming professionals",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/iii/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/iii-con",
                "text_version": {
                    "id": 1142707,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142707/",
                    "content": "as they see fit to purchase a beardcoin from a customer",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/iii-con/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/iv",
                "text_version": {
                    "id": 1142708,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142708/",
                    "content": "whose beard they have removed,",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/iv/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
            {
                "heading": "",
                "start_date": "2013-07-18",
                "node": "/test/acts/47/11/iv-con",
                "text_version": {
                    "id": 1142709,
                    "url": "https://authorityspoke.com/api/v1/textversions/1142709/",
                    "content": "and to resell those beardcoins to the Department of Beards.",
                },
                "url": "https://authorityspoke.com/api/v1/test/acts/47/11/iv-con/",
                "end_date": None,
                "children": [],
                "citations": [],
            },
        ],
        "citations": [],
        "parent": "https://authorityspoke.com/api/v1/test/acts/47/",
    }


@pytest.fixture(scope="module")
def fifth_a():
    return {
        "heading": "AMENDMENT V.",
        "content": "No person shall be held to answer for a capital, or otherwise infamous crime, unless on a presentment or indictment of a Grand Jury, except in cases arising in the land or naval forces, or in the Militia, when in actual service in time of War or public danger; nor shall any person be subject for the same offence to be twice put in jeopardy of life or limb; nor shall be compelled in any Criminal Case to be a witness against himself; nor be deprived of life, liberty, or property, without due process of law; nor shall private property be taken for public use, without just compensation.",
        "children": [],
        "end_date": None,
        "node": "/us/const/amendment/V",
        "start_date": "1791-12-15",
        "url": "https://authorityspoke.com/api/v1/us/const/amendment/V/",
        "parent": "https://authorityspoke.com/api/v1/us/const/amendment/",
        "selection": True,
    }


@pytest.fixture(scope="class")
def make_entity() -> Dict[str, Entity]:
    return {
        "motel": Entity("Hideaway Lodge"),
        "motel_specific": Entity("Hideaway Lodge", generic=False),
        "watt": Entity("Wattenburg"),
        "trees": Entity("the stockpile of trees"),
        "trees_specific": Entity("the stockpile of trees", generic=False),
        "tree_search": Entity("officers' search of the stockpile of trees"),
        "tree_search_specific": Entity(
            "officers' search of the stockpile of trees", generic=False
        ),
        "alice": Entity("Alice"),
        "bob": Entity("Bob"),
        "craig": Entity("Craig"),
        "dan": Entity("Dan"),
        "circus": Entity("circus"),
    }


@pytest.fixture(scope="class")
def make_predicate() -> Dict[str, Predicate]:

    return {
        "p1": Predicate("$place was a motel"),
        "p1_again": Predicate("$place was a motel"),
        "p2": Predicate("$person operated and lived at $place"),
        "p2_reflexive": Predicate("$person operated and lived at $person"),
        "p2_no_truth": Predicate("$person operated and lived at $place", truth=None),
        "p2_false": Predicate("$person operated and lived at $place", truth=False),
        "p3": Predicate("$place was ${person}’s abode"),
        "p3_false": Predicate("$place was ${person}’s abode", truth=False),
        "p4": Predicate("$thing was on the premises of $place"),
        "p5": Predicate("$thing was a stockpile of Christmas trees"),
        "p6": Predicate("$thing was among some standing trees"),
        "p7": Comparison(
            "the distance between $place1 and $place2 was",
            truth=False,
            sign=">",
            expression=Q_("35 feet"),
        ),
        "p7_obverse": Comparison(
            "the distance between $place1 and $place2 was",
            truth=True,
            sign="<=",
            expression=Q_("35 feet"),
        ),
        "p7_opposite": Comparison(
            "the distance between $place1 and $place2 was",
            truth=True,
            sign=">",
            expression=Q_("35 feet"),
        ),
        "p7_not_equal": Comparison(
            "the distance between $place1 and $place2 was",
            truth=True,
            sign="<>",
            expression=Q_("35 feet"),
        ),
        "p7_true": Comparison(
            "the distance between $place1 and $place2 was",
            truth=True,
            sign="<",
            expression=Q_("35 feet"),
        ),
        "p8": Comparison(
            "the distance between $place1 and $place2 was",
            sign=">=",
            expression=Q_("20 feet"),
        ),
        "p8_no_truth": Comparison(
            "the distance between $place1 and $place2 was",
            truth=None,
            sign=">=",
            expression=Q_("20 feet"),
        ),
        "p8_exact": Comparison(
            "the distance between $place1 and $place2 was",
            sign="==",
            expression=Q_("25 feet"),
        ),
        "p8_less": Comparison(
            "the distance between $place1 and $place2 was",
            sign="<=",
            expression=Q_("20 feet"),
        ),
        "p8_meters": Comparison(
            "the distance between $place1 and $place2 was",
            sign=">=",
            expression=Q_("10 meters"),
        ),
        "p8_int": Comparison(
            "the distance between $place1 and $place2 was",
            sign=">=",
            expression=20,
        ),
        "p8_float": Comparison(
            "the distance between $place1 and $place2 was",
            sign=">=",
            expression=20.0,
        ),
        "p8_higher_int": Comparison(
            "the distance between $place1 and $place2 was",
            sign=">=",
            expression=30,
        ),
        "p9": Comparison(
            "the distance between $thing and a parking area used by personnel and patrons of $place was",
            sign="<=",
            expression=Q_("5 feet"),
        ),
        "p9_exact": Comparison(
            "the distance between $thing and a parking area used by personnel and patrons of $place was",
            sign="=",
            expression=Q_("5 feet"),
        ),
        "p9_miles": Comparison(
            "the distance between $thing and a parking area used by personnel and patrons of $place was",
            sign="<=",
            expression=Q_("5 miles"),
        ),
        "p9_more": Comparison(
            "the distance between $thing and a parking area used by personnel and patrons of $place was",
            sign=">",
            expression=Q_("5 feet"),
        ),
        "p9_acres": Comparison(
            "the distance between $thing and a parking area used by personnel and patrons of $place was",
            sign="<=",
            expression=Q_("5 acres"),
        ),
        "p10": Predicate("$thing was within the curtilage of $place"),
        "p10_false": Predicate(
            "$thing was within the curtilage of $place", truth=False
        ),
        "p11": Predicate("$act was a warrantless search and seizure"),
        "p12": Predicate("$act was performed by federal law enforcement officers"),
        "p13": Predicate("$act constituted an intrusion upon $place"),
        "p14": Predicate("$person sought to preserve $thing as private"),
        "p15": Predicate("$thing was in an area adjacent to $place"),
        "p16": Predicate("$thing was in an area accessible to the public"),
        "p17": Predicate(
            "In $act, several law enforcement officials meticulously went through $thing"
        ),
        "p18": Comparison(
            "the length of time that $act continued was",
            sign=">=",
            expression=Q_("385 minutes"),
        ),
        "p19": Predicate("$act continued after night fell"),
        # Use the irrelevant predicates/factors to make sure they don't affect an outcome.
        "p_irrelevant_0": Predicate("$person was a clown"),
        "p_irrelevant_1": Predicate("$person was a bear"),
        "p_irrelevant_2": Predicate("$place was a circus"),
        "p_irrelevant_3": Predicate("$person performed at $place"),
        "p_crime": Predicate("$person committed a crime"),
        "p_murder": Predicate("$shooter murdered $victim"),
        "p_murder_whether": Predicate("$shooter murdered $victim", truth=None),
        "p_murder_false": Predicate("$shooter murdered $victim", truth=False),
        "p_irrelevant": Predicate("$evidence is relevant to show $fact", truth=False),
        "p_relevant": Predicate("$evidence is relevant to show $fact"),
        "p_relevant_whether": Predicate(
            "$evidence is relevant to show $fact", truth=None
        ),
        "p_shooting": Predicate("$shooter shot $victim"),
        "p_shooting_self": Predicate("$shooter shot $shooter"),
        "p_no_shooting": Predicate("$shooter shot $victim", truth=False),
        "p_shooting_whether": Predicate("$shooter shot $victim", truth=None),
        "p_no_crime": Predicate("$person1 committed a crime", truth=False),
        "p_three_entities": Predicate("$planner told $intermediary to hire $shooter"),
        "p_small_weight": Comparison(
            "the amount of gold $person possessed was",
            sign=">=",
            expression=Q_("1 gram"),
        ),
        "p_large_weight": Comparison(
            "the amount of gold $person possessed was",
            sign=">=",
            expression=Q_("100 kilograms"),
        ),
        "p_friends": Predicate("$person1 and $person2 were friends"),
        "p_reliable": Predicate("$evidence was reliable"),
        "p_quantity=3": Comparison("The number of mice was", sign="==", expression=3),
        "p_quantity>=4": Comparison("The number of mice was", sign=">=", expression=4),
        "p_quantity>5": Comparison("The number of mice was", sign=">", expression=5),
        "p_no_context": Predicate("context was included", truth=False),
    }


@pytest.fixture(scope="class")
def watt_mentioned(make_entity) -> Tuple[Entity, ...]:
    e = make_entity
    return (e["motel"], e["watt"], e["trees"], e["tree_search"], e["motel_specific"])


@pytest.fixture(scope="class")
def watt_factor(make_predicate, make_entity, watt_mentioned) -> Dict[str, Factor]:
    p = make_predicate

    c = watt_mentioned

    return {
        "f1": build_fact(p["p1"], case_factors=c),
        "f2": build_fact(p["p2"], (1, 0), case_factors=c),
        "f3": build_fact(p["p3"], case_factors=c),
        "f4": build_fact(p["p4"], (2, 0), case_factors=c),
        "f5": build_fact(p["p5"], 2, case_factors=c),
        "f6": build_fact(p["p6"], 2, case_factors=c),
        "f7": build_fact(p["p7"], (0, 2), case_factors=c),
        "f8": build_fact(p["p8"], (0, 2), case_factors=c),
        "f9": build_fact(p["p9"], (2, 0), case_factors=c),
        "f10": build_fact(p["p10"], (2, 0), case_factors=c),
        "f1_entity_order": build_fact(p["p1"], (1,), case_factors=c),
        "f1_different_entity": build_fact(p["p1"], (2,), case_factors=c),
        "f1_specific": build_fact(p["p1"], (4,), case_factors=c),
        "f1b": build_fact(p["p1"], case_factors=c),
        "f1c": build_fact(p["p1_again"], case_factors=c),
        "f2_preponderance_of_evidence": build_fact(
            p["p2"],
            (1, 0),
            standard_of_proof="preponderance of evidence",
            case_factors=c,
        ),
        "f2_clear_and_convincing": build_fact(
            p["p2"], (1, 0), standard_of_proof="clear and convincing", case_factors=c
        ),
        "f2_beyond_reasonable_doubt": build_fact(
            p["p2"], (1, 0), standard_of_proof="beyond reasonable doubt", case_factors=c
        ),
        "f2_different_entity": build_fact(p["p2"], (1, 2), case_factors=c),
        "f2_entity_order": build_fact(p["p2"], case_factors=c),
        "f2_no_truth": build_fact(p["p2_no_truth"], (1, 0), case_factors=c),
        "f2_false": build_fact(p["p2_false"], case_factors=c),
        "f2_false_absent": build_fact(p["p2_false"], absent=True, case_factors=c),
        "f2_reflexive": build_fact(p["p2_reflexive"], case_factors=c),
        "f2_generic": build_fact(p["p2"], generic=True, case_factors=c),
        "f2_false_generic": build_fact(p["p2_false"], generic=True, case_factors=c),
        "f3_generic": build_fact(p["p3"], generic=True, case_factors=c),
        "f3_different_entity": build_fact(p["p3"], (2, 1), case_factors=c),
        "f3_entity_order": build_fact(p["p3"], (1, 0), case_factors=c),
        "f3_absent": build_fact(p["p3"], absent=True, case_factors=c),
        "f4_h4": build_fact(p["p4"], (3, 0), case_factors=c),
        "f4_swap_entities": build_fact(p["p4"], (0, 2), case_factors=c),
        "f4_swap_entities_4": build_fact(p["p4"], (1, 4), case_factors=c),
        "f5_h4": build_fact(p["p5"], (3,), case_factors=c),
        "f5_swap_entities": build_fact(p["p5"], (0,), case_factors=c),
        "f6_swap_entities": build_fact(p["p6"], (0,), case_factors=c),
        "f7_opposite": build_fact(p["p7_opposite"], (0, 2), case_factors=c),
        "f7_swap_entities": build_fact(p["p7"], (2, 0), case_factors=c),
        "f7_swap_entities_4": build_fact(p["p7"], (1, 4), case_factors=c),
        "f7_true": build_fact(p["p7_true"], (0, 2), case_factors=c),
        "f8_absent": build_fact(p["p8"], (0, 2), absent=True, case_factors=c),
        "f8_exact": build_fact(p["p8_exact"], (0, 2), case_factors=c),
        "f8_exact_swap_entities": build_fact(p["p8_exact"], (2, 0), case_factors=c),
        "f8_float": build_fact(p["p8_float"], (0, 2), case_factors=c),
        "f8_higher_int": build_fact(p["p8_higher_int"], (0, 2), case_factors=c),
        "f8_int": build_fact(p["p8_int"], (0, 2), case_factors=c),
        "f8_less": build_fact(p["p8_less"], (0, 2), case_factors=c),
        "f8_less_absent": build_fact(p["p8_less"], (0, 2), absent=True, case_factors=c),
        "f8_meters": build_fact(p["p8_meters"], (0, 2), case_factors=c),
        "f8_no_truth": build_fact(p["p8_no_truth"], (0, 2), case_factors=c),
        "f9_absent": build_fact(p["p9"], (2, 0), absent=True, case_factors=c),
        "f9_miles": build_fact(p["p9_miles"], (2, 0), case_factors=c),
        "f9_absent_miles": build_fact(
            p["p9_miles"], (2, 0), absent=True, case_factors=c
        ),
        "f9_more_different_entity": Fact(
            p["p9_more"], (make_entity["circus"], make_entity["motel"])
        ),
        "f9_swap_entities": build_fact(p["p9"], (0, 2), case_factors=c),
        "f9_swap_entities_4": build_fact(p["p9"], (1, 4), case_factors=c),
        "f10_absent": build_fact(p["p10"], (2, 0), absent=True, case_factors=c),
        "f10_false": build_fact(p["p10_false"], (2, 0), case_factors=c),
        "f10_absent_false": build_fact(p["p10_false"], absent=True, case_factors=c),
        "f10_swap_entities": build_fact(p["p10"], (0, 2), case_factors=c),
        "f10_swap_entities_4": build_fact(p["p10"], (1, 4), case_factors=c),
        "f11": build_fact(p["p11"], 3, case_factors=c),
        "f12": build_fact(p["p12"], 3, case_factors=c),
        "f13": build_fact(p["p13"], (3, 2), case_factors=c),
        "f14": build_fact(p["p14"], (1, 2), case_factors=c),
        "f15": build_fact(p["p15"], (2, 0), case_factors=c),
        "f16": build_fact(p["p16"], 2, case_factors=c),
        "f17": build_fact(p["p17"], (2, 3), case_factors=c),
        "f18": build_fact(p["p18"], 2, case_factors=c),
        "f19": build_fact(p["p19"], 2, case_factors=c),
    }


@pytest.fixture(scope="class")
def make_factor(make_predicate, make_entity) -> Dict[str, Factor]:
    p = make_predicate
    e = make_entity

    c = (e["alice"], e["bob"], e["craig"], e["dan"], e["circus"])

    return {
        "f_irrelevant_0": build_fact(p["p_irrelevant_0"], (2,), case_factors=c),
        "f_irrelevant_1": build_fact(p["p_irrelevant_1"], (3,), case_factors=c),
        "f_irrelevant_2": build_fact(p["p_irrelevant_2"], (4,), case_factors=c),
        "f_irrelevant_3": build_fact(p["p_irrelevant_3"], (2, 4), case_factors=c),
        "f_irrelevant_3_new_context": build_fact(
            p["p_irrelevant_3"], (3, 4), case_factors=c
        ),
        "f_irrelevant_3_context_0": build_fact(
            p["p_irrelevant_3"], (3, 0), case_factors=c
        ),
        "f_crime": build_fact(p["p_crime"], case_factors=c),
        "f_crime_craig_poe": Fact(
            p["p_crime"],
            terms=e["craig"],
            standard_of_proof="preponderance of evidence",
        ),
        "f_watt_crime": build_fact(p["p_crime"], case_factors=make_entity["watt"]),
        "f_no_crime": build_fact(p["p_no_crime"], case_factors=c),
        "f_no_crime_entity_order": build_fact(p["p_no_crime"], (1,), case_factors=c),
        "f_murder": build_fact(p["p_murder"], case_factors=c),
        "f_no_murder": build_fact(p["p_murder_false"], case_factors=c),
        "f_murder_entity_swap": build_fact(p["p_murder"], (1, 0), case_factors=c),
        "f_murder_craig": build_fact(p["p_murder"], (2, 3), case_factors=c),
        "f_murder_whether": build_fact(p["p_murder_whether"], case_factors=c),
        "f_shooting": build_fact(p["p_shooting"], case_factors=c),
        "f_shooting_self": build_fact(p["p_shooting_self"], case_factors=c),
        "f_shooting_craig": build_fact(p["p_shooting"], (2, 3), case_factors=c),
        "f_shooting_craig_poe": build_fact(
            p["p_shooting"],
            (2, 3),
            case_factors=c,
            standard_of_proof="preponderance of evidence",
        ),
        "f_shooting_craig_brd": build_fact(
            p["p_shooting"],
            (2, 3),
            case_factors=c,
            standard_of_proof="beyond reasonable doubt",
        ),
        "f_shooting_entity_order": build_fact(p["p_shooting"], (1, 0), case_factors=c),
        "f_no_shooting": build_fact(p["p_no_shooting"], case_factors=c),
        "f_shooting_whether": build_fact(p["p_shooting_whether"], case_factors=c),
        "f_no_shooting_entity_order": build_fact(
            p["p_no_shooting"], (1, 0), case_factors=c
        ),
        "f_three_entities": build_fact(
            p["p_three_entities"], (0, 1, 2), case_factors=c
        ),
        "f_large_weight": build_fact(p["p_large_weight"], (0,), case_factors=c),
        "f_small_weight": build_fact(p["p_small_weight"], (0,), case_factors=c),
        "f_friends": build_fact(p["p_friends"], (0, 1), case_factors=c),
        "f_no_context": build_fact(p["p_no_context"], case_factors=c),
    }


@pytest.fixture(scope="class")
def make_exhibit(
    make_entity, make_predicate, make_factor, watt_factor, make_complex_fact
) -> Dict[str, Exhibit]:
    e = make_entity
    f = make_factor
    w = watt_factor
    c = make_complex_fact

    return {
        "shooting_affidavit": Exhibit(
            form="affidavit",
            statement=f["f_shooting"],
            statement_attribution=e["alice"],
        ),
        "shooting_testimony": Exhibit(
            form="testimony",
            statement=f["f_shooting"],
            statement_attribution=e["alice"],
        ),
        "no_shooting_testimony": Exhibit(
            form="testimony",
            statement=f["f_no_shooting"],
            statement_attribution=e["alice"],
        ),
        "no_shooting_entity_order_testimony": Exhibit(
            form="testimony",
            statement=f["f_no_shooting_entity_order"],
            statement_attribution=e["bob"],
        ),
        "no_shooting_witness_unknown_testimony": Exhibit(
            form="testimony", statement=f["f_no_shooting"]
        ),
        "no_shooting_witness_unknown_absent_testimony": Exhibit(
            form="testimony", statement=f["f_no_shooting"], absent=True
        ),
        "no_shooting_different_witness_testimony": Exhibit(
            form="testimony",
            statement=f["f_no_shooting"],
            statement_attribution=e["bob"],
        ),
        "reciprocal_testimony": Exhibit(
            form="testimony", statement=w["f8"], statement_attribution=e["craig"]
        ),
        "reciprocal_declaration": Exhibit(
            form="declaration", statement=w["f8"], statement_attribution=e["craig"]
        ),
        "reciprocal_testimony_absent": Exhibit(
            form="testimony",
            statement=w["f8"],
            statement_attribution=e["craig"],
            absent=True,
        ),
        "reciprocal_testimony_less": Exhibit(
            form="testimony", statement=w["f8_less"], statement_attribution=e["craig"]
        ),
        "reciprocal_testimony_specific": Exhibit(
            form="testimony", statement=w["f8_meters"], statement_attribution=e["craig"]
        ),
        "reciprocal_testimony_specific_absent": Exhibit(
            form="testimony",
            statement=w["f8_meters"],
            statement_attribution=e["craig"],
            absent=True,
        ),
        "relevant_murder_testimony": Exhibit(
            form="testimony",
            statement=c["f_relevant_murder"],
            statement_attribution=e["alice"],
        ),
        "relevant_murder_nested_swap_testimony": Exhibit(
            form="testimony",
            statement=c["f_relevant_murder_nested_swap"],
            statement_attribution=e["bob"],
        ),
        "relevant_murder_alice_craig_testimony": Exhibit(
            form="testimony",
            statement=c["f_relevant_murder_alice_craig"],
            statement_attribution=e["alice"],
        ),
        "large_weight_testimony": Exhibit(
            form="testimony",
            statement=f["f_large_weight"],
            statement_attribution=e["bob"],
        ),
        "small_weight_testimony": Exhibit(
            form="testimony",
            statement=f["f_small_weight"],
            statement_attribution=e["bob"],
        ),
        "generic_exhibit": Exhibit(generic=True),
        "specific_but_featureless": Exhibit(),
        "testimony_no_statement": Exhibit(form="testimony"),
    }


@pytest.fixture(scope="class")
def make_complex_fact(make_predicate, make_factor) -> Dict[str, Fact]:
    p = make_predicate
    f = make_factor

    return {
        "f_irrelevant_murder": Fact(
            p["p_irrelevant"], (f["f_shooting"], f["f_murder"])
        ),
        "f_relevant_murder": Fact(p["p_relevant"], (f["f_shooting"], f["f_murder"])),
        "f_relevant_murder_swap_entities": Fact(
            p["p_relevant"], (f["f_shooting"], f["f_murder"])
        ),
        "f_relevant_murder_nested_swap": Fact(
            p["p_relevant"], (f["f_shooting_entity_order"], f["f_murder_entity_swap"])
        ),
        "f_relevant_murder_whether": Fact(
            p["p_relevant"], (f["f_shooting"], f["f_murder_whether"])
        ),
        "f_whether_relevant_murder_whether": Fact(
            p["p_relevant"], (f["f_shooting_whether"], f["f_murder_whether"])
        ),
        "f_relevant_murder_swap": Fact(
            p["p_relevant"], (f["f_shooting"], f["f_murder_entity_swap"])
        ),
        "f_relevant_murder_craig": Fact(
            p["p_relevant"], (f["f_shooting_craig"], f["f_murder_craig"])
        ),
        "f_relevant_murder_alice_craig": Fact(
            p["p_relevant"], (f["f_shooting"], f["f_murder_craig"])
        ),
    }


@pytest.fixture(scope="class")
def make_fact_about_exhibit(make_predicate, make_exhibit) -> Dict[str, Evidence]:
    p = make_predicate
    e = make_exhibit
    return {
        "f_reliable_large_weight": Fact(
            p["p_reliable"], (e["large_weight_testimony"],)
        ),
        "f_reliable_small_weight": Fact(
            p["p_reliable"], (e["small_weight_testimony"],)
        ),
    }


@pytest.fixture(scope="class")
def make_complex_rule(
    make_factor, make_exhibit, make_complex_fact, make_fact_about_exhibit
) -> Dict[str, Rule]:
    return {
        "accept_relevance_testimony": Rule(
            Procedure(
                inputs=make_exhibit["relevant_murder_testimony"],
                outputs=make_complex_fact["f_relevant_murder"],
            )
        ),
        "accept_relevance_testimony_ALL": Rule(
            Procedure(
                inputs=make_exhibit["relevant_murder_testimony"],
                outputs=make_complex_fact["f_relevant_murder"],
            ),
            universal=True,
        ),
        "accept_murder_fact_from_relevance": Rule(
            Procedure(
                inputs=make_complex_fact["f_relevant_murder"],
                outputs=make_factor["f_murder"],
            )
        ),
        "accept_murder_fact_from_relevance_and_shooting": Rule(
            Procedure(
                inputs=[
                    make_complex_fact["f_relevant_murder"],
                    make_factor["f_shooting"],
                ],
                outputs=make_factor["f_murder"],
            )
        ),
        "accept_murder_fact_from_relevance_and_shooting_craig": Rule(
            Procedure(
                inputs=[
                    make_complex_fact["f_relevant_murder_craig"],
                    make_factor["f_shooting_craig"],
                ],
                outputs=make_factor["f_murder_craig"],
            )
        ),
        "accept_small_weight_reliable": Rule(
            Procedure(
                inputs=[make_factor["f_small_weight"], make_factor["f_friends"]],
                outputs=make_fact_about_exhibit["f_reliable_small_weight"],
            ),
            universal=True,
        ),
        "accept_small_weight_reliable_more_evidence": Rule(
            Procedure(
                inputs=[make_factor["f_large_weight"], make_factor["f_friends"]],
                outputs=make_fact_about_exhibit["f_reliable_small_weight"],
            ),
            universal=True,
        ),
        "accept_large_weight_reliable": Rule(
            Procedure(
                inputs=[make_factor["f_small_weight"], make_factor["f_friends"]],
                outputs=make_fact_about_exhibit["f_reliable_large_weight"],
            ),
            universal=True,
        ),
    }


@pytest.fixture(scope="class")
def make_evidence(
    make_predicate, make_factor, watt_factor, make_exhibit
) -> Dict[str, Evidence]:
    p = make_predicate
    f = make_factor
    w = watt_factor
    x = make_exhibit
    return {
        "shooting": Evidence(x["shooting_testimony"], to_effect=f["f_crime"]),
        "shooting_no_effect": Evidence(x["shooting_testimony"]),
        "no_shooting": Evidence(x["no_shooting_testimony"], to_effect=f["f_no_crime"]),
        "no_shooting_absent": Evidence(
            x["no_shooting_testimony"], to_effect=f["f_no_crime"], absent=True
        ),
        "no_shooting_entity_order": Evidence(
            x["no_shooting_entity_order_testimony"],
            to_effect=f["f_no_crime_entity_order"],
        ),
        "no_shooting_witness_unknown": Evidence(
            x["no_shooting_witness_unknown_testimony"], to_effect=f["f_no_crime"]
        ),
        "no_shooting_witness_unknown_absent": Evidence(
            x["no_shooting_witness_unknown_testimony"],
            to_effect=f["f_no_crime"],
            absent=True,
        ),
        # Here the Exhibit is absent, not the Evidence. Pointless distinction?
        "no_shooting_witness_unknown_absent_exhibit": Evidence(
            x["no_shooting_witness_unknown_absent_testimony"], to_effect=f["f_no_crime"]
        ),
        "no_shooting_no_effect_entity_order": Evidence(
            x["no_shooting_entity_order_testimony"]
        ),
        "no_shooting_different_witness": Evidence(
            x["no_shooting_different_witness_testimony"], to_effect=f["f_no_crime"]
        ),
        "reciprocal": Evidence(x["reciprocal_testimony"], to_effect=f["f_no_crime"]),
        "crime": Evidence(
            x["generic_exhibit"], to_effect=f["f_watt_crime"], generic=True
        ),
        "crime_absent": Evidence(
            x["generic_exhibit"], to_effect=f["f_watt_crime"], absent=True, generic=True
        ),
        "generic": Evidence(x["generic_exhibit"], generic=True),
        "generic_absent": Evidence(x["generic_exhibit"], absent=True, generic=True),
    }


@pytest.fixture(scope="class")
def make_pleading(make_entity) -> Dict[str, Dict[str, Pleading]]:
    return {"craig": Pleading(filer=make_entity["craig"])}


@pytest.fixture(scope="class")
def make_allegation(make_pleading, make_factor) -> Dict[str, Dict[str, Allegation]]:
    return {
        "shooting": Allegation(
            statement=make_factor["f_shooting"], pleading=make_pleading["craig"]
        )
    }


@pytest.fixture(scope="module")
def make_selector() -> Dict[str, TextQuoteSelector]:
    return {
        "bad_selector": TextQuoteSelector(exact="text that doesn't exist in the code"),
        "preexisting material": TextQuoteSelector(
            exact=(
                "protection for a work employing preexisting material in which "
                + "copyright subsists does not extend to any part of the work in "
                + "which such material has been used unlawfully."
            )
        ),
        "copyright": TextQuoteSelector(suffix="idea, procedure,"),
        "copyright_requires_originality": TextQuoteSelector(
            suffix="fixed in any tangible"
        ),
    }


@pytest.fixture(scope="module")
def make_response() -> Dict[str, Dict]:
    """Mock api responses"""
    this_directory = os.path.dirname(os.path.abspath(__file__))
    parent_directory = os.path.dirname(this_directory)
    responses_filepath = parent_directory + "/example_data/responses/usc.json"
    with open(responses_filepath, "r") as f:
        responses = json.load(f)
    return responses


@pytest.fixture(scope="module")
def fake_usc_client() -> FakeClient:
    return FakeClient.from_file("usc.json")


@pytest.fixture(scope="module")
def beard_response() -> Dict[str, Dict]:
    """Mock api responses"""
    this_directory = os.path.dirname(os.path.abspath(__file__))
    parent_directory = os.path.dirname(this_directory)
    responses_filepath = parent_directory + "/example_data/responses/beard_act.json"
    with open(responses_filepath, "r") as f:
        responses = json.load(f)
    return responses


@pytest.fixture(scope="module")
def fake_beard_client() -> FakeClient:
    return FakeClient.from_file("beard_act.json")


@pytest.fixture(scope="module")
def e_fourth_a(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/amendment/IV"]["1791-12-15"])
    enactment.select_all()
    return enactment


@pytest.fixture(scope="module")
def e_search_clause(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/amendment/IV"]["1791-12-15"])
    selector = TextQuoteSelector(suffix=", and no Warrants shall issue")
    enactment.select(selector)
    return enactment


@pytest.fixture(scope="module")
def e_warrants_clause(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/amendment/IV"]["1791-12-15"])
    enactment.select("shall not be violated, and no Warrants shall issue,")
    return enactment


@pytest.fixture(scope="module")
def e_due_process_5(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/amendment/V"]["1791-12-15"])
    enactment.select("life, liberty, or property, without due process of law")
    return enactment


@pytest.fixture(scope="module")
def e_due_process_14(make_response):
    schema = EnactmentSchema()
    fourteenth = make_response["/us/const/amendment/XIV"]["1868-07-28"]
    enactment = schema.load(fourteenth["children"][0])
    enactment.select("life, liberty, or property, without due process of law")
    return enactment


@pytest.fixture(scope="module")
def e_securing_for_authors(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/article/I/8/8"]["1788-09-13"])
    selector = TextQuoteSelector(
        exact=(
            "To promote the Progress of Science and "
            + "useful Arts, by securing for limited Times to Authors"
        )
    )
    enactment.select(selector)
    return enactment


@pytest.fixture(scope="module")
def e_securing_exclusive_right_to_writings(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/article/I/8/8"]["1788-09-13"])
    enactment.select(
        "To promote the Progress of Science and useful Arts, by securing for limited Times to Authors"
    )
    enactment.select_more("the exclusive Right to their respective Writings")
    return enactment


@pytest.fixture(scope="module")
def e_and_inventors(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/article/I/8/8"]["1788-09-13"])
    enactment.select("and Inventors")
    return enactment


@pytest.fixture(scope="module")
def e_right_to_writings(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/const/article/I/8/8"]["1788-09-13"])
    enactment.select("the exclusive Right to their respective Writings")
    return enactment


@pytest.fixture(scope="module")
def e_copyright_protection(make_response, make_selector):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/a"]["2013-07-18"])
    enactment.select(TextQuoteSelector(suffix="Works of authorship include"))
    return enactment


@pytest.fixture(scope="module")
def e_copyright_requires_originality(make_response, make_selector):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/a"]["2013-07-18"])
    enactment.select(make_selector["copyright_requires_originality"])
    return enactment


@pytest.fixture(scope="module")
def e_copyright(make_response, make_selector):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/b"]["2013-07-18"])
    enactment.select(make_selector["copyright"])
    return enactment


@pytest.fixture(scope="module")
def e_copyright_exceptions(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/b"]["2013-07-18"])
    enactment.select(TextQuoteSelector(suffix="idea, procedure, process"))
    return enactment


@pytest.fixture(scope="module")
def e_copyright_exceptions_full(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/b"]["2013-07-18"])
    return enactment


@pytest.fixture(scope="module")
def e_in_no_case(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/b"]["2013-07-18"])
    enactment.select(
        "In no case does copyright protection for an original work of authorship extend to any"
    )
    return enactment


@pytest.fixture(scope="module")
def e_method_of_operation(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s102/b"]["2013-07-18"])
    enactment.select(TextQuoteSelector(exact="method of operation"))
    return enactment


@pytest.fixture(scope="module")
def e_compilation(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s103/b"]["2013-07-18"])
    enactment.select("The copyright in a compilation")
    return enactment


@pytest.fixture(scope="module")
def e_preexisting_material(make_response):
    schema = EnactmentSchema()
    enactment = schema.load(make_response["/us/usc/t17/s103/b"]["2013-07-18"])
    enactment.select(
        "extends only to the material contributed by the author "
        "of such work, as distinguished from the preexisting "
        "material employed in the work, and does not imply any "
        "exclusive right in the preexisting material."
    )
    return enactment


@pytest.fixture(scope="class")
def make_procedure(make_evidence, make_factor, watt_factor) -> Dict[str, Procedure]:
    e = make_evidence
    f = watt_factor
    m = make_factor

    return {
        "c1": Procedure(outputs=(f["f3"],), inputs=(f["f1"], f["f2"])),
        "c2": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c3": Procedure(
            outputs=e["crime_absent"],
            inputs=(f["f3"], f["f11"], f["f12"], f["f13"], f["f14"], f["f15"]),
            despite=(f["f16"]),
        ),
        "c4": Procedure(
            outputs=f["f13"],
            inputs=(
                f["f1"],
                f["f2"],
                f["f4_h4"],
                f["f5_h4"],
                f["f11"],
                f["f12"],
                f["f17"],
                f["f18"],
                f["f19"],
            ),
        ),
        "c1_again": Procedure(outputs=(f["f3"],), inputs=(f["f1"], f["f2"])),
        "c1_entity_order": Procedure(
            outputs=(f["f3_entity_order"],),
            inputs=(f["f2_entity_order"], f["f1_entity_order"]),
        ),
        "c1_factor_and_entity_order": Procedure(
            outputs=(f["f3_entity_order"],),
            inputs=(f["f1_entity_order"], f["f2_entity_order"]),
        ),
        "c1_easy": Procedure(outputs=(f["f3"],), inputs=(f["f2"])),
        "c2_absent_despite": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"]),
            despite=(f["f8_exact"], f["f9_absent"]),
        ),
        "c2_exact_quantity": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f8_exact"], f["f9"]),
        ),
        "c2_higher_quantity": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f8_meters"], f["f9"]),
        ),
        "c2_exact_in_despite": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8_exact"],),
        ),
        "c2_exact_in_despite_entity_order": Procedure(
            outputs=(f["f10_swap_entities"],),
            inputs=(
                f["f4_swap_entities"],
                f["f5_swap_entities"],
                f["f6_swap_entities"],
                f["f7_swap_entities"],
                f["f9_swap_entities"],
            ),
            despite=(f["f8_exact_swap_entities"],),
        ),
        "c2_irrelevant_inputs": Procedure(
            outputs=(f["f10"],),
            inputs=(
                f["f4"],
                f["f5"],
                f["f6"],
                f["f7"],
                f["f9"],
                m["f_irrelevant_0"],
                m["f_irrelevant_1"],
                m["f_irrelevant_2"],
                m["f_irrelevant_3"],
                m["f_irrelevant_3_new_context"],
            ),
            despite=(f["f8"],),
        ),
        "c2_irrelevant_outputs": Procedure(
            outputs=(
                f["f10"],
                m["f_irrelevant_0"],
                m["f_irrelevant_1"],
                m["f_irrelevant_2"],
                m["f_irrelevant_3"],
                m["f_irrelevant_3_context_0"],
            ),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
        ),
        "c2_irrelevant_despite": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(
                f["f8"],
                m["f_irrelevant_0"],
                m["f_irrelevant_1"],
                m["f_irrelevant_2"],
                m["f_irrelevant_3"],
                m["f_irrelevant_3_new_context"],
            ),
        ),
        "c2_reciprocal_swap": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7_swap_entities"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c2_nonreciprocal_swap": Procedure(
            outputs=(f["f10"],),
            inputs=(f["f4_swap_entities"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c2_broad_output": Procedure(
            outputs=(f["f8_int"],), inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"])
        ),
        "c2_narrow_output": Procedure(
            outputs=(f["f8_higher_int"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
        ),
        "c2_output_absent": Procedure(
            outputs=(f["f10_absent"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c2_output_false": Procedure(
            outputs=(f["f10_false"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c2_output_absent_false": Procedure(
            outputs=(f["f10_absent_false"],),
            inputs=(f["f4"], f["f5"], f["f6"], f["f7"], f["f9"]),
            despite=(f["f8"],),
        ),
        "c_near_means_no_curtilage": Procedure(
            outputs=(f["f10_false"],), inputs=(f["f7_true"])
        ),
        "c_nearer_means_curtilage": Procedure(
            outputs=(f["f10"],), inputs=(f["f8_less"])
        ),
        "c_near_means_curtilage": Procedure(outputs=(f["f10"],), inputs=(f["f7"])),
        "c_near_means_curtilage_even_if": Procedure(
            outputs=(f["f10"],), inputs=(f["f7"]), despite=(f["f8"])
        ),
        "c_exact_means_no_curtilage": Procedure(
            outputs=(f["f10_false"],), inputs=(f["f8_exact"])
        ),
        "c_far_means_no_curtilage": Procedure(
            outputs=(f["f10_false"],), inputs=(f["f8"]), despite=(f["f7"])
        ),
        "c3_fewer_inputs": Procedure(
            outputs=e["crime_absent"],
            inputs=(f["f3"], f["f11"], f["f12"], f["f15"]),
            despite=(f["f16"]),
        ),
        "c_output_distance_less": Procedure(outputs=(f["f9"]), inputs=(f["f1"])),
        "c_output_farther_different_entity": Procedure(
            outputs=(f["f9_more_different_entity"]), inputs=(f["f1"])
        ),
        "c_no_context": Procedure(outputs=m["f_no_context"]),
    }


@pytest.fixture(scope="class")
def real_holding(make_procedure, e_search_clause) -> Dict[str, Rule]:
    """These holdings can be changed in case they don't accurately reflect
    what's in real cases, or in case there are API improvements that
    allow them to become more accurate. I'll try not to write any tests
    that depend on them remaining the same."""

    c = make_procedure

    return {
        "h1": Holding(rule=Rule(c["c1"], enactments=e_search_clause, mandatory=True)),
        "h2": Holding(rule=Rule(c["c2"], enactments=e_search_clause, mandatory=True)),
        "h3": Holding(rule=Rule(c["c3"], enactments=e_search_clause, mandatory=True)),
        "h4": Holding(rule=Rule(c["c4"], enactments=e_search_clause, mandatory=True)),
    }


@pytest.fixture(scope="class")
def make_rule(
    make_procedure, e_fourth_a, e_search_clause, e_due_process_5
) -> Dict[str, Rule]:
    c = make_procedure

    return {
        "h1": Rule(c["c1"], enactments=e_search_clause),
        "h2": Rule(c["c2"], enactments=e_search_clause),
        "h3": Rule(c["c3"], enactments=e_search_clause),
        "h1_again": Rule(c["c1"], enactments=e_search_clause),
        "h1_entity_order": Rule(c["c1_entity_order"], enactments=e_search_clause),
        "h1_easy": Rule(c["c1_easy"], enactments=e_search_clause),
        "h2_without_cite": Rule(c["c2"]),
        "h2_fourth_a_cite": Rule(c["c2"], enactments=e_fourth_a),
        "h2_despite_due_process": Rule(
            c["c2"],
            enactments=e_search_clause,
            enactments_despite=e_due_process_5,
        ),
        "h2_ALL_due_process": Rule(
            c["c2"],
            enactments=(e_search_clause, e_due_process_5),
            mandatory=False,
            universal=True,
        ),
        "h2_ALL": Rule(
            c["c2"], enactments=e_search_clause, mandatory=False, universal=True
        ),
        "h2_ALL_MAY_output_false": Rule(
            c["c2_output_false"],
            enactments=e_search_clause,
            mandatory=False,
            universal=True,
        ),
        "h2_ALL_MUST": Rule(
            c["c2"], enactments=e_search_clause, mandatory=True, universal=True
        ),
        "h2_ALL_MUST_output_false": Rule(
            c["c2_output_false"],
            enactments=e_search_clause,
            mandatory=True,
            universal=True,
        ),
        "h2_exact_quantity": Rule(c["c2_exact_quantity"], enactments=e_search_clause),
        "h2_irrelevant_inputs": Rule(
            c["c2_irrelevant_inputs"], enactments=e_search_clause
        ),
        "h2_irrelevant_inputs_ALL": Rule(
            c["c2_irrelevant_inputs"], enactments=e_search_clause, universal=True
        ),
        "h2_irrelevant_inputs_MUST": Rule(
            c["c2_irrelevant_inputs"], enactments=e_search_clause, mandatory=True
        ),
        "h2_irrelevant_inputs_ALL_MUST": Rule(
            c["c2_irrelevant_inputs"],
            enactments=e_search_clause,
            mandatory=True,
            universal=True,
        ),
        "h2_reciprocal_swap": Rule(c["c2_reciprocal_swap"], enactments=e_search_clause),
        "h2_exact_in_despite": Rule(
            c["c2_exact_in_despite"], enactments=e_search_clause
        ),
        "h2_exact_in_despite_ALL": Rule(
            c["c2_exact_in_despite"],
            enactments=e_search_clause,
            mandatory=False,
            universal=True,
        ),
        "h2_exact_in_despite_ALL_entity_order": Rule(
            c["c2_exact_in_despite_entity_order"],
            enactments=e_search_clause,
            mandatory=False,
            universal=True,
        ),
        "h2_exact_quantity_ALL": Rule(
            c["c2_exact_quantity"],
            enactments=e_search_clause,
            mandatory=False,
            universal=True,
        ),
        "h2_MUST": Rule(
            c["c2"], enactments=e_search_clause, mandatory=True, universal=False
        ),
        "h2_output_absent": Rule(c["c2_output_absent"], enactments=e_search_clause),
        "h2_output_false": Rule(c["c2_output_false"], enactments=e_search_clause),
        "h2_output_false_ALL": Rule(
            c["c2_output_false"], enactments=e_search_clause, universal=True
        ),
        "h2_output_false_ALL_MUST": Rule(
            c["c2_output_false"],
            enactments=e_search_clause,
            mandatory=True,
            universal=True,
        ),
        "h2_output_absent_false": Rule(
            c["c2_output_absent_false"], enactments=e_search_clause
        ),
        "h2_SOME_MUST_output_false": Rule(
            c["c2_output_false"],
            enactments=e_search_clause,
            mandatory=True,
            universal=False,
        ),
        "h2_SOME_MUST_output_absent": Rule(
            c["c2_output_absent"],
            enactments=e_search_clause,
            mandatory=True,
            universal=False,
        ),
        "h3_ALL": Rule(c["c3"], enactments=e_search_clause, universal=True),
        "h3_fewer_inputs": Rule(c["c3_fewer_inputs"], enactments=e_search_clause),
        "h3_fewer_inputs_ALL": Rule(
            c["c3_fewer_inputs"], enactments=e_search_clause, universal=True
        ),
        "h_near_means_curtilage": Rule(
            c["c_near_means_curtilage"], enactments=e_search_clause
        ),
        "h_near_means_curtilage_even_if": Rule(
            c["c_near_means_curtilage_even_if"], enactments=e_search_clause
        ),
        "h_near_means_curtilage_ALL": Rule(
            c["c_near_means_curtilage"], enactments=e_search_clause, universal=True
        ),
        "h_near_means_curtilage_ALL_MUST": Rule(
            c["c_near_means_curtilage"],
            enactments=e_search_clause,
            mandatory=True,
            universal=True,
        ),
        "h_near_means_no_curtilage": Rule(
            c["c_near_means_no_curtilage"], enactments=e_search_clause
        ),
        "h_near_means_no_curtilage_ALL": Rule(
            c["c_near_means_no_curtilage"],
            enactments=e_search_clause,
            universal=True,
        ),
        "h_near_means_no_curtilage_ALL_MUST": Rule(
            c["c_near_means_no_curtilage"],
            enactments=e_search_clause,
            mandatory=True,
            universal=True,
        ),
        "h_nearer_means_curtilage": Rule(
            c["c_nearer_means_curtilage"], enactments=e_search_clause
        ),
        "h_nearer_means_curtilage_ALL": Rule(
            c["c_nearer_means_curtilage"], enactments=e_search_clause, universal=True
        ),
        "h_nearer_means_curtilage_MUST": Rule(
            c["c_nearer_means_curtilage"], enactments=e_search_clause, mandatory=True
        ),
        "h_far_means_no_curtilage": Rule(c["c_far_means_no_curtilage"]),
        "h_exact_means_no_curtilage_ALL": Rule(
            c["c_exact_means_no_curtilage"], universal=True
        ),
        "h_far_means_no_curtilage_ALL": Rule(
            c["c_far_means_no_curtilage"], universal=True
        ),
        "h_output_distance_less": Rule(
            c["c_output_distance_less"], universal=True, mandatory=True
        ),
        "h_output_farther_different_entity": Rule(
            c["c_output_farther_different_entity"]
        ),
    }


@pytest.fixture(scope="class")
def make_beard_rule(beard_response) -> List[Rule]:
    """Rules from the "Beard Tax Act" example statutes."""
    client = FakeClient(responses=beard_response)
    beard_dictionary = loaders.load_holdings("beard_rules.json")
    return readers.read_rules(beard_dictionary, client=client)


@pytest.fixture(scope="class")
def make_holding(make_rule) -> Dict[str, Holding]:
    holdings: Dict[str, Holding] = {}
    for name, rule in make_rule.items():
        holdings[name] = Holding(rule=rule)

    new_holdings = {
        "h1_opposite": Holding(rule=make_rule["h1"], rule_valid=False),
        "h2_undecided": Holding(rule=make_rule["h2"], decided=False),
        "h2_invalid_undecided": Holding(
            rule=make_rule["h2"], rule_valid=False, decided=False
        ),
        "h2_MUST_undecided": Holding(rule=make_rule["h2_MUST"], decided=False),
        "h2_MUST_invalid": Holding(rule=make_rule["h2_MUST"], rule_valid=False),
        "h2_invalid": Holding(rule=make_rule["h2"], rule_valid=False),
        "h2_ALL_invalid": Holding(rule=make_rule["h2_ALL"], rule_valid=False),
        "h2_irrelevant_inputs_undecided": Holding(
            rule=make_rule["h2_irrelevant_inputs"], decided=False
        ),
        "h2_irrelevant_inputs_invalid": Holding(
            rule=make_rule["h2_irrelevant_inputs"], rule_valid=False
        ),
        "h2_irrelevant_inputs_ALL_invalid": Holding(
            rule=make_rule["h2_irrelevant_inputs_ALL"], rule_valid=False
        ),
        "h2_irrelevant_inputs_ALL_MUST_invalid": Holding(
            rule=make_rule["h2_irrelevant_inputs_ALL_MUST"], rule_valid=False
        ),
        "h2_irrelevant_inputs_MUST_invalid": Holding(
            rule=make_rule["h2_irrelevant_inputs_MUST"], rule_valid=False
        ),
        "h2_ALL_due_process_invalid": Holding(
            rule=make_rule["h2_ALL_due_process"], rule_valid=False
        ),
        "h3_undecided": Holding(rule=make_rule["h3"], decided=False),
        "h3_ALL_undecided": Holding(rule=make_rule["h3_ALL"], decided=False),
        "h3_fewer_inputs_undecided": Holding(
            rule=make_rule["h3_fewer_inputs"], decided=False
        ),
        "h3_fewer_inputs_ALL_undecided": Holding(
            rule=make_rule["h3_fewer_inputs_ALL"], decided=False
        ),
        "h_near_means_curtilage_ALL_undecided": Holding(
            rule=make_rule["h_near_means_curtilage_ALL"], decided=False
        ),
    }

    holdings.update(new_holdings)
    return holdings


TEST_CASES = ("feist", "lotus", "oracle", "brad", "cardenas", "watt")


def load_decisions_for_fixtures():
    decisions = {}
    for case in TEST_CASES:
        decision = loaders.load_decision(f"{case}_h.json")
        built = readers.read_decision(decision)
        decisions[case] = built
    return decisions


@pytest.fixture(scope="class")
def make_decision():
    return load_decisions_for_fixtures()


@pytest.fixture(scope="class")
def make_decision_with_holding(make_response):
    client_without_api_access = FakeClient(responses=make_response)
    decisions = load_decisions_for_fixtures()
    for case in TEST_CASES:
        (
            holdings,
            holding_anchors,
            named_anchors,
            enactment_anchors,
        ) = loaders.read_anchored_holdings_from_file(
            f"holding_{case}.json",
            client=client_without_api_access,
        )
        decisions[case].majority.posit(
            holdings,
            holding_anchors=holding_anchors,
            named_anchors=named_anchors,
            enactment_anchors=enactment_anchors,
        )
    return decisions


@pytest.fixture(scope="class")
def make_opinion(make_decision) -> Dict[str, Opinion]:
    opinions = {}
    for case in TEST_CASES:
        for opinion in make_decision[case].opinions:
            opinions[f"{case}_{opinion.position}"] = opinion
    return opinions


@pytest.fixture(scope="class")
def make_opinion_with_holding(make_decision_with_holding) -> Dict[str, Opinion]:
    opinions = {}
    for case in TEST_CASES:
        for opinion in make_decision_with_holding[case].opinions:
            opinions[f"{case}_{opinion.position}"] = opinion
    return opinions


@pytest.fixture(scope="class")
def make_analysis() -> Dict[str, Dict[str, Any]]:
    """Example user analysis data."""
    return {
        "minimal": {
            "holdings": [
                {
                    "anchors": "upholding searches in |open fields or grounds|around a house",
                    "outputs": {
                        "type": "fact",
                        "content": "{Bradley} made a minimal holding object",
                    },
                }
            ],
        },
        "no anchors": {
            "holdings": [
                {
                    "outputs": {
                        "type": "fact",
                        "content": "this holding has no text anchors",
                    }
                }
            ]
        },
    }


@pytest.fixture(scope="function")
def raw_factor() -> RawFactor:
    """
    Example user data that needs to be processed and loaded as a Factor.
    """
    return {
        "relevant": {
            "content": "{} is relevant to show {}",
            "type": "Fact",
            "name": "relevant fact",
            "terms": [
                {"content": "{Short Name} shot {Longer Name}", "type": "Fact"},
                {
                    "content": "{} murdered {}",
                    "terms": ["Short Name", "Longer Name"],
                    "type": "Fact",
                },
            ],
        }
    }


@pytest.fixture(scope="function")
def raw_holding() -> RawHolding:
    return {
        "bradley_house": {
            "inputs": {"type": "fact", "content": "{Bradley} lived at Bradley's house"},
            "outputs": {
                "type": "evidence",
                "to_effect": {
                    "type": "fact",
                    "name": "fact that Bradley committed a crime",
                    "content": "Bradley committed a crime",
                },
                "name": "evidence of Bradley's guilt",
                "absent": True,
            },
        },
        "stolen watch": {
            "anchors": [
                {"exact": "Mark stole a watch"},
                {"exact": "a watch was stolen by Mark"},
            ],
            "outputs": [
                {"type": "Fact", "content": "{Mark} stole a watch"},
            ],
            "inputs": [
                {
                    "type": "Evidence",
                    "to_effect": {"type": "Fact", "content": "{Mark} stole a watch"},
                }
            ],
        },
    }


@pytest.fixture(scope="function")
def make_context_register() -> ContextRegister:
    context_names = ContextRegister()
    context_names.insert_pair(key=Entity("Alice"), value=Entity("Craig"))
    context_names.insert_pair(key=Entity("Bob"), value=Entity("Dan"))
    return context_names