WerkoutAPI/generator/tests/test_movement_enforcement.py

"""
Tests for _build_working_supersets() — Items #4, #6, #7:
- Movement pattern enforcement (WorkoutStructureRule merging)
- Modality consistency check (duration_bias warning)
- Straight-set strength (first superset = single main lift)
"""
from datetime import date
from django.contrib.auth import get_user_model
from django.test import TestCase
from unittest.mock import patch, MagicMock, PropertyMock

from generator.models import (
    MuscleGroupSplit,
    MovementPatternOrder,
    UserPreference,
    WorkoutStructureRule,
    WorkoutType,
)
from generator.services.workout_generator import (
    FINAL_CONFORMANCE_MAX_RETRIES,
    WorkoutGenerator,
    STRENGTH_WORKOUT_TYPES,
    WORKOUT_TYPE_DEFAULTS,
)
from generator.rules_engine import RuleViolation, validate_workout
from registered_user.models import RegisteredUser

User = get_user_model()


class MovementEnforcementTestBase(TestCase):
    """Shared setup for movement enforcement tests."""

    @classmethod
    def setUpTestData(cls):
        cls.auth_user = User.objects.create_user(
            username='testmove', password='testpass123',
        )
        cls.registered_user = RegisteredUser.objects.create(
            first_name='Test', last_name='Move', user=cls.auth_user,
        )

        # Create workout types
        cls.strength_type = WorkoutType.objects.create(
            name='traditional strength',
            typical_rest_between_sets=120,
            typical_intensity='high',
            rep_range_min=3,
            rep_range_max=6,
            duration_bias=0.0,
            superset_size_min=1,
            superset_size_max=3,
        )
        cls.hiit_type = WorkoutType.objects.create(
            name='hiit',
            typical_rest_between_sets=30,
            typical_intensity='high',
            rep_range_min=10,
            rep_range_max=20,
            duration_bias=0.7,
            superset_size_min=3,
            superset_size_max=6,
        )
        cls.core_type = WorkoutType.objects.filter(name='core_training').first()
        if cls.core_type is None:
            cls.core_type = WorkoutType.objects.create(
                name='core_training',
                typical_rest_between_sets=30,
                typical_intensity='medium',
                rep_range_min=10,
                rep_range_max=20,
                duration_bias=0.5,
                superset_size_min=3,
                superset_size_max=5,
            )

        # Create MovementPatternOrder records
        MovementPatternOrder.objects.create(
            position='early', movement_pattern='lower push - squat',
            frequency=20, section_type='working',
        )
        MovementPatternOrder.objects.create(
            position='early', movement_pattern='upper push - horizontal',
            frequency=15, section_type='working',
        )
        MovementPatternOrder.objects.create(
            position='middle', movement_pattern='upper pull',
            frequency=18, section_type='working',
        )
        MovementPatternOrder.objects.create(
            position='late', movement_pattern='isolation',
            frequency=12, section_type='working',
        )

        # Create WorkoutStructureRule for strength
        cls.strength_rule = WorkoutStructureRule.objects.create(
            workout_type=cls.strength_type,
            section_type='working',
            movement_patterns=['lower push - squat', 'hip hinge', 'upper push - horizontal'],
            typical_rounds=5,
            typical_exercises_per_superset=2,
            goal_type='general_fitness',
        )

    def _make_preference(self, **kwargs):
        """Create a UserPreference for testing."""
        defaults = {
            'registered_user': self.registered_user,
            'days_per_week': 3,
            'fitness_level': 2,
            'primary_goal': 'general_fitness',
        }
        defaults.update(kwargs)
        return UserPreference.objects.create(**defaults)

    def _make_generator(self, pref):
        """Create a WorkoutGenerator with mocked dependencies."""
        with patch('generator.services.workout_generator.ExerciseSelector') as MockSelector, \
             patch('generator.services.workout_generator.PlanBuilder'):
            gen = WorkoutGenerator(pref)
            # Make the exercise selector return mock exercises
            self.mock_selector = gen.exercise_selector
        return gen

    def _create_mock_exercise(self, name='Mock Exercise', is_duration=False,
                              is_weight=True, is_reps=True, is_compound=True,
                              exercise_tier='primary', movement_patterns='lower push - squat',
                              hr_elevation_rating=5):
        """Create a mock Exercise object."""
        ex = MagicMock()
        ex.pk = id(ex)  # unique pk
        ex.name = name
        ex.is_duration = is_duration
        ex.is_weight = is_weight
        ex.is_reps = is_reps
        ex.is_compound = is_compound
        ex.exercise_tier = exercise_tier
        ex.movement_patterns = movement_patterns
        ex.hr_elevation_rating = hr_elevation_rating
        ex.side = None
        ex.stretch_position = 'mid'
        return ex


class TestMovementPatternEnforcement(MovementEnforcementTestBase):
    """Item #4: WorkoutStructureRule patterns merged with position patterns."""

    def test_movement_patterns_passed_to_selector(self):
        """select_exercises should receive combined movement pattern preferences
        when both position patterns and structure rule patterns exist."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        # Setup mock exercises
        mock_exercises = [
            self._create_mock_exercise(f'Exercise {i}')
            for i in range(3)
        ]
        gen.exercise_selector.select_exercises.return_value = mock_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises

        muscle_split = {
            'muscles': ['chest', 'back'],
            'split_type': 'full_body',
            'label': 'Full Body',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        supersets = gen._build_working_supersets(
            muscle_split, self.strength_type, wt_params,
        )

        # Verify select_exercises was called
        self.assertTrue(gen.exercise_selector.select_exercises.called)

        # Check the movement_pattern_preference argument in the first call
        first_call_kwargs = gen.exercise_selector.select_exercises.call_args_list[0]
        # The call could be positional or keyword - check kwargs
        if first_call_kwargs.kwargs.get('movement_pattern_preference') is not None:
            patterns = first_call_kwargs.kwargs['movement_pattern_preference']
            # Should be combined patterns (intersection of position + rule, or rule[:3])
            self.assertIsInstance(patterns, list)
            self.assertTrue(len(patterns) > 0)

        pref.delete()

    def test_retries_when_superset_has_duplicate_focus(self):
        """Generator should retry when a working superset repeats focus family."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        curl_a = self._create_mock_exercise(
            'Alternating Bicep Curls',
            movement_patterns='upper pull',
            is_compound=False,
            exercise_tier='accessory',
        )
        curl_b = self._create_mock_exercise(
            'Bicep Curls',
            movement_patterns='upper pull',
            is_compound=False,
            exercise_tier='accessory',
        )
        pull = self._create_mock_exercise('Bent Over Row', movement_patterns='upper pull')
        hinge = self._create_mock_exercise('Romanian Deadlift', movement_patterns='hip hinge')

        gen.exercise_selector.select_exercises.side_effect = [
            [curl_a, curl_b],  # rejected: duplicate focus
            [pull, hinge],     # accepted
        ]
        gen.exercise_selector.balance_stretch_positions.side_effect = lambda exs, **_: exs

        muscle_split = {
            'muscles': ['upper back', 'biceps'],
            'split_type': 'pull',
            'label': 'Pull',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])
        wt_params['num_supersets'] = (1, 1)
        wt_params['exercises_per_superset'] = (2, 2)
        wt_params['duration_bias'] = 0.0

        supersets = gen._build_working_supersets(muscle_split, self.hiit_type, wt_params)
        self.assertEqual(len(supersets), 1)
        self.assertGreaterEqual(gen.exercise_selector.select_exercises.call_count, 2)

        names = [
            entry['exercise'].name
            for entry in supersets[0].get('exercises', [])
        ]
        self.assertNotEqual(
            set(names),
            {'Alternating Bicep Curls', 'Bicep Curls'},
            f'Expected duplicate-focus superset to be retried, got {names}',
        )

        pref.delete()


class TestStrengthStraightSets(MovementEnforcementTestBase):
    """Item #7: First working superset in strength = single main lift."""

    def test_strength_first_superset_single_exercise(self):
        """For traditional strength, the first working superset should request
        exactly 1 exercise (straight set of a main lift)."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_ex = self._create_mock_exercise('Barbell Squat')
        gen.exercise_selector.select_exercises.return_value = [mock_ex]
        gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]

        muscle_split = {
            'muscles': ['quads', 'hamstrings'],
            'split_type': 'lower',
            'label': 'Lower',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        supersets = gen._build_working_supersets(
            muscle_split, self.strength_type, wt_params,
        )

        self.assertGreaterEqual(len(supersets), 1)

        # First superset should have been requested with count=1
        first_call = gen.exercise_selector.select_exercises.call_args_list[0]
        self.assertEqual(first_call.kwargs.get('count', first_call.args[1] if len(first_call.args) > 1 else None), 1)

        pref.delete()

    def test_strength_first_superset_more_rounds(self):
        """First superset of a strength workout should have 4-6 rounds."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_ex = self._create_mock_exercise('Deadlift')
        gen.exercise_selector.select_exercises.return_value = [mock_ex]
        gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]

        muscle_split = {
            'muscles': ['hamstrings', 'glutes'],
            'split_type': 'lower',
            'label': 'Lower',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        # Run multiple times to check round ranges
        round_counts = set()
        for _ in range(50):
            gen.exercise_selector.select_exercises.return_value = [mock_ex]
            supersets = gen._build_working_supersets(
                muscle_split, self.strength_type, wt_params,
            )
            if supersets:
                round_counts.add(supersets[0]['rounds'])

        # All first-superset round counts should be in [4, 6]
        for r in round_counts:
            self.assertGreaterEqual(r, 4, f"Rounds {r} below minimum 4")
            self.assertLessEqual(r, 6, f"Rounds {r} above maximum 6")

        pref.delete()

    def test_strength_first_superset_rest_period(self):
        """First superset of a strength workout should use the workout type's
        typical_rest_between_sets for rest."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_ex = self._create_mock_exercise('Bench Press')
        gen.exercise_selector.select_exercises.return_value = [mock_ex]
        gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]

        muscle_split = {
            'muscles': ['chest', 'triceps'],
            'split_type': 'push',
            'label': 'Push',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        supersets = gen._build_working_supersets(
            muscle_split, self.strength_type, wt_params,
        )

        if supersets:
            # typical_rest_between_sets for our strength_type is 120
            self.assertEqual(supersets[0]['rest_between_rounds'], 120)

        pref.delete()

    def test_strength_accessories_still_superset(self):
        """2nd+ supersets in strength workouts should still have 2+ exercises
        (the min_ex_per_ss rule still applies to non-first supersets)."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_exercises = [
            self._create_mock_exercise(f'Accessory {i}', exercise_tier='accessory')
            for i in range(3)
        ]
        gen.exercise_selector.select_exercises.return_value = mock_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises

        muscle_split = {
            'muscles': ['chest', 'back', 'shoulders'],
            'split_type': 'upper',
            'label': 'Upper',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        supersets = gen._build_working_supersets(
            muscle_split, self.strength_type, wt_params,
        )

        # Should have multiple supersets
        if len(supersets) >= 2:
            # Retries may add extra calls; assert at least one non-first
            # working-superset request asks for 2+ exercises.
            observed_counts = []
            for call in gen.exercise_selector.select_exercises.call_args_list:
                count_arg = call.kwargs.get('count')
                if count_arg is None and len(call.args) > 1:
                    count_arg = call.args[1]
                if count_arg is not None:
                    observed_counts.append(count_arg)
            self.assertTrue(
                any(c >= 2 for c in observed_counts),
                f"Expected at least one accessory superset request >=2 exercises, got {observed_counts}",
            )

        pref.delete()

    def test_non_strength_no_single_exercise_override(self):
        """Non-strength workouts should NOT have the single-exercise first superset."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_exercises = [
            self._create_mock_exercise(f'HIIT Move {i}', is_duration=True, is_weight=False)
            for i in range(5)
        ]
        gen.exercise_selector.select_exercises.return_value = mock_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises

        muscle_split = {
            'muscles': ['chest', 'back', 'quads'],
            'split_type': 'full_body',
            'label': 'Full Body',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])

        supersets = gen._build_working_supersets(
            muscle_split, self.hiit_type, wt_params,
        )

        # First call to select_exercises should NOT have count=1
        first_call = gen.exercise_selector.select_exercises.call_args_list[0]
        count_arg = first_call.kwargs.get('count')
        if count_arg is None and len(first_call.args) > 1:
            count_arg = first_call.args[1]
        self.assertGreater(count_arg, 1, "Non-strength first superset should have > 1 exercise")

        pref.delete()

    def test_strength_first_superset_survives_post_processing(self):
        """generate_single_workout should preserve first strength straight set."""
        pref = self._make_preference(primary_goal='strength')
        gen = self._make_generator(pref)

        main_lift = self._create_mock_exercise('Back Squat', exercise_tier='primary')
        accessory_1 = self._create_mock_exercise('DB Row', exercise_tier='secondary')
        accessory_2 = self._create_mock_exercise('RDL', exercise_tier='secondary')
        accessory_3 = self._create_mock_exercise('Lat Pulldown', exercise_tier='accessory')

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._get_final_conformance_violations = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)
        gen._build_working_supersets = MagicMock(return_value=[
            {
                'name': 'Working Set 1',
                'rounds': 5,
                'rest_between_rounds': 120,
                'modality': 'reps',
                'exercises': [
                    {'exercise': main_lift, 'reps': 5, 'order': 1},
                ],
            },
            {
                'name': 'Working Set 2',
                'rounds': 3,
                'rest_between_rounds': 90,
                'modality': 'reps',
                'exercises': [
                    {'exercise': accessory_1, 'reps': 10, 'order': 1},
                    {'exercise': accessory_2, 'reps': 10, 'order': 2},
                    {'exercise': accessory_3, 'reps': 12, 'order': 3},
                ],
            },
        ])

        muscle_split = {
            'muscles': ['quads', 'hamstrings'],
            'split_type': 'lower',
            'label': 'Lower',
        }
        workout_spec = gen.generate_single_workout(
            muscle_split=muscle_split,
            workout_type=self.strength_type,
            scheduled_date=date(2026, 3, 2),
        )

        working = [
            ss for ss in workout_spec.get('supersets', [])
            if ss.get('name', '').startswith('Working')
        ]
        self.assertGreaterEqual(len(working), 1)
        self.assertEqual(
            len(working[0].get('exercises', [])),
            1,
            f'Expected first strength working set to stay at 1 exercise, got: {working[0]}',
        )

        pref.delete()


class TestModalityConsistency(MovementEnforcementTestBase):
    """Item #6: Modality consistency warning for duration-dominant workouts."""

    def test_duration_dominant_warns_on_low_ratio(self):
        """When duration_bias >= 0.6 and most exercises are rep-based,
        a warning should be appended to self.warnings."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        # Create mostly rep-based (non-duration) exercises
        mock_exercises = [
            self._create_mock_exercise(f'Rep Exercise {i}', is_duration=False)
            for i in range(4)
        ]
        gen.exercise_selector.select_exercises.return_value = mock_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises

        muscle_split = {
            'muscles': ['chest', 'back'],
            'split_type': 'full_body',
            'label': 'Full Body',
        }

        # Use HIIT params (duration_bias = 0.7 >= 0.6)
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])
        # But force rep-based by setting duration_bias low in actual randomization
        # We need to make superset_is_duration = False for all supersets
        # Override duration_bias to be very low so random.random() > it
        # But wt_params['duration_bias'] stays at 0.7 for the post-check

        # Actually, the modality check uses wt_params['duration_bias'] which is 0.7
        # The rep-based exercises come from select_exercises mock returning
        # exercises with is_duration=False

        supersets = gen._build_working_supersets(
            muscle_split, self.hiit_type, wt_params,
        )

        # The exercises are not is_duration so the check should fire
        # Look for the modality mismatch warning
        modality_warnings = [
            w for w in gen.warnings if 'Modality mismatch' in w
        ]
        # Note: This depends on whether the supersets ended up rep-based
        # Since duration_bias is 0.7, most supersets will be duration-based
        # and our mock exercises don't have is_duration=True, so they'd be
        # skipped in the duration superset builder (the continue clause).
        # The modality check counts exercises that ARE is_duration.
        # With is_duration=False mocks in duration supersets, they'd be skipped.
        # So total_exercises could be 0 (if all were skipped).

        # Let's verify differently: the test should check the logic directly.
        # Create a scenario where the duration check definitely triggers:
        # Set duration_bias high but exercises are rep-based
        gen.warnings = []  # Reset warnings

        # Create supersets manually to test the post-check
        # Simulate: wt_params has high duration_bias, but exercises are rep-based
        wt_params_high_dur = dict(wt_params)
        wt_params_high_dur['duration_bias'] = 0.8

        # Return exercises that won't be skipped (rep-based supersets with non-duration exercises)
        rep_exercises = [
            self._create_mock_exercise(f'Rep Ex {i}', is_duration=False)
            for i in range(3)
        ]
        gen.exercise_selector.select_exercises.return_value = rep_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = rep_exercises

        # Force non-strength workout type with low actual random for duration
        # Use a non-strength type so is_strength_workout is False
        # Create a real WorkoutType to avoid MagicMock pk issues with Django ORM
        non_strength_type = WorkoutType.objects.create(
            name='circuit training',
            typical_rest_between_sets=30,
            duration_bias=0.7,
        )

        # Patch random to make all supersets rep-based despite high duration_bias
        import random
        original_random = random.random
        random.random = lambda: 0.99  # Always > duration_bias, so rep-based

        try:
            supersets = gen._build_working_supersets(
                muscle_split, non_strength_type, wt_params_high_dur,
            )
        finally:
            random.random = original_random

        # Now check warnings
        modality_warnings = [
            w for w in gen.warnings if 'Modality mismatch' in w
        ]
        if supersets and any(ss.get('exercises') for ss in supersets):
            self.assertTrue(
                len(modality_warnings) > 0,
                f"Expected modality mismatch warning but got: {gen.warnings}",
            )

        pref.delete()

    def test_no_warning_when_duration_bias_low(self):
        """When duration_bias < 0.6, no modality consistency warning
        should be emitted even if exercises are all rep-based."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        mock_exercises = [
            self._create_mock_exercise(f'Rep Exercise {i}', is_duration=False)
            for i in range(3)
        ]
        gen.exercise_selector.select_exercises.return_value = mock_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises

        muscle_split = {
            'muscles': ['chest', 'back'],
            'split_type': 'full_body',
            'label': 'Full Body',
        }

        # Use strength params (duration_bias = 0.0 < 0.6)
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])

        supersets = gen._build_working_supersets(
            muscle_split, self.strength_type, wt_params,
        )

        modality_warnings = [
            w for w in gen.warnings if 'Modality mismatch' in w
        ]
        self.assertEqual(
            len(modality_warnings), 0,
            f"Should not have modality warning for low duration_bias but got: {modality_warnings}",
        )

        pref.delete()

    def test_no_warning_when_duration_ratio_sufficient(self):
        """When duration_bias >= 0.6 and duration exercises >= 50%,
        no warning should be emitted."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        # Create mostly duration exercises
        duration_exercises = [
            self._create_mock_exercise(f'Duration Ex {i}', is_duration=True, is_weight=False)
            for i in range(4)
        ]
        gen.exercise_selector.select_exercises.return_value = duration_exercises
        gen.exercise_selector.balance_stretch_positions.return_value = duration_exercises

        muscle_split = {
            'muscles': ['chest', 'back'],
            'split_type': 'full_body',
            'label': 'Full Body',
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])

        supersets = gen._build_working_supersets(
            muscle_split, self.hiit_type, wt_params,
        )

        modality_warnings = [
            w for w in gen.warnings if 'Modality mismatch' in w
        ]
        self.assertEqual(
            len(modality_warnings), 0,
            f"Should not have modality warning when duration ratio is sufficient but got: {modality_warnings}",
        )

        pref.delete()


class TestFinalConformance(MovementEnforcementTestBase):
    """Strict final conformance enforcement for assembled workouts."""

    def test_core_workout_respects_type_max_exercise_cap(self):
        """Core workouts should be trimmed to the calibrated max (8 working exercises)."""
        pref = self._make_preference(primary_goal='general_fitness')
        gen = self._make_generator(pref)

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._get_final_conformance_violations = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)

        working_exercises = [
            {'exercise': self._create_mock_exercise(f'Core Push {i}', movement_patterns='upper push, core'), 'reps': 12, 'order': i + 1}
            for i in range(6)
        ]
        more_working_exercises = [
            {'exercise': self._create_mock_exercise(f'Core Pull {i}', movement_patterns='upper pull, core'), 'reps': 12, 'order': i + 1}
            for i in range(6)
        ]

        gen._build_working_supersets = MagicMock(return_value=[
            {
                'name': 'Working Set 1',
                'rounds': 3,
                'rest_between_rounds': 30,
                'modality': 'reps',
                'exercises': working_exercises,
            },
            {
                'name': 'Working Set 2',
                'rounds': 3,
                'rest_between_rounds': 30,
                'modality': 'reps',
                'exercises': more_working_exercises,
            },
        ])

        workout_spec = gen.generate_single_workout(
            muscle_split={
                'muscles': ['core', 'abs', 'obliques'],
                'split_type': 'core',
                'label': 'Core Day',
            },
            workout_type=self.core_type,
            scheduled_date=date(2026, 3, 2),
        )

        working = [
            ss for ss in workout_spec.get('supersets', [])
            if ss.get('name', '').startswith('Working')
        ]
        total_working = sum(len(ss.get('exercises', [])) for ss in working)
        self.assertLessEqual(
            total_working, 8,
            f'Expected core workout to cap at 8 working exercises, got {total_working}',
        )

        pref.delete()

    def test_core_cap_removes_extra_minimum_supersets(self):
        """When all sets are already at minimum size, remove trailing sets to hit cap."""
        pref = self._make_preference(primary_goal='general_fitness')
        gen = self._make_generator(pref)

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._get_final_conformance_violations = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)

        working_supersets = []
        for idx in range(6):
            push = self._create_mock_exercise(
                f'Push {idx}',
                movement_patterns='upper push',
            )
            pull = self._create_mock_exercise(
                f'Pull {idx}',
                movement_patterns='upper pull',
            )
            working_supersets.append({
                'name': f'Working Set {idx + 1}',
                'rounds': 3,
                'rest_between_rounds': 30,
                'modality': 'reps',
                'exercises': [
                    {'exercise': push, 'reps': 12, 'order': 1},
                    {'exercise': pull, 'reps': 12, 'order': 2},
                ],
            })

        gen._build_working_supersets = MagicMock(return_value=working_supersets)

        workout_spec = gen.generate_single_workout(
            muscle_split={
                'muscles': ['core', 'abs', 'obliques'],
                'split_type': 'core',
                'label': 'Core Day',
            },
            workout_type=self.core_type,
            scheduled_date=date(2026, 3, 2),
        )

        working = [
            ss for ss in workout_spec.get('supersets', [])
            if ss.get('name', '').startswith('Working')
        ]
        total_working = sum(len(ss.get('exercises', [])) for ss in working)
        self.assertLessEqual(total_working, 8)
        self.assertLessEqual(len(working), 4)

        pref.delete()

    def test_pad_to_fill_respects_type_cap(self):
        """Padding should stop when workout-type max working-exercise cap is reached."""
        pref = self._make_preference(primary_goal='general_fitness')
        gen = self._make_generator(pref)

        gen._estimate_total_time = MagicMock(return_value=0)
        gen.exercise_selector.select_exercises.return_value = [
            self._create_mock_exercise('Pad Exercise', movement_patterns='upper pull')
        ]

        base_ex_a = self._create_mock_exercise('Base A', movement_patterns='upper push')
        base_ex_b = self._create_mock_exercise('Base B', movement_patterns='upper pull')
        workout_spec = {
            'supersets': [
                {
                    'name': 'Working Set 1',
                    'rounds': 3,
                    'rest_between_rounds': 30,
                    'modality': 'reps',
                    'exercises': [
                        {'exercise': base_ex_a, 'reps': 12, 'order': 1},
                        {'exercise': base_ex_b, 'reps': 12, 'order': 2},
                        {'exercise': base_ex_a, 'reps': 12, 'order': 3},
                    ],
                },
                {
                    'name': 'Working Set 2',
                    'rounds': 3,
                    'rest_between_rounds': 30,
                    'modality': 'reps',
                    'exercises': [
                        {'exercise': base_ex_b, 'reps': 12, 'order': 1},
                        {'exercise': base_ex_a, 'reps': 12, 'order': 2},
                        {'exercise': base_ex_b, 'reps': 12, 'order': 3},
                    ],
                },
            ],
        }
        wt_params = dict(WORKOUT_TYPE_DEFAULTS['core'])
        wt_params['duration_bias'] = 0.0

        padded = gen._pad_to_fill(
            workout_spec=workout_spec,
            max_duration_sec=3600,
            muscle_split={
                'muscles': ['core', 'abs'],
                'split_type': 'core',
                'label': 'Core Day',
            },
            wt_params=wt_params,
            workout_type=self.core_type,
        )

        total_working = sum(
            len(ss.get('exercises', []))
            for ss in padded.get('supersets', [])
            if ss.get('name', '').startswith('Working')
        )
        self.assertLessEqual(total_working, 8)

        pref.delete()

    def test_compound_ordering_uses_validator_definition(self):
        """Accessory-tagged entries should not be treated as compounds in ordering."""
        pref = self._make_preference(primary_goal='general_fitness')
        gen = self._make_generator(pref)

        accessory_flagged_compound = self._create_mock_exercise(
            'Accessory Marked Compound',
            is_compound=True,
            exercise_tier='accessory',
            movement_patterns='upper push',
        )
        true_compound = self._create_mock_exercise(
            'Primary Compound',
            is_compound=True,
            exercise_tier='secondary',
            movement_patterns='upper pull',
        )
        workout_spec = {
            'supersets': [
                {
                    'name': 'Working Set 1',
                    'rounds': 3,
                    'rest_between_rounds': 45,
                    'modality': 'reps',
                    'exercises': [
                        {'exercise': accessory_flagged_compound, 'reps': 10, 'order': 1},
                        {'exercise': true_compound, 'reps': 8, 'order': 2},
                    ],
                },
            ],
        }

        gen._enforce_compound_first_order(workout_spec, is_strength_workout=False)
        violations = validate_workout(workout_spec, 'hiit', 'general_fitness')
        compound_order_violations = [
            v for v in violations
            if v.rule_id == 'compound_before_isolation'
        ]
        self.assertEqual(len(compound_order_violations), 0)

        pref.delete()

    def test_final_warning_triggers_regeneration(self):
        """A final warning should trigger full regeneration before returning."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)

        ex = self._create_mock_exercise('Balanced Pull', movement_patterns='upper pull')
        gen._build_working_supersets = MagicMock(return_value=[
            {
                'name': 'Working Set 1',
                'rounds': 3,
                'rest_between_rounds': 45,
                'modality': 'reps',
                'exercises': [{'exercise': ex, 'reps': 10, 'order': 1}],
            },
        ])

        gen._get_final_conformance_violations = MagicMock(side_effect=[
            [RuleViolation(
                rule_id='exercise_count_cap',
                severity='warning',
                message='Too many exercises',
            )],
            [],
        ])

        gen.generate_single_workout(
            muscle_split={
                'muscles': ['upper back', 'lats'],
                'split_type': 'pull',
                'label': 'Pull Day',
            },
            workout_type=self.hiit_type,
            scheduled_date=date(2026, 3, 3),
        )

        self.assertEqual(
            gen._build_working_supersets.call_count, 2,
            'Expected regeneration after final warning.',
        )
        pref.delete()

    def test_unresolved_final_violations_raise_error(self):
        """Generator should fail fast when conformance cannot be achieved."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)

        ex = self._create_mock_exercise('Push Only', movement_patterns='upper push')
        gen._build_working_supersets = MagicMock(return_value=[
            {
                'name': 'Working Set 1',
                'rounds': 3,
                'rest_between_rounds': 45,
                'modality': 'reps',
                'exercises': [{'exercise': ex, 'reps': 10, 'order': 1}],
            },
        ])
        gen._get_final_conformance_violations = MagicMock(return_value=[
            RuleViolation(
                rule_id='push_pull_ratio',
                severity='warning',
                message='Pull:push ratio too low',
            ),
        ])

        with self.assertRaises(ValueError):
            gen.generate_single_workout(
                muscle_split={
                    'muscles': ['chest', 'triceps'],
                    'split_type': 'push',
                    'label': 'Push Day',
                },
                workout_type=self.hiit_type,
                scheduled_date=date(2026, 3, 4),
            )

        self.assertEqual(
            gen._build_working_supersets.call_count,
            FINAL_CONFORMANCE_MAX_RETRIES + 1,
        )
        pref.delete()

    def test_info_violation_is_not_blocking(self):
        """Info-level rules should not fail generation in strict mode."""
        pref = self._make_preference()
        gen = self._make_generator(pref)

        gen._build_warmup = MagicMock(return_value=None)
        gen._build_cooldown = MagicMock(return_value=None)
        gen._check_quality_gates = MagicMock(return_value=[])
        gen._adjust_to_time_target = MagicMock(side_effect=lambda spec, *_args, **_kwargs: spec)

        ex = self._create_mock_exercise('Compound Lift', movement_patterns='upper pull')
        gen._build_working_supersets = MagicMock(return_value=[
            {
                'name': 'Working Set 1',
                'rounds': 3,
                'rest_between_rounds': 45,
                'modality': 'reps',
                'exercises': [{'exercise': ex, 'reps': 8, 'order': 1}],
            },
        ])
        gen._get_final_conformance_violations = MagicMock(return_value=[
            RuleViolation(
                rule_id='compound_before_isolation',
                severity='info',
                message='Compound exercises should generally appear before isolation.',
            ),
        ])

        workout = gen.generate_single_workout(
            muscle_split={
                'muscles': ['upper back'],
                'split_type': 'pull',
                'label': 'Pull Day',
            },
            workout_type=self.strength_type,
            scheduled_date=date(2026, 3, 5),
        )

        self.assertIsInstance(workout, dict)
        self.assertEqual(gen._build_working_supersets.call_count, 1)
        pref.delete()