Hardening follow-up: N+1 elimination, type validation, diversify fix

Additional fixes from parallel hardening streams:

- exercise/serializers: remove unused WorkoutEquipment import, add prefetch docs
- generator/serializers: N+1 fix in GeneratedWorkoutDetailSerializer (inline workout dict, prefetch-aware supersets)
- generator/services/plan_builder: eliminate redundant .save() after .create() via single create_kwargs dict
- generator/services/workout_generator: proper type-match validation for HIIT/cardio/core/flexibility; fix diversify type count to account for removed entry
- generator/views: request-level caching for get_registered_user helper; prefetch chain for accept_workout
- superset/serializers: guard against dangling FK in SupersetExerciseSerializer
- workout/helpers: use prefetched data instead of re-querying per superset

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

generator/services/workout_generator.py

@@ -1398,18 +1398,31 @@ class WorkoutGenerator:
                 break
 
             replaced = False
+            removed_type = (result[idx].get('split_type') or 'full_body').strip().lower()
+            removed_sig = self._split_signature(result[idx])
             for candidate in candidates:
                 candidate_type = (candidate.get('split_type') or 'full_body').strip().lower()
                 candidate_sig = self._split_signature(candidate)
-                current_sig = self._split_signature(result[idx])
-                if candidate_sig == current_sig:
+                if candidate_sig == removed_sig:
                     continue
 
-                new_type_count = type_counts[candidate_type] + (0 if candidate_type == (result[idx].get('split_type') or 'full_body').strip().lower() else 1)
+                # Account for the removal of the old entry when counting
+                # the new type: subtract 1 for the removed type if it
+                # matches the candidate type, add 1 for the candidate.
+                if candidate_type == removed_type:
+                    new_type_count = type_counts[candidate_type]  # net zero: -1 removed +1 added
+                else:
+                    new_type_count = type_counts[candidate_type] + 1
                 if new_type_count > max_same_type:
                     continue
 
-                if sig_counts[candidate_sig] >= max_same_signature:
+                # Same accounting for signatures: the removed signature
+                # frees a slot, so only block if the candidate sig count
+                # (after removing the old entry) is still at max.
+                effective_sig_count = sig_counts[candidate_sig]
+                if candidate_sig == removed_sig:
+                    effective_sig_count -= 1
+                if effective_sig_count >= max_same_signature:
                     continue
 
                 result[idx] = dict(candidate)
@@ -2987,7 +3000,12 @@ class WorkoutGenerator:
             return []
 
         wt_name_lower = workout_type.name.strip().lower()
+        wt_key = _normalize_type_key(wt_name_lower)
         is_strength = wt_name_lower in STRENGTH_WORKOUT_TYPES
+        is_hiit = wt_key == 'high_intensity_interval_training'
+        is_cardio = wt_key == 'cardio'
+        is_core = wt_key == 'core_training'
+        is_flexibility = wt_key == 'flexibility'
         threshold = GENERATION_RULES['workout_type_match_pct']['value']
 
         total_exercises = 0
@@ -3001,7 +3019,33 @@ class WorkoutGenerator:
                 if is_strength:
                     if getattr(ex, 'is_weight', False) or getattr(ex, 'is_compound', False):
                         matching_exercises += 1
+                elif is_hiit:
+                    # HIIT: favor high HR, compound, or duration-capable exercises
+                    hr = getattr(ex, 'hr_elevation_rating', None) or 0
+                    if hr >= 5 or getattr(ex, 'is_compound', False) or getattr(ex, 'is_duration', False):
+                        matching_exercises += 1
+                elif is_cardio:
+                    # Cardio: favor duration-capable or high-HR exercises
+                    hr = getattr(ex, 'hr_elevation_rating', None) or 0
+                    if getattr(ex, 'is_duration', False) or hr >= 5:
+                        matching_exercises += 1
+                elif is_core:
+                    # Core: check if exercise targets core muscles
+                    muscles = (getattr(ex, 'muscle_groups', '') or '').lower()
+                    patterns = (getattr(ex, 'movement_patterns', '') or '').lower()
+                    if any(tok in muscles for tok in ('core', 'abs', 'oblique')):
+                        matching_exercises += 1
+                    elif 'core' in patterns or 'anti' in patterns:
+                        matching_exercises += 1
+                elif is_flexibility:
+                    # Flexibility: favor duration-based, stretch/mobility exercises
+                    patterns = (getattr(ex, 'movement_patterns', '') or '').lower()
+                    if getattr(ex, 'is_duration', False) or any(
+                        tok in patterns for tok in ('stretch', 'mobility', 'yoga', 'flexibility')
+                    ):
+                        matching_exercises += 1
                 else:
+                    # Unknown type -- count all as matching (no false negatives)
                     matching_exercises += 1
 
         violations = []