Update randomization pattern for Adversarial simulation

sdk/evaluation/azure-ai-evaluation/CHANGELOG.md

@@ -11,6 +11,7 @@
 ### Other Changes
 - Refined error messages for serviced-based evaluators and simulators.
 - Introduced environment variable `AI_EVALS_DISABLE_EXPERIMENTAL_WARNING` to disable the warning message for experimental features.
+- Changed the randomization pattern for `AdversarialSimulator` such that there is an equal number of Adversarial harm categories (e.g. Hate + Unfairness, Self-Harm, Violence, Sex) represented in the  `AdversarialSimulator` outputs. Previously, for 200 `max_simulation_results` a user might see 140 results belonging to the 'Hate + Unfairness' category and 40 results belonging to the 'Self-Harm' category. Now, user will see 50 results for each of Hate + Unfairness, Self-Harm, Violence, and Sex. 
 
 ## 1.0.0b5 (2024-10-28)
 

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/simulator/_adversarial_simulator.py

@@ -7,6 +7,7 @@
 import logging
 import random
 from typing import Any, Callable, Dict, List, Literal, Optional, Union, cast
+from itertools import zip_longest
 
 from tqdm import tqdm
 
@@ -215,17 +216,18 @@ async def __call__(
             ncols=100,
             unit="simulations",
         )
-        for template in templates:
-            parameter_order = list(range(len(template.template_parameters)))
-            if randomize_order:
-                # The template parameter lists are persistent across sim runs within a session,
-                # So randomize a the selection instead of the parameter list directly,
-                # or a potentially large deep copy.
-                if randomization_seed is not None:
-                    random.seed(randomization_seed)
-                random.shuffle(parameter_order)
-            for index in parameter_order:
-                parameter = template.template_parameters[index].copy()
+
+        if randomize_order:
+            # The template parameter lists are persistent across sim runs within a session,
+            # So randomize a the selection instead of the parameter list directly,
+            # or a potentially large deep copy.
+            if randomization_seed is not None:
+                random.seed(randomization_seed)
+            random.shuffle(templates)
+        parameter_lists = [t.template_parameters for t in templates]
+        zipped_parameters = list(zip_longest(*parameter_lists))
+        for param_group in zipped_parameters:
+            for template, parameter in zip(templates, param_group):
                 if _jailbreak_type == "upia":
                     parameter = self._join_conversation_starter(parameter, random.choice(jailbreak_dataset))
                 tasks.append(