import json
import random
import re
from typing import Dict, List, Optional, Tuple, Union
import wandb
from datasets import load_dataset, Dataset # Added Dataset for dummy data
from tqdm.asyncio import tqdm_asyncio
from langdetect import detect, LangDetectException
from atroposlib.envs.base import (
APIServerConfig,
BaseEnv,
BaseEnvConfig,
EvalHandlingEnum,
Item,
ScoredDataGroup,
)
from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
# System prompt can be reused or adapted for instruction following tasks
system_prompt = (
"You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the "
"problem and deliberate with yourself via systematic reasoning processes to help come to a correct "
"solution prior to answering. You should enclose your thoughts and internal monologue inside "
" tags, and then provide your solution or response to the problem."
)
class InstructionFollowingEnv(BaseEnv):
def __init__(
self,
config: BaseEnvConfig,
server_configs: List[APIServerConfig],
slurm=True,
testing=False,
):
super().__init__(config, server_configs, slurm, testing)
self.percent_correct_buffer = list()
self.eval_metrics = list()
self.rollouts_for_wandb = []
# self.completion_lengths = [] # Kept from SingleToolCallingEnv, assess utility
@classmethod
def config_init(self) -> Tuple[BaseEnvConfig, List[APIServerConfig]]:
# Configuration for the Instruction Following Environment
env_config = BaseEnvConfig(
tokenizer_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
group_size=16,
use_wandb=True,
rollout_server_url="http://localhost:8000",
total_steps=500,
batch_size=1024,
steps_per_eval=20,
max_token_length=1024 * 16,
inference_weight=1.0,
wandb_name="instruction_following_rlvr_ifeval", # Specific WandB project name
eval_handling=EvalHandlingEnum.LIMIT_TRAIN,
eval_limit_ratio=0.1,
dataset_name="allenai/RLVR-IFeval", # Default dataset
dataset_config_name=None, # RLVR-IFeval doesn't have a specific config name, uses 'default'
test_set_ratio=0.05 # The ratio of the selelcted dataset in %
)
# Server configurations can be similar to SingleToolCallingEnv or adjusted
server_configs = [
APIServerConfig(
model_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
base_url="http://localhost:9004/v1",
api_key="x",
num_max_requests_at_once=32,
num_requests_for_eval=256,
)
]
return env_config, server_configs
async def create_rollout_table(self, wandb_metrics):
# Logs rollouts to a WandB table for visualization
if len(self.rollouts_for_wandb) > 0:
table = wandb.Table(columns=["text", "score", "constraint_details"])
for group in self.rollouts_for_wandb:
for item in group:
# item[0] is model output, item[1] is score, item[2] is constraint info
table.add_data(item[0], item[1], json.dumps(item[2]))
wandb_metrics["train/rollouts"] = table
self.rollouts_for_wandb = []
return wandb_metrics
async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
# Logs metrics to WandB
if wandb_metrics is None:
wandb_metrics = dict()
try:
wandb_metrics["train/percent_correct"] = sum(
self.percent_correct_buffer
) / len(self.percent_correct_buffer)
except ZeroDivisionError:
pass # Buffer might be empty
self.percent_correct_buffer = list()
for item in self.eval_metrics:
wandb_metrics[item[0]] = item[1]
self.eval_metrics = list()
await super().wandb_log(wandb_metrics)
async def setup(self):
"""
Load and preprocess the dataset for instruction following.
This method is specifically tailored to process 'allenai/RLVR-IFeval' dataset structure.
Each item from RLVR-IFeval is expected to have:
- 'messages': A list of dictionaries, e.g., [{'role': 'user', 'content': 'instruction...'}]
- 'ground_truth': A JSON string containing 'func_name' and arguments for the verifier.
The method will parse these to produce items for the environment with:
- 'prompt': The user's instruction string.
- 'func_name': The string name of the verifier function.
- 'args': A dictionary of arguments for that verifier function.
"""
dataset_name = getattr(self.config, "dataset_name", "allenai/RLVR-IFeval")
dataset_config_name = getattr(self.config, "dataset_config_name", None) # Default is None, RLVR-IFeval has no sub-config
processed_items = []
try:
print(f"Attempting to load dataset: {dataset_name}, config: {dataset_config_name if dataset_config_name else 'default'}")
if dataset_config_name:
full_dataset_raw = load_dataset(dataset_name, dataset_config_name, split="train", trust_remote_code=True)
else:
full_dataset_raw = load_dataset(dataset_name, split="train", trust_remote_code=True)
print(f"Successfully loaded raw dataset. Number of items: {len(full_dataset_raw)}")
for i, item in enumerate(full_dataset_raw):
# Extract prompt from 'messages' field
item_messages = item.get("messages")
if not item_messages or not isinstance(item_messages, list) or len(item_messages) == 0:
print(f"Warning: Item {i} has invalid or empty 'messages' field. Skipping. Item: {item}")
continue
# Assuming the relevant prompt is the content of the first message in the list
# (or last, if multiple user messages were possible, but IFEval is typically single user instruction)
prompt_text = item_messages[0].get("content")
if not prompt_text:
print(f"Warning: Item {i} '{item_messages[0]}' has no content. Skipping.")
continue
# Get the ground_truth JSON string
ground_truth_json_str = item.get("ground_truth")
if not ground_truth_json_str or not isinstance(ground_truth_json_str, str):
print(f"Warning: Item {i} missing or has invalid 'ground_truth' string. Skipping. Prompt: {prompt_text[:50]}...")
continue
try:
parsed_gt = json.loads(ground_truth_json_str)
if not isinstance(parsed_gt, dict):
raise ValueError("Parsed ground_truth is not a dictionary.")
except (json.JSONDecodeError, ValueError) as e:
print(f"Warning: Could not parse 'ground_truth' JSON for item {i}. Error: {e}. GT String: '{ground_truth_json_str}'. Prompt: {prompt_text[:50]}... Skipping.")
continue
func_name_from_gt = parsed_gt.get("func_name")
if not func_name_from_gt:
print(f"Warning: Item {i} parsed 'ground_truth' has no 'func_name'. GT: {parsed_gt}. Prompt: {prompt_text[:50]}... Skipping.")
continue
if func_name_from_gt not in IF_FUNCTIONS_MAP:
print(f"Warning: func_name '{func_name_from_gt}' in item {i} not in IF_FUNCTIONS_MAP. Prompt: {prompt_text[:50]}... Skipping.")
continue
# Prepare args for the verifier function: remove func_name and keep others.
# Verifier functions will only use args they expect.
args_dict = {k: v for k, v in parsed_gt.items() if k != "func_name" and v is not None}
processed_items.append({
"prompt": prompt_text,
"func_name": func_name_from_gt,
"args": args_dict,
"original_constraints_for_logging": str(item.get("constraint", "")), # For logging, from RLVR-IFeval structure
"expected_response_for_logging": "" # RLVR-IFeval doesn't seem to have a sample good response directly
})
if not processed_items:
print("Warning: No items successfully processed from the dataset. Check dataset format/content or parsing logic.")
raise ValueError("Dataset processing resulted in no valid items for RLVR-IFeval. Cannot proceed without data.")
full_dataset = Dataset.from_list(processed_items)
print(f"Successfully processed {len(full_dataset)} items from dataset '{dataset_name}'.")
except Exception as e:
# This block is a fallback if the primary dataset loading/processing fails catastrophically.
# For RLVR-IFeval, a failure here suggests issues with Hugging Face access, dataset integrity, or fundamental code errors.
print(f"CRITICAL: Failed to load or process primary dataset '{dataset_name}': {e}. Using a DUMMY dataset as fallback.")
dummy_data_for_fallback = [
{
"prompt": "Dummy Instruction 1: Ensure your response contains the word 'example'.",
"func_name": "verify_keywords",
"args": {"keyword_list": ["example"]},
"original_constraints_for_logging": "Contains 'example'",
"expected_response_for_logging": "This is an example response."
},
{
"prompt": "Dummy Instruction 2: Output a valid JSON with key 'data' and value 'test'.",
"func_name": "validate_json_format",
"args": {},
"original_constraints_for_logging": "Output valid JSON.",
"expected_response_for_logging": "{\\\"data\\\": \\\"test\\\"}"
}
]
full_dataset = Dataset.from_list(dummy_data_for_fallback)
print(f"Initialized with DUMMY dataset of {len(full_dataset)} items due to previous errors.")
full_dataset = full_dataset.shuffle(seed=42)
actual_test_size = self.config.test_set_ratio # Read from config
num_items = len(full_dataset)
if num_items == 0:
print("ERROR: Dataset is empty. Cannot create train/test split.")
self.train = Dataset.from_list([])
self.test = Dataset.from_list([])
elif num_items == 1:
print("Warning: Dataset has only 1 item. Using it for both train and test.")
self.train = full_dataset
self.test = full_dataset
else: # num_items > 1
# Ensure test_size results in at least 1 item for test set if possible, but not more than train set
if num_items < 5 : # For 2,3,4 items, make test size 1
min_test_items = 1
else: # For 5+ items, 20% is fine
min_test_items = max(1, int(num_items * actual_test_size))
# Ensure test split is not too large, e.g. not more than 50% unless dataset is very small
# And ensure train always has at least one item if num_items > 1
calculated_test_size = min_test_items / num_items
if calculated_test_size >= 0.5 and num_items > 2: # If test is 50% or more and we have 3+ items
calculated_test_size = (num_items -1) / num_items # Make train have at least 1
split_dataset = full_dataset.train_test_split(test_size=calculated_test_size, seed=42)
self.train = split_dataset["train"]
self.test = split_dataset["test"]
# Final check for empty train/test after split, should not happen with logic above if num_items > 0
if len(self.train) == 0 and len(self.test) > 0:
print("Warning: Train set empty after split, test set has data. This is unusual. Swapping.")
self.train = self.test # Fallback, though indicates issue
elif len(self.test) == 0 and len(self.train) > 0:
print("Warning: Test set empty after split, train set has data. Using full train set for test as well.")
self.test = self.train
self.iter = 0
print(f"Dataset setup complete. Train size: {len(self.train)}, Test size: {len(self.test)}")
async def _get_score_from_verifier(self, model_response_text: str, func_name: str, args: Dict) -> float:
"""Helper to call verifier function and get a numerical score."""
if func_name not in IF_FUNCTIONS_MAP:
print(f"Warning: Verifier function '{func_name}' not found in IF_FUNCTIONS_MAP.")
return 0.0
verifier_func = IF_FUNCTIONS_MAP[func_name]
raw_score = None
try:
# For validate_response_language, langdetect is now imported at the top.
# Specific argument handling for functions that don't take generic **args
# or have special return types should be done before the generic call.
if func_name == "validate_placeholders":
# validate_placeholders expects 'text' and 'N', returns (bool, list)
raw_score = verifier_func(model_response_text, N=args.get("N"))
elif func_name == "verify_bullet_points":
# verify_bullet_points expects 'text' and 'N', returns bool (or was (bool,str) in one doc)
# Assuming it now consistently returns bool as per our integrated version
raw_score = verifier_func(model_response_text, N=args.get("N"))
# Add other specific handlers here if necessary, otherwise use generic **args
else:
raw_score = verifier_func(model_response_text, **args)
except LangDetectException: # Specifically catch for language detection issues
print(f"Warning: langdetect failed for func_name '{func_name}'. Scoring as incorrect.")
return 0.0
except ImportError as e:
# This might happen if a function tries a lazy import that fails (not langdetect now)
print(f"Warning: ImportError during verifier function '{func_name}': {e}. Check dependencies.")
return 0.0
except TypeError as e:
print(f"TypeError calling {func_name} with args {args}: {e}. Text: '{model_response_text[:100]}...'")
return 0.0
except Exception as e: # Catch any other unexpected error from a verifier
print(f"Unexpected error in verifier function '{func_name}' with args {args}: {e}")
return 0.0
# Convert boolean or tuple[boolean, ...] to float score
if isinstance(raw_score, tuple):
score_value = float(raw_score[0]) # Assuming the first element is the boolean score
elif isinstance(raw_score, bool):
score_value = float(raw_score)
else:
print(f"Warning: Verifier '{func_name}' returned unexpected type: {type(raw_score)}. Expected bool or tuple.")
score_value = 0.0
return score_value
async def rollout_and_score_eval(self, test_item: Dict):
# test_item is a dictionary from the test set, processed by setup()
# It should contain 'prompt', 'func_name', 'args'
instruction_prompt_text = test_item["prompt"]
func_name = test_item["func_name"]
args_for_verifier = test_item["args"]
messages = [{"role": "system", "content": system_prompt}]
messages.append({"role": "user", "content": instruction_prompt_text})
prompt_str = self.tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=False
)
completion = await self.server.completion(
prompt=prompt_str,
n=1,
max_tokens=self.config.max_token_length, # Use config for max_tokens
temperature=0.7, # Temperature for eval, can be 0 for deterministic
split="eval",
)
model_response_text = completion.choices[0].text
score_value = await self._get_score_from_verifier(model_response_text, func_name, args_for_verifier)
return score_value # Returns 1.0 for correct, 0.0 for incorrect based on verifier
async def evaluate(self, *args, **kwargs):
# Evaluates the model on the test set
if not self.test or len(self.test) == 0:
print("Warning: Test set is empty. Skipping evaluation.")
self.eval_metrics.append(("eval/percent_correct", 0.0))
return
eval_tasks = []
for test_item_dict in self.test: # self.test contains dicts after setup
eval_tasks.append(self.rollout_and_score_eval(test_item_dict))
scores = await tqdm_asyncio.gather(*eval_tasks)
if not scores: # If gather returns empty list
percent_correct = 0.0
else:
percent_correct = sum(scores) / len(scores)
self.eval_metrics.append(("eval/percent_correct", percent_correct))
print(f"Evaluation percent correct: {percent_correct}")
async def collect_trajectories(self, item: Item) -> Tuple[Optional[ScoredDataGroup], List]:
# item = (prompt_messages_tuple, answer_info_dict)
# answer_info_dict = {"func_name": ..., "args": ...}
prompt_messages_list = [dict(msg_fset) for msg_fset in item[0]]
answer_info = item[1]
prompt_str = self.tokenizer.apply_chat_template(
prompt_messages_list, add_generation_prompt=True, tokenize=False
)
completions = await self.server.completion(
prompt=prompt_str,
n=self.config.group_size,
max_tokens=self.config.max_token_length,
temperature=0.8, # Temperature for diverse responses during training rollouts
)
to_score_list = []
for choice in completions.choices:
trajectory_messages = [dict(msg_fset) for msg_fset in item[0]] # Fresh copy
trajectory_messages.append({"role": "assistant", "content": choice.text})
to_score_list.append((tuple(trajectory_messages), answer_info)) # Pass answer_info
if not to_score_list:
return None, []
scored_data = await self.score(to_score_list)
to_backlog = [] # Backlog not currently used but part of signature
return scored_data, to_backlog
def save_checkpoint(self, step, data=None):
if data is None:
data = {}
data["iter"] = self.iter
super().save_checkpoint(step, data)
async def score(
self, rollout_group_data: List[Tuple[tuple, Dict]]
) -> Optional[ScoredDataGroup]:
# rollout_group_data is a list of (trajectory_messages_tuple, answer_info_dict)
# answer_info_dict = {"func_name": ..., "args": ...}
scores_container = ScoredDataGroup()
scores_container["tokens"] = list()
scores_container["masks"] = list()
scores_container["scores"] = list()
if not rollout_group_data:
return None
# The 'answer_info' (func_name, args) is consistent for all items in this group,
# as it comes from the same initial prompt.
# We can extract it once if needed, but it's passed per item.
random.shuffle(rollout_group_data) # Shuffle to avoid bias
for trajectory_item in rollout_group_data:
full_trajectory_messages = trajectory_item[0]
answer_info = trajectory_item[1] # {"func_name": ..., "args": ...}
model_response_text = full_trajectory_messages[-1]["content"]
func_name = answer_info["func_name"]
args_for_verifier = answer_info["args"]
# Get score (1.0 for correct, 0.0 for incorrect from verifier)
score_value = await self._get_score_from_verifier(model_response_text, func_name, args_for_verifier)
# Map to reward: 1.0 for correct, -1.0 for incorrect
reward = 1.0 if score_value == 1.0 else -1.0
# Tokenize the conversation for PPO training
# Ensure full_trajectory_messages is a list of dicts
list_of_dicts_trajectory = [dict(msg) for msg in full_trajectory_messages]
out_dict = tokenize_for_trainer(self.tokenizer, list_of_dicts_trajectory)
tokens = out_dict["tokens"]
masks = out_dict["masks"]
# Filter out examples with insufficient context (too short)
if sum(1 for m_val in masks if m_val != -100) < 10: # At least 10 non-masked tokens
continue
scores_container["tokens"].append(tokens)
scores_container["masks"].append(masks)
scores_container["scores"].append(reward)
# Stop if we have enough examples for the group
if len(scores_container["tokens"]) >= self.config.group_size:
break
if not scores_container["tokens"]: # No valid items collected
return None
# Record success rate for logging (based on positive rewards)
for rwd in scores_container["scores"]:
self.percent_correct_buffer.append(max(0, rwd)) # If reward is 1.0, it's a success
# Optional: Apply length penalty if all responses are correct (reward 1.0)
# This logic is from SingleToolCallingEnv, may need adjustment for IF
if all(s == 1.0 for s in scores_container["scores"]):
token_lengths = [len(t) for t in scores_container["tokens"]]
if not token_lengths or max(token_lengths) == 0:
return scores_container # Avoid division by zero, or if all empty
max_allowed_length = self.config.max_token_length
# Threshold can be adjusted, e.g., 75% of max_token_length
length_threshold = max_allowed_length * 0.75
penalized_scores = []
for i, length in enumerate(token_lengths):
original_score = scores_container["scores"][i] # Should be 1.0 here
if length <= length_threshold:
penalized_scores.append(original_score)
else:
# Linear penalty for exceeding threshold
penalty_factor = (length - length_threshold) / (max_allowed_length - length_threshold)
penalty_factor = min(penalty_factor, 1.0) # Cap penalty factor at 1
# Penalized score scales from original_score down to original_score * (1-1) = 0
penalized_scores.append(original_score * (1.0 - penalty_factor))
scores_container["scores"] = penalized_scores
# If all scores are identical after potential penalties, no learning signal
if len(set(scores_container["scores"])) <= 1 and len(scores_container["scores"]) > 1 :
return None # Avoid sending data with no variance
return scores_container
async def get_next_item(self) -> Item:
# Fetches the next preprocessed item from the training set
if not self.train or len(self.train) == 0:
# This case should be handled by setup, but as a safeguard:
print("Error: Training data is empty in get_next_item.")
# Return a dummy item to prevent crashes, though this indicates a setup issue
dummy_prompt_messages = (
frozenset({"role": "system", "content": system_prompt}.items()),
frozenset({"role": "user", "content": "Dummy instruction: say hello."}.items())
)
dummy_answer_info = {"func_name": "verify_keywords", "args": {"keyword_list": ["hello"]}}
return (dummy_prompt_messages, dummy_answer_info)
raw_item = self.train[self.iter % len(self.train)] # raw_item is a dict
self.iter += 1
instruction_prompt_text = raw_item["prompt"]
# Construct messages for the LLM (prompt tuple part of Item)
# Using frozenset as required by BaseEnv's Item type hint
prompt_messages_tuple = (
frozenset({"role": "system", "content": system_prompt}.items()),
frozenset({"role": "user", "content": instruction_prompt_text}.items()),
)
# The "answer" part for scoring purposes (answer_info dict part of Item)
answer_info = {
"func_name": raw_item["func_name"],
"args": raw_item["args"],
# Optionally include other info for logging/debugging if needed from raw_item
"original_constraints_for_logging": raw_item.get("original_constraints", ""),
"expected_response_for_logging": raw_item.get("expected_response_for_logging", "")
}
return (prompt_messages_tuple, answer_info)
async def add_rollouts_for_wandb(
self,
scored_data: ScoredDataGroup, # Assuming single ScoredDataGroup here
item: Item = None, # item = (prompt_messages_tuple, answer_info_dict)
):
# Saves rollouts for WandB logging
num_keep = self.config.num_rollouts_per_group_for_logging
if num_keep == -1: # Log all rollouts in the group
num_keep = len(scored_data["tokens"])
# item[1] is the answer_info_dict containing func_name and args
constraint_details_for_log = item[1] if item else {}
rollout_batch = []
for i in range(min(num_keep, len(scored_data["tokens"]))):
decoded_text = self.tokenizer.decode(scored_data["tokens"][i], skip_special_tokens=True)
score = scored_data["scores"][i]
rollout_batch.append((decoded_text, score, constraint_details_for_log))
self.rollouts_for_wandb.append(rollout_batch)
# Limit the number of rollout groups stored
if len(self.rollouts_for_wandb) > self.config.num_rollouts_to_keep:
self.rollouts_for_wandb.pop(0)
# ----- IFEval Verifier Functions and Map -----
# adapted from https://github.com/allenai/open-instruct/blob/main/scripts/eval_constraints/if_functions.py
# Helper function for verify_keyword_frequency, moved import re to top level
def _extract_words(text: str) -> List[str]:
return re.findall(r"\\b\\w+\\b", text.lower())
# include keywords: Include keywords {keyword1}, {keyword2} in your response
def verify_keywords(text: str, keyword_list: List[str]) -> bool:
response_lower = text.lower()
return all(keyword.lower() in response_lower for keyword in keyword_list)
# Keyword Frequency: In your response, the word {word} should appear {N} times.
def verify_keyword_frequency(text: str, word: str, N: int) -> bool:
text_lower = text.lower()
keyword_lower = word.lower()
words = _extract_words(text_lower)
actual_count = sum(1 for w in words if w == keyword_lower)
return actual_count == N
# Forbidden Words: Do not include keywords {forbidden words} in the response.
def validate_forbidden_words(text: str, forbidden_words: List[str]) -> bool:
text_lower = text.lower()
return not any(word.lower() in text_lower for word in forbidden_words)
# Letter Frequency : In your response, the letter {letter} should appear {N} times.
def verify_letter_frequency(text: str, letter: str, N: int) -> bool:
if len(letter) != 1:
# This should ideally raise ValueError, but for RL reward, return False
return False
actual_count = text.count(letter)
return actual_count == N
# Response Language: Your ENTIRE response should be in {language}, no other language is allowed.
def validate_response_language(text: str, language: str) -> bool:
try:
detected_language = detect(text)
return detected_language == language
except LangDetectException: # Catching specific exception from detect()
print(f"Warning: langdetect failed to detect language for text: '{text[:50]}...'")
return False
# Number Paragraphs: Your response should contain {N} paragraphs. You separate paragraphs using the markdown divider:
# * * *
def verify_paragraph_count(text: str, N: int) -> bool:
def clean_text(txt: str) -> str:
return "\\n".join(line.strip() for line in txt.splitlines()).strip()
cleaned_text = clean_text(text)
# Paragraphs are separated by '* * *'. N dividers mean N+1 paragraphs.
# If the text IS paragraphs, then N paragraphs will have N-1 dividers.
# The prompt implies N paragraphs are expected.
# If N=1, 0 dividers. If N=2, 1 divider. So, count of parts = N.
paragraphs = cleaned_text.split("* * *")
actual_count = len(paragraphs)
# Verify each split resulted in non-empty content, if text itself is not empty
if not cleaned_text and N == 0 : return True # 0 paragraphs, empty text
if not cleaned_text and N > 0 : return False
valid_paragraphs = [p.strip() for p in paragraphs if p.strip()]
# This check might be too strict if empty paragraphs are allowed by the constraint definition
# If "paragraph" implies non-empty content:
# return len(valid_paragraphs) == N and actual_count == N
# If constraint just means N segments separated by dividers:
return actual_count == N
# Number Words: Answer with at least / around / at most {N} words
def validate_word_constraint(text: str, N: int, quantifier: str) -> bool:
words = text.strip().split()
actual_count = len(words)
tolerance = max(round(N * 0.1), 1) # For 'around'
if quantifier == "at least":
return actual_count >= N
elif quantifier == "at most":
return actual_count <= N
elif quantifier == "around":
return abs(actual_count - N) <= tolerance
return False
# Number Sentences: Answer with at least / around / at most {N} sentences.
def verify_sentence_constraint(text: str, N: int, quantifier: str) -> bool:
# Basic sentence splitting, might need more robust NLP for complex cases
sentences = re.split(r'(?= N
elif quantifier == "around":
# "around" for sentences usually means exact or +/-1
return abs(actual_count - N) <= 1
elif quantifier == "at most":
return actual_count <= N
return False
# Number Paragraphs + First Word in i-th Paragraph
def validate_paragraphs(text: str, N: int, first_word: str, i: int) -> bool:
# Paragraphs separated by double line breaks
paragraphs = text.split("\\n\\n")
if len(paragraphs) != N:
return False
# i is 1-indexed for paragraph number
if not (1 <= i <= len(paragraphs)):
return False
# Check first word of the i-th paragraph
# .strip() to handle leading/trailing whitespace in paragraph
# .split()[0] to get the first word
try:
actual_first_word = paragraphs[i - 1].strip().split()[0]
# Case-insensitive comparison for first_word might be more robust
return actual_first_word.lower() == first_word.lower()
except IndexError: # Handles empty paragraph or paragraph without words
return False
# Postscript: At the end of your response, please explicitly add a postscript starting with {postscript marker}
def verify_postscript(text: str, postscript_marker: str) -> bool:
marker_index = text.rfind(postscript_marker) # Find last occurrence
if marker_index == -1:
return False
# Check if it's truly a postscript (i.e., near the end, and has content after marker)
# This interpretation: marker exists, and something follows it OR it's at the very end.
# The original IFEval might have a stricter definition (e.g. specific distance from end)
# A simple check: marker is present and the text from marker to end is mostly the postscript.
# For RL, simpler: marker is present and is not just prefix of a word.
# Test if the marker is at a word boundary if it's not the start of the string
if marker_index > 0 and text[marker_index-1].isalnum() and postscript_marker[0].isalnum():
# Avoid matching mid-word, e.g. "script" in "postscript" if marker is "script"
# This check is heuristic. A regex with word boundaries might be better.
pass # Heuristic, might need refinement
# Check if content exists after marker, or if marker itself is the end
remaining_text = text[marker_index:].strip()
return len(remaining_text) >= len(postscript_marker.strip())
# Number Placeholder: The response must contain at least {N} placeholders ... [address].
def validate_placeholders(text: str, N: int) -> Tuple[bool, List[str]]:
placeholders_found = re.findall(r'\\[(.*?)\\]', text) # Matches [content]
return len(placeholders_found) >= N, placeholders_found
# Number Bullets: Your answer must contain exactly {N} bullet points. * This is a point.
def verify_bullet_points(text: str, N: int) -> bool: # Original had tuple[bool,str] in doc, bool in code
lines = text.splitlines()
# Markdown bullets usually start with '*', '-', or '+' followed by a space.
bullet_points = [line.strip() for line in lines if re.match(r'^(\\s*)[\\*\\-\\+]\\s+', line.strip())]
return len(bullet_points) == N
# Title: Your answer must contain a title, wrapped in double angular brackets, such as <>.
def validate_title(text: str) -> bool:
return bool(re.search(r'<<(.*?)>>', text))
# Choose: From Answer with one of the following options: {options}
def validate_choice(text: str, options: List[str]) -> bool:
# Assuming 'text' should be one of the 'options' exactly, or contain one of them.
# The original prompt "Answer with one of..." implies the response *is* one of the options.
# Case-insensitive comparison for robustness.
text_cleaned = text.strip().lower()
return any(text_cleaned == opt.strip().lower() for opt in options)
# Minimum Number Highlighted Section: Highlight at least {N} sections ... *highlighted section*
def validate_highlighted_sections(text: str, N: int) -> bool:
# Markdown italics/bold *highlight* or **highlight**
# This regex looks for single asterisks: *content*
matches = re.findall(r'\\*(.*?)\\*(?= N
# Multiple Sections: Your response must have {N} sections. Mark ... with {section splitter} X.
def validate_sections(text: str, N: int, section_splitter: str) -> bool:
# Example: section_splitter = "Section" -> "Section 1", "Section 2"
# This implies the splitter itself might include a number or be just the prefix.
# If splitter is "---", then text.split("---").
# If splitter is "Topic X:", this is more complex.
# Assuming a simple string split is intended by the original IFEval function.
# The prompt phrasing "Mark the beginning of each section with {section splitter} X"
# suggests counting occurrences of the splitter pattern.
# If section_splitter is like "SECTION", we'd look for "SECTION 1", "SECTION 2", ...
# This is hard to generalize perfectly without knowing how IFEval defines 'X'.
# Simplest: count occurrences of the base splitter string.
# sections = text.split(section_splitter)
# num_sections = len(sections) -1 if sections[0].strip() == "" else len(sections)
# A slightly more robust way for "Splitter X":
# Count how many times "splitter" followed by something (like a number) appears.
# Example: if splitter is "Chapter", we look for "Chapter 1", "Chapter ...".
# This regex is a placeholder for more specific logic IFEval might use.
# Let's use a simple count of the splitter string for now.
# This might need to be adjusted based on IFEval's exact expectation for "X".
# For "SECTION 1.", "SECTION 2.", if splitter is "SECTION ":
actual_sections = len(re.findall(re.escape(section_splitter) + r'\\s*\\d*[:\\.\\s]', text, re.IGNORECASE))
# If N=0 and no splitters, it's true. If N>0 and no splitters, false.
if N == 0: return actual_sections == 0
return actual_sections == N
# JSON Format : Entire output should be wrapped in JSON format.
def validate_json_format(text: str) -> bool:
try:
json.loads(text.strip()) # .strip() to handle leading/trailing whitespace
return True
except json.JSONDecodeError:
return False
# Repeat Prompt: First, repeat the request without change, then give your answer
def validate_repeat_prompt(text: str, original_prompt: str) -> bool:
# Normalize whitespace for comparison robustness
text_norm = " ".join(text.strip().split())
original_prompt_norm = " ".join(original_prompt.strip().split())
return text_norm.startswith(original_prompt_norm)
# Two Responses: Give two different responses. Separated by 6 asterisk symbols: ******.
def validate_two_responses(text: str) -> bool:
if text.count("******") == 1:
parts = text.split("******")
if len(parts) == 2:
# Check if parts are non-empty and different
resp1 = parts[0].strip()
resp2 = parts[1].strip()
return bool(resp1 and resp2 and resp1 != resp2)
return False
# All Uppercase: Your entire response should be in English, capital letters only.
def validate_uppercase(text: str) -> bool:
# Check if it has letters and all letters are uppercase
if not any(c.isalpha() for c in text): # No letters, technically not violating "all capital"
return True # Or False, depending on interpretation of "response"
return text == text.upper()
# All Lowercase: Your entire response should be in English, and in all lowercase letters.
def validate_lowercase(text: str) -> bool:
if not any(c.isalpha() for c in text):
return True
return text == text.lower()
# Frequency of All-capital Words
def validate_frequency_capital_words(text: str, N: int, quantifier: str) -> bool:
# Words with all capital letters, e.g., "NASA", "AI". Min 2 chars to be a "word".
capital_words = re.findall(r'\\b[A-Z]{2,}\\b', text)
actual_count = len(capital_words)
tolerance = max(round(N * 0.1), 1) # For 'around'
if quantifier == "at least":
return actual_count >= N
elif quantifier == "at most":
return actual_count <= N
elif quantifier == "around": # Using exact for 'around' with capital words unless specified
return abs(actual_count - N) <= tolerance # Or just actual_count == N
return False
# End Checker: Finish your response with this exact phrase {end phrase}.
def validate_end(text: str, end_phrase: str) -> bool:
# Normalize whitespace at the end of text for robustness
return text.strip().endswith(end_phrase.strip())
# Quotation: Wrap your entire response with double quotation marks.
def validate_quotation(text: str) -> bool:
stripped_text = text.strip()
return stripped_text.startswith('"') and stripped_text.endswith('"')
# No Commas: In your entire response, refrain from the use of any commas.
def validate_no_commas(text: str) -> bool:
return "," not in text
IF_FUNCTIONS_MAP = {
"verify_keywords": verify_keywords,
"verify_keyword_frequency": verify_keyword_frequency,
"validate_forbidden_words": validate_forbidden_words,
"verify_letter_frequency": verify_letter_frequency,
"validate_response_language": validate_response_language,
"verify_paragraph_count": verify_paragraph_count,
"validate_word_constraint": validate_word_constraint,
"verify_sentence_constraint": verify_sentence_constraint,
"validate_paragraphs": validate_paragraphs,
"verify_postscript": verify_postscript,
"validate_placeholders": validate_placeholders,
"verify_bullet_points": verify_bullet_points,
"validate_title": validate_title,
"validate_choice": validate_choice,
"validate_highlighted_sections": validate_highlighted_sections,
"validate_sections": validate_sections,
"validate_json_format": validate_json_format,
"validate_repeat_prompt": validate_repeat_prompt,
"validate_two_responses": validate_two_responses,
"validate_uppercase": validate_uppercase,
"validate_lowercase": validate_lowercase,
"validate_frequency_capital_words": validate_frequency_capital_words,
"validate_end": validate_end,
"validate_quotation": validate_quotation,
"validate_no_commas": validate_no_commas,
}
if __name__ == "__main__":
InstructionFollowingEnv.cli()