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import re
import random
import torch
import numpy as np
def set_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # For CUDA if using GPUs
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False # Ensures deterministic behavior
def topk_indices(arr, k):
"""
Returns the top-K values and their (x, y) indices from a 2D NumPy array.
Args:
arr (np.ndarray): 2D input array.
k (int): Number of top elements to retrieve.
Returns:
list: List of (value, x, y) tuples sorted in descending order.
"""
flat_indices = np.argpartition(arr.ravel(), -k)[-k:] # Get indices of top-K elements (unordered)
sorted_indices = flat_indices[np.argsort(arr.ravel()[flat_indices])][::-1] # Sort them in descending order
topk_coords = [(arr.flat[i], i // arr.shape[1], i % arr.shape[1]) for i in sorted_indices] # Convert to (value, x, y)
return topk_coords
def trim_output(output):
instruction_prefix = "Answer the following question"
question_prefix = 'Question:'
comment_prefix = 'Comment:' # for some reason, Llama 13B likes to generate these comments indefinitely
for prefix in [instruction_prefix, question_prefix, comment_prefix]:
if prefix in output:
output = output.split(prefix)[0]
return output
def extract_box(pred_str):
ans = pred_str.split("boxed")[-1]
if len(ans) == 0:
return ""
elif ans[0] == "{":
stack = 1
a = ""
for c in ans[1:]:
if c == "{":
stack += 1
a += c
elif c == "}":
stack -= 1
if stack == 0:
break
a += c
else:
a += c
else:
a = ans.split("$")[0].strip()
return a
def extract_last_number(pred_str):
o = re.sub(r"(\d),(\d)", r"\1\2", pred_str)
numbers = re.findall(r"[-+]?\d*\.\d+|\d+", o)
if numbers:
ans = numbers[-1]
else:
ans = None
return ans
def calculate_token_cost(results, tokenizer):
token_budget = []
for output in results:
token_cost = tokenizer.encode(output[0])
token_budget.append(len(token_cost))
print(f"Samples: {len(token_budget)} Total token cost: {np.mean(token_budget)}")
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