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from collections import Counter
from itertools import chain
import math
import torch
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
def ngrams(sequence, n):
return [tuple(sequence[i:i+n]) for i in range(len(sequence)-n+1)]
def count_ngrams(sequence, max_n):
counts = Counter()
for n in range(1, max_n + 1):
counts.update(ngrams(sequence, n))
return counts
def self_bleu(outputs):
smoothing_function = SmoothingFunction().method1
scores = []
for i in range(len(outputs)):
references = outputs[:i] + outputs[i+1:]
# Avoid calculating BLEU score for empty references
if references:
scores.append(sentence_bleu(references, outputs[i], smoothing_function=smoothing_function))
# If all references are empty, return a default value
if not scores:
return 0
return sum(scores) / len(scores)
def dist_n(outputs, n):
all_ngrams = list(chain(*[ngrams(output, n) for output in outputs]))
unique_ngrams = set(all_ngrams)
return len(unique_ngrams) / len(all_ngrams) if all_ngrams else 0
def perplexity(model, tokenizer, texts):
encodings = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
max_length = model.config.n_positions
stride = 512
lls = []
for i in range(0, encodings.input_ids.size(1), stride):
begin_loc = max(i + stride - max_length, 0)
end_loc = i + stride
trg_len = end_loc - i
input_ids = encodings.input_ids[:, begin_loc:end_loc].to(model.device)
target_ids = input_ids.clone()
target_ids[:, :-trg_len] = -100
with torch.no_grad():
outputs = model(input_ids, labels=target_ids)
log_likelihood = outputs.loss * trg_len
lls.append(log_likelihood)
ppl = torch.exp(torch.stack(lls).sum() / end_loc)
return ppl.item()
def js_divergence(p, q):
def kl_divergence(p, q):
return sum(p[i] * math.log(p[i] / q[i]) for i in range(len(p)) if p[i] != 0 and q[i] != 0)
p_norm = [float(i)/sum(p) for i in p]
q_norm = [float(i)/sum(q) for i in q]
m = [(p_norm[i] + q_norm[i]) / 2 for i in range(len(p_norm))]
return (kl_divergence(p_norm, m) + kl_divergence(q_norm, m)) / 2
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