:j ddlmZddlZddlmZdededdfdZddededeedeeeffd Zd ed edefd Zddededeedefd Z y))OptionalN)TensorpredstargetreturncVt|jdk7r"tdt|jdt|jdk7r"tdt|jd|jdd|jk7r)td|jddd|jd|jst d |j d|j t jk7r*t d t jd |j dy) a3Check shape and type consistency of input vectors. Args: preds: Logits or a unnormalized score assigned to each token in a sequence with shape [batch_size, seq_len, vocab_size]. Scores will be normalized internally using softmax. target: Ground truth values with a shape [batch_size, seq_len]. Raises: ValueError: If ``preds`` tensor has no 3 dimensions. ValueError: If ``target`` tensor has no 2 dimensions. ValueError: If the first two dimensions of ``preds`` and ``target`` do not equal. TypeError: If ``preds`` dtype is not one of ``(torch.float16, torch.float32, torch.float64)`` TypeError: If ``target`` is not of a type LongTensor (torch.int64) zbInput tensor `preds` is expected to have 3 dimensions, [batch_size, seq_len, vocab_size], but got .zWInput tensor `target` is expected to have 2 dimensions, [batch_size, seq_len], but got NzvInput tensors `preds` and `target` are expected to have equaling first two dimensions, [batch_size, seq_len], but got z and zFInput tensor `preds` is expected to be of floating point type but got z2Input tensor `target` is expected to be of a type z but got )lenshape ValueErroris_floating_point TypeErrordtypetorchint64)rrs |/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/text/perplexity.py!_check_shape_and_type_consistencyrs/. 5;;1 EKK() ,   6<<A FLL)*! -   {{2A&,,& //4{{2A.?uV\\NRS U    " " $`afalal`mmnopp ||u{{"LU[[MYbcicocobppqrss# ignore_indexcbt||tjjj |j d|j dd}|j d}|F|j|}|j||k7tjd|j}n%tj|tj}|tj|j|f|}|jj! }|j!}||fS)a]Compute intermediate statistics for Perplexity. Args: preds: Logits or a unnormalized score assigned to each token in a sequence with shape [batch_size, seq_len, vocab_size]. Scores will be normalized internally using softmax. target: Ground truth values with a shape [batch_size, seq_len]. ignore_index: Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score. Returns: Log probabilities, summed over all samples Number of samples )dimr)device)r)rrnn functionalsoftmaxreshaper newheretensorr ones_likeboolarangenumellogsum)rrrprobsmasktotal_log_probscounts r_perplexity_updater.As$&eV4 HH   ' ' b%++b/(JPQ ' RE ^^B Fyy&f 4ell1V]]6[\vUZZ8 %,,v||~.6 7 =Eyy{((O HHJE E !!rtotalr-c2tj||z S)zCompute the Perplexity. Args: total: Log probabilities, summed over all samples count: Number of samples Returns: Perplexity )rexp)r/r-s r_perplexity_computer2es 99UU] ##rc:t|||\}}t||S)aPerplexity measures how well a language model predicts a text sample. This metric is calculated as the average number of bits per word a model needs to represent the sample. Args: preds: Logits or a unnormalized score assigned to each token in a sequence with shape [batch_size, seq_len, vocab_size], which is the output of a language model. Scores will be normalized internally using softmax. target: Ground truth values with a shape [batch_size, seq_len]. ignore_index: Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score. Returns: Perplexity value Examples: >>> from torch import rand, randint >>> preds = rand(2, 8, 5) >>> target = randint(5, (2, 8)) >>> target[0, 6:] = -100 >>> perplexity(preds, target, ignore_index=-100) tensor(5.8540) )r.r2)rrrr/r-s r perplexityr4rs#6&eV\BLE5 ue ,,r)N) typingrrrrinttupler.r2r4rrr9s )tV)tV)t)tX!"f!"f!"HSM!"]bcikqcq]r!"H $v $f $ $-f-f-HSM-U[-r