o .wi @sddlmZddlZddlmZdededdfddZddeded eedeeeffd d Zd ed edefddZddeded eedefddZ dS))OptionalN)TensorpredstargetreturncCst|jdkrtdt|jdt|jdkr$tdt|jd|jdd|jkr?td|jddd|jd|sLtd |jd|jtjkr_td tjd |jddS) 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)rrrd/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/text/perplexity.py!_check_shape_and_type_consistencys2  r ignore_indexcCst||tjjj|d|jddd}|d}|dur2||}|||ktj d|j d}ntj |tj d}|t ||f|}| }|}||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 )dimNr)device)r)rrnn functionalsoftmaxreshaper newheretensorr ones_likeboolarangenumellogsum)rrrprobsmasktotal_log_probscountrrr_perplexity_updateAs    r+totalr*cCst||S)zCompute the Perplexity. Args: total: Log probabilities, summed over all samples count: Number of samples Returns: Perplexity )rexp)r,r*rrr_perplexity_computees r.cCst|||\}}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+r.)rrrr,r*rrr perplexityrs r/)N) typingrrrrinttupler+r.r/rrrrs  (,$$