a  d}@sddlmZmZddlZddlmZddlmZejej fZ eeddddZ deeee eeefdd d Z eeed d d Zdeeee edddZdS))OptionalTupleN)Tensor) functional)predstargetreturncCst|jdkr$tdt|jdt|jdkrHtdt|jd|jdd|jkr~td|jddd|jd|jtvrtd td |jd|jtjkrtd 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 z8Input tensor `preds` is expected to be of a type one of z but got z2Input tensor `target` is expected to be of a type )lenshape ValueErrordtype_TORCH_FLOAT_OR_DOUBLE TypeErrortorchint64)rrrp/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/text/perplexity.py!_check_shape_and_type_consistencys4   r)rr ignore_indexrcCst||tj|d|jddd}|d}|dur`||}|||ktjd|j d}ntj |tj d}|dd|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 )ZdimNr)device)r)rFZsoftmaxZreshaper newhererZtensorrZ ones_likeboolZdiagonallogsum)rrrZprobsmaskZtotal_log_probscountrrr_perplexity_updateFs   r#)totalr"rcCst||S)zCompute the Perplexity. Args: total: Log probabilities, summed over all samples count: Number of samples Returns: Perplexity )rexp)r$r"rrr_perplexity_computejs r&cCst|||\}}t||S)a Perplexity 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: >>> import torch >>> gen = torch.manual_seed(42) >>> preds = torch.rand(2, 8, 5, generator=gen) >>> target = torch.randint(5, (2, 8), generator=gen) >>> target[0, 6:] = -100 >>> perplexity(preds, target, ignore_index=-100) tensor(5.8540) )r#r&)rrrr$r"rrr perplexitywsr')N)N)typingrrrrZtorch.nnrrfloat32float64rrintr#r&r'rrrrs   ."$