add correct name

hyenal · Apr 29, 2024 · 3381fa1 · 3381fa1
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diff --git a/docs/source/en/perf_infer_gpu_one.md b/docs/source/en/perf_infer_gpu_one.md
@@ -190,7 +190,7 @@ FlashAttention is more memory efficient, meaning you can train on much larger se
 PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is used by default for `torch>=2.1.1` when an implementation is available. You may also set `attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
 
 For now, Transformers supports SDPA inference and training for the following architectures:
-* [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio_spectrogram_transformer#transformers.ASTModel)
+* [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTModel)
 * [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
 * [Bert](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertModel)
 * [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)

diff --git a/utils/check_support_list.py b/utils/check_support_list.py
@@ -84,7 +84,7 @@ def check_sdpa_support_list():
                 archs_supporting_sdpa.append(model_name)
 
     for arch in archs_supporting_sdpa:
-        if arch not in doctext:
+        if arch not in doctext and arch not in doctext.replace("-", "_"):
             raise ValueError(
                 f"{arch} should be in listed in the SDPA documentation but is not. Please update the documentation."
             )