| As an example, let's use WhisperModel.forward's docstring: | |
| thon | |
| r""" | |
| Returns: | |
| Example: | |
| thon | |
| >>> import torch | |
| >>> from transformers import WhisperModel, WhisperFeatureExtractor | |
| >>> from datasets import load_dataset | |
| >>> model = WhisperModel.from_pretrained("openai/whisper-base") | |
| >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base") | |
| >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") | |
| >>> input_features = inputs.input_features | |
| >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id | |
| >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state | |
| >>> list(last_hidden_state.shape) | |
| [1, 2, 512] | |
| ```""" | |
| Just run the following line to automatically test every docstring example in the desired file: | |
| pytest --doctest-modules <path_to_file_or_dir> | |
| If the file has a markdown extention, you should add the --doctest-glob="*.md" argument. |