(*docstr, tokenizer_class=None, checkpoint=None)
| 415 | |
| 416 | |
| 417 | def add_code_sample_docstrings(*docstr, tokenizer_class=None, checkpoint=None): |
| 418 | def docstring_decorator(fn): |
| 419 | model_class = fn.__qualname__.split(".")[0] |
| 420 | is_tf_class = model_class[:2] == "TF" |
| 421 | |
| 422 | if "SequenceClassification" in model_class: |
| 423 | code_sample = TF_SEQUENCE_CLASSIFICATION_SAMPLE if is_tf_class else PT_SEQUENCE_CLASSIFICATION_SAMPLE |
| 424 | elif "QuestionAnswering" in model_class: |
| 425 | code_sample = TF_QUESTION_ANSWERING_SAMPLE if is_tf_class else PT_QUESTION_ANSWERING_SAMPLE |
| 426 | elif "TokenClassification" in model_class: |
| 427 | code_sample = TF_TOKEN_CLASSIFICATION_SAMPLE if is_tf_class else PT_TOKEN_CLASSIFICATION_SAMPLE |
| 428 | elif "MultipleChoice" in model_class: |
| 429 | code_sample = TF_MULTIPLE_CHOICE_SAMPLE if is_tf_class else PT_MULTIPLE_CHOICE_SAMPLE |
| 430 | elif "MaskedLM" in model_class: |
| 431 | code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE |
| 432 | elif "LMHead" in model_class: |
| 433 | code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE |
| 434 | elif "Model" in model_class: |
| 435 | code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE |
| 436 | else: |
| 437 | raise ValueError(f"Docstring can't be built for model {model_class}") |
| 438 | |
| 439 | built_doc = code_sample.format(model_class=model_class, tokenizer_class=tokenizer_class, checkpoint=checkpoint) |
| 440 | fn.__doc__ = (fn.__doc__ or "") + "".join(docstr) + built_doc |
| 441 | return fn |
| 442 | |
| 443 | return docstring_decorator |
| 444 | |
| 445 | |
| 446 | def is_remote_url(url_or_filename): |
nothing calls this directly
no outgoing calls
no test coverage detected