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| from llamafactory.data import Role
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| from llamafactory.data.converter import get_dataset_converter
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| from llamafactory.data.parser import DatasetAttr
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| from llamafactory.hparams import DataArguments
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| def test_alpaca_converter():
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| dataset_attr = DatasetAttr("hf_hub", "llamafactory/tiny-supervised-dataset")
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| data_args = DataArguments()
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| example = {
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| "instruction": "Solve the math problem.",
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| "input": "3 + 4",
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| "output": "The answer is 7.",
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| }
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| dataset_converter = get_dataset_converter("alpaca", dataset_attr, data_args)
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| assert dataset_converter(example) == {
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| "_prompt": [{"role": Role.USER.value, "content": "Solve the math problem.\n3 + 4"}],
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| "_response": [{"role": Role.ASSISTANT.value, "content": "The answer is 7."}],
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| "_system": "",
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| "_tools": "",
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| "_images": None,
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| "_videos": None,
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| "_audios": None,
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| }
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| def test_sharegpt_converter():
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| dataset_attr = DatasetAttr("hf_hub", "llamafactory/tiny-supervised-dataset")
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| data_args = DataArguments()
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| example = {
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| "conversations": [
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| {"from": "system", "value": "You are a helpful assistant."},
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| {"from": "human", "value": "Solve the math problem.\n3 + 4"},
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| {"from": "gpt", "value": "The answer is 7."},
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| ]
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| }
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| dataset_converter = get_dataset_converter("sharegpt", dataset_attr, data_args)
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| assert dataset_converter(example) == {
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| "_prompt": [{"role": Role.USER.value, "content": "Solve the math problem.\n3 + 4"}],
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| "_response": [{"role": Role.ASSISTANT.value, "content": "The answer is 7."}],
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| "_system": "You are a helpful assistant.",
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| "_tools": "",
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| "_images": None,
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| "_videos": None,
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| "_audios": None,
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| }
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