chatlearn/models/vllm_module.py [446:463]:
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        num_requests = len(prompts)
        assert num_requests == len(prompt_token_ids), \
            ("The lengths of prompts and prompt_token_ids must be the same.")

        inputs = []
        for i in range(num_requests):
            if prompts[i] is None:
                assert isinstance(prompt_token_ids[i], List[int]), \
                    f"Expect prompt_token_ids[{i}] is List[int] when prompt is None, while {prompt_token_ids[i]}."
            if prompt_token_ids[i] is None:
                assert isinstance(prompts[i], str), \
                    f"Expect prompts[{i}] is a string when prompt_token_ids is None, while {prompts[i]}."
            item = TextTokensPrompt(
                prompt=prompts[i],
                prompt_token_ids=prompt_token_ids[i])
            inputs.append(item)

        return inputs
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chatlearn/models/vllm_module_v2.py [326:343]:
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        num_requests = len(prompts)
        assert num_requests == len(prompt_token_ids), \
            ("The lengths of prompts and prompt_token_ids must be the same.")

        inputs = []
        for i in range(num_requests):
            if prompts[i] is None:
                assert isinstance(prompt_token_ids[i], List[int]), \
                    f"Expect prompt_token_ids[{i}] is List[int] when prompt is None, while {prompt_token_ids[i]}."
            if prompt_token_ids[i] is None:
                assert isinstance(prompts[i], str), \
                    f"Expect prompts[{i}] is a string when prompt_token_ids is None, while {prompts[i]}."
            item = TextTokensPrompt(
                prompt=prompts[i],
                prompt_token_ids=prompt_token_ids[i])
            inputs.append(item)

        return inputs
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