def METHOD_NAME(data_path: str) -> None:
    # Load dataset
    with open(data_path) as f:
        dataset = [json.loads(line) for line in f]
    # We can inspect the data quickly by checking the number
    # of examples and the first item
    # Initial dataset stats
    print("Num examples:", len(dataset))
    print("First example:")
    for message in dataset[0]["messages"]:
        print(message)
    # Now that we have a sense of the data, we need to go through all the different
    # examples and check to make sure the formatting is correct and matches the Chat
    # completions message structure
    # Format error checks
    format_errors: Dict[str, int] = defaultdict(int)
    for ex in dataset:
        if not isinstance(ex, dict):
            format_errors["data_type"] += 1
            continue
        messages = ex.get("messages", None)
        if not messages:
            format_errors["missing_messages_list"] += 1
            continue
        for message in messages:
            if "role" not in message or "content" not in message:
                format_errors["message_missing_key"] += 1
            if any(k not in ("role", "content", "name") for k in message):
                format_errors["message_unrecognized_key"] += 1
            if message.get("role", None) not in ("system", "user", "assistant"):
                format_errors["unrecognized_role"] += 1
            content = message.get("content", None)
            if not content or not isinstance(content, str):
                format_errors["missing_content"] += 1
        if not any(message.get("role", None) == "assistant" for message in messages):
            format_errors["example_missing_assistant_message"] += 1
    if format_errors:
        print("Found errors:")
        for k, v in format_errors.items():
            print(f"{k}: {v}")
    else:
        print("No errors found")
    # Beyond the structure of the message, we also need to ensure that the length does
    # not exceed the 4096 token limit.
    # Token counting functions
    encoding = tiktoken.get_encoding("cl100k_base")
    # not exact!
    # simplified from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
    def num_tokens_from_messages(
        messages: List[dict], tokens_per_message: int = 3, tokens_per_name: int = 1
    ) -> int:
        num_tokens = 0
        for message in messages:
            num_tokens += tokens_per_message
            for key, value in message.items():
                num_tokens += len(encoding.encode(value))
                if key == "name":
                    num_tokens += tokens_per_name
        num_tokens += 3
        return num_tokens
    def num_assistant_tokens_from_messages(messages: List[dict]) -> int:
        num_tokens = 0
        for message in messages:
            if message["role"] == "assistant":
                num_tokens += len(encoding.encode(message["content"]))
        return num_tokens
    def print_distribution(values: list, name: str) -> None:
        print(f"\n#### Distribution of {name}:")
        print(f"min / max: {min(values)}, {max(values)}")
        print(f"mean / median: {np.mean(values)}, {np.median(values)}")
        print(f"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}")
    # Last, we can look at the results of the different formatting operations before
    # proceeding with creating a fine-tuning job:
    # Warnings and tokens counts
    n_missing_system = 0
    n_missing_user = 0
    n_messages = []
    convo_lens = []
    assistant_message_lens = []
    for ex in dataset:
        messages = ex["messages"]
        if not any(message["role"] == "system" for message in messages):
            n_missing_system += 1
        if not any(message["role"] == "user" for message in messages):
            n_missing_user += 1
        n_messages.append(len(messages))
        convo_lens.append(num_tokens_from_messages(messages))
        assistant_message_lens.append(num_assistant_tokens_from_messages(messages))
    print("Num examples missing system message:", n_missing_system)
    print("Num examples missing user message:", n_missing_user)
    print_distribution(n_messages, "num_messages_per_example")
    print_distribution(convo_lens, "num_total_tokens_per_example")
    print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
    n_too_long = sum(length > 4096 for length in convo_lens)
    print(
        f"\n{n_too_long} examples may be over the 4096 token limit, "
        "they will be truncated during fine-tuning"
    )
    # Pricing and default n_epochs estimate
    MAX_TOKENS_PER_EXAMPLE = 4096
    MIN_TARGET_EXAMPLES = 100
    MAX_TARGET_EXAMPLES = 25000
    TARGET_EPOCHS = 3
    MIN_EPOCHS = 1
    MAX_EPOCHS = 25
    n_epochs = TARGET_EPOCHS
    n_train_examples = len(dataset)
    if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
        n_epochs = min(MAX_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
    elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
        n_epochs = max(MIN_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)
    n_billing_tokens_in_dataset = sum(
        min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens
    )
    print(
        f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will "
        "be charged for during training"
    )
    print(f"By default, you'll train for {n_epochs} epochs on this dataset")
    print(
        "By default, you'll be charged for "
        f"~{n_epochs * n_billing_tokens_in_dataset} tokens"
    )
    print("As of Augest 22, 2023, fine-tuning gpt-3.5-turbo is $0.008 / 1K Tokens.")
    print(
        "This means your total cost for training will be "
        f"${n_billing_tokens_in_dataset * 0.008 / 1000} per epoch."
    )