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| import tiktoken | |
| # Mapping of model names to their respective encodings | |
| ENCODINGS = { | |
| "gpt-4": tiktoken.get_encoding("cl100k_base"), | |
| "gpt-3.5-turbo": tiktoken.get_encoding("cl100k_base"), | |
| "gpt-3.5-turbo-0301": tiktoken.get_encoding("cl100k_base"), | |
| "text-davinci-003": tiktoken.get_encoding("p50k_base"), | |
| "text-davinci-002": tiktoken.get_encoding("p50k_base"), | |
| "text-davinci-001": tiktoken.get_encoding("r50k_base"), | |
| "text-curie-001": tiktoken.get_encoding("r50k_base"), | |
| "text-babbage-001": tiktoken.get_encoding("r50k_base"), | |
| "text-ada-001": tiktoken.get_encoding("r50k_base"), | |
| "davinci": tiktoken.get_encoding("r50k_base"), | |
| "curie": tiktoken.get_encoding("r50k_base"), | |
| "babbage": tiktoken.get_encoding("r50k_base"), | |
| "ada": tiktoken.get_encoding("r50k_base"), | |
| } | |
| # Mapping of model names to their respective maximum context lengths | |
| MAX_LENGTH = { | |
| "gpt-4": 8192, | |
| "gpt-3.5-turbo": 4096, | |
| "gpt-3.5-turbo-0301": 4096, | |
| "text-davinci-003": 4096, | |
| "text-davinci-002": 4096, | |
| "text-davinci-001": 2049, | |
| "text-curie-001": 2049, | |
| "text-babbage-001": 2049, | |
| "text-ada-001": 2049, | |
| "davinci": 2049, | |
| "curie": 2049, | |
| "babbage": 2049, | |
| "ada": 2049 | |
| } | |
| def count_tokens(model_name, text): | |
| """ | |
| Count the number of tokens for a given model and text. | |
| Parameters: | |
| - model_name (str): The name of the model. | |
| - text (str): The input text. | |
| Returns: | |
| - int: The number of tokens. | |
| """ | |
| if model_name not in ENCODINGS: | |
| raise ValueError(f"Model name '{model_name}' not found in encodings.") | |
| return len(ENCODINGS[model_name].encode(text)) | |
| def get_max_context_length(model_name): | |
| """ | |
| Get the maximum context length for a given model. | |
| Parameters: | |
| - model_name (str): The name of the model. | |
| Returns: | |
| - int: The maximum context length. | |
| """ | |
| if model_name not in MAX_LENGTH: | |
| raise ValueError(f"Model name '{model_name}' not found in max length dictionary.") | |
| return MAX_LENGTH[model_name] | |
| def get_token_ids_for_text(model_name, text): | |
| """ | |
| Get unique token IDs for a given text using the specified model's encoding. | |
| Parameters: | |
| - model_name (str): The name of the model. | |
| - text (str): The input text. | |
| Returns: | |
| - list: A list of unique token IDs. | |
| """ | |
| if model_name not in ENCODINGS: | |
| raise ValueError(f"Model name '{model_name}' not found in encodings.") | |
| encoded_tokens = ENCODINGS[model_name].encode(text) | |
| return list(set(encoded_tokens)) | |
| def get_token_ids_for_task_parsing(model_name): | |
| """ | |
| Get unique token IDs for task parsing. | |
| Parameters: | |
| - model_name (str): The name of the model. | |
| Returns: | |
| - list: A list of unique token IDs for task parsing. | |
| """ | |
| text = '''{"task": "text-classification", "token-classification", "text2text-generation", "summarization", "translation", "question-answering", "conversational", "text-generation", "sentence-similarity", "tabular-classification", "object-detection", "image-classification", "image-to-image", "image-to-text", "text-to-image", "visual-question-answering", "document-question-answering", "image-segmentation", "text-to-speech", "text-to-video", "automatic-speech-recognition", "audio-to-audio", "audio-classification", "canny-control", "hed-control", "mlsd-control", "normal-control", "openpose-control", "canny-text-to-image", "depth-text-to-image", "hed-text-to-image", "mlsd-text-to-image", "normal-text-to-image", "openpose-text-to-image |