prompter.py

import os
import ast
import time
from enums import PromptType  # also supports imports from this file from other files

non_hf_types = ['gpt4all_llama', 'llama', 'gptj']

prompt_type_to_model_name = {
    'plain': [
        'EleutherAI/gpt-j-6B',
        'EleutherAI/pythia-6.9b',
        'EleutherAI/pythia-12b',
        'EleutherAI/pythia-12b-deduped',
        'EleutherAI/gpt-neox-20b',
        'openlm-research/open_llama_7b_700bt_preview',
        'decapoda-research/llama-7b-hf',
        'decapoda-research/llama-13b-hf',
        'decapoda-research/llama-30b-hf',
        'decapoda-research/llama-65b-hf',
        'facebook/mbart-large-50-many-to-many-mmt',
        'philschmid/bart-large-cnn-samsum',
        'philschmid/flan-t5-base-samsum',
        'gpt2',
        'distilgpt2',
        'mosaicml/mpt-7b-storywriter',
    ],
    'gptj': ['gptj', 'gpt4all_llama'],
    'prompt_answer': [
        'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
        'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
        'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
        'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
        'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b-v2',
        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
        'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
        'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
        'h2oai/h2ogpt-gm-oasst1-multilang-xgen-7b-8k',
        'TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GPTQ',
    ],
    'prompt_answer_openllama': [
        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
        'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
    ],
    'instruct': ['TheBloke/llama-30b-supercot-SuperHOT-8K-fp16'],  # https://huggingface.co/TheBloke/llama-30b-supercot-SuperHOT-8K-fp16#prompting
    'instruct_with_end': ['databricks/dolly-v2-12b'],
    'quality': [],
    'human_bot': [
        'h2oai/h2ogpt-oasst1-512-12b',
        'h2oai/h2ogpt-oasst1-512-20b',
        'h2oai/h2ogpt-oig-oasst1-256-6_9b',
        'h2oai/h2ogpt-oig-oasst1-512-6_9b',
        'h2oai/h2ogpt-oig-oasst1-256-6.9b',  # legacy
        'h2oai/h2ogpt-oig-oasst1-512-6.9b',  # legacy
        'h2oai/h2ogpt-research-oasst1-512-30b',
        'h2oai/h2ogpt-research-oasst1-llama-65b',
        'h2oai/h2ogpt-oasst1-falcon-40b',
        'h2oai/h2ogpt-oig-oasst1-falcon-40b',
    ],
    'dai_faq': [],
    'summarize': [],
    'simple_instruct': ['t5-small', 't5-large', 'google/flan-t5', 'google/flan-t5-xxl', 'google/flan-ul2'],
    'instruct_vicuna': ['AlekseyKorshuk/vicuna-7b', 'TheBloke/stable-vicuna-13B-HF', 'junelee/wizard-vicuna-13b'],
    'human_bot_orig': ['togethercomputer/GPT-NeoXT-Chat-Base-20B'],
    "open_assistant": ['OpenAssistant/oasst-sft-7-llama-30b-xor', 'oasst-sft-7-llama-30b'],
    "wizard_lm": ['ehartford/WizardLM-7B-Uncensored', 'ehartford/WizardLM-13B-Uncensored'],
    "wizard_mega": ['openaccess-ai-collective/wizard-mega-13b'],
    "instruct_simple": ['JosephusCheung/Guanaco'],
    "wizard_vicuna": ['ehartford/Wizard-Vicuna-13B-Uncensored'],
    "wizard2": ['llama'],
    "mptinstruct": ['mosaicml/mpt-30b-instruct', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-30b-instruct'],
    "mptchat": ['mosaicml/mpt-7b-chat', 'mosaicml/mpt-30b-chat', 'TheBloke/mpt-30B-chat-GGML'],
    "vicuna11": ['lmsys/vicuna-33b-v1.3'],
    "falcon": ['tiiuae/falcon-40b-instruct', 'tiiuae/falcon-40b', 'tiiuae/falcon-7b-instruct', 'tiiuae/falcon-7b'],
    "llama2": [
        'meta-llama/Llama-2-7b-chat-hf',
        'meta-llama/Llama-2-13b-chat-hf',
        'meta-llama/Llama-2-34b-chat-hf',
        'meta-llama/Llama-2-70b-chat-hf',
    ],
    # could be plain, but default is correct prompt_type for default TheBloke model ggml-wizardLM-7B.q4_2.bin
}
if os.getenv('OPENAI_API_KEY'):
    prompt_type_to_model_name.update({
        "openai": ["text-davinci-003", "text-curie-001", "text-babbage-001", "text-ada-001"],
        "openai_chat": ["gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
    })

inv_prompt_type_to_model_name = {v.strip(): k for k, l in prompt_type_to_model_name.items() for v in l}
inv_prompt_type_to_model_lower = {v.strip().lower(): k for k, l in prompt_type_to_model_name.items() for v in l}

prompt_types_strings = []
for p in PromptType:
    prompt_types_strings.extend([p.name])

prompt_types = []
for p in PromptType:
    prompt_types.extend([p.name, p.value, str(p.value)])


def get_prompt(prompt_type, prompt_dict, chat, context, reduced, making_context, return_dict=False):
    prompt_dict_error = ''
    generates_leading_space = False

    if prompt_type == PromptType.custom.name and not isinstance(prompt_dict, dict):
        try:
            prompt_dict = ast.literal_eval(prompt_dict)
        except BaseException as e:
            prompt_dict_error = str(e)
    if prompt_dict_error:
        promptA = None
        promptB = None
        PreInstruct = None
        PreInput = ''
        PreResponse = ''
        terminate_response = None
        chat_sep = ''
        chat_turn_sep = ''
        humanstr = ''
        botstr = ''
        generates_leading_space = False
    elif prompt_type in [PromptType.custom.value, str(PromptType.custom.value),
                         PromptType.custom.name]:
        promptA = prompt_dict.get('promptA', '')
        promptB = prompt_dict.get('promptB', '')
        PreInstruct = prompt_dict.get('PreInstruct', '')
        PreInput = prompt_dict.get('PreInput', '')
        PreResponse = prompt_dict.get('PreResponse', '')
        terminate_response = prompt_dict.get('terminate_response', None)
        chat_sep = prompt_dict.get('chat_sep', '\n')
        chat_turn_sep = prompt_dict.get('chat_turn_sep', '\n')
        humanstr = prompt_dict.get('humanstr', '')
        botstr = prompt_dict.get('botstr', '')
    elif prompt_type in [PromptType.plain.value, str(PromptType.plain.value),
                         PromptType.plain.name]:
        promptA = promptB = PreInstruct = PreInput = PreResponse = None
        terminate_response = []
        chat_turn_sep = chat_sep = ''
        # plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
        humanstr = None
        botstr = None
    elif prompt_type == 'simple_instruct':
        promptA = promptB = PreInstruct = PreInput = PreResponse = None
        terminate_response = []
        chat_turn_sep = chat_sep = '\n'
        humanstr = None
        botstr = None
    elif prompt_type in [PromptType.instruct.value, str(PromptType.instruct.value),
                         PromptType.instruct.name] + [PromptType.instruct_with_end.value,
                                                      str(PromptType.instruct_with_end.value),
                                                      PromptType.instruct_with_end.name]:
        promptA = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n' if not (
                chat and reduced) else ''
        promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
                chat and reduced) else ''

        PreInstruct = """
### Instruction:
"""

        PreInput = """
### Input:
"""

        PreResponse = """
### Response:
"""
        if prompt_type in [PromptType.instruct_with_end.value, str(PromptType.instruct_with_end.value),
                           PromptType.instruct_with_end.name]:
            terminate_response = ['### End']
        else:
            terminate_response = None
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.quality.value, str(PromptType.quality.value),
                         PromptType.quality.name]:
        promptA = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction as applied on the Input.\n' if not (
                chat and reduced) else ''
        promptB = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction.\n' if not (
                chat and reduced) else ''

        PreInstruct = """
### Instruction:
"""

        PreInput = """
### Input:
"""

        PreResponse = """
### Response:
"""
        terminate_response = None
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct  # first thing human says
        botstr = PreResponse  # first thing bot says
    elif prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
                         PromptType.human_bot.name] + [PromptType.human_bot_orig.value,
                                                       str(PromptType.human_bot_orig.value),
                                                       PromptType.human_bot_orig.name]:
        human = '<human>:'
        bot = "<bot>:"
        if reduced or context or prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
                                                 PromptType.human_bot.name]:
            preprompt = ''
        else:
            cur_date = time.strftime('%Y-%m-%d')
            cur_time = time.strftime('%H:%M:%S %p %Z')

            PRE_PROMPT = """\
Current Date: {}
Current Time: {}

"""
            preprompt = PRE_PROMPT.format(cur_date, cur_time)
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)

        PreInstruct = human + ' '

        PreInput = None

        if making_context:
            # when making context, want it to appear as-if LLM generated, which starts with space after :
            PreResponse = bot + ' '
        else:
            # normally LLM adds space after this, because was how trained.
            # if add space here, non-unique tokenization will often make LLM produce wrong output
            PreResponse = bot

        terminate_response = ['\n' + human, '\n' + bot, human, bot, PreResponse]
        chat_turn_sep = chat_sep = '\n'
        humanstr = human  # tag before human talks
        botstr = bot  # tag before bot talks
        generates_leading_space = True
    elif prompt_type in [PromptType.dai_faq.value, str(PromptType.dai_faq.value),
                         PromptType.dai_faq.name]:
        promptA = ''
        promptB = 'Answer the following Driverless AI question.\n'

        PreInstruct = """
### Driverless AI frequently asked question:
"""

        PreInput = None

        PreResponse = """
### Driverless AI documentation answer:
"""
        terminate_response = ['\n\n']
        chat_turn_sep = chat_sep = terminate_response
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.summarize.value, str(PromptType.summarize.value),
                         PromptType.summarize.name]:
        promptA = promptB = PreInput = ''
        PreInstruct = '## Main Text\n\n'
        PreResponse = '\n\n## Summary\n\n'
        terminate_response = None
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.instruct_vicuna.value, str(PromptType.instruct_vicuna.value),
                         PromptType.instruct_vicuna.name]:
        promptA = promptB = "A chat between a curious human and an artificial intelligence assistant. " \
                            "The assistant gives helpful, detailed, and polite answers to the human's questions." if not (
                chat and reduced) else ''

        PreInstruct = """
### Human:
"""

        PreInput = None

        PreResponse = """
### Assistant:
"""
        terminate_response = [
            '### Human:']  # but only allow terminate after prompt is found correctly, else can't terminate
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.prompt_answer.value, str(PromptType.prompt_answer.value),
                         PromptType.prompt_answer.name]:
        preprompt = ''
        prompt_tokens = "<|prompt|>"
        answer_tokens = "<|answer|>"
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = prompt_tokens
        PreInput = None
        PreResponse = answer_tokens
        eos = '<|endoftext|>'  # neox eos
        humanstr = prompt_tokens
        botstr = answer_tokens
        terminate_response = [humanstr, PreResponse, eos]
        chat_sep = eos
        chat_turn_sep = eos
    elif prompt_type in [PromptType.prompt_answer_openllama.value, str(PromptType.prompt_answer_openllama.value),
                         PromptType.prompt_answer_openllama.name]:
        preprompt = ''
        prompt_tokens = "<|prompt|>"
        answer_tokens = "<|answer|>"
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = prompt_tokens
        PreInput = None
        PreResponse = answer_tokens
        eos = '</s>'  # llama eos
        humanstr = prompt_tokens
        botstr = answer_tokens
        terminate_response = [humanstr, PreResponse, eos]
        chat_sep = eos
        chat_turn_sep = eos
    elif prompt_type in [PromptType.open_assistant.value, str(PromptType.open_assistant.value),
                         PromptType.open_assistant.name]:
        # From added_tokens.json
        preprompt = ''
        prompt_tokens = "<|prompter|>"
        answer_tokens = "<|assistant|>"
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = prompt_tokens
        PreInput = None
        PreResponse = answer_tokens
        pend = "<|prefix_end|>"
        eos = "</s>"
        humanstr = prompt_tokens
        botstr = answer_tokens
        terminate_response = [humanstr, PreResponse, pend, eos]
        chat_turn_sep = chat_sep = eos
    elif prompt_type in [PromptType.wizard_lm.value, str(PromptType.wizard_lm.value),
                         PromptType.wizard_lm.name]:
        # https://github.com/ehartford/WizardLM/blob/main/src/train_freeform.py
        preprompt = ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = ""
        PreInput = None
        PreResponse = "\n\n### Response\n"
        eos = "</s>"
        terminate_response = [PreResponse, eos]
        chat_turn_sep = chat_sep = eos
        humanstr = promptA
        botstr = PreResponse
    elif prompt_type in [PromptType.wizard_mega.value, str(PromptType.wizard_mega.value),
                         PromptType.wizard_mega.name]:
        preprompt = ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = """
### Instruction:
"""
        PreInput = None
        PreResponse = """
### Assistant:
"""
        terminate_response = [PreResponse]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.instruct_vicuna2.value, str(PromptType.instruct_vicuna2.value),
                         PromptType.instruct_vicuna2.name]:
        promptA = promptB = "" if not (chat and reduced) else ''

        PreInstruct = """
HUMAN:
"""

        PreInput = None

        PreResponse = """
ASSISTANT:
"""
        terminate_response = [
            'HUMAN:']  # but only allow terminate after prompt is found correctly, else can't terminate
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.instruct_vicuna3.value, str(PromptType.instruct_vicuna3.value),
                         PromptType.instruct_vicuna3.name]:
        promptA = promptB = "" if not (chat and reduced) else ''

        PreInstruct = """
### User:
"""

        PreInput = None

        PreResponse = """
### Assistant:
"""
        terminate_response = [
            '### User:']  # but only allow terminate after prompt is found correctly, else can't terminate
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.wizard2.value, str(PromptType.wizard2.value),
                         PromptType.wizard2.name]:
        # https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML
        preprompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.""" if not (
                chat and reduced) else ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = """
### Instruction:
"""
        PreInput = None
        PreResponse = """
### Response:
"""
        terminate_response = [PreResponse]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.wizard3.value, str(PromptType.wizard3.value),
                         PromptType.wizard3.name]:
        # https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML
        preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" if not (
                chat and reduced) else ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = """USER: """
        PreInput = None
        PreResponse = """ASSISTANT: """
        terminate_response = [PreResponse]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.wizard_vicuna.value, str(PromptType.wizard_vicuna.value),
                         PromptType.wizard_vicuna.name]:
        preprompt = ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = """USER: """
        PreInput = None
        PreResponse = """ASSISTANT: """
        terminate_response = [PreResponse]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse

    elif prompt_type in [PromptType.instruct_simple.value, str(PromptType.instruct_simple.value),
                         PromptType.instruct_simple.name]:
        promptB = promptA = '' if not (chat and reduced) else ''

        PreInstruct = """
### Instruction:
"""

        PreInput = """
### Input:
"""

        PreResponse = """
### Response:
"""
        terminate_response = None
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.openai.value, str(PromptType.openai.value),
                         PromptType.openai.name]:
        preprompt = """The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.""" if not (
                chat and reduced) else ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = "\nHuman: "
        PreInput = None
        PreResponse = "\nAI:"
        terminate_response = [PreResponse] + [" Human:", " AI:"]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.gptj.value, str(PromptType.gptj.value),
                         PromptType.gptj.name]:
        preprompt = "### Instruction:\n The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response." if not (
                chat and reduced) else ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = "\n### Prompt: "
        PreInput = None
        PreResponse = "\n### Response: "
        terminate_response = [PreResponse] + ["Prompt:", "Response:"]
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.openai_chat.value, str(PromptType.openai_chat.value),
                         PromptType.openai_chat.name]:
        # prompting and termination all handled by endpoint
        preprompt = """"""
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        PreInstruct = ""
        PreInput = None
        PreResponse = ""
        terminate_response = []
        chat_turn_sep = chat_sep = '\n'
        humanstr = None
        botstr = None
    elif prompt_type in [PromptType.vicuna11.value, str(PromptType.vicuna11.value),
                         PromptType.vicuna11.name]:
        preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. """ if not (
                chat and reduced) else ''
        start = ''
        promptB = promptA = '%s%s' % (preprompt, start)
        eos = '</s>'
        PreInstruct = """USER: """
        PreInput = None
        PreResponse = """ASSISTANT:"""
        terminate_response = [PreResponse]
        chat_sep = ' '
        chat_turn_sep = eos
        humanstr = PreInstruct
        botstr = PreResponse

        if making_context:
            # when making context, want it to appear as-if LLM generated, which starts with space after :
            PreResponse = PreResponse + ' '
        else:
            # normally LLM adds space after this, because was how trained.
            # if add space here, non-unique tokenization will often make LLM produce wrong output
            PreResponse = PreResponse
    elif prompt_type in [PromptType.mptinstruct.value, str(PromptType.mptinstruct.value),
                         PromptType.mptinstruct.name]:
        # https://huggingface.co/mosaicml/mpt-30b-instruct#formatting
        promptA = promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
                chat and reduced) else ''

        PreInstruct = """
### Instruction
"""

        PreInput = """
### Input
"""

        PreResponse = """
### Response
"""
        terminate_response = None
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.mptchat.value, str(PromptType.mptchat.value),
                         PromptType.mptchat.name]:
        # https://huggingface.co/TheBloke/mpt-30B-chat-GGML#prompt-template
        promptA = promptB = """<|im_start|>system\nA conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.\n<|im_end|>""" if not (
                chat and reduced) else ''

        PreInstruct = """<|im_start|>user
"""

        PreInput = None

        PreResponse = """<|im_end|><|im_start|>assistant
"""
        terminate_response = ['<|im_end|>']
        chat_sep = ''
        chat_turn_sep = '<|im_end|>'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.falcon.value, str(PromptType.falcon.value),
                         PromptType.falcon.name]:
        promptA = promptB = "" if not (chat and reduced) else ''

        PreInstruct = """User: """

        PreInput = None

        PreResponse = """Assistant:"""
        terminate_response = ['\nUser', "<|endoftext|>"]
        chat_sep = '\n\n'
        chat_turn_sep = '\n\n'
        humanstr = PreInstruct
        botstr = PreResponse
        if making_context:
            # when making context, want it to appear as-if LLM generated, which starts with space after :
            PreResponse = 'Assistant: '
        else:
            # normally LLM adds space after this, because was how trained.
            # if add space here, non-unique tokenization will often make LLM produce wrong output
            PreResponse = PreResponse
        # generates_leading_space = True
    elif prompt_type in [PromptType.guanaco.value, str(PromptType.guanaco.value),
                         PromptType.guanaco.name]:
        # https://huggingface.co/TheBloke/guanaco-65B-GPTQ
        promptA = promptB = "" if not (chat and reduced) else ''

        PreInstruct = """### Human: """

        PreInput = None

        PreResponse = """### Assistant:"""
        terminate_response = ['### Human:']  # but only allow terminate after prompt is found correctly, else can't terminate
        chat_turn_sep = chat_sep = '\n'
        humanstr = PreInstruct
        botstr = PreResponse
    elif prompt_type in [PromptType.llama2.value, str(PromptType.llama2.value),
                         PromptType.llama2.name]:
        PreInstruct = ""
        llama2_sys = "<<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
        prompt = "<s>[INST] "
        enable_sys = False  # too much safety, hurts accuracy
        if not (chat and reduced):
            if enable_sys:
                promptA = promptB = prompt + llama2_sys
            else:
                promptA = promptB = prompt
        else:
            promptA = promptB = ''
        PreInput = None
        PreResponse = ""
        terminate_response = ["[INST]", "</s>"]
        chat_sep = ' [/INST]'
        chat_turn_sep = ' </s><s>[INST] '
        humanstr = PreInstruct
        botstr = PreResponse
        if making_context:
            PreResponse += " "
    else:
        raise RuntimeError("No such prompt_type=%s" % prompt_type)

    if isinstance(terminate_response, (tuple, list)):
        assert '' not in terminate_response, "Bad terminate_response"

    ret_dict = dict(promptA=promptA, promptB=promptB, PreInstruct=PreInstruct, PreInput=PreInput,
                    PreResponse=PreResponse, terminate_response=terminate_response, chat_sep=chat_sep,
                    chat_turn_sep=chat_turn_sep,
                    humanstr=humanstr, botstr=botstr,
                    generates_leading_space=generates_leading_space)

    if return_dict:
        return ret_dict, prompt_dict_error
    else:
        return tuple(list(ret_dict.values()))


def generate_prompt(data_point, prompt_type, prompt_dict, chat, reduced, making_context):
    context = data_point.get('context')
    if context is None:
        context = ''
    instruction = data_point.get('instruction')
    input = data_point.get('input')
    output = data_point.get('output')
    prompt_type = data_point.get('prompt_type', prompt_type)
    prompt_dict = data_point.get('prompt_dict', prompt_dict)
    assert prompt_type in prompt_types, "Bad prompt type: %s" % prompt_type
    promptA, promptB, PreInstruct, PreInput, PreResponse, \
        terminate_response, chat_sep, chat_turn_sep, humanstr, botstr, \
        generates_leading_space = get_prompt(prompt_type, prompt_dict, chat,
                                             context, reduced, making_context)

    # could avoid if reduce=True, but too complex for parent functions to handle
    prompt = context

    if input and promptA:
        prompt += f"""{promptA}"""
    elif promptB:
        prompt += f"""{promptB}"""

    if instruction and PreInstruct is not None and input and PreInput is not None:
        prompt += f"""{PreInstruct}{instruction}{PreInput}{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif instruction and input and PreInstruct is None and PreInput is not None:
        prompt += f"""{PreInput}{instruction}
{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input and instruction and PreInput is None and PreInstruct is not None:
        prompt += f"""{PreInstruct}{instruction}
{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif instruction and PreInstruct is not None:
        prompt += f"""{PreInstruct}{instruction}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input and PreInput is not None:
        prompt += f"""{PreInput}{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input and instruction and PreInput is not None:
        prompt += f"""{PreInput}{instruction}{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input and instruction and PreInstruct is not None:
        prompt += f"""{PreInstruct}{instruction}{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input and instruction:
        # i.e. for simple_instruct
        prompt += f"""{instruction}: {input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif input:
        prompt += f"""{input}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
    elif instruction:
        prompt += f"""{instruction}"""
        prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)

    if PreResponse is not None:
        prompt += f"""{PreResponse}"""
        pre_response = PreResponse  # Don't use strip
    else:
        pre_response = ''

    if output:
        prompt += f"""{output}"""

    return prompt, pre_response, terminate_response, chat_sep, chat_turn_sep


def inject_chatsep(prompt_type, prompt, chat_sep=None):
    if chat_sep:
        # only add new line if structured prompt, while 'plain' is just generation of next tokens from input
        prompt += chat_sep
    return prompt


class Prompter(object):
    def __init__(self, prompt_type, prompt_dict, debug=False, chat=False, stream_output=False, repeat_penalty=True,
                 allowed_repeat_line_length=10):
        self.prompt_type = prompt_type
        self.prompt_dict = prompt_dict
        self.debug = debug
        self.chat = chat
        self.stream_output = stream_output
        self.repeat_penalty = repeat_penalty
        self.allowed_repeat_line_length = allowed_repeat_line_length
        self.prompt = None
        context = ""  # not for chat context
        reduced = False  # not for chat context
        making_context = False  # not for chat context
        self.promptA, self.promptB, self.PreInstruct, self.PreInput, self.PreResponse, \
            self.terminate_response, self.chat_sep, self.chat_turn_sep, self.humanstr, self.botstr, \
            self.generates_leading_space = \
            get_prompt(self.prompt_type, self.prompt_dict, chat, context, reduced, making_context)
        self.pre_response = self.PreResponse

    def generate_prompt(self, data_point, reduced=None):
        """
        data_point['context'] is assumed to be like a system prompt or pre-conversation, not inserted after user prompt
        :param data_point:
        :param reduced:
        :return:
        """
        reduced = data_point.get('context') not in ['', None] if reduced is None else reduced
        making_context = False  # whether really making final prompt or just generating context
        prompt, _, _, _, _ = generate_prompt(data_point, self.prompt_type, self.prompt_dict, self.chat, reduced,
                                             making_context)
        if self.debug:
            print("prompt: %s" % prompt, flush=True)
        # if have context, should have always reduced and only preappend promptA/B here
        if data_point.get('context'):
            if data_point.get('input') and self.promptA:
                prompt = self.promptA + prompt
            elif self.promptB:
                prompt = self.promptB + prompt

        self.prompt = prompt
        return prompt

    def get_response(self, outputs, prompt=None, sanitize_bot_response=False):
        if isinstance(outputs, str):
            outputs = [outputs]
        if self.debug:
            print("output:\n%s" % '\n\n'.join(outputs), flush=True)
        if prompt is not None:
            self.prompt = prompt

        def clean_response(response):
            meaningless_words = ['<pad>', '</s>', '<|endoftext|>']
            for word in meaningless_words:
                response = response.replace(word, "")
            if sanitize_bot_response:
                from better_profanity import profanity
                response = profanity.censor(response)
            if self.generates_leading_space and isinstance(response, str) and len(response) > 0 and response[0] == ' ':
                response = response[1:]
            return response

        def clean_repeats(response):
            lines = response.split('\n')
            new_lines = []
            [new_lines.append(line) for line in lines if
             line not in new_lines or len(line) < self.allowed_repeat_line_length]
            if self.debug and len(lines) != len(new_lines):
                print("cleaned repeats: %s %s" % (len(lines), len(new_lines)), flush=True)
            response = '\n'.join(new_lines)
            return response

        multi_output = len(outputs) > 1

        for oi, output in enumerate(outputs):
            if self.prompt_type in [PromptType.plain.value, str(PromptType.plain.value), PromptType.plain.name]:
                output = clean_response(output)
            elif prompt is None:
                # then use most basic parsing like pipeline
                if not self.botstr:
                    pass
                elif self.botstr in output:
                    if self.humanstr:
                        output = clean_response(output.split(self.botstr)[1].split(self.humanstr)[0])
                    else:
                        # i.e. use after bot but only up to next bot
                        output = clean_response(output.split(self.botstr)[1].split(self.botstr)[0])
                else:
                    # output = clean_response(output)
                    # assume just not printed yet
                    output = ""
            else:
                # find first instance of prereponse
                # prompt sometimes has odd characters, that mutate length,
                # so can't go by length alone
                if self.pre_response:
                    outputi = output.find(prompt)
                    if outputi >= 0:
                        output = output[outputi + len(prompt):]
                        allow_terminate = True
                    else:
                        # subtraction is risky due to space offsets sometimes, so only do if necessary
                        output = output[len(prompt) - len(self.pre_response):]
                        # [1] to avoid repeated pre_response, just take first (after prompt - pre_response for chat)
                        if self.pre_response in output:
                            output = output.split(self.pre_response)[1]
                            allow_terminate = True
                        else:
                            if output:
                                print("Failure of parsing or not enough output yet: %s" % output, flush=True)
                            allow_terminate = False
                else:
                    allow_terminate = True
                    output = output[len(prompt):]
                # clean after subtract prompt out, so correct removal of pre_response
                output = clean_response(output)
                if self.repeat_penalty:
                    output = clean_repeats(output)
                if self.terminate_response and allow_terminate:
                    finds = []
                    for term in self.terminate_response:
                        finds.append(output.find(term))
                    finds = [x for x in finds if x >= 0]
                    if len(finds) > 0:
                        termi = finds[0]
                        output = output[:termi]
                    else:
                        output = output
            if multi_output:
                # prefix with output counter
                output = "\n=========== Output %d\n\n" % (1 + oi) + output
                if oi > 0:
                    # post fix outputs with seperator
                    output += '\n'
            output = self.fix_text(self.prompt_type, output)
            outputs[oi] = output
        # join all outputs, only one extra new line between outputs
        output = '\n'.join(outputs)
        if self.debug:
            print("outputclean:\n%s" % '\n\n'.join(outputs), flush=True)
        return output

    @staticmethod
    def fix_text(prompt_type1, text1):
        if prompt_type1 == 'human_bot':
            # hack bug in vLLM with stopping, stops right, but doesn't return last token
            hfix = '<human'
            if text1.endswith(hfix):
                text1 = text1[:-len(hfix)]
        return text1