gen.py

import ast
import copy
import functools
import glob
import inspect
import queue
import sys
import os
import time
import traceback
import typing
import warnings
from datetime import datetime
import filelock
import requests
import psutil
from requests import ConnectTimeout, JSONDecodeError
from urllib3.exceptions import ConnectTimeoutError, MaxRetryError, ConnectionError
from requests.exceptions import ConnectionError as ConnectionError2
from requests.exceptions import ReadTimeout as ReadTimeout2

if os.path.dirname(os.path.abspath(__file__)) not in sys.path:
    sys.path.append(os.path.dirname(os.path.abspath(__file__)))

os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
os.environ['BITSANDBYTES_NOWELCOME'] = '1'
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')

from evaluate_params import eval_func_param_names, no_default_param_names
from enums import DocumentSubset, LangChainMode, no_lora_str, model_token_mapping, no_model_str, source_prefix, \
    source_postfix, LangChainAction, LangChainAgent, DocumentChoice
from loaders import get_loaders
from utils import set_seed, clear_torch_cache, save_generate_output, NullContext, wrapped_partial, EThread, get_githash, \
    import_matplotlib, get_device, makedirs, get_kwargs, start_faulthandler, get_hf_server, FakeTokenizer, remove, \
    have_langchain, set_openai, load_collection_enum, cuda_vis_check

start_faulthandler()
import_matplotlib()

SEED = 1236
set_seed(SEED)

from typing import Union

import fire
import torch
from transformers import GenerationConfig, AutoModel, TextIteratorStreamer

from prompter import Prompter, inv_prompt_type_to_model_lower, non_hf_types, PromptType, get_prompt, generate_prompt
from stopping import get_stopping

langchain_actions = [x.value for x in list(LangChainAction)]

langchain_agents_list = [x.value for x in list(LangChainAgent)]

scratch_base_dir = '/tmp/'


def main(
        load_8bit: bool = False,
        load_4bit: bool = False,
        load_half: bool = True,
        load_gptq: str = '',
        load_exllama: bool = False,
        use_safetensors: bool = False,
        revision: str = None,
        use_gpu_id: bool = True,
        base_model: str = '',
        tokenizer_base_model: str = '',
        lora_weights: str = "",
        gpu_id: int = 0,
        compile_model: bool = True,
        use_cache: bool = None,
        inference_server: str = "",
        prompt_type: Union[int, str] = None,
        prompt_dict: typing.Dict = None,

        model_lock: typing.List[typing.Dict[str, str]] = None,
        model_lock_columns: int = None,
        fail_if_cannot_connect: bool = False,

        # input to generation
        temperature: float = None,
        top_p: float = None,
        top_k: int = None,
        num_beams: int = None,
        repetition_penalty: float = None,
        num_return_sequences: int = None,
        do_sample: bool = None,
        max_new_tokens: int = None,
        min_new_tokens: int = None,
        early_stopping: Union[bool, str] = None,
        max_time: float = None,

        memory_restriction_level: int = None,
        debug: bool = False,
        save_dir: str = None,
        share: bool = False,
        local_files_only: bool = False,
        resume_download: bool = True,
        use_auth_token: Union[str, bool] = False,
        trust_remote_code: Union[str, bool] = True,
        rope_scaling: dict = None,
        max_seq_len: int = None,
        offload_folder: str = "offline_folder",

        src_lang: str = "English",
        tgt_lang: str = "Russian",

        cli: bool = False,
        cli_loop: bool = True,
        gradio: bool = True,
        gradio_offline_level: int = 0,
        root_path: str = "",
        chat: bool = True,
        chat_context: bool = False,
        stream_output: bool = True,
        async_output: bool = True,
        num_async: int = 3,
        show_examples: bool = None,
        verbose: bool = False,
        h2ocolors: bool = True,
        dark: bool = False,  # light tends to be best
        height: int = 600,
        show_lora: bool = True,
        login_mode_if_model0: bool = False,
        block_gradio_exit: bool = True,
        concurrency_count: int = 1,
        api_open: bool = False,
        allow_api: bool = True,
        input_lines: int = 1,
        gradio_size: str = None,
        auth: typing.List[typing.Tuple[str, str]] = None,
        max_max_time=None,
        max_max_new_tokens=None,

        sanitize_user_prompt: bool = False,
        sanitize_bot_response: bool = False,

        extra_model_options: typing.List[str] = [],
        extra_lora_options: typing.List[str] = [],
        extra_server_options: typing.List[str] = [],

        score_model: str = 'auto',

        eval_filename: str = None,
        eval_prompts_only_num: int = 0,
        eval_prompts_only_seed: int = 1234,
        eval_as_output: bool = False,

        langchain_mode: str = None,
        langchain_action: str = LangChainAction.QUERY.value,
        langchain_agents: list = [],
        force_langchain_evaluate: bool = False,
        langchain_modes: list = [x.value for x in list(LangChainMode)],
        visible_langchain_modes: list = ['UserData', 'MyData'],
        # WIP:
        # visible_langchain_actions: list = langchain_actions.copy(),
        visible_langchain_actions: list = [LangChainAction.QUERY.value, LangChainAction.SUMMARIZE_MAP.value],
        visible_langchain_agents: list = langchain_agents_list.copy(),
        document_subset: str = DocumentSubset.Relevant.name,
        document_choice: list = [DocumentChoice.ALL.value],
        user_path: str = None,
        langchain_mode_paths: dict = {'UserData': None},
        detect_user_path_changes_every_query: bool = False,
        use_llm_if_no_docs: bool = True,
        load_db_if_exists: bool = True,
        keep_sources_in_context: bool = False,
        use_system_prompt: bool = False,
        db_type: str = 'chroma',
        use_openai_embedding: bool = False,
        use_openai_model: bool = False,
        hf_embedding_model: str = None,
        cut_distance: float = 1.64,
        add_chat_history_to_context: bool = True,
        allow_upload_to_user_data: bool = True,
        reload_langchain_state: bool = True,
        allow_upload_to_my_data: bool = True,
        enable_url_upload: bool = True,
        enable_text_upload: bool = True,
        enable_sources_list: bool = True,
        chunk: bool = True,
        chunk_size: int = 512,
        top_k_docs: int = None,
        reverse_docs: bool = True,
        auto_reduce_chunks: bool = True,
        max_chunks: int = 100,
        n_jobs: int = -1,
        enable_captions: bool = True,
        captions_model: str = "Salesforce/blip-image-captioning-base",
        pre_load_caption_model: bool = False,
        caption_gpu: bool = True,
        enable_ocr: bool = False,
        enable_pdf_ocr: str = 'auto',
):
    """

    :param load_8bit: load model in 8-bit using bitsandbytes
    :param load_4bit: load model in 4-bit using bitsandbytes
    :param load_half: load model in float16
    :param load_gptq: to load model with GPTQ, put model_basename here, e.g. gptq_model-4bit--1g
    :param load_exllama: whether to use exllama (only applicable to LLaMa1/2 models with 16-bit or GPTQ
    :param use_safetensors: to use safetensors version (assumes file/HF points to safe tensors version)
    :param revision: Which HF revision to use
    :param use_gpu_id: whether to control devices with gpu_id.  If False, then spread across GPUs
    :param base_model: model HF-type name.  If use --base_model to preload model, cannot unload in gradio in models tab
    :param tokenizer_base_model: tokenizer HF-type name.  Usually not required, inferred from base_model.
    :param lora_weights: LORA weights path/HF link
    :param gpu_id: if use_gpu_id, then use gpu_id for cuda device ID, or auto mode if gpu_id != -1
    :param compile_model Whether to compile the model
    :param use_cache: Whether to use caching in model (some models fail when multiple threads use)
    :param inference_server: Consume base_model as type of model at this address
                             Address can be text-generation-server hosting that base_model
                             e.g. python generate.py --inference_server="http://192.168.1.46:6112" --base_model=h2oai/h2ogpt-oasst1-512-12b
                             Or Address can be "openai_chat" or "openai" for OpenAI API
                             e.g. python generate.py --inference_server="openai_chat" --base_model=gpt-3.5-turbo
                             e.g. python generate.py --inference_server="openai" --base_model=text-davinci-003
                             Or Address can be "vllm:IP:port" or "vllm:IP:port" for OpenAI-compliant vLLM endpoint
                             Note: vllm_chat not supported by vLLM project.
    :param prompt_type: type of prompt, usually matched to fine-tuned model or plain for foundational model
    :param prompt_dict: If prompt_type=custom, then expects (some) items returned by get_prompt(..., return_dict=True)
    :param model_lock: Lock models to specific combinations, for ease of use and extending to many models
           Only used if gradio = True
           List of dicts, each dict has base_model, tokenizer_base_model, lora_weights, inference_server, prompt_type, and prompt_dict
           If all models have same prompt_type, and prompt_dict, can still specify that once in CLI outside model_lock as default for dict
           Can specify model_lock instead of those items on CLI
           As with CLI itself, base_model can infer prompt_type and prompt_dict if in prompter.py.
             Also, tokenizer_base_model and lora_weights are optional.
             Also, inference_server is optional if loading model from local system.
           All models provided will automatically appear in compare model mode
           Model loading-unloading and related choices will be disabled.  Model/lora/server adding will be disabled
    :param model_lock_columns: How many columns to show if locking models (and so showing all at once)
           If None, then defaults to up to 3
           if -1, then all goes into 1 row
           Maximum value is 4 due to non-dynamic gradio rendering elements
    :param fail_if_cannot_connect: if doing model locking (e.g. with many models), fail if True.  Otherwise ignore.
           Useful when many endpoints and want to just see what works, but still have to wait for timeout.
    :param temperature: generation temperature
    :param top_p: generation top_p
    :param top_k: generation top_k
    :param num_beams: generation number of beams
    :param repetition_penalty: generation repetition penalty
    :param num_return_sequences: generation number of sequences (1 forced for chat)
    :param do_sample: generation sample
    :param max_new_tokens: generation max new tokens
    :param min_new_tokens: generation min tokens
    :param early_stopping: generation early stopping
    :param max_time: maximum time to allow for generation
    :param memory_restriction_level: 0 = no restriction to tokens or model, 1 = some restrictions on token 2 = HF like restriction 3 = very low memory case
    :param debug: enable debug mode
    :param save_dir: directory chat data is saved to
    :param share: whether to share the gradio app with sharable URL
    :param local_files_only: whether to only use local files instead of doing to HF for models
    :param resume_download: whether to resume downloads from HF for models
    :param use_auth_token: whether to use HF auth token (requires CLI did huggingface-cli login before)
    :param trust_remote_code: whether to use trust any code needed for HF model
    :param rope_scaling:
           For HF transformers model: scaling for rope-based models, e.g. --rope_scaling="{'type':'dynamic', 'factor':4}"
           For exllama model: --rope_scaling="{'alpha_value':4}" .  This automatically scales max_seq_len for exllama
    :param offload_folder: path for spilling model onto disk
    :param src_lang: source languages to include if doing translation (None = all)
    :param tgt_lang: target languages to include if doing translation (None = all)
    :param cli: whether to use CLI (non-gradio) interface.
    :param cli_loop: whether to loop for CLI (False usually only for testing)
    :param gradio: whether to enable gradio, or to enable benchmark mode
    :param gradio_offline_level: > 0, then change fonts so full offline
           == 1 means backend won't need internet for fonts, but front-end UI might if font not cached
           == 2 means backend and frontend don't need internet to download any fonts.
           Note: Some things always disabled include HF telemetry, gradio telemetry, chromadb posthog that involve uploading.
           This option further disables google fonts for downloading, which is less intrusive than uploading,
           but still required in air-gapped case.  The fonts don't look as nice as google fonts, but ensure full offline behavior.
           Also set --share=False to avoid sharing a gradio live link.
    :param root_path: The root path (or "mount point") of the application,
           if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy 
           that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", 
           the `root_path` should be set to "/myapp".
    :param chat: whether to enable chat mode with chat history
    :param chat_context: whether to use extra helpful context if human_bot
    :param stream_output: whether to stream output
    :param async_output: Whether to do asyncio handling
           For summarization
           Applicable to HF TGI server
           Only if stream_output=False in CLI, UI, or API
    :param num_async: Number of simultaneously allowed asyncio calls to make for async_output
           Too many will overload inference server, too few will be too slow
    :param show_examples: whether to show clickable examples in gradio
    :param verbose: whether to show verbose prints
    :param h2ocolors: whether to use H2O.ai theme
    :param dark: whether to use dark mode for UI by default (still controlled in UI)
    :param height: height of chat window
    :param show_lora: whether to show LORA options in UI (expert so can be hard to understand)
    :param login_mode_if_model0: set to True to load --base_model after client logs in, to be able to free GPU memory when model is swapped
    :param block_gradio_exit: whether to block gradio exit (used for testing)
    :param concurrency_count: gradio concurrency count (1 is optimal for LLMs)
    :param api_open: If False, don't let API calls skip gradio queue
    :param allow_api: whether to allow API calls at all to gradio server
    :param input_lines: how many input lines to show for chat box (>1 forces shift-enter for submit, else enter is submit)
    :param gradio_size: Overall size of text and spaces: "xsmall", "small", "medium", "large".
           Small useful for many chatbots in model_lock mode
    :param auth: gradio auth for launcher in form [(user1, pass1), (user2, pass2), ...]
                 e.g. --auth=[('jon','password')] with no spaces
    :param max_max_time: Maximum max_time for gradio slider
    :param max_max_new_tokens: Maximum max_new_tokens for gradio slider
    :param sanitize_user_prompt: whether to remove profanity from user input (slows down input processing)
      Requires optional packages:
      pip install alt-profanity-check==1.2.2 better-profanity==0.7.0
    :param sanitize_bot_response: whether to remove profanity and repeat lines from bot output (about 2x slower generation for long streaming cases due to better_profanity being slow)
    :param extra_model_options: extra models to show in list in gradio
    :param extra_lora_options: extra LORA to show in list in gradio
    :param extra_server_options: extra servers to show in list in gradio
    :param score_model: which model to score responses
           None: no response scoring
           'auto': auto mode, '' (no model) for CPU or 1 GPU, 'OpenAssistant/reward-model-deberta-v3-large-v2' for >=2 GPUs,
            because on CPU takes too much compute just for scoring response
    :param eval_filename: json file to use for evaluation, if None is sharegpt
    :param eval_prompts_only_num: for no gradio benchmark, if using eval_filename prompts for eval instead of examples
    :param eval_prompts_only_seed: for no gradio benchmark, seed for eval_filename sampling
    :param eval_as_output: for no gradio benchmark, whether to test eval_filename output itself
    :param langchain_mode: Data source to include.  Choose "UserData" to only consume files from make_db.py.
           None: auto mode, check if langchain package exists, at least do LLM if so, else Disabled
           WARNING: wiki_full requires extra data processing via read_wiki_full.py and requires really good workstation to generate db, unless already present.
    :param langchain_action: Mode langchain operations in on documents.
            Query: Make query of document(s)
            Summarize or Summarize_map_reduce: Summarize document(s) via map_reduce
            Summarize_all: Summarize document(s) using entire document at once
            Summarize_refine: Summarize document(s) using entire document, and try to refine before returning summary
    :param langchain_agents: Which agents to use
            'search': Use Web Search as context for LLM response, e.g. SERP if have SERPAPI_API_KEY in env
    :param force_langchain_evaluate: Whether to force langchain LLM use even if not doing langchain, mostly for testing.
    :param user_path: user path to glob from to generate db for vector search, for 'UserData' langchain mode.
           If already have db, any new/changed files are added automatically if path set, does not have to be same path used for prior db sources
    :param langchain_mode_paths: dict of langchain_mode keys and disk path values to use for source of documents
           E.g. "{'UserData2': 'userpath2'}"
           Can be None even if existing DB, to avoid new documents being added from that path, source links that are on disk still work.
           If user_path is not None, that path is used for 'UserData' instead of the value in this dict
    :param detect_user_path_changes_every_query: whether to detect if any files changed or added every similarity search (by file hashes).
           Expensive for large number of files, so not done by default.  By default only detect changes during db loading.
    :param langchain_modes: names of collections/dbs to potentially have
    :param visible_langchain_modes: dbs to generate at launch to be ready for LLM
           Can be up to ['wiki', 'wiki_full', 'UserData', 'MyData', 'github h2oGPT', 'DriverlessAI docs']
           But wiki_full is expensive and requires preparation
           To allow scratch space only live in session, add 'MyData' to list
           Default: If only want to consume local files, e.g. prepared by make_db.py, only include ['UserData']
           If have own user modes, need to add these here or add in UI.
           A state file is stored in visible_langchain_modes.pkl containing last UI-selected values of:
              langchain_modes, visible_langchain_modes, and langchain_mode_paths
              Delete the file if you want to start fresh,
              but in any case the user_path passed in CLI is used for UserData even if was None or different
    :param visible_langchain_actions: Which actions to allow
    :param visible_langchain_agents: Which agents to allow
    :param document_subset: Default document choice when taking subset of collection
    :param document_choice: Chosen document(s) by internal name, 'All' means use all docs
    :param use_llm_if_no_docs: Whether to use LLM even if no documents, when langchain_mode=UserData or MyData or custom
    :param load_db_if_exists: Whether to load chroma db if exists or re-generate db
    :param keep_sources_in_context: Whether to keep url sources in context, not helpful usually
    :param use_system_prompt: Whether to use system prompt (e.g. llama2 safe system prompt)
    :param db_type: 'faiss' for in-memory or 'chroma' or 'weaviate' for persisted on disk
    :param use_openai_embedding: Whether to use OpenAI embeddings for vector db
    :param use_openai_model: Whether to use OpenAI model for use with vector db
    :param hf_embedding_model: Which HF embedding model to use for vector db
           Default is instructor-large with 768 parameters per embedding if have GPUs, else all-MiniLM-L6-v2 if no GPUs
           Can also choose simpler model with 384 parameters per embedding: "sentence-transformers/all-MiniLM-L6-v2"
           Can also choose even better embedding with 1024 parameters: 'hkunlp/instructor-xl'
           We support automatically changing of embeddings for chroma, with a backup of db made if this is done
    :param cut_distance: Distance to cut off references with larger distances when showing references.
           1.64 is good to avoid dropping references for all-MiniLM-L6-v2, but instructor-large will always show excessive references.
           For all-MiniLM-L6-v2, a value of 1.5 can push out even more references, or a large value of 100 can avoid any loss of references.
    :param add_chat_history_to_context: Include chat context when performing action
           Not supported yet for openai_chat when using document collection instead of LLM
           Also not supported when using CLI mode
    :param allow_upload_to_user_data: Whether to allow file uploads to update shared vector db (UserData or custom user dbs)
    :param reload_langchain_state: Whether to reload visible_langchain_modes.pkl file that contains any new user collections.
    :param allow_upload_to_my_data: Whether to allow file uploads to update scratch vector db
    :param enable_url_upload: Whether to allow upload from URL
    :param enable_text_upload: Whether to allow upload of text
    :param enable_sources_list: Whether to allow list (or download for non-shared db) of list of sources for chosen db
    :param chunk: Whether to chunk data (True unless know data is already optimally chunked)
    :param chunk_size: Size of chunks, with typically top-4 passed to LLM, so needs to be in context length
    :param top_k_docs: number of chunks to give LLM
    :param reverse_docs: whether to reverse docs order so most relevant is closest to question.
           Best choice for sufficiently smart model, and truncation occurs for oldest context, so best then too.
           But smaller 6_9 models fail to use newest context and can get stuck on old information.
    :param auto_reduce_chunks: Whether to automatically reduce top_k_docs to fit context given prompt
    :param max_chunks: If top_k_docs=-1, maximum number of chunks to allow
    :param n_jobs: Number of processors to use when consuming documents (-1 = all, is default)
    :param enable_captions: Whether to support captions using BLIP for image files as documents, then preloads that model
    :param captions_model: Which model to use for captions.
           captions_model: str = "Salesforce/blip-image-captioning-base",  # continue capable
           captions_model: str = "Salesforce/blip2-flan-t5-xl",   # question/answer capable, 16GB state
           captions_model: str = "Salesforce/blip2-flan-t5-xxl",  # question/answer capable, 60GB state
           Note: opt-based blip2 are not permissive license due to opt and Meta license restrictions
           Disabled for CPU since BLIP requires CUDA
    :param pre_load_caption_model: Whether to preload caption model, or load after forking parallel doc loader
           parallel loading disabled if preload and have images, to prevent deadlocking on cuda context
           Recommended if using larger caption model
    :param caption_gpu: If support caption, then use GPU if exists
    :param enable_ocr: Whether to support OCR on images
    :param enable_pdf_ocr: 'auto' means only use OCR if normal text extraction fails.  Useful for pure image-based PDFs with text
                            'on' means always do OCR as additional parsing of same documents
                            'off' means don't do OCR (e.g. because it's slow even if 'auto' only would trigger if nothing else worked)
    :return:
    """
    if base_model is None:
        base_model = ''
    if tokenizer_base_model is None:
        tokenizer_base_model = ''
    if lora_weights is None:
        lora_weights = ''
    if inference_server is None:
        inference_server = ''

    # listen to env if set
    model_lock = os.getenv('model_lock', str(model_lock))
    model_lock = ast.literal_eval(model_lock)

    if model_lock:
        assert gradio, "model_lock only supported for gradio=True"
        if len(model_lock) > 1:
            assert chat, "model_lock only works for multiple models for chat=True"
        assert not cli, "model_lock only supported for cli=False"
        assert not (not cli and not gradio), "model_lock only supported for eval (cli=gradio=False)"
        assert not base_model, "Don't specify model_lock and base_model"
        assert not tokenizer_base_model, "Don't specify model_lock and tokenizer_base_model"
        assert not lora_weights, "Don't specify model_lock and lora_weights"
        assert not inference_server, "Don't specify model_lock and inference_server"
        # assert not prompt_type, "Don't specify model_lock and prompt_type"
        # assert not prompt_dict, "Don't specify model_lock and prompt_dict"

    n_jobs = int(os.getenv('n_jobs', str(n_jobs)))
    is_hf = bool(int(os.getenv("HUGGINGFACE_SPACES", '0')))
    is_gpth2oai = bool(int(os.getenv("GPT_H2O_AI", '0')))
    is_public = is_hf or is_gpth2oai  # multi-user case with fixed model and disclaimer
    if memory_restriction_level is None:
        memory_restriction_level = 2 if is_hf else 0  # 2 assumes run on 24GB consumer GPU
    else:
        assert 0 <= memory_restriction_level <= 3, "Bad memory_restriction_level=%s" % memory_restriction_level
    if is_public and os.getenv('n_jobs') is None:
        n_jobs = max(1, min(os.cpu_count() // 2, 8))
    admin_pass = os.getenv("ADMIN_PASS")
    # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result
    # but becomes unrecoverable sometimes if raise, so just be silent for now
    raise_generate_gpu_exceptions = True

    if isinstance(rope_scaling, str):
        rope_scaling = ast.literal_eval(rope_scaling)

    # allow set token directly
    use_auth_token = os.environ.get("HUGGINGFACE_API_TOKEN", use_auth_token)
    allow_upload_to_user_data = bool(
        int(os.environ.get("allow_upload_to_user_data", str(int(allow_upload_to_user_data)))))
    allow_upload_to_my_data = bool(int(os.environ.get("allow_upload_to_my_data", str(int(allow_upload_to_my_data)))))
    height = int(os.environ.get("HEIGHT", height))
    h2ocolors = bool(int(os.getenv('h2ocolors', h2ocolors)))

    # allow enabling langchain via ENV
    # FIRST PLACE where LangChain referenced, but no imports related to it
    langchain_mode = os.environ.get("LANGCHAIN_MODE", langchain_mode)
    if langchain_mode is not None:
        assert langchain_mode in langchain_modes, "Invalid langchain_mode %s" % langchain_mode
    visible_langchain_modes = ast.literal_eval(os.environ.get("visible_langchain_modes", str(visible_langchain_modes)))
    if langchain_mode not in visible_langchain_modes and langchain_mode in langchain_modes:
        if langchain_mode is not None:
            visible_langchain_modes += [langchain_mode]

    # update
    if isinstance(langchain_mode_paths, str):
        langchain_mode_paths = ast.literal_eval(langchain_mode_paths)
        assert isinstance(langchain_mode_paths, dict)
    if user_path:
        langchain_mode_paths['UserData'] = user_path
        makedirs(user_path)

    if is_public:
        allow_upload_to_user_data = False
        if LangChainMode.USER_DATA.value in visible_langchain_modes:
            visible_langchain_modes.remove(LangChainMode.USER_DATA.value)

    # in-place, for non-scratch dbs
    if allow_upload_to_user_data:
        update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, '')
        # always listen to CLI-passed user_path if passed
        if user_path:
            langchain_mode_paths['UserData'] = user_path

    assert langchain_action in langchain_actions, "Invalid langchain_action %s" % langchain_action
    assert len(
        set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents

    # if specifically chose not to show My or User Data, disable upload, so gradio elements are simpler
    if LangChainMode.MY_DATA.value not in visible_langchain_modes:
        allow_upload_to_my_data = False
    if LangChainMode.USER_DATA.value not in visible_langchain_modes:
        allow_upload_to_user_data = False

    # auto-set langchain_mode
    if have_langchain and langchain_mode is None:
        # start in chat mode, in case just want to chat and don't want to get "No documents to query" by default.
        langchain_mode = LangChainMode.LLM.value
        if allow_upload_to_user_data and not is_public and langchain_mode_paths['UserData']:
            print("Auto set langchain_mode=%s.  Could use UserData instead." % langchain_mode, flush=True)
        elif allow_upload_to_my_data:
            print("Auto set langchain_mode=%s.  Could use MyData instead."
                  "  To allow UserData to pull files from disk,"
                  " set user_path or langchain_mode_paths, and ensure allow_upload_to_user_data=True" % langchain_mode,
                  flush=True)
        else:
            raise RuntimeError("Please pass --langchain_mode=<chosen mode> out of %s" % langchain_modes)
    if not have_langchain and langchain_mode not in [None, LangChainMode.DISABLED.value, LangChainMode.LLM.value]:
        raise RuntimeError("Asked for LangChain mode but langchain python package cannot be found.")
    if langchain_mode is None:
        # if not set yet, disable
        langchain_mode = LangChainMode.DISABLED.value
        print("Auto set langchain_mode=%s  Have langchain package: %s" % (langchain_mode, have_langchain), flush=True)

    if is_public:
        allow_upload_to_user_data = False
        input_lines = 1  # ensure set, for ease of use
        temperature = 0.2 if temperature is None else temperature
        top_p = 0.85 if top_p is None else top_p
        top_k = 70 if top_k is None else top_k
        if is_hf:
            do_sample = True if do_sample is None else do_sample
            top_k_docs = 3 if top_k_docs is None else top_k_docs
        else:
            # by default don't sample, too chatty
            do_sample = False if do_sample is None else do_sample
            top_k_docs = 4 if top_k_docs is None else top_k_docs

        if memory_restriction_level == 2:
            if not base_model and not inference_server and not model_lock:
                base_model = 'h2oai/h2ogpt-oasst1-512-12b'
                # don't set load_8bit if passed base_model, doesn't always work so can't just override
                load_8bit = True
                load_4bit = False  # FIXME - consider using 4-bit instead of 8-bit
        elif not inference_server:
            top_k_docs = 10 if top_k_docs is None else top_k_docs
    if memory_restriction_level >= 2:
        load_8bit = True
        load_4bit = False  # FIXME - consider using 4-bit instead of 8-bit
        if hf_embedding_model is None:
            hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
        top_k_docs = 3 if top_k_docs is None else top_k_docs
    if top_k_docs is None:
        top_k_docs = 3
    if is_public:
        if not max_time:
            max_time = 60 * 2
        if not max_max_time:
            max_max_time = max_time
        if not max_new_tokens:
            max_new_tokens = 256
        if not max_max_new_tokens:
            max_max_new_tokens = 512
    else:
        if not max_max_time:
            max_max_time = 60 * 20
        if not max_max_new_tokens:
            max_max_new_tokens = 1024
    if is_hf:
        # must override share if in spaces
        share = False
        if not max_time:
            max_time = 60 * 1
        if not max_max_time:
            max_max_time = max_time
        # HF accounted for later in get_max_max_new_tokens()
    save_dir = os.getenv('SAVE_DIR', save_dir)
    score_model = os.getenv('SCORE_MODEL', score_model)
    if str(score_model) == 'None':
        score_model = ''
    concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count))
    api_open = bool(int(os.getenv('API_OPEN', str(int(api_open)))))
    allow_api = bool(int(os.getenv('ALLOW_API', str(int(allow_api)))))

    n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
    n_gpus, gpu_ids = cuda_vis_check(n_gpus)
    if n_gpus == 0:
        print("No GPUs detected", flush=True)
        enable_captions = False
        gpu_id = None
        load_8bit = False
        load_4bit = False
        load_half = False
        load_gptq = ''
        load_exllama = False
        use_safetensors = False
        revision = None
        use_gpu_id = False
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = False
        torch.set_default_dtype(torch.float32)
        if psutil.virtual_memory().available < 94 * 1024 ** 3 and not inference_server and not model_lock:
            # 12B uses ~94GB
            # 6.9B uses ~47GB
            base_model = 'h2oai/h2ogpt-oig-oasst1-512-6_9b' if not base_model else base_model
        if hf_embedding_model is None:
            # if no GPUs, use simpler embedding model to avoid cost in time
            hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
        if score_model == 'auto':
            score_model = ''
    else:
        if score_model == 'auto':
            if n_gpus >= 2:
                # will by default place scoring model on last GPU
                score_model = 'OpenAssistant/reward-model-deberta-v3-large-v2'
            else:
                score_model = ''
        if hf_embedding_model is None:
            # if still None, then set default
            hf_embedding_model = 'hkunlp/instructor-large'

    # get defaults
    if base_model:
        model_lower = base_model.lower()
    elif model_lock:
        # have 0th model be thought of as normal model
        assert len(model_lock) > 0 and model_lock[0]['base_model']
        model_lower = model_lock[0]['base_model'].lower()
    else:
        model_lower = ''
    if not gradio:
        # force, else not single response like want to look at
        stream_output = False
        # else prompt removal can mess up output
        chat = False
    # hard-coded defaults
    first_para = False
    text_limit = None

    if offload_folder:
        offload_folder = makedirs(offload_folder, exist_ok=True, tmp_ok=True)

    placeholder_instruction, placeholder_input, \
        stream_output, show_examples, \
        prompt_type, prompt_dict, \
        temperature, top_p, top_k, num_beams, \
        max_new_tokens, min_new_tokens, early_stopping, max_time, \
        repetition_penalty, num_return_sequences, \
        do_sample, \
        src_lang, tgt_lang, \
        examples, \
        task_info = \
        get_generate_params(model_lower,
                            chat,
                            stream_output, show_examples,
                            prompt_type, prompt_dict,
                            temperature, top_p, top_k, num_beams,
                            max_new_tokens, min_new_tokens, early_stopping, max_time,
                            repetition_penalty, num_return_sequences,
                            do_sample,
                            top_k_docs,
                            chunk,
                            chunk_size,
                            verbose,
                            )

    git_hash = get_githash() if is_public or os.getenv('GET_GITHASH') else "GET_GITHASH"
    locals_dict = locals()
    locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()])
    if verbose:
        print(f"Generating model with params:\n{locals_print}", flush=True)
        print("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), git_hash), flush=True)

    if langchain_mode != "Disabled":
        # SECOND PLACE where LangChain referenced, but all imports are kept local so not required
        from gpt_langchain import prep_langchain, get_some_dbs_from_hf
        if is_hf:
            get_some_dbs_from_hf()
        dbs = {}
        for langchain_mode1 in visible_langchain_modes:
            if langchain_mode1 in ['MyData']:  # FIXME: Remove other custom temp dbs
                # don't use what is on disk, remove it instead
                for gpath1 in glob.glob(os.path.join(scratch_base_dir, 'db_dir_%s*' % langchain_mode1)):
                    if os.path.isdir(gpath1):
                        print("Removing old MyData: %s" % gpath1, flush=True)
                        remove(gpath1)
                continue
            if langchain_mode1 in ['All']:
                # FIXME: All should be avoided until scans over each db, shouldn't be separate db
                continue
            persist_directory1 = 'db_dir_%s' % langchain_mode1  # single place, no special names for each case
            try:
                db = prep_langchain(persist_directory1,
                                    load_db_if_exists,
                                    db_type, use_openai_embedding,
                                    langchain_mode1, langchain_mode_paths,
                                    hf_embedding_model,
                                    kwargs_make_db=locals())
            finally:
                # in case updated embeddings or created new embeddings
                clear_torch_cache()
            dbs[langchain_mode1] = db
        # remove None db's so can just rely upon k in dbs for if hav db
        dbs = {k: v for k, v in dbs.items() if v is not None}
    else:
        dbs = {}
        # import control
        if os.environ.get("TEST_LANGCHAIN_IMPORT"):
            assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
            assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"

    model_state_none = dict(model=None, tokenizer=None, device=None,
                            base_model=None, tokenizer_base_model=None, lora_weights=None,
                            inference_server=None, prompt_type=None, prompt_dict=None)
    my_db_state0 = {LangChainMode.MY_DATA.value: [None, None]}
    selection_docs_state0 = dict(visible_langchain_modes=visible_langchain_modes,
                                 langchain_mode_paths=langchain_mode_paths,
                                 langchain_modes=langchain_modes)
    selection_docs_state = selection_docs_state0
    langchain_modes0 = langchain_modes
    langchain_mode_paths0 = langchain_mode_paths
    visible_langchain_modes0 = visible_langchain_modes

    if cli:
        from cli import run_cli
        return run_cli(**get_kwargs(run_cli, exclude_names=['model_state0'], **locals()))
    elif not gradio:
        from eval import run_eval
        return run_eval(**get_kwargs(run_eval, exclude_names=['model_state0'], **locals()))
    elif gradio:
        # imported here so don't require gradio to run generate
        from gradio_runner import go_gradio

        # get default model
        model_states = []
        model_list = [dict(base_model=base_model, tokenizer_base_model=tokenizer_base_model, lora_weights=lora_weights,
                           inference_server=inference_server, prompt_type=prompt_type, prompt_dict=prompt_dict)]
        model_list0 = copy.deepcopy(model_list)  # just strings, safe to deepcopy
        model_state0 = model_state_none.copy()
        assert len(model_state_none) == len(model_state0)
        if model_lock:
            model_list = model_lock
        for model_dict in reversed(model_list):
            # do reverse, so first is default base_model etc., so some logic works in go_gradio() more easily
            # handles defaults user didn't have to pass
            model_dict['base_model'] = base_model1 = model_dict.get('base_model', '')
            model_dict['tokenizer_base_model'] = tokenizer_base_model1 = model_dict.get('tokenizer_base_model', '')
            model_dict['lora_weights'] = lora_weights1 = model_dict.get('lora_weights', '')
            model_dict['inference_server'] = inference_server1 = model_dict.get('inference_server', '')
            prompt_type1 = model_dict.get('prompt_type', model_list0[0]['prompt_type'])  # don't use mutated value
            # try to infer, ignore empty initial state leading to get_generate_params -> 'plain'
            if model_dict.get('prompt_type') is None:
                model_lower1 = base_model1.lower()
                if model_lower1 in inv_prompt_type_to_model_lower:
                    prompt_type1 = inv_prompt_type_to_model_lower[model_lower1]
                    prompt_dict1, error0 = get_prompt(prompt_type1, '',
                                                      chat=False, context='', reduced=False, making_context=False,
                                                      return_dict=True)
                else:
                    prompt_dict1 = prompt_dict
            else:
                prompt_dict1 = prompt_dict
            model_dict['prompt_type'] = prompt_type1
            model_dict['prompt_dict'] = prompt_dict1 = model_dict.get('prompt_dict', prompt_dict1)
            all_kwargs = locals().copy()
            all_kwargs.update(dict(base_model=base_model1, tokenizer_base_model=tokenizer_base_model1,
                                   lora_weights=lora_weights1, inference_server=inference_server1))
            if base_model1 and not login_mode_if_model0:
                model0, tokenizer0, device = get_model(reward_type=False,
                                                       **get_kwargs(get_model, exclude_names=['reward_type'],
                                                                    **all_kwargs))
            else:
                # if empty model, then don't load anything, just get gradio up
                model0, tokenizer0, device = None, None, None
            if model0 is None:
                if fail_if_cannot_connect:
                    raise RuntimeError("Could not connect, see logs")
                # skip
                if isinstance(model_lock, list):
                    model_lock.remove(model_dict)
                continue
            model_state_trial = dict(model=model0, tokenizer=tokenizer0, device=device)
            model_state_trial.update(model_dict)
            assert len(model_state_none) == len(model_state_trial)
            print("Model %s" % model_dict, flush=True)
            if model_lock:
                # last in iteration will be first
                model_states.insert(0, model_state_trial)
                # fill model_state0 so go_gradio() easier, manage model_states separately
                model_state0 = model_state_trial.copy()
            else:
                model_state0 = model_state_trial.copy()
            assert len(model_state_none) == len(model_state0)

        # get score model
        all_kwargs = locals().copy()
        smodel, stokenizer, sdevice = get_score_model(reward_type=True,
                                                      **get_kwargs(get_score_model, exclude_names=['reward_type'],
                                                                   **all_kwargs))
        score_model_state0 = dict(model=smodel, tokenizer=stokenizer, device=sdevice,
                                  base_model=score_model, tokenizer_base_model='', lora_weights='',
                                  inference_server='', prompt_type='', prompt_dict='')

        if enable_captions:
            if pre_load_caption_model:
                from image_captions import H2OImageCaptionLoader
                caption_loader = H2OImageCaptionLoader(caption_gpu=caption_gpu).load_model()
            else:
                caption_loader = 'gpu' if caption_gpu else 'cpu'
        else:
            caption_loader = False

        # assume gradio needs everything
        go_gradio(**locals())


def get_config(base_model,
               use_auth_token=False,
               trust_remote_code=True,
               offload_folder=None,
               revision=None,
               rope_scaling=None,
               triton_attn=False,
               long_sequence=True,
               return_model=False,
               raise_exception=False,
               max_seq_len=None,
               verbose=False,
               ):
    from accelerate import init_empty_weights
    with init_empty_weights():
        from transformers import AutoConfig
        try:
            config = AutoConfig.from_pretrained(base_model, use_auth_token=use_auth_token,
                                                trust_remote_code=trust_remote_code,
                                                offload_folder=offload_folder,
                                                revision=revision,
                                                rope_scaling=rope_scaling)
        except OSError as e:
            if raise_exception:
                raise
            if 'not a local folder and is not a valid model identifier listed on' in str(
                    e) or '404 Client Error' in str(e):
                # e.g. llama, gpjt, etc.
                # e.g. HF TGI but not model on HF or private etc.
                if max_seq_len is None and base_model.lower() in non_hf_types:
                    print("Could not determine --max_seq_len, setting to 2048.  Pass if not correct", flush=True)
                    max_seq_len = 2048
                # HF TGI server only should really require prompt_type, not HF model state
                return None, None, max_seq_len
            else:
                raise
        if triton_attn and 'mpt-' in base_model.lower():
            config.attn_config['attn_impl'] = 'triton'
        if long_sequence:
            if 'mpt-7b-storywriter' in base_model.lower():
                config.update({"max_seq_len": 83968})
            if 'mosaicml/mpt-7b-chat' in base_model.lower():
                config.update({"max_seq_len": 4096})
            if 'mpt-30b' in base_model.lower():
                config.update({"max_seq_len": 2 * 8192})
        if return_model and \
                issubclass(config.__class__, tuple(AutoModel._model_mapping.keys())):
            model = AutoModel.from_config(
                config,
                trust_remote_code=trust_remote_code,
            )
        else:
            # can't infer
            model = None
    if 'falcon' in base_model.lower():
        config.use_cache = False

    # allow override
    if max_seq_len is not None:
        print("Overriding max_seq_len %d -> %d" % (max_seq_len, max_seq_len), flush=True)
    else:
        if hasattr(config, 'max_seq_len'):
            max_seq_len = int(config.max_seq_len)
        elif hasattr(config, 'max_position_embeddings') and isinstance(config.max_position_embeddings, int):
            # help automatically limit inputs to generate
            max_seq_len = config.max_position_embeddings
            if verbose:
                print("Used max_position_embeddings=%s as base model (pre-rope) max_seq_len."
                      "  If not desired, pass --max_seq_len and set to some integer value." % config.max_position_embeddings,
                      flush=True)
        else:
            print("Could not determine --max_seq_len, setting to 2048.  Pass if not correct", flush=True)
            max_seq_len = 2048
            # FIXME:
            # raise RuntimeError("Could not determine max_seq_len,"
            #                   " please pass --max_seq_len and set to some value, e.g. 2048.")

        if rope_scaling:
            if rope_scaling.get('factor'):
                # HF transformers
                max_seq_len *= rope_scaling.get('factor')
            elif rope_scaling.get('alpha_value'):
                # exllama
                # Note: exllama's own tokenizer has this set correctly in loaders.py, this config will be unused
                max_seq_len *= rope_scaling.get('alpha_value')
            print("Automatically setting max_seq_len=%d for RoPE scaling" % max_seq_len, flush=True)

    return config, model, max_seq_len


def get_non_lora_model(base_model, model_loader, load_half,
                       load_gptq,
                       load_exllama,
                       use_safetensors,
                       revision,
                       model_kwargs, reward_type,
                       config, model,
                       gpu_id=0,
                       ):
    """
    Ensure model gets on correct device
    """

    if model is not None:
        # NOTE: Can specify max_memory={0: max_mem, 1: max_mem}, to shard model
        # NOTE: Some models require avoiding sharding some layers,
        # then would pass no_split_module_classes and give list of those layers.
        from accelerate import infer_auto_device_map
        device_map = infer_auto_device_map(
            model,
            dtype=torch.float16 if load_half else torch.float32,
        )
        if hasattr(model, 'model'):
            device_map_model = infer_auto_device_map(
                model.model,
                dtype=torch.float16 if load_half else torch.float32,
            )
            device_map.update(device_map_model)
    else:
        device_map = "auto"

    n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0

    if n_gpus > 0:
        if gpu_id >= 0:
            # FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set.
            # So avoid for now, just put on first GPU, unless score_model, put on last
            if reward_type:
                device_map = {'': n_gpus - 1}
            else:
                device_map = {'': min(n_gpus - 1, gpu_id)}
        if gpu_id == -1:
            device_map = {'': 'cuda'}
    else:
        device_map = {'': 'cpu'}
        model_kwargs['load_in_8bit'] = False
        model_kwargs['load_in_4bit'] = False
    print('device_map: %s' % device_map, flush=True)

    load_in_8bit = model_kwargs.get('load_in_8bit', False)
    load_in_4bit = model_kwargs.get('load_in_4bit', False)
    model_kwargs['device_map'] = device_map
    model_kwargs['use_safetensors'] = use_safetensors
    model_kwargs['revision'] = revision
    pop_unused_model_kwargs(model_kwargs)

    if load_exllama:
        model = model_loader
    elif load_gptq:
        if 'Llama-2-70B-chat-GPTQ' in base_model:
            model_kwargs.update(dict(inject_fused_attention=False))
        model_kwargs.pop('torch_dtype', None)
        model_kwargs.pop('device_map')
        model = model_loader(
            model_name_or_path=base_model,
            model_basename=load_gptq,
            **model_kwargs,
        )
    elif load_in_8bit or load_in_4bit or not load_half:
        model = model_loader(
            base_model,
            config=config,
            **model_kwargs,
        )
    else:
        model = model_loader(
            base_model,
            config=config,
            **model_kwargs,
        ).half()
    return model


def get_client_from_inference_server(inference_server, base_model=None, raise_connection_exception=False):
    inference_server, headers = get_hf_server(inference_server)
    # preload client since slow for gradio case especially
    from gradio_utils.grclient import GradioClient
    gr_client = None
    hf_client = None
    if headers is None:
        try:
            print("GR Client Begin: %s %s" % (inference_server, base_model), flush=True)
            # first do sanity check if alive, else gradio client takes too long by default
            requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT', '30')))
            gr_client = GradioClient(inference_server)
            print("GR Client End: %s" % inference_server, flush=True)
        except (OSError, ValueError) as e:
            # Occurs when wrong endpoint and should have been HF client, so don't hard raise, just move to HF
            gr_client = None
            print("GR Client Failed %s %s: %s" % (inference_server, base_model, str(e)), flush=True)
        except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2,
                JSONDecodeError, ReadTimeout2, KeyError) as e:
            t, v, tb = sys.exc_info()
            ex = ''.join(traceback.format_exception(t, v, tb))
            print("GR Client Failed %s %s: %s" % (inference_server, base_model, str(ex)), flush=True)
            if raise_connection_exception:
                raise

    if gr_client is None:
        res = None
        from text_generation import Client as HFClient
        print("HF Client Begin: %s %s" % (inference_server, base_model))
        try:
            hf_client = HFClient(inference_server, headers=headers, timeout=int(os.getenv('REQUEST_TIMEOUT', '30')))
            # quick check valid TGI endpoint
            res = hf_client.generate('What?', max_new_tokens=1)
            hf_client = HFClient(inference_server, headers=headers, timeout=300)
        except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2,
                JSONDecodeError, ReadTimeout2, KeyError) as e:
            hf_client = None
            t, v, tb = sys.exc_info()
            ex = ''.join(traceback.format_exception(t, v, tb))
            print("HF Client Failed %s %s: %s" % (inference_server, base_model, str(ex)))
            if raise_connection_exception:
                raise
        print("HF Client End: %s %s : %s" % (inference_server, base_model, res))
    return inference_server, gr_client, hf_client


def get_model(
        load_8bit: bool = False,
        load_4bit: bool = False,
        load_half: bool = True,
        load_gptq: str = '',
        load_exllama: bool = False,
        use_safetensors: bool = False,
        revision: str = None,
        use_gpu_id: bool = True,
        base_model: str = '',
        inference_server: str = "",
        tokenizer_base_model: str = '',
        lora_weights: str = "",
        gpu_id: int = 0,

        reward_type: bool = None,
        local_files_only: bool = False,
        resume_download: bool = True,
        use_auth_token: Union[str, bool] = False,
        trust_remote_code: bool = True,
        offload_folder: str = None,
        rope_scaling: dict = None,
        max_seq_len: int = None,
        compile_model: bool = True,

        verbose: bool = False,
):
    """

    :param load_8bit: load model in 8-bit, not supported by all models
    :param load_4bit: load model in 4-bit, not supported by all models
    :param load_half: load model in 16-bit
    :param load_gptq: GPTQ model_basename
    :param load_exllama: whether to use exllama
    :param use_safetensors: use safetensors file
    :param revision:
    :param use_gpu_id: Use torch infer of optimal placement of layers on devices (for non-lora case)
           For non-LORA case, False will spread shards across multiple GPUs, but this can lead to cuda:x cuda:y mismatches
           So it is not the default
    :param base_model: name/path of base model
    :param inference_server: whether base_model is hosted locally ('') or via http (url)
    :param tokenizer_base_model: name/path of tokenizer
    :param lora_weights: name/path
    :param gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1)
    :param reward_type: reward type model for sequence classification
    :param local_files_only: use local files instead of from HF
    :param resume_download: resume downloads from HF
    :param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo
    :param trust_remote_code: trust code needed by model
    :param offload_folder: offload folder
    :param rope_scaling: scaling for rope-based models, e.g. "{'type':'dynamic', 'factor':4}"
    :param max_seq_len: override for maximum sequence length for model
    :param compile_model: whether to compile torch model
    :param max_seq_len: if set, use as max_seq_len for model
    :param verbose:
    :return:
    """
    print("Starting get_model: %s %s" % (base_model, inference_server), flush=True)

    triton_attn = False
    long_sequence = True
    config_kwargs = dict(use_auth_token=use_auth_token,
                         trust_remote_code=trust_remote_code,
                         offload_folder=offload_folder,
                         rope_scaling=rope_scaling,
                         triton_attn=triton_attn,
                         long_sequence=long_sequence,
                         revision=revision,
                         max_seq_len=max_seq_len,
                         verbose=verbose)
    config, _, max_seq_len = get_config(base_model, **config_kwargs, raise_exception=False)

    if base_model in non_hf_types:
        assert config is None, "Expected config None for %s" % base_model

    llama_type_from_config = 'llama' in str(config).lower()
    llama_type_from_name = "llama" in base_model.lower()
    llama_type = llama_type_from_config or llama_type_from_name
    if "xgen" in base_model.lower() or 'llama2' in base_model.lower() or 'llama-2' in base_model.lower():
        llama_type = False
    if llama_type:
        if verbose:
            print("Detected as llama type from"
                  " config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True)

    model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type,
                                                 load_gptq=load_gptq, load_exllama=load_exllama, config=config,
                                                 rope_scaling=rope_scaling, max_seq_len=max_seq_len)

    tokenizer_kwargs = dict(local_files_only=local_files_only,
                            resume_download=resume_download,
                            use_auth_token=use_auth_token,
                            trust_remote_code=trust_remote_code,
                            offload_folder=offload_folder,
                            revision=revision,
                            padding_side='left',
                            config=config,
                            )
    if not tokenizer_base_model:
        tokenizer_base_model = base_model

    if load_exllama:
        tokenizer = tokenizer_loader
    elif config is not None and tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
        if load_exllama:
            tokenizer = tokenizer_loader
        else:
            tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model, **tokenizer_kwargs)
            # sets raw (no cushion) limit
            # If using RoPE with scaling, then for non-exllama models (e.g. HF models),
            #  then config -> tokenizer will set model_max_length correctly
            set_model_max_len(max_seq_len, tokenizer, verbose=False)
            # if using fake tokenizer, not really accurate when lots of numbers, give a bit of buffer, else get:
            # Generation Failed: Input validation error: `inputs` must have less than 2048 tokens. Given: 2233
            tokenizer.model_max_length = tokenizer.model_max_length - 50
    else:
        tokenizer = FakeTokenizer()

    if isinstance(inference_server, str) and inference_server.startswith("http"):
        inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server,
                                                                                  base_model=base_model)
        client = gr_client or hf_client
        # Don't return None, None for model, tokenizer so triggers
        return client, tokenizer, 'http'
    if isinstance(inference_server, str) and (
            inference_server.startswith('openai') or inference_server.startswith('vllm')):
        if inference_server.startswith('openai'):
            assert os.getenv('OPENAI_API_KEY'), "Set environment for OPENAI_API_KEY"
            # Don't return None, None for model, tokenizer so triggers
            # include small token cushion
            tokenizer = FakeTokenizer(model_max_length=model_token_mapping[base_model] - 50)
        return inference_server, tokenizer, inference_server
    assert not inference_server, "Malformed inference_server=%s" % inference_server
    if base_model in non_hf_types:
        from gpt4all_llm import get_model_tokenizer_gpt4all
        model, tokenizer, device = get_model_tokenizer_gpt4all(base_model)
        return model, tokenizer, device
    if load_exllama:
        return model_loader, tokenizer, 'cuda'

    # get local torch-HF model
    return get_hf_model(load_8bit=load_8bit,
                        load_4bit=load_4bit,
                        load_half=load_half,
                        load_gptq=load_gptq,
                        use_safetensors=use_safetensors,
                        revision=revision,
                        use_gpu_id=use_gpu_id,
                        base_model=base_model,
                        tokenizer_base_model=tokenizer_base_model,
                        lora_weights=lora_weights,
                        gpu_id=gpu_id,

                        reward_type=reward_type,
                        local_files_only=local_files_only,
                        resume_download=resume_download,
                        use_auth_token=use_auth_token,
                        trust_remote_code=trust_remote_code,
                        offload_folder=offload_folder,
                        rope_scaling=rope_scaling,
                        compile_model=compile_model,

                        llama_type=llama_type,
                        config_kwargs=config_kwargs,
                        tokenizer_kwargs=tokenizer_kwargs,

                        verbose=verbose)


def get_hf_model(load_8bit: bool = False,
                 load_4bit: bool = False,
                 load_half: bool = True,
                 load_gptq: str = '',
                 use_safetensors: bool = False,
                 revision: str = None,
                 use_gpu_id: bool = True,
                 base_model: str = '',
                 tokenizer_base_model: str = '',
                 lora_weights: str = "",
                 gpu_id: int = 0,

                 reward_type: bool = None,
                 local_files_only: bool = False,
                 resume_download: bool = True,
                 use_auth_token: Union[str, bool] = False,
                 trust_remote_code: bool = True,
                 offload_folder: str = None,
                 rope_scaling: dict = None,
                 compile_model: bool = True,

                 llama_type: bool = False,
                 config_kwargs=None,
                 tokenizer_kwargs=None,

                 verbose: bool = False,
                 ):
    assert config_kwargs is not None
    assert tokenizer_kwargs is not None

    load_exllama = False  # Never should be in HF code for exllama

    if lora_weights is not None and lora_weights.strip():
        if verbose:
            print("Get %s lora weights" % lora_weights, flush=True)
    device = get_device()

    if 'gpt2' in base_model.lower():
        # RuntimeError: where expected condition to be a boolean tensor, but got a tensor with dtype Half
        load_8bit = False
        load_4bit = False

    assert base_model.strip(), (
        "Please choose a base model with --base_model (CLI) or load one from Models Tab (gradio)"
    )

    model_loader, tokenizer_loader = get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type,
                                                 load_gptq=load_gptq, load_exllama=load_exllama)

    config, _, max_seq_len = get_config(base_model, return_model=False, raise_exception=True, **config_kwargs)

    if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
        if load_exllama:
            tokenizer = tokenizer_loader
        else:
            tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
                                                         **tokenizer_kwargs)
    else:
        tokenizer = tokenizer_loader

    if isinstance(tokenizer, str):
        # already a pipeline, tokenizer_loader is string for task
        model = model_loader(tokenizer,
                             model=base_model,
                             device=0 if device == "cuda" else -1,
                             torch_dtype=torch.float16 if device == 'cuda' else torch.float32)
    else:
        assert device in ["cuda", "cpu", "mps"], "Unsupported device %s" % device
        model_kwargs = dict(local_files_only=local_files_only,
                            torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
                            resume_download=resume_download,
                            use_auth_token=use_auth_token,
                            trust_remote_code=trust_remote_code,
                            offload_folder=offload_folder,
                            revision=revision,
                            # rope_scaling=rope_scaling,  # only put into config
                            )
        if 'mbart-' not in base_model.lower() and 'mpt-' not in base_model.lower():
            if use_gpu_id and gpu_id is not None and gpu_id >= 0 and device == 'cuda':
                device_map = {"": gpu_id}
            else:
                device_map = "auto"
            model_kwargs.update(dict(load_in_8bit=load_8bit,
                                     load_in_4bit=load_4bit,
                                     device_map=device_map,
                                     ))
        if 'mpt-' in base_model.lower() and gpu_id is not None and gpu_id >= 0:
            # MPT doesn't support spreading over GPUs
            model_kwargs.update(dict(device_map={"": gpu_id} if device == 'cuda' else "cpu"))

        if 'OpenAssistant/reward-model'.lower() in base_model.lower():
            # FIXME: could put on other GPUs
            model_kwargs['device_map'] = {"": 0} if device == 'cuda' else {"": 'cpu'}
            model_kwargs.pop('torch_dtype', None)
        pop_unused_model_kwargs(model_kwargs)

        if not lora_weights:
            # torch.device context uses twice memory for AutoGPTQ
            context = NullContext if load_gptq else torch.device
            with context(device):

                if use_gpu_id:
                    config, model, max_seq_len = get_config(base_model,
                                                            return_model=True, raise_exception=True, **config_kwargs)
                    model = get_non_lora_model(base_model, model_loader, load_half, load_gptq,
                                               load_exllama,
                                               use_safetensors,
                                               revision,
                                               model_kwargs, reward_type,
                                               config, model,
                                               gpu_id=gpu_id,
                                               )
                else:
                    config, _, max_seq_len = get_config(base_model, **config_kwargs)
                    if load_half and not (load_8bit or load_4bit or load_gptq):
                        model = model_loader(
                            base_model,
                            config=config,
                            **model_kwargs).half()
                    else:
                        model = model_loader(
                            base_model,
                            config=config,
                            **model_kwargs)
        elif load_8bit or load_4bit:
            config, _, max_seq_len = get_config(base_model, **config_kwargs)
            model = model_loader(
                base_model,
                config=config,
                **model_kwargs
            )
            from peft import PeftModel  # loads cuda, so avoid in global scope
            model = PeftModel.from_pretrained(
                model,
                lora_weights,
                torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
                local_files_only=local_files_only,
                resume_download=resume_download,
                use_auth_token=use_auth_token,
                trust_remote_code=trust_remote_code,
                offload_folder=offload_folder,
                rope_scaling=rope_scaling,
                revision=revision,
                device_map={"": 0} if device == 'cuda' else {"": 'cpu'},  # seems to be required
            )
        else:
            with torch.device(device):
                config, _, max_seq_len = get_config(base_model, raise_exception=True, **config_kwargs)
                model = model_loader(
                    base_model,
                    config=config,
                    **model_kwargs
                )
                from peft import PeftModel  # loads cuda, so avoid in global scope
                model = PeftModel.from_pretrained(
                    model,
                    lora_weights,
                    torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
                    local_files_only=local_files_only,
                    resume_download=resume_download,
                    use_auth_token=use_auth_token,
                    trust_remote_code=trust_remote_code,
                    offload_folder=offload_folder,
                    rope_scaling=rope_scaling,
                    device_map="auto",
                )
                if load_half and not load_gptq:
                    model.half()

    # unwind broken decapoda-research config
    if llama_type:
        model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
        model.config.bos_token_id = 1
        model.config.eos_token_id = 2
    if 'gpt2' in base_model.lower():
        # add special tokens that otherwise all share the same id
        tokenizer.add_special_tokens({'bos_token': '<bos>',
                                      'eos_token': '<eos>',
                                      'pad_token': '<pad>'})

    if not isinstance(tokenizer, str):
        model.eval()
        if torch.__version__ >= "2" and sys.platform != "win32" and compile_model:
            model = torch.compile(model)

    set_model_max_len(max_seq_len, tokenizer, verbose=False, reward_type=reward_type)

    return model, tokenizer, device


def set_model_max_len(max_seq_len, tokenizer, verbose=False, reward_type=False):
    if reward_type:
        # limit deberta, else uses too much memory and not worth response score
        tokenizer.model_max_length = 512
        return

    tokenizer.model_max_length = max_seq_len
    if verbose:
        print("model_max_length=%s" % tokenizer.model_max_length, flush=True)
    # for bug in HF transformers
    if tokenizer.model_max_length > 100000000:
        tokenizer.model_max_length = 2048


def pop_unused_model_kwargs(model_kwargs):
    """
    in-place pop unused kwargs that are not dependency-upgrade friendly
    no point passing in False, is default, and helps avoid needing to update requirements for new deps
    :param model_kwargs:
    :return:
    """
    check_list = ['load_in_8bit', 'load_in_4bit']
    for k in check_list:
        if k in model_kwargs and not model_kwargs[k]:
            model_kwargs.pop(k)


def get_score_model(score_model: str = None,
                    load_8bit: bool = False,
                    load_4bit: bool = False,
                    load_half: bool = True,
                    load_gptq: str = '',
                    load_exllama: bool = False,
                    use_gpu_id: bool = True,
                    base_model: str = '',
                    inference_server: str = '',
                    tokenizer_base_model: str = '',
                    lora_weights: str = "",
                    gpu_id: int = 0,

                    reward_type: bool = None,
                    local_files_only: bool = False,
                    resume_download: bool = True,
                    use_auth_token: Union[str, bool] = False,
                    trust_remote_code: bool = True,
                    offload_folder: str = None,
                    rope_scaling: dict = None,
                    compile_model: bool = True,

                    verbose: bool = False,
                    ):
    if score_model is not None and score_model.strip():
        load_8bit = False
        load_4bit = False
        load_half = False
        load_gptq = ''
        load_exllama = False
        use_safetensors = False
        revision = None
        base_model = score_model.strip()
        tokenizer_base_model = ''
        lora_weights = ''
        inference_server = ''
        llama_type = False
        max_seq_len = None
        compile_model = False
        smodel, stokenizer, sdevice = get_model(reward_type=True,
                                                **get_kwargs(get_model, exclude_names=['reward_type'], **locals()))
    else:
        smodel, stokenizer, sdevice = None, None, None
    return smodel, stokenizer, sdevice


def evaluate(
        model_state,
        my_db_state,
        selection_docs_state,
        # START NOTE: Examples must have same order of parameters
        instruction,
        iinput,
        context,
        stream_output,
        prompt_type,
        prompt_dict,
        temperature,
        top_p,
        top_k,
        num_beams,
        max_new_tokens,
        min_new_tokens,
        early_stopping,
        max_time,
        repetition_penalty,
        num_return_sequences,
        do_sample,
        chat,
        instruction_nochat,
        iinput_nochat,
        langchain_mode,
        add_chat_history_to_context,
        langchain_action,
        langchain_agents,
        top_k_docs,
        chunk,
        chunk_size,
        document_subset,
        document_choice,
        # END NOTE: Examples must have same order of parameters
        async_output=None,
        num_async=None,
        src_lang=None,
        tgt_lang=None,
        debug=False,
        concurrency_count=None,
        save_dir=None,
        sanitize_bot_response=False,
        model_state0=None,
        langchain_modes0=None,
        langchain_mode_paths0=None,
        visible_langchain_modes0=None,
        memory_restriction_level=None,
        max_max_new_tokens=None,
        is_public=None,
        max_max_time=None,
        raise_generate_gpu_exceptions=None,
        chat_context=None,
        lora_weights=None,
        use_llm_if_no_docs=True,
        load_db_if_exists=True,
        dbs=None,
        detect_user_path_changes_every_query=None,
        use_openai_embedding=None,
        use_openai_model=None,
        hf_embedding_model=None,
        cut_distance=None,
        db_type=None,
        n_jobs=None,
        first_para=None,
        text_limit=None,
        verbose=False,
        cli=False,
        reverse_docs=True,
        use_cache=None,
        auto_reduce_chunks=None,
        max_chunks=None,
        model_lock=None,
        force_langchain_evaluate=None,
        model_state_none=None,
        load_exllama=None,
):
    # ensure passed these
    assert concurrency_count is not None
    assert memory_restriction_level is not None
    assert raise_generate_gpu_exceptions is not None
    assert chat_context is not None
    assert use_openai_embedding is not None
    assert use_openai_model is not None
    assert hf_embedding_model is not None
    assert db_type is not None
    assert top_k_docs is not None and isinstance(top_k_docs, int)
    assert chunk is not None and isinstance(chunk, bool)
    assert chunk_size is not None and isinstance(chunk_size, int)
    assert n_jobs is not None
    assert first_para is not None
    assert isinstance(add_chat_history_to_context, bool)
    assert load_exllama is not None

    if selection_docs_state is not None:
        langchain_modes = selection_docs_state.get('langchain_modes', langchain_modes0)
        langchain_mode_paths = selection_docs_state.get('langchain_mode_paths', langchain_mode_paths0)
        visible_langchain_modes = selection_docs_state.get('visible_langchain_modes', visible_langchain_modes0)
    else:
        langchain_modes = langchain_modes0
        langchain_mode_paths = langchain_mode_paths0
        visible_langchain_modes = visible_langchain_modes0

    if debug:
        locals_dict = locals().copy()
        locals_dict.pop('model_state', None)
        locals_dict.pop('model_state0', None)
        locals_dict.pop('model_states', None)
        print(locals_dict)

    no_model_msg = "Please choose a base model with --base_model (CLI) or load in Models Tab (gradio).\n" \
                   "Then start New Conversation"

    if model_state is None:
        model_state = model_state_none.copy()
    if model_state0 is None:
        # e.g. for no gradio case, set dummy value, else should be set
        model_state0 = model_state_none.copy()

    # model_state['model] is only 'model' if should use model_state0
    # model could also be None
    have_model_lock = model_lock is not None
    have_fresh_model = model_state['model'] not in [None, 'model', no_model_str]
    # for gradio UI control, expect model_state and model_state0 to match, so if have_model_lock=True, then should have_fresh_model=True
    # but gradio API control will only use nochat api etc. and won't use fresh model, so can't assert in general
    # if have_model_lock:
    #    assert have_fresh_model, "Expected model_state and model_state0 to match if have_model_lock"
    have_cli_model = model_state0['model'] not in [None, 'model', no_model_str]

    if have_fresh_model:
        # USE FRESH MODEL
        if not have_model_lock:
            # model_state0 is just one of model_state if model_lock, so don't nuke
            # try to free-up original model (i.e. list was passed as reference)
            if model_state0['model'] and hasattr(model_state0['model'], 'cpu'):
                model_state0['model'].cpu()
                model_state0['model'] = None
            # try to free-up original tokenizer (i.e. list was passed as reference)
            if model_state0['tokenizer']:
                model_state0['tokenizer'] = None
            clear_torch_cache()
        chosen_model_state = model_state
    elif have_cli_model:
        # USE MODEL SETUP AT CLI
        assert isinstance(model_state['model'], str)  # expect no fresh model
        chosen_model_state = model_state0
    else:
        raise AssertionError(no_model_msg)
    # get variables
    model = chosen_model_state['model']
    tokenizer = chosen_model_state['tokenizer']
    device = chosen_model_state['device']
    base_model = chosen_model_state['base_model']
    tokenizer_base_model = chosen_model_state['tokenizer_base_model']
    lora_weights = chosen_model_state['lora_weights']
    inference_server = chosen_model_state['inference_server']
    # prefer use input from API over model state
    prompt_type = prompt_type or chosen_model_state['prompt_type']
    prompt_dict = prompt_dict or chosen_model_state['prompt_dict']

    if base_model is None:
        raise AssertionError(no_model_msg)

    assert base_model.strip(), no_model_msg
    assert model, "Model is missing"
    assert tokenizer, "Tokenizer is missing"

    # choose chat or non-chat mode
    if not chat:
        instruction = instruction_nochat
        iinput = iinput_nochat

    # in some cases, like lean nochat API, don't want to force sending prompt_type, allow default choice
    model_lower = base_model.lower()
    if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom':
        prompt_type = inv_prompt_type_to_model_lower[model_lower]
        if verbose:
            print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True)
    assert prompt_type is not None, "prompt_type was None"

    # Control generation hyperparameters
    # adjust for bad inputs, e.g. in case also come from API that doesn't get constrained by gradio sliders
    # below is for TGI server, not required for HF transformers
    # limits are chosen similar to gradio_runner.py sliders/numbers
    top_p = min(max(1e-3, top_p), 1.0 - 1e-3)
    top_k = min(max(1, int(top_k)), 100)
    temperature = min(max(0.01, temperature), 2.0)
    # FIXME: https://github.com/h2oai/h2ogpt/issues/106
    num_beams = 1 if stream_output else num_beams  # See max_beams in gradio_runner
    max_max_new_tokens = get_max_max_new_tokens(chosen_model_state,
                                                memory_restriction_level=memory_restriction_level,
                                                max_new_tokens=max_new_tokens,
                                                max_max_new_tokens=max_max_new_tokens)
    model_max_length = get_model_max_length(chosen_model_state)
    max_new_tokens = min(max(1, int(max_new_tokens)), max_max_new_tokens)
    min_new_tokens = min(max(0, int(min_new_tokens)), max_new_tokens)
    max_time = min(max(0, max_time), max_max_time)
    repetition_penalty = min(max(0.01, repetition_penalty), 3.0)
    num_return_sequences = 1 if chat else min(max(1, int(num_return_sequences)), 10)
    min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public)
    top_k_docs = min(max(min_top_k_docs, int(top_k_docs)), max_top_k_docs)
    chunk_size = min(max(128, int(chunk_size)), 2048)
    if not context:
        # get hidden context if have one
        context = get_context(chat_context, prompt_type)

    # restrict instruction, typically what has large input
    from h2oai_pipeline import H2OTextGenerationPipeline
    instruction, num_prompt_tokens1 = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer)
    context, num_prompt_tokens2 = H2OTextGenerationPipeline.limit_prompt(context, tokenizer)
    iinput, num_prompt_tokens3 = H2OTextGenerationPipeline.limit_prompt(iinput, tokenizer)
    num_prompt_tokens = (num_prompt_tokens1 or 0) + (num_prompt_tokens2 or 0) + (num_prompt_tokens3 or 0)

    # get prompt
    prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output)
    data_point = dict(context=context, instruction=instruction, input=iinput)
    prompt = prompter.generate_prompt(data_point)

    # THIRD PLACE where LangChain referenced, but imports only occur if enabled and have db to use
    assert langchain_mode in langchain_modes, "Invalid langchain_mode %s" % langchain_mode
    assert langchain_action in langchain_actions, "Invalid langchain_action %s" % langchain_action
    assert len(
        set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents
    if dbs is not None and langchain_mode in dbs:
        db = dbs[langchain_mode]
    elif my_db_state is not None and langchain_mode in my_db_state:
        db1 = my_db_state[langchain_mode]
        if db1 is not None and len(db1) == 2:
            db = db1[0]
        else:
            db = None
    else:
        db = None
    langchain_only_model = base_model in non_hf_types or load_exllama
    do_langchain_path = langchain_mode not in [False, 'Disabled', 'LLM'] or \
                        langchain_only_model or \
                        force_langchain_evaluate
    if do_langchain_path:
        outr = ""
        # use smaller cut_distance for wiki_full since so many matches could be obtained, and often irrelevant unless close
        from gpt_langchain import run_qa_db
        gen_hyper_langchain = dict(do_sample=do_sample,
                                   temperature=temperature,
                                   repetition_penalty=repetition_penalty,
                                   top_k=top_k,
                                   top_p=top_p,
                                   num_beams=num_beams,
                                   min_new_tokens=min_new_tokens,
                                   max_new_tokens=max_new_tokens,
                                   early_stopping=early_stopping,
                                   max_time=max_time,
                                   num_return_sequences=num_return_sequences,
                                   )
        t_generate = time.time()
        for r in run_qa_db(query=instruction,
                           iinput=iinput,
                           context=context,
                           model_name=base_model, model=model, tokenizer=tokenizer,
                           inference_server=inference_server,
                           langchain_only_model=langchain_only_model,
                           stream_output=stream_output,
                           async_output=async_output,
                           num_async=num_async,
                           prompter=prompter,
                           use_llm_if_no_docs=use_llm_if_no_docs,
                           load_db_if_exists=load_db_if_exists,
                           db=db,
                           langchain_mode_paths=langchain_mode_paths,
                           detect_user_path_changes_every_query=detect_user_path_changes_every_query,
                           cut_distance=1.1 if langchain_mode in ['wiki_full'] else cut_distance,
                           add_chat_history_to_context=add_chat_history_to_context,
                           use_openai_embedding=use_openai_embedding,
                           use_openai_model=use_openai_model,
                           hf_embedding_model=hf_embedding_model,
                           first_para=first_para,
                           text_limit=text_limit,
                           chunk=chunk,
                           chunk_size=chunk_size,
                           langchain_mode=langchain_mode,
                           langchain_action=langchain_action,
                           langchain_agents=langchain_agents,
                           document_subset=document_subset,
                           document_choice=document_choice,
                           db_type=db_type,
                           top_k_docs=top_k_docs,

                           **gen_hyper_langchain,

                           prompt_type=prompt_type,
                           prompt_dict=prompt_dict,
                           n_jobs=n_jobs,
                           verbose=verbose,
                           cli=cli,
                           sanitize_bot_response=sanitize_bot_response,
                           reverse_docs=reverse_docs,

                           lora_weights=lora_weights,

                           auto_reduce_chunks=auto_reduce_chunks,
                           max_chunks=max_chunks,
                           ):
            outr, extra = r  # doesn't accumulate, new answer every yield, so only save that full answer
            yield dict(response=outr, sources=extra)
        if save_dir:
            # estimate using tiktoken
            ntokens = FakeTokenizer().num_tokens_from_string(outr)
            extra_dict = gen_hyper_langchain.copy()
            extra_dict.update(prompt_type=prompt_type,
                              inference_server=inference_server,
                              langchain_mode=langchain_mode,
                              langchain_action=langchain_action,
                              langchain_agents=langchain_agents,
                              document_subset=document_subset,
                              document_choice=document_choice,
                              num_prompt_tokens=num_prompt_tokens,
                              instruction=instruction,
                              iinput=iinput,
                              context=context,
                              t_generate=time.time() - t_generate,
                              ntokens=ntokens,
                              tokens_persecond=ntokens / (time.time() - t_generate),
                              )
            save_generate_output(prompt=prompt,
                                 output=outr, base_model=base_model, save_dir=save_dir,
                                 where_from='run_qa_db',
                                 extra_dict=extra_dict)
            if verbose:
                print(
                    'Post-Generate Langchain: %s decoded_output: %s' % (str(datetime.now()), len(outr) if outr else -1),
                    flush=True)
        if outr or langchain_only_model:
            # if got no response (e.g. not showing sources and got no sources,
            # so nothing to give to LLM), then slip through and ask LLM
            # Or if llama/gptj, then just return since they had no response and can't go down below code path
            # clear before return, since .then() never done if from API
            clear_torch_cache()
            return

    if inference_server.startswith('vllm') or inference_server.startswith('openai') or inference_server.startswith(
            'http'):
        t_generate = time.time()
        if inference_server.startswith('vllm') or inference_server.startswith('openai'):
            where_from = "openai_client"
            openai, inf_type = set_openai(inference_server)

            terminate_response = prompter.terminate_response or []
            stop_sequences = list(set(terminate_response + [prompter.PreResponse]))
            stop_sequences = [x for x in stop_sequences if x]
            # OpenAI will complain if ask for too many new tokens, takes it as min in some sense, wrongly so.
            max_new_tokens_openai = min(max_new_tokens, model_max_length - num_prompt_tokens)
            gen_server_kwargs = dict(temperature=temperature if do_sample else 0,
                                     max_tokens=max_new_tokens_openai,
                                     top_p=top_p if do_sample else 1,
                                     frequency_penalty=0,
                                     n=num_return_sequences,
                                     presence_penalty=1.07 - repetition_penalty + 0.6,  # so good default
                                     )
            if inf_type == 'vllm' or inference_server == 'openai':
                response = openai.Completion.create(
                    model=base_model,
                    prompt=prompt,
                    **gen_server_kwargs,
                    stop=stop_sequences,
                    stream=stream_output,
                )
                if not stream_output:
                    text = response['choices'][0]['text']
                    yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                              sanitize_bot_response=sanitize_bot_response),
                               sources='')
                else:
                    collected_events = []
                    text = ''
                    for event in response:
                        collected_events.append(event)  # save the event response
                        event_text = event['choices'][0]['text']  # extract the text
                        text += event_text  # append the text
                        yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                                  sanitize_bot_response=sanitize_bot_response),
                                   sources='')
            elif inf_type == 'vllm_chat' or inference_server == 'openai_chat':
                if inf_type == 'vllm_chat':
                    raise NotImplementedError('%s not supported by vLLM' % inf_type)
                response = openai.ChatCompletion.create(
                    model=base_model,
                    messages=[
                        {"role": "system", "content": "You are a helpful assistant."},
                        {'role': 'user',
                         'content': prompt,
                         }
                    ],
                    stream=stream_output,
                    **gen_server_kwargs,
                )
                if not stream_output:
                    text = response["choices"][0]["message"]["content"]
                    yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                              sanitize_bot_response=sanitize_bot_response),
                               sources='')
                else:
                    text = ""
                    for chunk in response:
                        delta = chunk["choices"][0]["delta"]
                        if 'content' in delta:
                            text += delta['content']
                            yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                                      sanitize_bot_response=sanitize_bot_response),
                                       sources='')
            else:
                raise RuntimeError("No such OpenAI mode: %s" % inference_server)
        elif inference_server.startswith('http'):
            inference_server, headers = get_hf_server(inference_server)
            from gradio_utils.grclient import GradioClient
            from text_generation import Client as HFClient
            if isinstance(model, GradioClient):
                gr_client = model
                hf_client = None
            elif isinstance(model, HFClient):
                gr_client = None
                hf_client = model
            else:
                inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server,
                                                                                          base_model=base_model)

            # quick sanity check to avoid long timeouts, just see if can reach server
            requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10')))

            if gr_client is not None:
                # Note: h2oGPT gradio server could handle input token size issues for prompt,
                # but best to handle here so send less data to server

                chat_client = False
                where_from = "gr_client"
                client_langchain_mode = 'Disabled'
                client_add_chat_history_to_context = True
                client_langchain_action = LangChainAction.QUERY.value
                client_langchain_agents = []
                gen_server_kwargs = dict(temperature=temperature,
                                         top_p=top_p,
                                         top_k=top_k,
                                         num_beams=num_beams,
                                         max_new_tokens=max_new_tokens,
                                         min_new_tokens=min_new_tokens,
                                         early_stopping=early_stopping,
                                         max_time=max_time,
                                         repetition_penalty=repetition_penalty,
                                         num_return_sequences=num_return_sequences,
                                         do_sample=do_sample,
                                         chat=chat_client,
                                         )
                # account for gradio into gradio that handles prompting, avoid duplicating prompter prompt injection
                if prompt_type in [None, '', PromptType.plain.name, PromptType.plain.value,
                                   str(PromptType.plain.value)]:
                    # if our prompt is plain, assume either correct or gradio server knows different prompt type,
                    # so pass empty prompt_Type
                    gr_prompt_type = ''
                    gr_prompt_dict = ''
                    gr_prompt = prompt  # already prepared prompt
                    gr_context = ''
                    gr_iinput = ''
                else:
                    # if already have prompt_type that is not plain, None, or '', then already applied some prompting
                    #  But assume server can handle prompting, and need to avoid double-up.
                    #  Also assume server can do better job of using stopping.py to stop early, so avoid local prompting, let server handle
                    #  So avoid "prompt" and let gradio server reconstruct from prompt_type we passed
                    # Note it's ok that prompter.get_response() has prompt+text, prompt=prompt passed,
                    #  because just means extra processing and removal of prompt, but that has no human-bot prompting doesn't matter
                    #  since those won't appear
                    gr_context = context
                    gr_prompt = instruction
                    gr_iinput = iinput
                    gr_prompt_type = prompt_type
                    gr_prompt_dict = prompt_dict
                client_kwargs = dict(instruction=gr_prompt if chat_client else '',  # only for chat=True
                                     iinput=gr_iinput,  # only for chat=True
                                     context=gr_context,
                                     # streaming output is supported, loops over and outputs each generation in streaming mode
                                     # but leave stream_output=False for simple input/output mode
                                     stream_output=stream_output,

                                     **gen_server_kwargs,

                                     prompt_type=gr_prompt_type,
                                     prompt_dict=gr_prompt_dict,

                                     instruction_nochat=gr_prompt if not chat_client else '',
                                     iinput_nochat=gr_iinput,  # only for chat=False
                                     langchain_mode=client_langchain_mode,
                                     add_chat_history_to_context=client_add_chat_history_to_context,
                                     langchain_action=client_langchain_action,
                                     langchain_agents=client_langchain_agents,
                                     top_k_docs=top_k_docs,
                                     chunk=chunk,
                                     chunk_size=chunk_size,
                                     document_subset=DocumentSubset.Relevant.name,
                                     document_choice=[DocumentChoice.ALL.value],
                                     )
                api_name = '/submit_nochat_api'  # NOTE: like submit_nochat but stable API for string dict passing
                if not stream_output:
                    res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name)
                    res_dict = ast.literal_eval(res)
                    text = res_dict['response']
                    sources = res_dict['sources']
                    yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                              sanitize_bot_response=sanitize_bot_response),
                               sources=sources)
                else:
                    job = gr_client.submit(str(dict(client_kwargs)), api_name=api_name)
                    text = ''
                    sources = ''
                    res_dict = dict(response=text, sources=sources)
                    while not job.done():
                        outputs_list = job.communicator.job.outputs
                        if outputs_list:
                            res = job.communicator.job.outputs[-1]
                            res_dict = ast.literal_eval(res)
                            text = res_dict['response']
                            sources = res_dict['sources']
                            if gr_prompt_type == 'plain':
                                # then gradio server passes back full prompt + text
                                prompt_and_text = text
                            else:
                                prompt_and_text = prompt + text
                            yield dict(response=prompter.get_response(prompt_and_text, prompt=prompt,
                                                                      sanitize_bot_response=sanitize_bot_response),
                                       sources=sources)
                        time.sleep(0.01)
                    # ensure get last output to avoid race
                    res_all = job.outputs()
                    if len(res_all) > 0:
                        res = res_all[-1]
                        res_dict = ast.literal_eval(res)
                        text = res_dict['response']
                        sources = res_dict['sources']
                    else:
                        # go with old text if last call didn't work
                        e = job.future._exception
                        if e is not None:
                            stre = str(e)
                            strex = ''.join(traceback.format_tb(e.__traceback__))
                        else:
                            stre = ''
                            strex = ''

                        print("Bad final response: %s %s %s %s %s: %s %s" % (base_model, inference_server,
                                                                             res_all, prompt, text, stre, strex),
                              flush=True)
                    if gr_prompt_type == 'plain':
                        # then gradio server passes back full prompt + text
                        prompt_and_text = text
                    else:
                        prompt_and_text = prompt + text
                    yield dict(response=prompter.get_response(prompt_and_text, prompt=prompt,
                                                              sanitize_bot_response=sanitize_bot_response),
                               sources=sources)
            elif hf_client:
                # HF inference server needs control over input tokens
                where_from = "hf_client"

                # prompt must include all human-bot like tokens, already added by prompt
                # https://github.com/huggingface/text-generation-inference/tree/main/clients/python#types
                terminate_response = prompter.terminate_response or []
                stop_sequences = list(set(terminate_response + [prompter.PreResponse]))
                stop_sequences = [x for x in stop_sequences if x]
                gen_server_kwargs = dict(do_sample=do_sample,
                                         max_new_tokens=max_new_tokens,
                                         # best_of=None,
                                         repetition_penalty=repetition_penalty,
                                         return_full_text=False,
                                         seed=SEED,
                                         stop_sequences=stop_sequences,
                                         temperature=temperature,
                                         top_k=top_k,
                                         top_p=top_p,
                                         # truncate=False,  # behaves oddly
                                         # typical_p=top_p,
                                         # watermark=False,
                                         # decoder_input_details=False,
                                         )
                # work-around for timeout at constructor time, will be issue if multi-threading,
                # so just do something reasonable or max_time if larger
                # lower bound because client is re-used if multi-threading
                hf_client.timeout = max(300, max_time)
                if not stream_output:
                    text = hf_client.generate(prompt, **gen_server_kwargs).generated_text
                    yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                              sanitize_bot_response=sanitize_bot_response),
                               sources='')
                else:
                    text = ""
                    for response in hf_client.generate_stream(prompt, **gen_server_kwargs):
                        if not response.token.special:
                            # stop_sequences
                            text_chunk = response.token.text
                            text += text_chunk
                            yield dict(response=prompter.get_response(prompt + text, prompt=prompt,
                                                                      sanitize_bot_response=sanitize_bot_response),
                                       sources='')
            else:
                raise RuntimeError("Failed to get client: %s" % inference_server)
        else:
            raise RuntimeError("No such inference_server  %s" % inference_server)

        if save_dir and text:
            # estimate using tiktoken
            ntokens = FakeTokenizer().num_tokens_from_string(text)
            # save prompt + new text
            extra_dict = gen_server_kwargs.copy()
            extra_dict.update(dict(inference_server=inference_server, num_prompt_tokens=num_prompt_tokens,
                                   t_generate=time.time() - t_generate,
                                   ntokens=ntokens,
                                   tokens_persecond=ntokens / (time.time() - t_generate),
                                   ))
            save_generate_output(prompt=prompt, output=text, base_model=base_model, save_dir=save_dir,
                                 where_from=where_from, extra_dict=extra_dict)
        return
    else:
        assert not inference_server, "inference_server=%s not supported" % inference_server

    if isinstance(tokenizer, str):
        # pipeline
        if tokenizer == "summarization":
            key = 'summary_text'
        else:
            raise RuntimeError("No such task type %s" % tokenizer)
        # NOTE: uses max_length only
        yield dict(response=model(prompt, max_length=max_new_tokens)[0][key], sources='')

    if 'mbart-' in base_model.lower():
        assert src_lang is not None
        tokenizer.src_lang = languages_covered()[src_lang]

    stopping_criteria = get_stopping(prompt_type, prompt_dict, tokenizer, device,
                                     model_max_length=tokenizer.model_max_length)

    inputs = tokenizer(prompt, return_tensors="pt")
    if debug and len(inputs["input_ids"]) > 0:
        print('input_ids length', len(inputs["input_ids"][0]), flush=True)
    input_ids = inputs["input_ids"].to(device)
    # CRITICAL LIMIT else will fail
    max_max_tokens = tokenizer.model_max_length
    max_input_tokens = max_max_tokens - min_new_tokens
    # NOTE: Don't limit up front due to max_new_tokens, let go up to max or reach max_max_tokens in stopping.py
    input_ids = input_ids[:, -max_input_tokens:]
    # required for falcon if multiple threads or asyncio accesses to model during generation
    if use_cache is None:
        use_cache = False if 'falcon' in base_model else True
    gen_config_kwargs = dict(temperature=float(temperature),
                             top_p=float(top_p),
                             top_k=top_k,
                             num_beams=num_beams,
                             do_sample=do_sample,
                             repetition_penalty=float(repetition_penalty),
                             num_return_sequences=num_return_sequences,
                             renormalize_logits=True,
                             remove_invalid_values=True,
                             use_cache=use_cache,
                             )
    token_ids = ['eos_token_id', 'pad_token_id', 'bos_token_id', 'cls_token_id', 'sep_token_id']
    for token_id in token_ids:
        if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None:
            gen_config_kwargs.update({token_id: getattr(tokenizer, token_id)})
    generation_config = GenerationConfig(**gen_config_kwargs)

    gen_kwargs = dict(input_ids=input_ids,
                      generation_config=generation_config,
                      return_dict_in_generate=True,
                      output_scores=True,
                      max_new_tokens=max_new_tokens,  # prompt + new
                      min_new_tokens=min_new_tokens,  # prompt + new
                      early_stopping=early_stopping,  # False, True, "never"
                      max_time=max_time,
                      stopping_criteria=stopping_criteria,
                      )
    if 'gpt2' in base_model.lower():
        gen_kwargs.update(dict(bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.eos_token_id))
    elif 'mbart-' in base_model.lower():
        assert tgt_lang is not None
        tgt_lang = languages_covered()[tgt_lang]
        gen_kwargs.update(dict(forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang]))
    else:
        token_ids = ['eos_token_id', 'bos_token_id', 'pad_token_id']
        for token_id in token_ids:
            if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None:
                gen_kwargs.update({token_id: getattr(tokenizer, token_id)})

    decoder_kwargs = dict(skip_special_tokens=True,
                          clean_up_tokenization_spaces=True)

    decoder = functools.partial(tokenizer.decode,
                                **decoder_kwargs
                                )
    decoder_raw_kwargs = dict(skip_special_tokens=False,
                              clean_up_tokenization_spaces=True)

    decoder_raw = functools.partial(tokenizer.decode,
                                    **decoder_raw_kwargs
                                    )

    t_generate = time.time()
    with torch.no_grad():
        have_lora_weights = lora_weights not in [no_lora_str, '', None]
        context_class_cast = NullContext if device == 'cpu' or have_lora_weights or device == 'mps' else torch.autocast
        with context_class_cast(device):
            # protection for gradio not keeping track of closed users,
            # else hit bitsandbytes lack of thread safety:
            # https://github.com/h2oai/h2ogpt/issues/104
            # but only makes sense if concurrency_count == 1
            context_class = NullContext  # if concurrency_count > 1 else filelock.FileLock
            if verbose:
                print('Pre-Generate: %s' % str(datetime.now()), flush=True)
            decoded_output = None
            with context_class("generate.lock"):
                if verbose:
                    print('Generate: %s' % str(datetime.now()), flush=True)
                # decoded tokenized prompt can deviate from prompt due to special characters
                inputs_decoded = decoder(input_ids[0])
                inputs_decoded_raw = decoder_raw(input_ids[0])
                if inputs_decoded == prompt:
                    # normal
                    pass
                elif inputs_decoded.lstrip() == prompt.lstrip():
                    # sometimes extra space in front, make prompt same for prompt removal
                    prompt = inputs_decoded
                elif inputs_decoded_raw == prompt:
                    # some models specify special tokens that are part of normal prompt, so can't skip them
                    inputs_decoded = prompt = inputs_decoded_raw
                    decoder = decoder_raw
                    decoder_kwargs = decoder_raw_kwargs
                elif inputs_decoded_raw.replace("<unk> ", "").replace("<unk>", "").replace('\n', ' ').replace(' ',
                                                                                                              '') == prompt.replace(
                    '\n', ' ').replace(' ', ''):
                    inputs_decoded = prompt = inputs_decoded_raw
                    decoder = decoder_raw
                    decoder_kwargs = decoder_raw_kwargs
                else:
                    if verbose:
                        print("WARNING: Special characters in prompt", flush=True)
                if stream_output:
                    skip_prompt = False  # means first output has prompt too
                    streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False,
                                                       **decoder_kwargs)
                    gen_kwargs.update(dict(streamer=streamer))
                    target = wrapped_partial(generate_with_exceptions, model.generate,
                                             prompt=prompt, inputs_decoded=inputs_decoded,
                                             raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
                                             **gen_kwargs)
                    bucket = queue.Queue()
                    thread = EThread(target=target, streamer=streamer, bucket=bucket)
                    thread.start()
                    outputs = ""
                    try:
                        for new_text in streamer:
                            if bucket.qsize() > 0 or thread.exc:
                                thread.join()
                            outputs += new_text
                            yield dict(response=prompter.get_response(outputs, prompt=inputs_decoded,
                                                                      sanitize_bot_response=sanitize_bot_response),
                                       sources='')
                    except BaseException:
                        # if any exception, raise that exception if was from thread, first
                        if thread.exc:
                            raise thread.exc
                        raise
                    finally:
                        # clear before return, since .then() never done if from API
                        clear_torch_cache()
                        # in case no exception and didn't join with thread yet, then join
                        if not thread.exc:
                            thread.join()
                    # in case raise StopIteration or broke queue loop in streamer, but still have exception
                    if thread.exc:
                        raise thread.exc
                    decoded_output = outputs
                    ntokens = len(outputs) // 4  # hack for now
                else:
                    try:
                        outputs = model.generate(**gen_kwargs)
                    finally:
                        clear_torch_cache()  # has to be here for API submit_nochat_api since.then() not called
                    ntokens = sum([len(s) for s in outputs.sequences]) if save_dir else -1
                    outputs = [decoder(s) for s in outputs.sequences]

                    yield dict(response=prompter.get_response(outputs, prompt=inputs_decoded,
                                                              sanitize_bot_response=sanitize_bot_response), sources='')
                    if outputs and len(outputs) >= 1:
                        decoded_output = prompt + outputs[0]
                if save_dir and decoded_output:
                    extra_dict = gen_config_kwargs.copy()
                    extra_dict.update(dict(num_prompt_tokens=num_prompt_tokens,
                                           t_generate=time.time() - t_generate,
                                           ntokens=ntokens,
                                           tokens_persecond=ntokens / (time.time() - t_generate),
                                           ))
                    save_generate_output(prompt=prompt, output=decoded_output, base_model=base_model, save_dir=save_dir,
                                         where_from="evaluate_%s" % str(stream_output),
                                         extra_dict=extra_dict)
            if verbose:
                print('Post-Generate: %s decoded_output: %s' % (
                    str(datetime.now()), len(decoded_output) if decoded_output else -1), flush=True)


inputs_list_names = list(inspect.signature(evaluate).parameters)
state_names = ['model_state', 'my_db_state', 'selection_docs_state']
inputs_kwargs_list = [x for x in inputs_list_names if x not in eval_func_param_names + state_names]


def get_cutoffs(memory_restriction_level, for_context=False, model_max_length=2048):
    # help to avoid errors like:
    # RuntimeError: The size of tensor a (2048) must match the size of tensor b (2049) at non-singleton dimension 3
    # RuntimeError: expected scalar type Half but found Float
    # with - 256
    if memory_restriction_level > 0:
        max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
    else:
        # at least give room for 1 paragraph output
        max_length_tokenize = model_max_length - 256
    cutoff_len = max_length_tokenize * 4  # if reaches limit, then can't generate new tokens
    output_smallest = 30 * 4
    max_prompt_length = cutoff_len - output_smallest

    if for_context:
        # then lower even more to avoid later chop, since just estimate tokens in context bot
        max_prompt_length = max(64, int(max_prompt_length * 0.8))

    return cutoff_len, output_smallest, max_length_tokenize, max_prompt_length


class H2OTextIteratorStreamer(TextIteratorStreamer):
    """
    normally, timeout required for now to handle exceptions, else get()
    but with H2O version of TextIteratorStreamer, loop over block to handle
    """

    def __init__(self, tokenizer, skip_prompt: bool = False, timeout: typing.Optional[float] = None,
                 block=True, **decode_kwargs):
        super().__init__(tokenizer, skip_prompt, **decode_kwargs)
        self.text_queue = queue.Queue()
        self.stop_signal = None
        self.do_stop = False
        self.timeout = timeout
        self.block = block

    def on_finalized_text(self, text: str, stream_end: bool = False):
        """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
        self.text_queue.put(text, timeout=self.timeout)
        if stream_end:
            self.text_queue.put(self.stop_signal, timeout=self.timeout)

    def __iter__(self):
        return self

    def __next__(self):
        while True:
            try:
                value = self.stop_signal  # value looks unused in pycharm, not true
                if self.do_stop:
                    print("hit stop", flush=True)
                    # could raise or break, maybe best to raise and make parent see if any exception in thread
                    self.clear_queue()
                    self.do_stop = False
                    raise StopIteration()
                    # break
                value = self.text_queue.get(block=self.block, timeout=self.timeout)
                break
            except queue.Empty:
                time.sleep(0.01)
        if value == self.stop_signal:
            self.clear_queue()
            self.do_stop = False
            raise StopIteration()
        else:
            return value

    def clear_queue(self):
        # make sure streamer is reusable after stop hit
        with self.text_queue.mutex:
            self.text_queue.queue.clear()

    def put(self, value):
        """
        Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
        # same as base class, except remove hack w.r.t. text.rfind(" ") that ruins LLaMa2
        """
        if len(value.shape) > 1 and value.shape[0] > 1:
            raise ValueError("TextStreamer only supports batch size 1")
        elif len(value.shape) > 1:
            value = value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
            self.next_tokens_are_prompt = False
            return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist())
        text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)

        # After the symbol for a new line, we flush the cache.
        if text.endswith("\n"):
            printable_text = text[self.print_len:]
            self.token_cache = []
            self.print_len = 0
        # If the last token is a CJK character, we print the characters.
        elif len(text) > 0 and self._is_chinese_char(ord(text[-1])):
            printable_text = text[self.print_len:]
            self.print_len += len(printable_text)
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
            # printable_text = text[self.print_len : text.rfind(" ") + 1]
            printable_text = text[self.print_len:]
            self.print_len += len(printable_text)

        self.on_finalized_text(printable_text)


def generate_with_exceptions(func, *args, prompt='', inputs_decoded='', raise_generate_gpu_exceptions=True, **kwargs):
    try:
        func(*args, **kwargs)
    except torch.cuda.OutOfMemoryError as e:
        print("GPU OOM 2: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
              flush=True)
        if 'input_ids' in kwargs:
            if kwargs['input_ids'] is not None:
                kwargs['input_ids'].cpu()
            kwargs['input_ids'] = None
        traceback.print_exc()
        clear_torch_cache()
        return
    except (Exception, RuntimeError) as e:
        if 'Expected all tensors to be on the same device' in str(e) or \
                'expected scalar type Half but found Float' in str(e) or \
                'probability tensor contains either' in str(e) or \
                'cublasLt ran into an error!' in str(e) or \
                'mat1 and mat2 shapes cannot be multiplied' in str(e):
            print(
                "GPU Error: prompt: %s inputs_decoded: %s exception: %s" % (prompt, inputs_decoded, str(e)),
                flush=True)
            traceback.print_exc()
            clear_torch_cache()
            if raise_generate_gpu_exceptions:
                raise
            return
        else:
            clear_torch_cache()
            if raise_generate_gpu_exceptions:
                raise


def get_generate_params(model_lower,
                        chat,
                        stream_output, show_examples,
                        prompt_type, prompt_dict,
                        temperature, top_p, top_k, num_beams,
                        max_new_tokens, min_new_tokens, early_stopping, max_time,
                        repetition_penalty, num_return_sequences,
                        do_sample,
                        top_k_docs, chunk, chunk_size,
                        verbose):
    use_defaults = False
    use_default_examples = True
    examples = []
    task_info = 'LLM'
    if model_lower:
        print(f"Using Model {model_lower}", flush=True)
    else:
        if verbose:
            print("No model defined yet", flush=True)

    min_new_tokens = min_new_tokens if min_new_tokens is not None else 0
    early_stopping = early_stopping if early_stopping is not None else False
    max_time_defaults = 60 * 3
    max_time = max_time if max_time is not None else max_time_defaults

    if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom':
        prompt_type = inv_prompt_type_to_model_lower[model_lower]
        if verbose:
            print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True)

    # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end
    if show_examples is None:
        if chat:
            show_examples = False
        else:
            show_examples = True

    summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"""

    use_placeholder_instruction_as_example = False
    if 'bart-large-cnn-samsum' in model_lower or 'flan-t5-base-samsum' in model_lower:
        placeholder_instruction = summarize_example1
        placeholder_input = ""
        use_defaults = True
        use_default_examples = False
        use_placeholder_instruction_as_example = True
        task_info = "Summarization"
    elif 't5-' in model_lower or 't5' == model_lower or 'flan-' in model_lower:
        placeholder_instruction = "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
        placeholder_input = ""
        use_defaults = True
        use_default_examples = True
        task_info = "Multi-Task: Q/A, translation, Chain-of-Thought, Logical Reasoning, Summarization, etc.  Best to use task prefix as trained on, e.g. `translate English to German: ` (space after colon)"
    elif 'mbart-' in model_lower:
        placeholder_instruction = "The girl has long hair."
        placeholder_input = ""
        use_defaults = True
        use_default_examples = False
        use_placeholder_instruction_as_example = True
    elif 'gpt2' in model_lower:
        placeholder_instruction = "The sky is"
        placeholder_input = ""
        prompt_type = prompt_type or 'plain'
        use_default_examples = True  # some will be odd "continuations" but can be ok
        use_placeholder_instruction_as_example = True
        task_info = "Auto-complete phrase, code, etc."
        use_defaults = True
    else:
        if chat:
            placeholder_instruction = ""
        else:
            placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter."
        placeholder_input = ""
        if model_lower in inv_prompt_type_to_model_lower:
            if prompt_type != 'custom':
                prompt_type = inv_prompt_type_to_model_lower[model_lower]
        elif model_lower:
            # default is plain, because might rely upon trust_remote_code to handle prompting
            prompt_type = prompt_type or 'plain'
        else:
            prompt_type = ''
        task_info = "No task"
        if prompt_type == 'instruct':
            task_info = "Answer question or follow imperative as instruction with optionally input."
        elif prompt_type == 'plain':
            task_info = "Auto-complete phrase, code, etc."
        elif prompt_type == 'human_bot':
            if chat:
                task_info = "Chat (Shift-Enter to give question/imperative, input concatenated with instruction)"
            else:
                task_info = "Ask question/imperative (input concatenated with instruction)"

    # revert to plain if still nothing
    prompt_type = prompt_type or 'plain'
    if use_defaults:
        temperature = 1.0 if temperature is None else temperature
        top_p = 1.0 if top_p is None else top_p
        top_k = 40 if top_k is None else top_k
        num_beams = num_beams or 1
        max_new_tokens = max_new_tokens or 512
        repetition_penalty = repetition_penalty or 1.07
        num_return_sequences = min(num_beams, num_return_sequences or 1)
        do_sample = False if do_sample is None else do_sample
    else:
        temperature = 0.1 if temperature is None else temperature
        top_p = 0.75 if top_p is None else top_p
        top_k = 40 if top_k is None else top_k
        num_beams = num_beams or 1
        max_new_tokens = max_new_tokens or 1024
        repetition_penalty = repetition_penalty or 1.07
        num_return_sequences = min(num_beams, num_return_sequences or 1)
        do_sample = False if do_sample is None else do_sample
    # doesn't include chat, instruction_nochat, iinput_nochat, added later
    params_list = ["",
                   stream_output,
                   prompt_type, prompt_dict,
                   temperature, top_p, top_k, num_beams,
                   max_new_tokens, min_new_tokens,
                   early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample]

    if use_placeholder_instruction_as_example:
        examples += [[placeholder_instruction, ''] + params_list]

    if use_default_examples:
        examples += [
            ["Translate English to French", "Good morning"] + params_list,
            ["Give detailed answer for whether Einstein or Newton is smarter.", ''] + params_list,
            ["Explain in detailed list, all the best practices for coding in python.", ''] + params_list,
            [
                "Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.",
                ''] + params_list,
            ['Translate to German:  My name is Arthur', ''] + params_list,
            ["Please answer to the following question. Who is going to be the next Ballon d'or?", ''] + params_list,
            ['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.',
             ''] + params_list,
            ['Please answer the following question. What is the boiling point of Nitrogen?', ''] + params_list,
            ['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', ''] + params_list,
            ["Simplify the following expression: (False or False and True). Explain your answer.", ''] + params_list,
            [
                "Premise: At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?",
                ''] + params_list,
            ['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', ''] + params_list,
            [
                'Answer the following question by reasoning step by step.  The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?',
                ''] + params_list,
            ["""def area_of_rectangle(a: float, b: float):
    \"\"\"Return the area of the rectangle.\"\"\"""", ''] + params_list,
            ["""# a function in native python:
def mean(a):
    return sum(a)/len(a)

# the same function using numpy:
import numpy as np
def mean(a):""", ''] + params_list,
            ["""X = np.random.randn(100, 100)
y = np.random.randint(0, 1, 100)

# fit random forest classifier with 20 estimators""", ''] + params_list,
        ]
    # add summary example
    examples += [
        [summarize_example1, 'Summarize' if prompt_type not in ['plain', 'instruct_simple'] else ''] + params_list]

    src_lang = "English"
    tgt_lang = "Russian"

    # move to correct position
    for example in examples:
        example += [chat, '', '', LangChainMode.DISABLED.value, True, LangChainAction.QUERY.value, [],
                    top_k_docs, chunk, chunk_size, DocumentSubset.Relevant.name, []
                    ]
        # adjust examples if non-chat mode
        if not chat:
            example[eval_func_param_names.index('instruction_nochat')] = example[
                eval_func_param_names.index('instruction')]
            example[eval_func_param_names.index('instruction')] = ''

            example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')]
            example[eval_func_param_names.index('iinput')] = ''
        assert len(example) == len(eval_func_param_names), "Wrong example: %s %s" % (
            len(example), len(eval_func_param_names))

    if prompt_type == PromptType.custom.name and not prompt_dict:
        raise ValueError("Unexpected to get non-empty prompt_dict=%s for prompt_type=%s" % (prompt_dict, prompt_type))

    # get prompt_dict from prompt_type, so user can see in UI etc., or for custom do nothing except check format
    prompt_dict, error0 = get_prompt(prompt_type, prompt_dict,
                                     chat=False, context='', reduced=False, making_context=False, return_dict=True)
    if error0:
        raise RuntimeError("Prompt wrong: %s" % error0)

    return placeholder_instruction, placeholder_input, \
        stream_output, show_examples, \
        prompt_type, prompt_dict, \
        temperature, top_p, top_k, num_beams, \
        max_new_tokens, min_new_tokens, early_stopping, max_time, \
        repetition_penalty, num_return_sequences, \
        do_sample, \
        src_lang, tgt_lang, \
        examples, \
        task_info


def languages_covered():
    # https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered
    covered = """Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)"""
    covered = covered.split(', ')
    covered = {x.split(' ')[0]: x.split(' ')[1].replace(')', '').replace('(', '') for x in covered}
    return covered


def get_context(chat_context, prompt_type):
    if chat_context and prompt_type == 'human_bot':
        context0 = """<bot>: I am an intelligent, helpful, truthful, and fair assistant named h2oGPT, who will give accurate, balanced, and reliable responses.  I will not respond with I don't know or I don't understand.
<human>: I am a human person seeking useful assistance and request all questions be answered completely, and typically expect detailed responses.  Give answers in numbered list format if several distinct but related items are being listed."""
    else:
        context0 = ''
    return context0


def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len):
    question = question[-cutoff_len:]
    answer = answer[-cutoff_len:]

    inputs = stokenizer(question, answer,
                        return_tensors="pt",
                        truncation=True,
                        max_length=max_length_tokenize).to(smodel.device)
    try:
        score = torch.sigmoid(smodel(**inputs.to(smodel.device)).logits[0].float()).cpu().detach().numpy()[0]
    except torch.cuda.OutOfMemoryError as e:
        print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True)
        del inputs
        traceback.print_exc()
        clear_torch_cache()
        return 'Response Score: GPU OOM'
    except (Exception, RuntimeError) as e:
        if 'Expected all tensors to be on the same device' in str(e) or \
                'expected scalar type Half but found Float' in str(e) or \
                'probability tensor contains either' in str(e) or \
                'cublasLt ran into an error!' in str(e) or \
                'device-side assert triggered' in str(e):
            print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)),
                  flush=True)
            traceback.print_exc()
            clear_torch_cache()
            return 'Response Score: GPU Error'
        else:
            raise
    os.environ['TOKENIZERS_PARALLELISM'] = 'true'
    return score


def check_locals(**kwargs):
    # ensure everything in evaluate is here
    can_skip_because_locally_generated = no_default_param_names + [
        # get_model:
        'reward_type'
    ]
    for k in eval_func_param_names:
        if k in can_skip_because_locally_generated:
            continue
        assert k in kwargs, "Missing %s" % k
    for k in inputs_kwargs_list:
        if k in can_skip_because_locally_generated:
            continue
        assert k in kwargs, "Missing %s" % k

    for k in list(inspect.signature(get_model).parameters):
        if k in can_skip_because_locally_generated:
            continue
        assert k in kwargs, "Missing %s" % k


def get_model_max_length(model_state):
    if not isinstance(model_state['tokenizer'], (str, type(None))):
        return model_state['tokenizer'].model_max_length
    else:
        return 2048


def get_max_max_new_tokens(model_state, **kwargs):
    if not isinstance(model_state['tokenizer'], (str, type(None))):
        max_max_new_tokens = model_state['tokenizer'].model_max_length
    else:
        max_max_new_tokens = None

    if kwargs['max_max_new_tokens'] is not None and max_max_new_tokens is not None:
        return min(max_max_new_tokens, kwargs['max_max_new_tokens'])
    elif kwargs['max_max_new_tokens'] is not None:
        return kwargs['max_max_new_tokens']
    elif kwargs['memory_restriction_level'] == 1:
        return 768
    elif kwargs['memory_restriction_level'] == 2:
        return 512
    elif kwargs['memory_restriction_level'] >= 3:
        return 256
    else:
        # FIXME: Need to update after new model loaded, so user can control with slider
        return 2048


def get_minmax_top_k_docs(is_public):
    if is_public:
        min_top_k_docs = 1
        max_top_k_docs = 3
        label_top_k_docs = "Number of document chunks"
    else:
        min_top_k_docs = -1
        max_top_k_docs = 100
        label_top_k_docs = "Number of document chunks (-1 = auto fill model context)"
    return min_top_k_docs, max_top_k_docs, label_top_k_docs


def history_to_context(history, langchain_mode1,
                       add_chat_history_to_context,
                       prompt_type1, prompt_dict1, chat1, model_max_length1,
                       memory_restriction_level1, keep_sources_in_context1,
                       use_system_prompt1):
    """
    consumes all history up to (but not including) latest history item that is presumed to be an [instruction, None] pair
    :param history:
    :param langchain_mode1:
    :param add_chat_history_to_context:
    :param prompt_type1:
    :param prompt_dict1:
    :param chat1:
    :param model_max_length1:
    :param memory_restriction_level1:
    :param keep_sources_in_context1:
    :param use_system_prompt1:
    :return:
    """
    # ensure output will be unique to models
    _, _, _, max_prompt_length = get_cutoffs(memory_restriction_level1,
                                             for_context=True, model_max_length=model_max_length1)
    context1 = ''
    if max_prompt_length is not None and add_chat_history_to_context:
        context1 = ''
        # - 1 below because current instruction already in history from user()
        for histi in range(0, len(history) - 1):
            data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
            prompt, pre_response, terminate_response, chat_sep, chat_turn_sep = \
                generate_prompt(data_point,
                                prompt_type1,
                                prompt_dict1,
                                chat1,
                                reduced=True,
                                making_context=True,
                                use_system_prompt=use_system_prompt1,
                                histi=histi)
            # md -> back to text, maybe not super important if model trained enough
            if not keep_sources_in_context1 and langchain_mode1 != 'Disabled' and prompt.find(source_prefix) >= 0:
                # FIXME: This is relatively slow even for small amount of text, like 0.3s each history item
                import re
                prompt = re.sub(f'{re.escape(source_prefix)}.*?{re.escape(source_postfix)}', '', prompt,
                                flags=re.DOTALL)
                if prompt.endswith('\n<p>'):
                    prompt = prompt[:-4]
            prompt = prompt.replace('<br>', chat_turn_sep)
            if not prompt.endswith(chat_turn_sep):
                prompt += chat_turn_sep
            # most recent first, add older if can
            # only include desired chat history
            if len(prompt + context1) > max_prompt_length:
                break
            context1 += prompt

        _, pre_response, terminate_response, chat_sep, chat_turn_sep = \
            generate_prompt({}, prompt_type1, prompt_dict1,
                            chat1, reduced=True,
                            making_context=True,
                            use_system_prompt=use_system_prompt1,
                            histi=-1)
        if context1 and not context1.endswith(chat_turn_sep):
            context1 += chat_turn_sep  # ensure if terminates abruptly, then human continues on next line
    return context1


def update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, extra):
    # update from saved state on disk
    langchain_modes_from_file, visible_langchain_modes_from_file, langchain_mode_paths_from_file = \
        load_collection_enum(extra)

    visible_langchain_modes_temp = visible_langchain_modes.copy() + visible_langchain_modes_from_file
    visible_langchain_modes.clear()  # don't lose original reference
    [visible_langchain_modes.append(x) for x in visible_langchain_modes_temp if x not in visible_langchain_modes]

    langchain_mode_paths.update(langchain_mode_paths_from_file)

    langchain_modes_temp = langchain_modes.copy() + langchain_modes_from_file
    langchain_modes.clear()  # don't lose original reference
    [langchain_modes.append(x) for x in langchain_modes_temp if x not in langchain_modes]


def entrypoint_main():
    """
    Examples:

    WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B
    python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B'
    python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B'
    
    # generate without lora weights, no prompt
    python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain'
    python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq'

    python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq'
    # OpenChatKit settings:
    python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0

    python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False
    python generate.py --base_model='t5-large' --prompt_type='simple_instruct'
    python generate.py --base_model='philschmid/bart-large-cnn-samsum'
    python generate.py --base_model='philschmid/flan-t5-base-samsum'
    python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt'

    python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28'

    must have 4*48GB GPU and run without 8bit in order for sharding to work with use_gpu_id=False
    can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned
    python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --use_gpu_id=False --prompt_type='human_bot'

    python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b
    """
    fire.Fire(main)


if __name__ == "__main__":
    entrypoint_main()