import ast import contextlib import functools import gc import getpass import hashlib import inspect import json import os import pathlib import pickle import platform import random import shutil import subprocess import sys import threading import time import traceback import zipfile import tarfile from concurrent.futures import ProcessPoolExecutor from datetime import datetime from typing import Tuple, Callable, Dict from queue import Queue, Empty from concurrent.futures import ThreadPoolExecutor import filelock import fire import numpy as np import pandas as pd import requests import uuid import tabulate from fire import inspectutils from joblib import Parallel from tqdm.auto import tqdm def H2O_Fire(component=None): config_prefix = "H2OGPT_" args = sys.argv[1:] query_args = [arg.split("=")[0].split(" ")[0].lstrip("-") for arg in args] fn_spec = inspectutils.GetFullArgSpec(component) for key, value in os.environ.items(): if not ( (key.startswith(config_prefix) or key.startswith(config_prefix.lower())) and len(key) > len(config_prefix) ): continue # ignore as non H2OGPT argument new_key = key[len(config_prefix):].lower() if new_key in query_args: continue # ignore as already passed as script argument if new_key not in fn_spec.args: continue # ignore as not a valid H2OGPT argument args.append(f"--{new_key}={value}") fire.Fire(component=component, command=args) def set_seed(seed: int): """ Sets the seed of the entire notebook so results are the same every time we run. This is for REPRODUCIBILITY. """ import torch np.random.seed(seed) random_state = np.random.RandomState(seed) random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False os.environ['PYTHONHASHSEED'] = str(seed) return random_state def flatten_list(lis): """Given a list, possibly nested to any level, return it flattened.""" new_lis = [] for item in lis: if type(item) == type([]): new_lis.extend(flatten_list(item)) else: new_lis.append(item) return new_lis def clear_torch_cache(allow_skip=False): if allow_skip and os.getenv('CLEAR_CLEAR_TORCH', '2') == '1' or os.getenv('CLEAR_CLEAR_TORCH', '2') == '0': return try: import torch if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() gc.collect() except RuntimeError as e: print("clear_torch_cache error: %s" % ''.join(traceback.format_tb(e.__traceback__)), flush=True) def ping(): try: print('Ping: %s' % str(datetime.now()), flush=True) except AttributeError: # some programs wrap print and will fail with flush passed pass def ping_gpu(): try: print('Ping_GPU: %s %s' % (str(datetime.now()), system_info()), flush=True) except AttributeError: # some programs wrap print and will fail with flush passed pass try: ping_gpu_memory() except Exception as e: print('Ping_GPU memory failure: %s' % str(e), flush=True) def ping_gpu_memory(): from models.gpu_mem_track import MemTracker gpu_tracker = MemTracker() # define a GPU tracker from torch.cuda import memory_summary gpu_tracker.track() def get_torch_allocated(): import torch return torch.cuda.memory_allocated() def get_device(n_gpus=None): import torch if torch.cuda.is_available() and n_gpus != 0: device = "cuda" elif torch.backends.mps.is_built(): device = "mps" else: device = "cpu" return device def system_info(): import psutil system = {} # https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard # https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749 try: temps = psutil.sensors_temperatures(fahrenheit=False) if 'coretemp' in temps: coretemp = temps['coretemp'] temp_dict = {k.label: k.current for k in coretemp} for k, v in temp_dict.items(): system['CPU_C/%s' % k] = v except AttributeError: pass # https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt try: from pynvml.smi import nvidia_smi nvsmi = nvidia_smi.getInstance() gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])} for k, v in gpu_power_dict.items(): system['GPU_W/%s' % k] = v gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])} for k, v in gpu_temp_dict.items(): system['GPU_C/%s' % k] = v gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])} gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])} gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict} for k, v in gpu_memory_frac_dict.items(): system[f'GPU_M/%s' % k] = v except (KeyError, ModuleNotFoundError): pass system['hash'] = get_githash() debug_mem = False if debug_mem: try: # pip install guppy3 from guppy import hpy h = hpy() print(h.heap()) print(h.heap().byvia) print(h.heap().byid) except: pass return system def system_info_print(): try: df = pd.DataFrame.from_dict(system_info(), orient='index') # avoid slamming GPUs time.sleep(1) return df.to_markdown() except Exception as e: return "Error: %s" % str(e) def zip_data(root_dirs=None, zip_file=None, base_dir='./', fail_any_exception=False): try: return _zip_data(zip_file=zip_file, base_dir=base_dir, root_dirs=root_dirs) except Exception as e: traceback.print_exc() print('Exception in zipping: %s' % str(e)) if not fail_any_exception: raise def _zip_data(root_dirs=None, zip_file=None, base_dir='./'): if isinstance(root_dirs, str): root_dirs = [root_dirs] if zip_file is None: datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_") host_name = os.getenv('HF_HOSTNAME', 'emptyhost') zip_file = "data_%s_%s.zip" % (datetime_str, host_name) assert root_dirs is not None base_path = os.path.dirname(zip_file) if not os.path.isdir(base_path) and os.path.dirname(zip_file): base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True) zip_file = os.path.join(base_path, os.path.basename(zip_file)) with zipfile.ZipFile(zip_file, "w") as expt_zip: for root_dir in root_dirs: if root_dir is None: continue for root, d, files in os.walk(root_dir): for file in files: file_to_archive = os.path.join(root, file) assert os.path.exists(file_to_archive) path_to_archive = os.path.relpath(file_to_archive, base_dir) expt_zip.write(filename=file_to_archive, arcname=path_to_archive) return zip_file, zip_file def tar_data(root_dirs=None, tar_file=None, base_dir='./', fail_any_exception=False): try: return _tar_data(tar_file=tar_file, base_dir=base_dir, root_dirs=root_dirs) except Exception as e: traceback.print_exc() print('Exception in tar archiving: %s' % str(e)) if not fail_any_exception: raise def _tar_data(root_dirs=None, tar_file=None, base_dir='./'): if isinstance(root_dirs, str): root_dirs = [root_dirs] if tar_file is None: datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_") host_name = os.getenv('HF_HOSTNAME', 'emptyhost') tar_file = "data_%s_%s.tar.gz" % (datetime_str, host_name) assert root_dirs is not None base_path = os.path.dirname(tar_file) if not os.path.isdir(base_path) and os.path.dirname(tar_file): base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True) tar_file = os.path.join(base_path, os.path.basename(tar_file)) with tarfile.open(tar_file, "w:gz") as expt_tar: for root_dir in root_dirs: if root_dir is None: continue for root, d, files in os.walk(root_dir): for file in files: file_to_archive = os.path.join(root, file) assert os.path.exists(file_to_archive) path_to_archive = os.path.relpath(file_to_archive, base_dir) expt_tar.add(name=file_to_archive, arcname=path_to_archive) return tar_file, tar_file def save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from', extra_dict={}, error='', sources=[], which_api='', valid_key=None, h2ogpt_key='', return_dict=False): if not save_dir: return try: return _save_generate_output(prompt=prompt, output=output, base_model=base_model, save_dir=save_dir, where_from=where_from, extra_dict=extra_dict, error=error, sources=sources, which_api=which_api, valid_key=valid_key, h2ogpt_key=h2ogpt_key, return_dict=return_dict) except Exception as e: traceback.print_exc() print('Exception in saving: %s' % str(e)) def _save_generate_tokens(response_no_refs, extra_dict): # tokenize at end if need to, so doesn't block generation in multi-generator case if extra_dict.get('ntokens') is None: extra_dict['ntokens'] = FakeTokenizer().num_tokens_from_string(str(response_no_refs)) # only do below if didn't already compute ntokens, else assume also computed rate if extra_dict.get('ntokens') is not None and extra_dict.get('t_generate') is not None: extra_dict['tokens_persecond'] = extra_dict['ntokens'] / extra_dict['t_generate'] return extra_dict def _save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from', extra_dict={}, error='', sources=[], which_api='', valid_key=None, h2ogpt_key='', return_dict=False): """ Save conversation to .json, row by row. json_file_path is path to final JSON file. If not in ., then will attempt to make directories. Appends if file exists """ prompt = '' if prompt is None else prompt output = '' if output is None else output extra_dict = _save_generate_tokens(output, extra_dict) dict_to_save = dict(prompt=prompt, text=output, time=time.ctime(), base_model=base_model, where_from=where_from, error=error, sources=sources, which_api=which_api, valid_key=valid_key, h2ogpt_key=h2ogpt_key, ) dict_to_save.update(extra_dict) if return_dict: return dict_to_save if os.path.exists(save_dir) and not os.path.isdir(save_dir): raise RuntimeError("save_dir already exists and is not a directory!") makedirs(save_dir, exist_ok=True) # already should be made, can't change at this point import json with filelock.FileLock("%s.lock" % os.path.basename(save_dir)): # lock logging in case have concurrency with open(os.path.join(save_dir, "history.json"), "a") as f: # just add [ at start, and ] at end, and have proper JSON dataset f.write( " " + json.dumps( dict_to_save ) + ",\n" ) def s3up(filename): try: return _s3up(filename) except Exception as e: traceback.print_exc() print('Exception for file %s in s3up: %s' % (filename, str(e))) return "Failed to upload %s: Error: %s" % (filename, str(e)) def _s3up(filename): import boto3 aws_access_key_id = os.getenv('AWS_SERVER_PUBLIC_KEY') aws_secret_access_key = os.getenv('AWS_SERVER_SECRET_KEY') bucket = os.getenv('AWS_BUCKET') assert aws_access_key_id, "Set AWS key" assert aws_secret_access_key, "Set AWS secret" assert bucket, "Set AWS Bucket" s3 = boto3.client('s3', aws_access_key_id=os.getenv('AWS_SERVER_PUBLIC_KEY'), aws_secret_access_key=os.getenv('AWS_SERVER_SECRET_KEY'), ) ret = s3.upload_file( Filename=filename, Bucket=os.getenv('AWS_BUCKET'), Key=filename, ) if ret in [None, '']: return "Successfully uploaded %s" % filename def get_githash(): try: githash = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE).stdout.decode('utf-8')[0:-1] except: try: with open('git_hash.txt', 'rt') as f: githash = f.read() except: githash = "GET_GITHASH" return githash def copy_code(run_id): """ copy code to track changes :param run_id: :return: """ rnd_num = str(random.randint(0, 2 ** 31)) run_id = 'run_' + str(run_id) os.makedirs(run_id, exist_ok=True) me_full = os.path.join(pathlib.Path(__file__).parent.resolve(), __file__) me_file = os.path.basename(__file__) new_me = os.path.join(run_id, me_file + '_' + get_githash()) if os.path.isfile(new_me): new_me = os.path.join(run_id, me_file + '_' + get_githash() + '_' + rnd_num) shutil.copy(me_full, new_me) else: shutil.copy(me_full, new_me) class NullContext(threading.local): """No-op context manager, executes block without doing any additional processing. Used as a stand-in if a particular block of code is only sometimes used with a normal context manager: """ def __init__(self, *args, **kwargs): pass def __enter__(self): return self def __exit__(self, exc_type, exc_value, exc_traceback): self.finally_act() def finally_act(self): pass def wrapped_partial(func, *args, **kwargs): """ Give partial properties of normal function, like __name__ attribute etc. :param func: :param args: :param kwargs: :return: """ partial_func = functools.partial(func, *args, **kwargs) functools.update_wrapper(partial_func, func) return partial_func class ThreadException(Exception): pass class EThread(threading.Thread): # Function that raises the custom exception def __init__(self, group=None, target=None, name=None, args=(), kwargs=None, *, daemon=None, streamer=None, bucket=None): self.bucket = bucket self.streamer = streamer self.exc = None self._return = None super().__init__(group=group, target=target, name=name, args=args, kwargs=kwargs, daemon=daemon) def run(self): # Variable that stores the exception, if raised by someFunction try: if self._target is not None: self._return = self._target(*self._args, **self._kwargs) except BaseException as e: print("thread exception: %s" % str(sys.exc_info())) self.bucket.put(sys.exc_info()) self.exc = e if self.streamer: print("make stop: %s" % str(sys.exc_info()), flush=True) self.streamer.do_stop = True finally: # Avoid a refcycle if the thread is running a function with # an argument that has a member that points to the thread. del self._target, self._args, self._kwargs def join(self, timeout=None): threading.Thread.join(self) # Since join() returns in caller thread # we re-raise the caught exception # if any was caught if self.exc: raise self.exc return self._return def import_matplotlib(): import matplotlib matplotlib.use('agg') # KEEP THESE HERE! START import matplotlib.pyplot as plt import pandas as pd # to avoid dlopen deadlock in fork import pandas.core.computation.expressions as pd_expressions import pandas._libs.groupby as pd_libgroupby import pandas._libs.reduction as pd_libreduction import pandas.core.algorithms as pd_algorithms import pandas.core.common as pd_com import numpy as np # KEEP THESE HERE! END def get_sha(value): return hashlib.md5(str(value).encode('utf-8')).hexdigest() def sanitize_filename(name, file_length_limit=250): """ Sanitize file *base* names. :param name: name to sanitize :param file_length_limit: bit smaller than 256 for safety :return: """ bad_chars = ['[', ']', ',', '/', '\\', '\\w', '\\s', '-', '+', '\"', '\'', '>', '<', ' ', '=', ')', '(', ':', '^'] for char in bad_chars: name = name.replace(char, "_") length = len(name) sha_length = 32 real_length_limit = file_length_limit - (sha_length + 2) assert real_length_limit > 0, "Bad file limit length: %s %s" % (file_length_limit, real_length_limit) if length > file_length_limit: sha = get_sha(name) half_real_length_limit = max(1, int(real_length_limit / 2)) name = name[0:half_real_length_limit] + "_" + sha + "_" + name[length - half_real_length_limit:length] return name def shutil_rmtree(*args, **kwargs): return shutil.rmtree(*args, **kwargs) def remove(path: str): try: if path is not None and os.path.exists(path): if os.path.isdir(path): shutil_rmtree(path, ignore_errors=True) else: with contextlib.suppress(FileNotFoundError): os.remove(path) except: pass def makedirs(path, exist_ok=True, tmp_ok=False, use_base=False): """ Avoid some inefficiency in os.makedirs() :param path: :param exist_ok: :param tmp_ok: use /tmp if can't write locally :param use_base: :return: """ if path is None: return path # if base path set, make relative to that, unless user_path absolute path if use_base: if os.path.normpath(path) == os.path.normpath(os.path.abspath(path)): pass else: if os.getenv('H2OGPT_BASE_PATH') is not None: base_dir = os.path.normpath(os.getenv('H2OGPT_BASE_PATH')) path = os.path.normpath(path) if not path.startswith(base_dir): path = os.path.join(os.getenv('H2OGPT_BASE_PATH', ''), path) path = os.path.normpath(path) if os.path.isdir(path) and os.path.exists(path): assert exist_ok, "Path already exists" return path try: os.makedirs(path, exist_ok=exist_ok) return path except FileExistsError: # e.g. soft link return path except PermissionError: if tmp_ok: path0 = path path = os.path.join('/tmp/', path) print("Permission denied to %s, using %s instead" % (path0, path), flush=True) os.makedirs(path, exist_ok=exist_ok) return path else: raise def atomic_move_simple(src, dst): try: shutil.move(src, dst) except (shutil.Error, FileExistsError): pass remove(src) def download_simple(url, dest=None, overwrite=False, verbose=False): if dest is None: dest = os.path.basename(url) base_path = os.path.dirname(dest) if base_path: # else local path base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True) dest = os.path.join(base_path, os.path.basename(dest)) if os.path.isfile(dest): if not overwrite: print("Already have %s from url %s, delete file if invalid" % (dest, str(url)), flush=True) return dest else: remove(dest) if verbose: print("BEGIN get url %s" % str(url), flush=True) if url.startswith("file://"): from requests_file import FileAdapter s = requests.Session() s.mount('file://', FileAdapter()) url_data = s.get(url, stream=True) else: url_data = requests.get(url, stream=True) if verbose: print("GOT url %s" % str(url), flush=True) if url_data.status_code != requests.codes.ok: msg = "Cannot get url %s, code: %s, reason: %s" % ( str(url), str(url_data.status_code), str(url_data.reason), ) raise requests.exceptions.RequestException(msg) url_data.raw.decode_content = True uuid_tmp = str(uuid.uuid4())[:6] dest_tmp = dest + "_dl_" + uuid_tmp + ".tmp" with open(dest_tmp, "wb") as f: shutil.copyfileobj(url_data.raw, f) atomic_move_simple(dest_tmp, dest) if verbose: print("DONE url %s" % str(url), flush=True) return dest def download(url, dest=None, dest_path=None): if dest_path is not None: dest = os.path.join(dest_path, os.path.basename(url)) if os.path.isfile(dest): print("already downloaded %s -> %s" % (url, dest)) return dest elif dest is not None: if os.path.exists(dest): print("already downloaded %s -> %s" % (url, dest)) return dest else: uuid_tmp = "dl2_" + str(uuid.uuid4())[:6] dest = uuid_tmp + os.path.basename(url) print("downloading %s to %s" % (url, dest)) if url.startswith("file://"): from requests_file import FileAdapter s = requests.Session() s.mount('file://', FileAdapter()) url_data = s.get(url, stream=True) else: url_data = requests.get(url, stream=True) if url_data.status_code != requests.codes.ok: msg = "Cannot get url %s, code: %s, reason: %s" % ( str(url), str(url_data.status_code), str(url_data.reason)) raise requests.exceptions.RequestException(msg) url_data.raw.decode_content = True dirname = os.path.dirname(dest) if dirname != "" and not os.path.isdir(dirname): base_path = os.path.dirname(dest) base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True) dest = os.path.join(base_path, os.path.basename(dest)) uuid_tmp = "dl3_" + str(uuid.uuid4())[:6] dest_tmp = dest + "_" + uuid_tmp + ".tmp" with open(dest_tmp, 'wb') as f: shutil.copyfileobj(url_data.raw, f) try: shutil.move(dest_tmp, dest) except FileExistsError: pass remove(dest_tmp) return dest def get_doc(x): return x.page_content def get_source(x): return x.metadata.get('source', "UNKNOWN SOURCE") def get_accordion_named(content, title, font_size=8): return f"""
{title}{content}
""" def hyde_titles(level): if level == 0: title = "HYDE 0: LLM" elif level == 1: title = "HYDE 1: Prompt+LLM embedding" elif level == 2: title = "HYDE 2: Prompt+LLM+HYDE 1 embedding" elif level == 3: title = "HYDE 3: Prompt+LLM+HYDE 1&2 embedding" else: title = "HYDE 4: Prompt+LLM+HYDE 1&2&3 embedding" return title def get_accordion(x, font_size=2, head_acc=50): title = x.page_content[:head_acc].replace("\n", ' ').replace("
", ' ').replace("

", ' ').replace("\r", ' ') content = x.page_content return f"""

{title}{content}
""" def get_url(x, from_str=False, short_name=False, font_size=2): if not from_str: source = x.metadata['source'] else: source = x if short_name: source_name = get_short_name(source) else: source_name = source if source.startswith('http://') or source.startswith('https://'): return """%s""" % ( font_size, source, source_name) elif ' maxl: allow_length = maxl - 3 half_allowed = max(1, int(allow_length / 2)) name = name[0:half_allowed] + "..." + name[length - half_allowed:length] return name def cuda_vis_check(total_gpus): """Helper function to count GPUs by environment variable Stolen from Jon's h2o4gpu utils """ cudavis = os.getenv("CUDA_VISIBLE_DEVICES") which_gpus = [] if cudavis is not None: # prune away white-space, non-numerics, # except commas for simple checking cudavis = "".join(cudavis.split()) import re cudavis = re.sub("[^0-9,]", "", cudavis) lencudavis = len(cudavis) if lencudavis == 0: total_gpus = 0 else: total_gpus = min( total_gpus, os.getenv("CUDA_VISIBLE_DEVICES").count(",") + 1) which_gpus = os.getenv("CUDA_VISIBLE_DEVICES").split(",") which_gpus = [int(x) for x in which_gpus] else: which_gpus = list(range(0, total_gpus)) return total_gpus, which_gpus def get_ngpus_vis(raise_if_exception=True): ngpus_vis1 = None shell = False if shell: cmd = "nvidia-smi -L 2> /dev/null" else: cmd = ["nvidia-smi", "-L"] try: timeout = 5 * 3 o = subprocess.check_output(cmd, shell=shell, timeout=timeout) lines = o.decode("utf-8").splitlines() ngpus_vis1 = 0 for line in lines: if 'Failed to initialize NVML' not in line: ngpus_vis1 += 1 except (FileNotFoundError, subprocess.CalledProcessError, OSError): # GPU systems might not have nvidia-smi, so can't fail pass except subprocess.TimeoutExpired as e: print('Failed get_ngpus_vis: %s' % str(e)) if raise_if_exception: raise if ngpus_vis1 is None: import torch if get_device() == 'cuda': ngpus_vis1 = torch.cuda.device_count() if torch.cuda.is_available() else 0 else: ngpus_vis1 = 0 ngpus_vis1, which_gpus = cuda_vis_check(ngpus_vis1) return ngpus_vis1 def get_mem_gpus(raise_if_exception=True, ngpus=None): totalmem_gpus1 = 0 usedmem_gpus1 = 0 freemem_gpus1 = 0 if ngpus == 0: return totalmem_gpus1, usedmem_gpus1, freemem_gpus1 try: cmd = "nvidia-smi -q 2> /dev/null | grep -A 3 'FB Memory Usage'" o = subprocess.check_output(cmd, shell=True, timeout=15) lines = o.decode("utf-8").splitlines() for line in lines: if 'Total' in line: totalmem_gpus1 += int(line.split()[2]) * 1024 ** 2 if 'Used' in line: usedmem_gpus1 += int(line.split()[2]) * 1024 ** 2 if 'Free' in line: freemem_gpus1 += int(line.split()[2]) * 1024 ** 2 except (FileNotFoundError, subprocess.CalledProcessError, OSError): # GPU systems might not have nvidia-smi, so can't fail pass except subprocess.TimeoutExpired as e: print('Failed get_mem_gpus: %s' % str(e)) if raise_if_exception: raise return totalmem_gpus1, usedmem_gpus1, freemem_gpus1 n_gpus_global = get_ngpus_vis() class ForkContext(threading.local): """ Set context for forking Ensures state is returned once done """ def __init__(self, args=None, kwargs=None, forkdata_capable=True): """ :param args: :param kwargs: :param forkdata_capable: whether fork is forkdata capable and will use copy-on-write forking of args/kwargs """ self.forkdata_capable = forkdata_capable if self.forkdata_capable: self.has_args = args is not None self.has_kwargs = kwargs is not None forkdatacontext.args = args forkdatacontext.kwargs = kwargs else: self.has_args = False self.has_kwargs = False def __enter__(self): try: # flush all outputs so doesn't happen during fork -- don't print/log inside ForkContext contexts! sys.stdout.flush() sys.stderr.flush() except BaseException as e: # exit not called if exception, and don't want to leave forkdatacontext filled in that case print("ForkContext failure on enter: %s" % str(e)) self.finally_act() raise return self def __exit__(self, exc_type, exc_value, exc_traceback): self.finally_act() def finally_act(self): """ Done when exception hit or exit is reached in context first reset forkdatacontext as crucial to have reset even if later 2 calls fail :return: None """ if self.forkdata_capable and (self.has_args or self.has_kwargs): forkdatacontext._reset() class _ForkDataContext(threading.local): def __init__( self, args=None, kwargs=None, ): """ Global context for fork to carry data to subprocess instead of relying upon copy/pickle/serialization :param args: args :param kwargs: kwargs """ assert isinstance(args, (tuple, type(None))) assert isinstance(kwargs, (dict, type(None))) self.__args = args self.__kwargs = kwargs @property def args(self) -> Tuple: """returns args""" return self.__args @args.setter def args(self, args): if self.__args is not None: raise AttributeError( "args cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs)) ) self.__args = args @property def kwargs(self) -> Dict: """returns kwargs""" return self.__kwargs @kwargs.setter def kwargs(self, kwargs): if self.__kwargs is not None: raise AttributeError( "kwargs cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs)) ) self.__kwargs = kwargs def _reset(self): """Reset fork arg-kwarg context to default values""" self.__args = None self.__kwargs = None def get_args_kwargs(self, func, args, kwargs) -> Tuple[Callable, Tuple, Dict]: if self.__args: args = self.__args[1:] if not func: assert len(self.__args) > 0, "if have no func, must have in args" func = self.__args[0] # should always be there if self.__kwargs: kwargs = self.__kwargs try: return func, args, kwargs finally: forkdatacontext._reset() @staticmethod def get_args_kwargs_for_traced_func(func, args, kwargs): """ Return args/kwargs out of forkdatacontext when using copy-on-write way of passing args/kwargs :param func: actual function ran by _traced_func, which itself is directly what mppool treats as function :param args: :param kwargs: :return: func, args, kwargs from forkdatacontext if used, else originals """ # first 3 lines are debug func_was_None = func is None args_was_None_or_empty = args is None or len(args) == 0 kwargs_was_None_or_empty = kwargs is None or len(kwargs) == 0 forkdatacontext_args_was_None = forkdatacontext.args is None forkdatacontext_kwargs_was_None = forkdatacontext.kwargs is None func, args, kwargs = forkdatacontext.get_args_kwargs(func, args, kwargs) using_forkdatacontext = func_was_None and func is not None # pulled func out of forkdatacontext.__args[0] assert forkdatacontext.args is None, "forkdatacontext.args should be None after get_args_kwargs" assert forkdatacontext.kwargs is None, "forkdatacontext.kwargs should be None after get_args_kwargs" proc_type = kwargs.get('proc_type', 'SUBPROCESS') if using_forkdatacontext: assert proc_type == "SUBPROCESS" or proc_type == "SUBPROCESS" if proc_type == "NORMAL": assert forkdatacontext_args_was_None, "if no fork, expect forkdatacontext.args None entering _traced_func" assert forkdatacontext_kwargs_was_None, "if no fork, expect forkdatacontext.kwargs None entering _traced_func" assert func is not None, "function should not be None, indicates original args[0] was None or args was None" return func, args, kwargs forkdatacontext = _ForkDataContext() # Add user info username = getpass.getuser() current_working_directory = os.getcwd() operating_system = platform.system() def _traced_func(func, *args, **kwargs): func, args, kwargs = forkdatacontext.get_args_kwargs_for_traced_func(func, args, kwargs) return func(*args, **kwargs) def call_subprocess_onetask(func, args=None, kwargs=None): if platform.system() in ['Darwin', 'Windows']: return func(*args, **kwargs) if isinstance(args, list): args = tuple(args) if args is None: args = () if kwargs is None: kwargs = {} args = list(args) args = [func] + args args = tuple(args) with ForkContext(args=args, kwargs=kwargs): args = (None,) kwargs = {} with ProcessPoolExecutor(max_workers=1) as executor: future = executor.submit(_traced_func, *args, **kwargs) return future.result() class ProgressParallel(Parallel): def __init__(self, use_tqdm=True, total=None, *args, **kwargs): self._use_tqdm = use_tqdm self._total = total super().__init__(*args, **kwargs) def __call__(self, *args, **kwargs): with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar: return Parallel.__call__(self, *args, **kwargs) def print_progress(self): if self._total is None: self._pbar.total = self.n_dispatched_tasks self._pbar.n = self.n_completed_tasks self._pbar.refresh() def get_kwargs(func, exclude_names=None, **kwargs): func_names = list(inspect.signature(func).parameters) missing_kwargs = [x for x in func_names if x not in kwargs] if exclude_names: for k in exclude_names: if k in missing_kwargs: missing_kwargs.remove(k) if k in func_names: func_names.remove(k) assert not missing_kwargs, "Missing %s" % missing_kwargs kwargs = {k: v for k, v in kwargs.items() if k in func_names} return kwargs from importlib.metadata import distribution, PackageNotFoundError have_faiss = False try: assert distribution('faiss') is not None have_faiss = True except (PackageNotFoundError, AssertionError): pass try: assert distribution('faiss_gpu') is not None have_faiss = True except (PackageNotFoundError, AssertionError): pass try: assert distribution('faiss_cpu') is not None have_faiss = True except (PackageNotFoundError, AssertionError): pass have_chromamigdb = False try: assert distribution('chromamigdb') is not None have_chromamigdb = True except (PackageNotFoundError, AssertionError): pass have_serpapi = False try: assert distribution('google-search-results') is not None have_serpapi = True except (PackageNotFoundError, AssertionError): pass def hash_file(file): try: import hashlib # BUF_SIZE is totally arbitrary, change for your app! BUF_SIZE = 65536 # lets read stuff in 64kb chunks! md5 = hashlib.md5() # sha1 = hashlib.sha1() if not os.path.isfile(file): md5.update(file.encode(encoding='UTF-8')) else: with open(file, 'rb') as f: while True: data = f.read(BUF_SIZE) if not data: break md5.update(data) # sha1.update(data) except BaseException as e: print("Cannot hash %s due to %s" % (file, str(e))) traceback.print_exc() return '' return md5.hexdigest() def start_faulthandler(): # If hit server or any subprocess with signal SIGUSR1, it'll print out all threads stack trace, but wont't quit or coredump # If more than one fork tries to write at same time, then looks corrupted. import faulthandler # SIGUSR1 in h2oai/__init__.py as well faulthandler.enable() if hasattr(faulthandler, 'register'): # windows/mac import signal faulthandler.register(signal.SIGUSR1) def get_hf_server(inference_server): inf_split = inference_server.split(" ") assert len(inf_split) == 1 or len(inf_split) == 3 inference_server = inf_split[0] if len(inf_split) == 3: headers = {"authorization": "%s %s" % (inf_split[1], inf_split[2])} else: headers = None return inference_server, headers class FakeTokenizer: """ 1) For keeping track of model_max_length 2) For when model doesn't directly expose tokenizer but need to count tokens """ def __init__(self, model_max_length=2048, encoding_name="cl100k_base", is_openai=False, is_anthropic=False, tokenizer=None, is_llama_cpp=False): if model_max_length is None: assert not (is_openai or is_anthropic), "Should have set model_max_length for OpenAI or Anthropic" model_max_length = 2048 self.is_openai = is_openai self.is_anthropic = is_anthropic self.is_llama_cpp = is_llama_cpp self.tokenizer = tokenizer self.model_max_length = model_max_length if not self.is_openai and not self.is_anthropic and not self.is_llama_cpp: # don't push limit, since if using fake tokenizer, only estimate, and seen underestimates by order 250 self.model_max_length -= 250 self.encoding_name = encoding_name # The first time this runs, it will require an internet connection to download. Later runs won't need an internet connection. if not self.is_anthropic: import tiktoken self.encoding = tiktoken.get_encoding(self.encoding_name) else: self.encoding = None def encode(self, x, *args, return_tensors="pt", **kwargs): if self.is_llama_cpp: # and len(x) < 4 * 4 * self.model_max_length: # don't use llama.cpp if too much input_ids = self.tokenizer.tokenize(b" " + x.encode("utf-8")) elif self.is_anthropic: from anthropic import Anthropic client = Anthropic() tokenizer = client.get_tokenizer() input_ids = tokenizer.encode(x).ids else: input_ids = self.encoding.encode(x, disallowed_special=()) if return_tensors == 'pt' and isinstance(input_ids, list): import torch input_ids = torch.tensor(input_ids) return dict(input_ids=input_ids) def decode(self, x, *args, **kwargs): if self.is_llama_cpp: # and len(x) < 4 * self.model_max_length: # don't use llama.cpp if too much return self.tokenizer.detokenize(x) elif self.is_anthropic: from anthropic import Anthropic client = Anthropic() tokenizer = client.get_tokenizer() return tokenizer.decode(x) # input is input_ids[0] form return self.encoding.decode(x) def num_tokens_from_string(self, prompt: str) -> int: """Returns the number of tokens in a text string.""" if self.is_anthropic: from anthropic import Anthropic client = Anthropic() return client.count_tokens(prompt) num_tokens = len(self.encode(prompt)['input_ids']) return num_tokens def __call__(self, x, *args, **kwargs): return self.encode(x, *args, **kwargs) def get_local_ip(): import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) try: # doesn't even have to be reachable s.connect(('10.255.255.255', 1)) IP = s.getsockname()[0] except Exception: IP = '127.0.0.1' finally: s.close() return IP try: assert distribution('langchain') is not None have_langchain = True except (PackageNotFoundError, AssertionError): have_langchain = False import distutils.spawn have_tesseract = distutils.spawn.find_executable("tesseract") have_libreoffice = distutils.spawn.find_executable("libreoffice") try: from weasyprint import HTML import doctr have_doctr = True except: have_doctr = False try: assert distribution('arxiv') is not None assert distribution('pymupdf') is not None have_arxiv = True except (PackageNotFoundError, AssertionError): have_arxiv = False try: assert distribution('pymupdf') is not None have_pymupdf = True except (PackageNotFoundError, AssertionError): have_pymupdf = False try: assert distribution('selenium') is not None have_selenium = True except (PackageNotFoundError, AssertionError): have_selenium = False try: assert distribution('pillow') is not None have_pillow = True except (PackageNotFoundError, AssertionError): have_pillow = False try: assert distribution('playwright') is not None have_playwright = True except (PackageNotFoundError, AssertionError): have_playwright = False try: assert distribution('jq') is not None have_jq = True except (PackageNotFoundError, AssertionError): have_jq = False try: assert distribution('optimum') is not None have_optimum = True except (PackageNotFoundError, AssertionError): have_optimum = False try: assert distribution('librosa') is not None have_librosa = True except (PackageNotFoundError, AssertionError): have_librosa = False try: assert distribution('wavio') is not None have_wavio = True except (PackageNotFoundError, AssertionError): have_wavio = False try: assert distribution('soundfile') is not None have_soundfile = True except (PackageNotFoundError, AssertionError): have_soundfile = False try: assert distribution('deepspeed') is not None have_deepspeed = True except (PackageNotFoundError, AssertionError): have_deepspeed = False try: assert distribution('emoji') is not None have_emoji = True except (PackageNotFoundError, AssertionError): have_emoji = False try: assert distribution('langid') is not None have_langid = True except (PackageNotFoundError, AssertionError): have_langid = False try: assert distribution('TTS') is not None have_TTS = True except (PackageNotFoundError, AssertionError): have_TTS = False try: assert distribution('faster_whisper') is not None have_use_faster = True except (PackageNotFoundError, AssertionError): have_use_faster = False try: assert distribution('flash_attn') is not None have_flash_attention = True have_flash_attention_2 = distribution('flash_attn').version.startswith('2.') except (PackageNotFoundError, AssertionError): have_flash_attention = False have_flash_attention_2 = False try: assert distribution('gradio') is not None have_gradio = True is_gradio_version4 = distribution('gradio').version.startswith('4.') except (PackageNotFoundError, AssertionError): have_gradio = False is_gradio_version4 = False try: assert distribution('gradio_pdf') is not None have_gradio_pdf = is_gradio_version4 except (PackageNotFoundError, AssertionError): have_gradio_pdf = False try: assert distribution('pyrubberband') is not None have_pyrubberband = True except (PackageNotFoundError, AssertionError): have_pyrubberband = False try: assert distribution('fiftyone') is not None have_fiftyone = True except (PackageNotFoundError, AssertionError): have_fiftyone = False try: assert distribution('diffusers') is not None have_diffusers = True except (PackageNotFoundError, AssertionError): have_diffusers = False only_unstructured_urls = os.environ.get("ONLY_UNSTRUCTURED_URLS", "0") == "1" only_selenium = os.environ.get("ONLY_SELENIUM", "0") == "1" only_playwright = os.environ.get("ONLY_PLAYWRIGHT", "0") == "1" def set_openai(inference_server, model_name=None): if inference_server.startswith('vllm'): import openvllm api_key = openvllm.api_key = "EMPTY" inf_type = inference_server.split(':')[0].strip() ip_port_vllm = ':'.join(inference_server.split(':')[1:]) if ip_port_vllm.startswith('https://') or ip_port_vllm.startswith('http://'): api_base = openvllm.api_base = ip_port_vllm else: ip_vllm = inference_server.split(':')[1].strip() port_vllm = inference_server.split(':')[2].strip() api_base = openvllm.api_base = f"http://{ip_vllm}:{port_vllm}/v1" from openvllm import vLLM, AsyncvLLM client_args = dict(base_url=api_base, api_key=api_key) client = vLLM(**client_args) async_client = AsyncvLLM(**client_args) if inf_type in ['vllm_chat']: client = client.chat.completions async_client = async_client.chat.completions else: client = client.completions async_client = async_client.completions return client, async_client, inf_type, None, api_base, None, api_key else: api_key = os.getenv("OPENAI_API_KEY") base_url = None deployment_type = None api_version = None inf_type = inference_server.split(':')[0].strip() if len(inference_server.split(':')) >= 2: deployment_type = inference_server.split(':')[1].strip() if len(inference_server.split(':')) >= 3: base_url = inference_server.split(':')[2].strip() base_url = 'https://' + base_url if len(inference_server.split(':')) >= 4: api_version = inference_server.split(':')[3].strip() if inference_server.startswith('openai_azure'): if api_version in ['None', None]: # for function tools support # https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview api_version = "2023-12-01-preview" if os.getenv('OPENAI_AZURE_KEY') is not None: # use this instead if exists api_key = os.getenv("OPENAI_AZURE_KEY") if len(inference_server.split(':')) >= 5: api_key0 = inference_server.split(':')[4].strip() if api_key0 not in ['None', None]: api_key = api_key0 if deployment_type == 'None': deployment_type = None if base_url == 'None': base_url = None if base_url == 'None': base_url = None # cannot use non-chat model, uses old openai. stuff if go through to H2OOpenAI with chat model if model_name: chat_model = (model_name.startswith("gpt-3.5-turbo") or model_name.startswith( "gpt-4")) and "-instruct" not in model_name if chat_model and inf_type == 'openai_azure': inf_type = 'openai_azure_chat' if chat_model and inf_type == 'openai': inf_type = 'openai_chat' from openai import OpenAI, AzureOpenAI, AsyncOpenAI, AsyncAzureOpenAI if inf_type in ['openai_azure', 'openai_azure_chat']: client_args = dict(azure_deployment=deployment_type, azure_endpoint=base_url, api_version=api_version, api_key=api_key) client = AzureOpenAI(**client_args) async_client = AsyncAzureOpenAI(**client_args) else: client_args = dict(base_url=base_url, api_key=api_key) client = OpenAI(**client_args) async_client = AsyncOpenAI(**client_args) if inf_type in ['openai_chat', 'openai_azure_chat']: client = client.chat.completions async_client = async_client.chat.completions else: client = client.completions async_client = async_client.completions return client, async_client, inf_type, deployment_type, base_url, api_version, api_key def get_list_or_str(x): if isinstance(x, list): return x elif isinstance(x, str): try: x1 = ast.literal_eval(x) assert isinstance(x1, list) return x1 except: return x else: return x def deepcopy_by_pickle_object(object): """ Faster deepcopy, can only work on things that are picklable. Naive Deepcopy is more general. Same method as for class Individual :param object: :return: """ gc.disable() new_object = pickle.loads(pickle.dumps(object, -1)) gc.enable() return new_object def url_alive(url): try: response = requests.head(url) except Exception as e: return False else: if response.status_code in [200, 301, 302, 307]: return True else: return False def return_good_url(url): # ignore status code, just see if exists or not for prefix in ['', 'http://', 'http://', 'https://www.', 'http://www.']: try: url_test = prefix + url response = requests.head(url_test) except Exception as e: response = None url_test = None if response is not None: # and response.status_code < 400: # don't do status check, if got status, then is real URL regardless of goodness, not text return url_test return None def dict_to_html(x, small=True, api=False): df = pd.DataFrame(x.items(), columns=['Key', 'Value']) df.index = df.index + 1 df.index.name = 'index' if api: return tabulate.tabulate(df, headers='keys') else: res = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml') if small: return "" + res + "" else: return res def text_to_html(x, api=False): if api: return x return """
%s
""" % x def lg_to_gr( **kwargs, ): # translate: import torch n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0 n_gpus, _ = cuda_vis_check(n_gpus) image_audio_loaders_options = ['Caption'] if n_gpus != 0: image_audio_loaders_options.extend(['CaptionBlip2', 'Pix2Struct']) if have_tesseract: image_audio_loaders_options.append('OCR') if have_doctr: image_audio_loaders_options.append('DocTR') if have_librosa: image_audio_loaders_options.append('ASR') if n_gpus != 0: image_audio_loaders_options.append('ASRLarge') if kwargs['enable_llava'] and kwargs['llava_model']: image_audio_loaders_options.append('LLaVa') image_audio_loaders_options0 = [] if have_tesseract and kwargs['enable_ocr']: image_audio_loaders_options0.append('OCR') if have_doctr and kwargs['enable_doctr']: image_audio_loaders_options0.append('DocTR') if kwargs['enable_captions']: if kwargs['max_quality'] and n_gpus > 0: # BLIP2 only on GPU image_audio_loaders_options0.append('CaptionBlip2') else: image_audio_loaders_options0.append('Caption') if have_librosa and kwargs['enable_transcriptions']: if kwargs['max_quality'] and n_gpus > 0: image_audio_loaders_options0.append('ASRLarge') else: image_audio_loaders_options0.append('ASR') if kwargs['enable_llava'] and kwargs['llava_model']: # and n_gpus > 0 # don't require local GPUs # LLaVa better and faster if present # and kwargs['max_quality'] image_audio_loaders_options0.append('LLaVa') if 'Caption' in image_audio_loaders_options0: image_audio_loaders_options0.remove('Caption') if 'CaptionBlip2' in image_audio_loaders_options0: image_audio_loaders_options0.remove('CaptionBlip2') pdf_loaders_options = ['Unstructured', 'PyPDF', 'TryHTML'] if have_pymupdf: pdf_loaders_options = ['PyMuPDF'] + pdf_loaders_options if have_tesseract: pdf_loaders_options.append('OCR') if have_doctr: pdf_loaders_options.append('DocTR') pdf_loaders_options0 = [] if have_pymupdf and kwargs['use_pymupdf'] in [True, 'auto', 'on']: pdf_loaders_options0.append('PyMuPDF') if kwargs['enable_pdf_ocr'] in [True, 'on']: pdf_loaders_options0.append('OCR') if have_doctr and kwargs['enable_pdf_doctr'] in [True, 'on']: pdf_loaders_options0.append('DocTR') # in case my pymupdf, use pypdf as backup default if kwargs['use_pypdf'] in [True, 'on'] and have_pymupdf or kwargs['use_pypdf'] in [True, 'auto', 'on'] and not have_pymupdf: pdf_loaders_options0.append('PyPDF') if kwargs['use_unstructured_pdf'] in [True, 'on']: pdf_loaders_options0.append('Unstructured') if kwargs['try_pdf_as_html'] in [True, 'on']: pdf_loaders_options0.append('TryHTML') url_loaders_options = [] if only_unstructured_urls: url_loaders_options.append('Unstructured') elif have_selenium and only_selenium: url_loaders_options.append('Selenium') elif have_playwright and only_playwright: url_loaders_options.append('PlayWright') else: url_loaders_options.append('Unstructured') if have_selenium: url_loaders_options.append('Selenium') if have_playwright: url_loaders_options.append('PlayWright') url_loaders_options.append('ScrapeWithPlayWright') url_loaders_options.append('ScrapeWithHttp') url_loaders_options0 = [url_loaders_options[0]] assert set(image_audio_loaders_options0).issubset(image_audio_loaders_options), "%s %s" % ( image_audio_loaders_options0, image_audio_loaders_options) assert set(pdf_loaders_options0).issubset(pdf_loaders_options), "%s %s" % ( pdf_loaders_options0, pdf_loaders_options) assert set(url_loaders_options0).issubset(url_loaders_options), "%s %s" % ( url_loaders_options0, url_loaders_options) return image_audio_loaders_options0, image_audio_loaders_options, \ pdf_loaders_options0, pdf_loaders_options, \ url_loaders_options0, url_loaders_options def fix_json(s): # Attempt to parse the string as-is. try: return json.loads(s) except json.JSONDecodeError: pass # Initialize variables. new_s = "" stack = [] is_inside_string = False escaped = False # Process each character in the string one at a time. for char in s: if is_inside_string: if char == '"' and not escaped: is_inside_string = False elif char == '\n' and not escaped: char = '\\n' # Replace the newline character with the escape sequence. elif char == '\\': escaped = not escaped else: escaped = False else: if char == '"': is_inside_string = True escaped = False elif char == '{': stack.append('}') elif char == '[': stack.append(']') elif char == '}' or char == ']': if stack and stack[-1] == char: stack.pop() else: # Mismatched closing character; the input is malformed. return None # Append the processed character to the new string. new_s += char # If we're still inside a string at the end of processing, we need to close the string. if is_inside_string: new_s += '"' # Close any remaining open structures in the reverse order that they were opened. for closing_char in reversed(stack): new_s += closing_char # Attempt to parse the modified string as JSON. try: return json.loads(new_s) except json.JSONDecodeError: # If we still can't parse the string as JSON, return None to indicate failure. return None def wrap_in_try_except(code): # Add import traceback code = "import traceback\n" + code # Parse the input code into an AST parsed_code = ast.parse(code) # Wrap the entire code's AST in a single try-except block try_except = ast.Try( body=parsed_code.body, handlers=[ ast.ExceptHandler( type=ast.Name(id="Exception", ctx=ast.Load()), name=None, body=[ ast.Expr( value=ast.Call( func=ast.Attribute(value=ast.Name(id="traceback", ctx=ast.Load()), attr="print_exc", ctx=ast.Load()), args=[], keywords=[] ) ), ] ) ], orelse=[], finalbody=[] ) # Assign the try-except block as the new body parsed_code.body = [try_except] # Convert the modified AST back to source code return ast.unparse(parsed_code) def enqueue_output(file, queue): for line in iter(file.readline, ''): queue.put(line) file.close() def read_popen_pipes(p): with ThreadPoolExecutor(2) as pool: q_stdout, q_stderr = Queue(), Queue() pool.submit(enqueue_output, p.stdout, q_stdout) pool.submit(enqueue_output, p.stderr, q_stderr) while True: if p.poll() is not None and q_stdout.empty() and q_stderr.empty(): break out_line = err_line = '' try: out_line = q_stdout.get_nowait() except Empty: pass try: err_line = q_stderr.get_nowait() except Empty: pass yield out_line, err_line def start_process(cmd): start_cmd = sys.executable + " -i -q -u" print_cmd = 'print("{}")' cmd = [start_cmd] + [cmd] process = subprocess.Popen(cmd, stdout=subprocess.PIPE) for c in iter(lambda: process.stdout.read(1), b''): sys.stdout.write(c) def str_to_list(x, allow_none=False): if isinstance(x, str): if len(x.strip()) > 0: if x.strip().startswith('['): x = ast.literal_eval(x.strip()) else: raise ValueError("Invalid str_to_list for %s" % x) else: x = [] elif x is None and not allow_none: x = [] if allow_none: assert isinstance(x, (type(None), list)) else: assert isinstance(x, list) return x def str_to_dict(x): if isinstance(x, str): if len(x.strip()) > 0: if x.strip().startswith('{'): x = ast.literal_eval(x.strip()) else: raise ValueError("Invalid str_to_dict for %s" % x) else: x = {} elif x is None: x = {} assert isinstance(x, dict) return x def get_token_count(x, tokenizer, token_count_fun=None): # NOTE: Somewhat duplicates H2OTextGenerationPipeline.get_token_count() # handle ambiguity in if get dict or list if tokenizer is not None: if hasattr(tokenizer, 'encode'): tokens = tokenizer.encode(x) else: tokens = tokenizer(x) if isinstance(tokens, dict) and 'input_ids' in tokens: tokens = tokens['input_ids'] if isinstance(tokens, list): n_tokens = len(tokens) elif len(tokens.shape) == 2: n_tokens = tokens.shape[1] elif len(tokens.shape) == 1: n_tokens = tokens.shape[0] else: raise RuntimeError("Cannot handle tokens: %s" % tokens) elif token_count_fun is not None: assert callable(token_count_fun) n_tokens = token_count_fun(x) else: tokenizer = FakeTokenizer() n_tokens = tokenizer.num_tokens_from_string(x) return n_tokens def reverse_ucurve_list(lst): if not lst: return [] if len(lst) == 1: return lst if len(lst) == 2: return [lst[1], lst[0]] front_list = [] end_list = [] for i, item in enumerate(lst): if i % 2 == 0: end_list.append(item) else: front_list.append(item) return front_list + end_list[::-1] def undo_reverse_ucurve_list(lst): if not lst: return [] if len(lst) == 1: return lst if len(lst) == 2: return [lst[1], lst[0]] # Split the list into two halves: the first half and the second half (reversed) mid = len(lst) // 2 first_half = lst[:mid] second_half = lst[mid:][::-1] # Merge the two halves by taking elements alternatively from the second half and then the first half result = [] for i in range(mid): result.append(second_half[i]) result.append(first_half[i]) # If the length of the list is odd, append the last element of the second half if len(lst) % 2 != 0: result.append(second_half[-1]) return result def get_size(start_path='.'): total_size = 0 for dirpath, dirnames, filenames in os.walk(start_path): for f in filenames: fp = os.path.join(dirpath, f) # skip if it is symbolic link if not os.path.islink(fp): total_size += os.path.getsize(fp) return total_size def get_test_name_core(): tn = os.environ['PYTEST_CURRENT_TEST'].split(':')[-1] tn = "_".join(tn.split(' ')[:-1]) # skip (call) at end return sanitize_filename(tn)