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import datetime
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import logging
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import logging.handlers
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import os
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import sys
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import requests
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from llava.constants import LOGDIR
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server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
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moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
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handler = None
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def build_logger(logger_name, logger_filename):
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global handler
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formatter = logging.Formatter(
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fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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if not logging.getLogger().handlers:
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logging.basicConfig(level=logging.INFO)
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logging.getLogger().handlers[0].setFormatter(formatter)
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stdout_logger = logging.getLogger("stdout")
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stdout_logger.setLevel(logging.INFO)
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sl = StreamToLogger(stdout_logger, logging.INFO)
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sys.stdout = sl
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stderr_logger = logging.getLogger("stderr")
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stderr_logger.setLevel(logging.ERROR)
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sl = StreamToLogger(stderr_logger, logging.ERROR)
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sys.stderr = sl
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.INFO)
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if handler is None:
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os.makedirs(LOGDIR, exist_ok=True)
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filename = os.path.join(LOGDIR, logger_filename)
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handler = logging.handlers.TimedRotatingFileHandler(
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filename, when='D', utc=True)
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handler.setFormatter(formatter)
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for name, item in logging.root.manager.loggerDict.items():
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if isinstance(item, logging.Logger):
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item.addHandler(handler)
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return logger
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class StreamToLogger(object):
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"""
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Fake file-like stream object that redirects writes to a logger instance.
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"""
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def __init__(self, logger, log_level=logging.INFO):
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self.terminal = sys.stdout
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self.logger = logger
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self.log_level = log_level
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self.linebuf = ''
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def __getattr__(self, attr):
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return getattr(self.terminal, attr)
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def write(self, buf):
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temp_linebuf = self.linebuf + buf
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self.linebuf = ''
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for line in temp_linebuf.splitlines(True):
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if line[-1] == '\n':
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self.logger.log(self.log_level, line.rstrip())
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else:
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self.linebuf += line
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def flush(self):
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if self.linebuf != '':
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self.logger.log(self.log_level, self.linebuf.rstrip())
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self.linebuf = ''
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def disable_torch_init():
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"""
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Disable the redundant torch default initialization to accelerate model creation.
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"""
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import torch
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setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
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setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
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def violates_moderation(text):
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"""
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Check whether the text violates OpenAI moderation API.
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"""
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url = "https://api.openai.com/v1/moderations"
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headers = {"Content-Type": "application/json",
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"Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
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text = text.replace("\n", "")
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data = "{" + '"input": ' + f'"{text}"' + "}"
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data = data.encode("utf-8")
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try:
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ret = requests.post(url, headers=headers, data=data, timeout=5)
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flagged = ret.json()["results"][0]["flagged"]
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except requests.exceptions.RequestException as e:
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flagged = False
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except KeyError as e:
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flagged = False
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return flagged
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def pretty_print_semaphore(semaphore):
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if semaphore is None:
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return "None"
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return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
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