"""
Common utilities.
"""
from asyncio import AbstractEventLoop
import json
import logging
import logging.handlers
import os
import platform
import sys
from typing import AsyncGenerator, Generator
import warnings

import requests
import torch

from fastchat.constants import LOGDIR


handler = None
visited_loggers = set()


def build_logger(logger_name, logger_filename):
    global handler

    formatter = logging.Formatter(
        fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
    )

    # Set the format of root handlers
    if not logging.getLogger().handlers:
        if sys.version_info[1] >= 9:
            # This is for windows
            logging.basicConfig(level=logging.INFO, encoding="utf-8")
        else:
            if platform.system() == "Windows":
                warnings.warn(
                    "If you are running on Windows, "
                    "we recommend you use Python >= 3.9 for UTF-8 encoding."
                )
            logging.basicConfig(level=logging.INFO)
    logging.getLogger().handlers[0].setFormatter(formatter)

    # Redirect stdout and stderr to loggers
    stdout_logger = logging.getLogger("stdout")
    stdout_logger.setLevel(logging.INFO)
    sl = StreamToLogger(stdout_logger, logging.INFO)
    sys.stdout = sl

    stderr_logger = logging.getLogger("stderr")
    stderr_logger.setLevel(logging.ERROR)
    sl = StreamToLogger(stderr_logger, logging.ERROR)
    sys.stderr = sl

    # Get logger
    logger = logging.getLogger(logger_name)
    logger.setLevel(logging.INFO)

    os.makedirs(LOGDIR, exist_ok=True)
    filename = os.path.join(LOGDIR, logger_filename)
    handler = logging.handlers.TimedRotatingFileHandler(
        filename, when="D", utc=True, encoding="utf-8"
    )
    handler.setFormatter(formatter)

    for logger in [stdout_logger, stderr_logger, logger]:
        if logger in visited_loggers:
            continue
        visited_loggers.add(logger)
        logger.addHandler(handler)

    return logger


class StreamToLogger(object):
    """
    Fake file-like stream object that redirects writes to a logger instance.
    """

    def __init__(self, logger, log_level=logging.INFO):
        self.terminal = sys.stdout
        self.logger = logger
        self.log_level = log_level
        self.linebuf = ""

    def __getattr__(self, attr):
        return getattr(self.terminal, attr)

    def write(self, buf):
        temp_linebuf = self.linebuf + buf
        self.linebuf = ""
        for line in temp_linebuf.splitlines(True):
            # From the io.TextIOWrapper docs:
            #   On output, if newline is None, any '\n' characters written
            #   are translated to the system default line separator.
            # By default sys.stdout.write() expects '\n' newlines and then
            # translates them so this is still cross platform.
            if line[-1] == "\n":
                encoded_message = line.encode("utf-8", "ignore").decode("utf-8")
                self.logger.log(self.log_level, encoded_message.rstrip())
            else:
                self.linebuf += line

    def flush(self):
        if self.linebuf != "":
            encoded_message = self.linebuf.encode("utf-8", "ignore").decode("utf-8")
            self.logger.log(self.log_level, encoded_message.rstrip())
        self.linebuf = ""


def disable_torch_init():
    """
    Disable the redundant torch default initialization to accelerate model creation.
    """
    import torch

    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)


def get_gpu_memory(max_gpus=None):
    """Get available memory for each GPU."""
    gpu_memory = []
    num_gpus = (
        torch.cuda.device_count()
        if max_gpus is None
        else min(max_gpus, torch.cuda.device_count())
    )

    for gpu_id in range(num_gpus):
        with torch.cuda.device(gpu_id):
            device = torch.cuda.current_device()
            gpu_properties = torch.cuda.get_device_properties(device)
            total_memory = gpu_properties.total_memory / (1024**3)
            allocated_memory = torch.cuda.memory_allocated() / (1024**3)
            available_memory = total_memory - allocated_memory
            gpu_memory.append(available_memory)
    return gpu_memory


def violates_moderation(text):
    """
    Check whether the text violates OpenAI moderation API.
    """
    import openai

    try:
        flagged = openai.Moderation.create(input=text)["results"][0]["flagged"]
    except openai.error.OpenAIError as e:
        flagged = False
    except (KeyError, IndexError) as e:
        flagged = False

    return flagged


def clean_flant5_ckpt(ckpt_path):
    """
    Flan-t5 trained with HF+FSDP saves corrupted  weights for shared embeddings,
    Use this function to make sure it can be correctly loaded.
    """
    index_file = os.path.join(ckpt_path, "pytorch_model.bin.index.json")
    index_json = json.load(open(index_file, "r"))

    weightmap = index_json["weight_map"]

    share_weight_file = weightmap["shared.weight"]
    share_weight = torch.load(os.path.join(ckpt_path, share_weight_file))[
        "shared.weight"
    ]

    for weight_name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]:
        weight_file = weightmap[weight_name]
        weight = torch.load(os.path.join(ckpt_path, weight_file))
        weight[weight_name] = share_weight
        torch.save(weight, os.path.join(ckpt_path, weight_file))


def pretty_print_semaphore(semaphore):
    """Print a semaphore in better format."""
    if semaphore is None:
        return "None"
    return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"


"""A javascript function to get url parameters for the gradio web server."""
get_window_url_params_js = """
function() {
    const params = new URLSearchParams(window.location.search);
    url_params = Object.fromEntries(params);
    console.log("url_params", url_params);
    return url_params;
    }
"""


def iter_over_async(
    async_gen: AsyncGenerator, event_loop: AbstractEventLoop
) -> Generator:
    """
    Convert async generator to sync generator

    :param async_gen: the AsyncGenerator to convert
    :param event_loop: the event loop to run on
    :returns: Sync generator
    """
    ait = async_gen.__aiter__()

    async def get_next():
        try:
            obj = await ait.__anext__()
            return False, obj
        except StopAsyncIteration:
            return True, None

    while True:
        done, obj = event_loop.run_until_complete(get_next())
        if done:
            break
        yield obj


def detect_language(text: str) -> str:
    """Detect the langauge of a string."""
    import polyglot  # pip3 install polyglot pyicu pycld2
    from polyglot.detect import Detector
    from polyglot.detect.base import logger as polyglot_logger
    import pycld2

    polyglot_logger.setLevel("ERROR")

    try:
        lang_code = Detector(text).language.name
    except (pycld2.error, polyglot.detect.base.UnknownLanguage):
        lang_code = "unknown"
    return lang_code


def parse_gradio_auth_creds(filename: str):
    """Parse a username:password file for gradio authorization."""
    gradio_auth_creds = []
    with open(filename, "r", encoding="utf8") as file:
        for line in file.readlines():
            gradio_auth_creds += [x.strip() for x in line.split(",") if x.strip()]
    if gradio_auth_creds:
        auth = [tuple(cred.split(":")) for cred in gradio_auth_creds]
    else:
        auth = None
    return auth


def is_partial_stop(output: str, stop_str: str):
    """Check whether the output contains a partial stop str."""
    for i in range(0, min(len(output), len(stop_str))):
        if stop_str.startswith(output[-i:]):
            return True
    return False


def run_cmd(cmd: str):
    """Run a bash command."""
    print(cmd)
    return os.system(cmd)


def is_sentence_complete(output: str):
    """Check whether the output is a complete sentence."""
    end_symbols = (".", "?", "!", "...", "。", "？", "！", "…", '"', "'", "”")
    return output.endswith(end_symbols)


def get_context_length(config):
    """Get the context length of a model from a huggingface model config."""
    if hasattr(config, "max_sequence_length"):
        return config.max_sequence_length
    elif hasattr(config, "seq_length"):
        return config.seq_length
    elif hasattr(config, "max_position_embeddings"):
        return config.max_position_embeddings
    else:
        return 2048