ai-content-maker/.venv/Lib/site-packages/TTS/config/__init__.py

import json
import os
import re
from typing import Dict

import fsspec
import yaml
from coqpit import Coqpit

from TTS.config.shared_configs import *
from TTS.utils.generic_utils import find_module


def read_json_with_comments(json_path):
    """for backward compat."""
    # fallback to json
    with fsspec.open(json_path, "r", encoding="utf-8") as f:
        input_str = f.read()
    # handle comments but not urls with //
    input_str = re.sub(r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str)
    return json.loads(input_str)

def register_config(model_name: str) -> Coqpit:
    """Find the right config for the given model name.

    Args:
        model_name (str): Model name.

    Raises:
        ModuleNotFoundError: No matching config for the model name.

    Returns:
        Coqpit: config class.
    """
    config_class = None
    config_name = model_name + "_config"

    # TODO: fix this
    if model_name == "xtts":
        from TTS.tts.configs.xtts_config import XttsConfig

        config_class = XttsConfig
    paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder.configs", "TTS.vc.configs"]
    for path in paths:
        try:
            config_class = find_module(path, config_name)
        except ModuleNotFoundError:
            pass
    if config_class is None:
        raise ModuleNotFoundError(f" [!] Config for {model_name} cannot be found.")
    return config_class


def _process_model_name(config_dict: Dict) -> str:
    """Format the model name as expected. It is a band-aid for the old `vocoder` model names.

    Args:
        config_dict (Dict): A dictionary including the config fields.

    Returns:
        str: Formatted modelname.
    """
    model_name = config_dict["model"] if "model" in config_dict else config_dict["generator_model"]
    model_name = model_name.replace("_generator", "").replace("_discriminator", "")
    return model_name


def load_config(config_path: str) -> Coqpit:
    """Import `json` or `yaml` files as TTS configs. First, load the input file as a `dict` and check the model name
    to find the corresponding Config class. Then initialize the Config.

    Args:
        config_path (str): path to the config file.

    Raises:
        TypeError: given config file has an unknown type.

    Returns:
        Coqpit: TTS config object.
    """
    config_dict = {}
    ext = os.path.splitext(config_path)[1]
    if ext in (".yml", ".yaml"):
        with fsspec.open(config_path, "r", encoding="utf-8") as f:
            data = yaml.safe_load(f)
    elif ext == ".json":
        try:
            with fsspec.open(config_path, "r", encoding="utf-8") as f:
                data = json.load(f)
        except json.decoder.JSONDecodeError:
            # backwards compat.
            data = read_json_with_comments(config_path)
    else:
        raise TypeError(f" [!] Unknown config file type {ext}")
    config_dict.update(data)
    model_name = _process_model_name(config_dict)
    config_class = register_config(model_name.lower())
    config = config_class()
    config.from_dict(config_dict)
    return config


def check_config_and_model_args(config, arg_name, value):
    """Check the give argument in `config.model_args` if exist or in `config` for
    the given value.

    Return False if the argument does not exist in `config.model_args` or `config`.
    This is to patch up the compatibility between models with and without `model_args`.

    TODO: Remove this in the future with a unified approach.
    """
    if hasattr(config, "model_args"):
        if arg_name in config.model_args:
            return config.model_args[arg_name] == value
    if hasattr(config, arg_name):
        return config[arg_name] == value
    return False


def get_from_config_or_model_args(config, arg_name):
    """Get the given argument from `config.model_args` if exist or in `config`."""
    if hasattr(config, "model_args"):
        if arg_name in config.model_args:
            return config.model_args[arg_name]
    return config[arg_name]


def get_from_config_or_model_args_with_default(config, arg_name, def_val):
    """Get the given argument from `config.model_args` if exist or in `config`."""
    if hasattr(config, "model_args"):
        if arg_name in config.model_args:
            return config.model_args[arg_name]
    if hasattr(config, arg_name):
        return config[arg_name]
    return def_val