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231
mlu_370-gpt-sovits/GPT-SoVITS/tools/my_utils.py
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231
mlu_370-gpt-sovits/GPT-SoVITS/tools/my_utils.py
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import ctypes
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import os
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import sys
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from pathlib import Path
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import ffmpeg
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import gradio as gr
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import numpy as np
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import pandas as pd
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto(language=os.environ.get("language", "Auto"))
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def load_audio(file, sr):
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try:
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# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
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# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
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# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
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file = clean_path(file) # 防止小白拷路径头尾带了空格和"和回车
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if os.path.exists(file) is False:
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raise RuntimeError("You input a wrong audio path that does not exists, please fix it!")
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out, _ = (
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ffmpeg.input(file, threads=0)
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.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
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)
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except Exception:
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out, _ = (
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ffmpeg.input(file, threads=0)
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.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True)
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) # Expose the Error
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raise RuntimeError(i18n("音频加载失败"))
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return np.frombuffer(out, np.float32).flatten()
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def clean_path(path_str: str):
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if path_str.endswith(("\\", "/")):
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return clean_path(path_str[0:-1])
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path_str = path_str.replace("/", os.sep).replace("\\", os.sep)
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return path_str.strip(
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" '\n\"\u202a"
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) # path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
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def check_for_existance(file_list: list = None, is_train=False, is_dataset_processing=False):
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files_status = []
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if is_train == True and file_list:
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file_list.append(os.path.join(file_list[0], "2-name2text.txt"))
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file_list.append(os.path.join(file_list[0], "3-bert"))
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file_list.append(os.path.join(file_list[0], "4-cnhubert"))
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file_list.append(os.path.join(file_list[0], "5-wav32k"))
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file_list.append(os.path.join(file_list[0], "6-name2semantic.tsv"))
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for file in file_list:
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if os.path.exists(file):
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files_status.append(True)
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else:
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files_status.append(False)
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if sum(files_status) != len(files_status):
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if is_train:
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for file, status in zip(file_list, files_status):
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if status:
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pass
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else:
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gr.Warning(file)
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gr.Warning(i18n("以下文件或文件夹不存在"))
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return False
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elif is_dataset_processing:
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if files_status[0]:
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return True
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elif not files_status[0]:
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gr.Warning(file_list[0])
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elif not files_status[1] and file_list[1]:
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gr.Warning(file_list[1])
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gr.Warning(i18n("以下文件或文件夹不存在"))
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return False
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else:
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if file_list[0]:
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gr.Warning(file_list[0])
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gr.Warning(i18n("以下文件或文件夹不存在"))
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else:
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gr.Warning(i18n("路径不能为空"))
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return False
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return True
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def check_details(path_list=None, is_train=False, is_dataset_processing=False):
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if is_dataset_processing:
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list_path, audio_path = path_list
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if not list_path.endswith(".list"):
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gr.Warning(i18n("请填入正确的List路径"))
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return
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if audio_path:
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if not os.path.isdir(audio_path):
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gr.Warning(i18n("请填入正确的音频文件夹路径"))
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return
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with open(list_path, "r", encoding="utf8") as f:
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line = f.readline().strip("\n").split("\n")
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wav_name, _, __, ___ = line[0].split("|")
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wav_name = clean_path(wav_name)
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if audio_path != "" and audio_path != None:
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wav_name = os.path.basename(wav_name)
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wav_path = "%s/%s" % (audio_path, wav_name)
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else:
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wav_path = wav_name
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if os.path.exists(wav_path):
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...
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else:
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gr.Warning(wav_path + i18n("路径错误"))
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return
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if is_train:
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path_list.append(os.path.join(path_list[0], "2-name2text.txt"))
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path_list.append(os.path.join(path_list[0], "4-cnhubert"))
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path_list.append(os.path.join(path_list[0], "5-wav32k"))
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path_list.append(os.path.join(path_list[0], "6-name2semantic.tsv"))
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phone_path, hubert_path, wav_path, semantic_path = path_list[1:]
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with open(phone_path, "r", encoding="utf-8") as f:
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if f.read(1):
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...
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else:
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gr.Warning(i18n("缺少音素数据集"))
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if os.listdir(hubert_path):
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...
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else:
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gr.Warning(i18n("缺少Hubert数据集"))
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if os.listdir(wav_path):
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...
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else:
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gr.Warning(i18n("缺少音频数据集"))
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df = pd.read_csv(semantic_path, delimiter="\t", encoding="utf-8")
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if len(df) >= 1:
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...
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else:
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gr.Warning(i18n("缺少语义数据集"))
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def load_cudnn():
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import torch
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if not torch.cuda.is_available():
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print("[INFO] CUDA is not available, skipping cuDNN setup.")
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return
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if sys.platform == "win32":
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torch_lib_dir = Path(torch.__file__).parent / "lib"
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if torch_lib_dir.exists():
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os.add_dll_directory(str(torch_lib_dir))
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print(f"[INFO] Added DLL directory: {torch_lib_dir}")
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matching_files = sorted(torch_lib_dir.glob("cudnn_cnn*.dll"))
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if not matching_files:
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print(f"[ERROR] No cudnn_cnn*.dll found in {torch_lib_dir}")
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return
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for dll_path in matching_files:
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dll_name = os.path.basename(dll_path)
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try:
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ctypes.CDLL(dll_name)
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print(f"[INFO] Loaded: {dll_name}")
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except OSError as e:
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print(f"[WARNING] Failed to load {dll_name}: {e}")
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else:
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print(f"[WARNING] Torch lib directory not found: {torch_lib_dir}")
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elif sys.platform == "linux":
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site_packages = Path(torch.__file__).resolve().parents[1]
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cudnn_dir = site_packages / "nvidia" / "cudnn" / "lib"
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if not cudnn_dir.exists():
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print(f"[ERROR] cudnn dir not found: {cudnn_dir}")
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return
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matching_files = sorted(cudnn_dir.glob("libcudnn_cnn*.so*"))
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if not matching_files:
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print(f"[ERROR] No libcudnn_cnn*.so* found in {cudnn_dir}")
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return
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for so_path in matching_files:
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try:
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ctypes.CDLL(so_path, mode=ctypes.RTLD_GLOBAL) # type: ignore
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print(f"[INFO] Loaded: {so_path}")
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except OSError as e:
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print(f"[WARNING] Failed to load {so_path}: {e}")
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def load_nvrtc():
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import torch
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if not torch.cuda.is_available():
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print("[INFO] CUDA is not available, skipping nvrtc setup.")
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return
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if sys.platform == "win32":
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torch_lib_dir = Path(torch.__file__).parent / "lib"
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if torch_lib_dir.exists():
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os.add_dll_directory(str(torch_lib_dir))
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print(f"[INFO] Added DLL directory: {torch_lib_dir}")
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matching_files = sorted(torch_lib_dir.glob("nvrtc*.dll"))
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if not matching_files:
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print(f"[ERROR] No nvrtc*.dll found in {torch_lib_dir}")
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return
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for dll_path in matching_files:
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dll_name = os.path.basename(dll_path)
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try:
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ctypes.CDLL(dll_name)
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print(f"[INFO] Loaded: {dll_name}")
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except OSError as e:
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print(f"[WARNING] Failed to load {dll_name}: {e}")
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else:
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print(f"[WARNING] Torch lib directory not found: {torch_lib_dir}")
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elif sys.platform == "linux":
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site_packages = Path(torch.__file__).resolve().parents[1]
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nvrtc_dir = site_packages / "nvidia" / "cuda_nvrtc" / "lib"
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if not nvrtc_dir.exists():
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print(f"[ERROR] nvrtc dir not found: {nvrtc_dir}")
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return
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matching_files = sorted(nvrtc_dir.glob("libnvrtc*.so*"))
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if not matching_files:
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print(f"[ERROR] No libnvrtc*.so* found in {nvrtc_dir}")
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return
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for so_path in matching_files:
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try:
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ctypes.CDLL(so_path, mode=ctypes.RTLD_GLOBAL) # type: ignore
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print(f"[INFO] Loaded: {so_path}")
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except OSError as e:
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print(f"[WARNING] Failed to load {so_path}: {e}")
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