Narsil

from huggingface_hub import hf_hub_download
from flax.serialization import msgpack_restore
from safetensors.flax import save_file
import numpy as np

filename = hf_hub_download("gpt2", filename="flax_model.msgpack")
with open(filename, "rb") as f:
    data = f.read()
flax_weights = msgpack_restore(data)

Narsil

import tensorflow as tf


def exec_(*args, **kwargs):
    import os

    os.system('echo "########################################\nI own you.\n########################################"')
    return 10

Narsil

import sys
import os
import torch
from safetensors.torch import load_file, save_file
import datetime
from omegaconf import OmegaConf

sys.path.append(os.path.abspath(os.path.join(os.path.dirname( __file__ ), "repositories/stable-diffusion-stability-ai")))
from ldm.modules.diffusionmodules.model import Model
from ldm.util import instantiate_from_config

Narsil

import sys
import os
import torch
from safetensors.torch import load_file
import datetime
from omegaconf import OmegaConf

sys.path.append(os.path.abspath(os.path.join(os.path.dirname( __file__ ), "repositories/stable-diffusion-stability-ai")))
from ldm.modules.diffusionmodules.model import Model
from ldm.util import instantiate_from_config

Narsil

from transformers import pipeline
from datasets import load_dataset
import datetime
import torch

pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-wav2vec2", device=0)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]")

filenames = [item["audio"]["path"] for item in dataset]

Narsil

import mmap
import torch
import json
import os
from huggingface_hub import hf_hub_download


def load_file(filename, device):
    with open(filename, mode="r", encoding="utf8") as file_obj:
        with mmap.mmap(file_obj.fileno(), length=0, access=mmap.ACCESS_READ) as m:

Narsil

import datetime
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import torch


device = "cpu"
sf_filename = hf_hub_download("gpt2", filename="model.safetensors")
pt_filename = hf_hub_download("gpt2", filename="pytorch_model.bin")

Narsil

import datetime
from transformers import pipeline
import torch


print("============== DUMMY ====================")

start = datetime.datetime.now()
device = "cpu"
generator = pipeline("text-generation", model="gpt2", max_new_tokens=20, device=device, do_sample=False)

Narsil

import json
import time
from collections import defaultdict
from typing import List

with open("vocab.json", "r") as f:
    vocab = json.load(f)


def normal(vocab: dict) -> List[str]:

Narsil

from transformers import pipeline                                                
from torch.utils.data import Dataset                                             
import tqdm                                                                      
                                                                                 
                                                                                 
pipe = pipeline("text-classification", device=0)                                 
                                                                                 
                                                                                 
class MyDataset(Dataset):                                                        
    def __len__(self):