patil-suraj

#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, DistributedSampler
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

def set_all_seed(seed):

patil-suraj

{"0": "tench, Tinca tinca",
 "1": "goldfish, Carassius auratus",
 "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
 "3": "tiger shark, Galeocerdo cuvieri",
 "4": "hammerhead, hammerhead shark",
 "5": "electric ray, crampfish, numbfish, torpedo",
 "6": "stingray",
 "7": "cock",
 "8": "hen",
 "9": "ostrich, Struthio camelus",

patil-suraj

import torch
import torch.utils.benchmark as benchmark
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.models.cross_attention import TorchAttentionProcessor

def benchmark_torch_function(f, *args, **kwargs):
    t0 = benchmark.Timer(
        stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
    )
    return round(t0.blocked_autorange(min_run_time=1).mean, 2)

patil-suraj

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse
import logging
from pathlib import Path
import shutil

patil-suraj

import inspect
import logging
import os
from pathlib import Path

import torch
from psutil import cpu_count
from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer
from transformers.generation_utils import GenerationMixin
from transformers.modeling_outputs import BaseModelOutputWithPast, Seq2SeqLMOutput

patil-suraj

import logging

from finetune_trainer import DataTrainingArguments, Seq2SeqTrainingArguments
from seq2seq_trainer import Seq2SeqTrainer
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from utils import Seq2SeqDataCollator, Seq2SeqDataset, build_compute_metrics_fn, freeze_embeds, freeze_params

# Setup logging
logging.basicConfig(level=logging.INFO)

patil-suraj

#!/usr/bin/env bash
pushd ~/swift/
rm -rf usr
popd
pushd ~/download/
rm swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz
wget https://storage.googleapis.com/s4tf-kokoro-artifact-testing/latest/swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz
tar -xf swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz 
mv usr/ ~/swift/
mv ~/swift/usr/lib/python3.6 ~/swift/usr/lib/python3.7

patil-suraj

from lxml import html  
import requests
from exceptions import ValueError
from time import sleep
import json
import argparse
from collections import OrderedDict
from time import sleep

def parse(ticker):

patil-suraj

//Flutter Modal Bottom Sheet
//Modified by Suvadeep Das
//Based on https://gist.github.com/andrelsmoraes/9e4af0133bff8960c1feeb0ead7fd749

import 'dart:async';

import 'package:flutter/material.dart';
import 'package:meta/meta.dart';

const Duration _kBottomSheetDuration = const Duration(milliseconds: 200);