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import types
from typing import List, Optional, Tuple, Union

import torch
from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

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import types
from typing import Union, List, Optional, Callable

import diffusers
import torch

from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput


@torch.inference_mode()

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/*
Package logger add level feature log for *log*.
*/
package logger

import (
	"fmt"
	"io"
	"log"
	"os"

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import toml
from argparse import ArgumentParser
from os import path

import collections

# python 3.8+ compatibility
try:
    collectionsAbc = collections.abc
except:

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train_size = int(0.8 * len(full_dataset))
test_size = len(full_dataset) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])

## or 

dataset = MyCustomDataset(my_path)
batch_size = 16
validation_split = .2
shuffle_dataset = True

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import models

model = getattr(models, 'AlexNet')()
# same as:
# model = models.AlexNet()

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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
import argparse
import scipy.io


def get_right_and_junk_index(query_label, gallery_labels, query_camera_label=None, gallery_camera_labels=None):
    same_label_index = np.argwhere(gallery_labels == query_label)

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# How to change a model's fc layer.
# with this code, you can get a model's features
del model.fc
model.fc = lambda x:x

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import os
from shutil import copyfile


def _make_sure_dir_exist(path):
    if not os.path.isdir(path):
        os.mkdir(path)


def refactor_dataset_folder(root_path, classes_map, img_path_root=None):

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