{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version 3.11.9 (main, Apr  6 2024, 17:59:24) [GCC 11.4.0]\n",
      "Keras Version 3.0.4 with jax backend \tJax Version 0.4.31\n",
      "Jax backend device gpu\n",
      "PyTorch version: 2.3.0+cpu\n"
     ]
    }
   ],
   "source": [
    "import typing as tp\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import random as r\n",
    "import numpy as np\n",
    "from dataclasses import dataclass\n",
    "from tqdm import trange\n",
    "import pickle\n",
    "import os\n",
    "from functools import partial\n",
    "import time\n",
    "import math\n",
    "\n",
    "import jax\n",
    "from jax import (\n",
    "    numpy as jnp,\n",
    "    Array,\n",
    "    random as jrand\n",
    ")\n",
    "import keras as nn\n",
    "nn.mixed_precision.set_dtype_policy(\"mixed_bfloat16\")\n",
    "import torch.utils.data\n",
    "import torchvision\n",
    "try:\n",
    "    from flash_attn_jax import flash_mha\n",
    "    USE_FLASH_ATT = True\n",
    "except:\n",
    "    USE_FLASH_ATT = False\n",
    "\n",
    "\n",
    "print(\"Python Version\", sys.version)\n",
    "print(f\"Keras Version {nn.__version__} with {nn.backend.backend()} backend \\tJax Version {jax.__version__}\")\n",
    "print(\"Jax backend device\", jax.default_backend())\n",
    "print(\"PyTorch version:\", torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(), # (H, W, C)/(H, W) -> (C, H, W) AND [0, 255] -> [0.0, 1.0]\n",
    "    torchvision.transforms.Normalize(mean=(0.5,), std=(0.5,), inplace=True) # [0.0, 1.0] -> [-1.0, 1.0]\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "valset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "\n",
    "NUM_CLASSES = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataLoader:\n",
    "    def __init__(self, ds):\n",
    "        self.ds = ds\n",
    "        \n",
    "    def iter_batches(self, batch_size):\n",
    "        while True:\n",
    "            self.dataset = torch.utils.data.DataLoader(\n",
    "                dataset=self.ds,\n",
    "                batch_size=batch_size,\n",
    "                shuffle=True,\n",
    "                pin_memory=True,\n",
    "                drop_last=True\n",
    "            )\n",
    "            for X_batch, y_batch in self.dataset:\n",
    "                yield X_batch.permute(0, 2, 3, 1).numpy(), y_batch.numpy() # (B, C, H, W), (B,) => (B, H, W, C), (B,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class config:\n",
    "    seed:int = 0\n",
    "\n",
    "    # Diffusion Args\n",
    "    var_range:tuple[int, int] = (1e-4, 2e-2)\n",
    "    num_timesteps:int = 400\n",
    "\n",
    "    # Vit Args\n",
    "    patch_size:int = 2\n",
    "    H:int = 28\n",
    "    W:int = 28\n",
    "    in_channels:int = 1\n",
    "    out_channels:int = in_channels\n",
    "    N:int = H*W//patch_size**2\n",
    "    assert N*patch_size**2 == H*W\n",
    "    \n",
    "    # weight init Args\n",
    "    linear_init:tp.Callable = nn.initializers.GlorotUniform(seed=seed+1) # also known as Xavier Uniform\n",
    "    bias_init:tp.Callable = nn.initializers.Zeros()\n",
    "\n",
    "    label_emb_init:tp.Callable = nn.initializers.RandomNormal(stddev=0.02, seed=seed+2)\n",
    "    timestep_mlp_init:tp.Callable = nn.initializers.RandomNormal(stddev=0.02, seed=seed+3)\n",
    "\n",
    "    adaLN_linear_bias_init:tp.Callable = nn.initializers.Zeros()\n",
    "    final_layer_linear_bias_init:tp.Callable = nn.initializers.Zeros()\n",
    "\n",
    "    # transformer Args\n",
    "    d_model:int = 288\n",
    "    num_heads:int = 6\n",
    "    assert d_model % 2 == 0\n",
    "    assert d_model % num_heads == 0\n",
    "    num_layers:int = 6\n",
    "    num_classes:int = NUM_CLASSES\n",
    "    dropout_rate:float = 0.0\n",
    "    use_flash_att:bool = USE_FLASH_ATT # Does it even speed up training?\n",
    "    maxlen:int = N\n",
    "\n",
    "    # Training Args\n",
    "    cfg_dropout_rate:float = 0.0\n",
    "\n",
    "    batch_size:int = 64\n",
    "    num_steps:int = 20_000\n",
    "    decay_steps:int = num_steps\n",
    "    warmup_steps:int = 100\n",
    "    max_lr:float = 1e-3\n",
    "    min_lr:float = 0.0*max_lr\n",
    "    no_decay:bool = False # keeping this True will increase the loss in between training\n",
    "    beta1:float = 0.9\n",
    "    beta2:float = 0.999\n",
    "    clipnorm:float = None\n",
    "    weight_decay:float = 0.0\n",
    "    # ema:float = 0.999\n",
    "    \n",
    "    patience:int = 10\n",
    "    num_grad_accumalation_steps:int = 1\n",
    "    ckpt_freq:int = 600\n",
    "    checkpoint_dir:str = \"checkpoints\"\n",
    "    return_best_train_states:bool = True\n",
    "\n",
    "os.makedirs(config.checkpoint_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diffusion Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DiffusionUtils:\n",
    "    def __init__(self, config:config):\n",
    "        self.num_timesteps = config.num_timesteps # (nT,)\n",
    "        self.beta_range = config.var_range\n",
    "        self.get_alpha()\n",
    "\n",
    "        self.H, self.W = config.H, config.W\n",
    "        self.in_channels = config.in_channels\n",
    "\n",
    "    def get_alpha(self):\n",
    "        self.beta = jnp.linspace(*self.beta_range, num=self.num_timesteps) # (nT,)\n",
    "        self.alpha = 1-self.beta # (nT,)\n",
    "        self.alpha_bar = jnp.concatenate((jnp.array([1.]), self.alpha.cumprod(axis=0)), axis=0) # (nT,)\n",
    "    \n",
    "    def noisy_it(self, X:Array, t:Array, key:Array): # (B, H, W, C), (B,)\n",
    "        noise = jrand.normal(key=key, shape=X.shape) # (B,)\n",
    "\n",
    "        alpha_bar_t = self.alpha_bar[t][:, None, None, None] # (B, 1, 1, 1) <= (B,) <= (nT,)\n",
    "        return {\n",
    "            \"noisy_images\": jnp.sqrt(alpha_bar_t)*X + jnp.sqrt(1 - alpha_bar_t) * noise,\n",
    "            \"timesteps\": t\n",
    "        }, noise\n",
    "    \n",
    "    def one_step_ddpm(self, xt:Array, pred_noise:Array, t:int, key:Array):\n",
    "        alpha_t, alpha_bar_t = self.alpha[t, None, None, None], self.alpha_bar[t, None, None, None]\n",
    "        xt_minus_1 = (\n",
    "            (1/jnp.sqrt(alpha_t))\n",
    "            *\n",
    "            (xt - (1-alpha_t)*pred_noise/jnp.sqrt(1-alpha_bar_t)\n",
    "            ) + jnp.sqrt(self.beta[t])*jrand.normal(key=key, shape=xt.shape)\n",
    "        )\n",
    "        return xt_minus_1\n",
    "    \n",
    "    def one_step_ddim(self, xt:Array, pred_noise:Array, t:int, key:Array) -> Array:\n",
    "        raise NotImplementedError\n",
    "    \n",
    "    def generate(\n",
    "        self, *,\n",
    "        model:nn.Model, # x:Array, # (B, H, W, C = 3 or 1) t:Array, # (B,) y:tp.Optional[Array]=None, # (B,) key:tp.Optional[Array]=None\n",
    "        trainable_vars:list[Array],\n",
    "        non_trainable_vars:list[Array],\n",
    "        labels:tp.Optional[list[int]]=None,\n",
    "        num_samples:int=1, # B\n",
    "        use_ddim:bool=False, # False for now until implemented\n",
    "        key:Array\n",
    "    ):\n",
    "        assert len(labels) == num_samples if labels is not None else True\n",
    "        sample_func = self.one_step_ddim if use_ddim else self.one_step_ddpm\n",
    "\n",
    "        print(f\"Generating images\", \"\" if labels is None else \"of \" + str(labels))\n",
    "        labels = jnp.array(labels) if labels is not None else None\n",
    "        x = jrand.normal(key, (num_samples, self.H, self.W, self.in_channels))\n",
    "\n",
    "        for i in trange(0, self.num_timesteps-1):\n",
    "            key, key2, key3 = jrand.split(key, num=3)\n",
    "            t = jnp.array([self.num_timesteps - i - 1]*num_samples) # (B,)\n",
    "            noise, non_trainable_vars = model.stateless_call(trainable_vars, non_trainable_vars, x, t, labels, key2, False)\n",
    "            x = sample_func(x, noise, t, key3)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-18 19:47:51.110858: W external/xla/xla/service/gpu/nvptx_compiler.cc:836] The NVIDIA driver's CUDA version is 12.2 which is older than the PTX compiler version (12.6.20). Because the driver is older than the PTX compiler version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eg = next(iter(DataLoader(trainset).iter_batches(1)))\n",
    "\n",
    "def show_diffusion():\n",
    "    # Determine the timesteps at which to display the images\n",
    "    timesteps = list(range(0, config.num_timesteps + 1, 25))\n",
    "\n",
    "    # Determine number of rows and columns for the grid\n",
    "    n_cols = 5  # Set the number of columns you want\n",
    "    n_rows = math.ceil(len(timesteps) / n_cols)\n",
    "\n",
    "    # Create subplots based on the number of images to display\n",
    "    plt.style.use('dark_background')  \n",
    "    fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 2, n_rows * 2))\n",
    "\n",
    "    # Flatten axes array for easy indexing if n_rows > 1\n",
    "    axes = axes.flatten() if n_rows > 1 else [axes]\n",
    "\n",
    "    for idx, (i, ax) in enumerate(zip(timesteps, axes)):\n",
    "        noisy_image, noise = DiffusionUtils(config).noisy_it(eg[0], jnp.array([i]), key=jrand.PRNGKey(i*100+1))\n",
    "        plt_image = noisy_image[\"noisy_images\"].squeeze()\n",
    "        \n",
    "        # Display image in the subplot\n",
    "        ax.imshow(plt_image, cmap=\"gray\")\n",
    "        ax.set_title(f\"Timestep:{i}\\nmean:{plt_image.mean().item():.2f}, std:{plt_image.std().item():.2f}\")\n",
    "        ax.axis('off')  # Hide axis labels\n",
    "\n",
    "    # Turn off any unused subplots\n",
    "    for j in range(len(timesteps), len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    # Adjust layout to avoid overlap\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "show_diffusion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test `generate` (using noisy_images, noise and timesteps)\n",
    "def show_generation_from_noise():\n",
    "    noisy_images, noises, times = [], [], {}\n",
    "    for idx, i in enumerate(range(0, config.num_timesteps-1)):\n",
    "        noisy_image, noise = DiffusionUtils(config).noisy_it(eg[0], jnp.array([i]), key=jrand.PRNGKey(i*101+1))\n",
    "        noisy_images.append(noisy_image[\"noisy_images\"].squeeze())\n",
    "        noises.append(noise.squeeze())\n",
    "        times[idx] = i\n",
    "\n",
    "    # Filter the timesteps for which images will be displayed\n",
    "    plot_indices = [i for i, t in enumerate(list(reversed(times))[:-1]) if i % 25 == 0 or i == len(times) - 2]\n",
    "\n",
    "    # Determine number of rows and columns for the grid\n",
    "    n_cols = 5  # Set the number of columns you want\n",
    "    n_rows = math.ceil(len(plot_indices) / n_cols)\n",
    "\n",
    "    # Create subplots based on the number of images to display\n",
    "    plt.style.use('dark_background')\n",
    "    fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols * 2, n_rows * 2))\n",
    "\n",
    "    # Flatten axes array for easy indexing if n_rows > 1\n",
    "    axes = axes.flatten() if n_rows > 1 else [axes]\n",
    "\n",
    "    for i, ax in zip(plot_indices, axes):\n",
    "        t = list(reversed(times))[:-1][i]\n",
    "        img = DiffusionUtils(config).one_step_ddpm(xt=noisy_images[t], pred_noise=noises[t], t=t, key=jrand.PRNGKey(t*102+10000))\n",
    "        \n",
    "        # Display image in the subplot\n",
    "        ax.imshow(img.squeeze(), cmap=\"gray\")\n",
    "        ax.set_title(f\"|Timestep:{t}\\nmean:{img.mean().item():.3f}, std:{img.std().item():.3f}|\".replace(\"\\n\", \"|\\n\"))\n",
    "        ax.axis('off')  # Hide axis labels\n",
    "\n",
    "    # Turn off any unused subplots\n",
    "    for j in range(len(plot_indices), len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    # Adjust layout to avoid overlap\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "show_generation_from_noise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positional and Time Embeddings\n",
    "\n",
    "* ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeEmbedding(nn.Layer):\n",
    "    def __init__(self, config:config, dim:int):\n",
    "        super().__init__()\n",
    "        self.mlp = nn.Sequential([\n",
    "            nn.layers.Dense(config.d_model, kernel_initializer=config.timestep_mlp_init, bias_initializer=config.bias_init),\n",
    "            nn.layers.Activation(jax.nn.selu),\n",
    "            nn.layers.Dense(config.d_model, kernel_initializer=config.timestep_mlp_init, bias_initializer=config.bias_init),\n",
    "        ])\n",
    "        self.half_dim = dim // 2\n",
    "\n",
    "    def sinusoidal_embeddings(self, x:Array):\n",
    "        \"\"\"Jax version of https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py\"\"\"\n",
    "        embeddings = jnp.exp(-math.log(10000) * jnp.arange(0, self.half_dim) / self.half_dim)\n",
    "        embeddings = x[:, None] * embeddings[None]\n",
    "        # in link implementation, concat(cos, sin, -1) is done\n",
    "        embeddings = jnp.concatenate([jnp.cos(embeddings), jnp.sin(embeddings)], -1)\n",
    "        return embeddings\n",
    "\n",
    "    def call(self, x:Array): # (B,)\n",
    "        x = self.sinusoidal_embeddings(x) # (B,) => (B, dim)\n",
    "        x = self.mlp(x) # (B, d_model)\n",
    "        return x # (B, d_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbedding:\n",
    "    def __init__(self, maxlen:int, dim:int):\n",
    "        p, i = jnp.meshgrid(jnp.arange(float(maxlen)), jnp.arange(dim/2)*2)\n",
    "        theta = (p/1e4**(i/dim)).T\n",
    "\n",
    "        self.pos_emb = jnp.stack([jnp.sin(theta), jnp.cos(theta)], axis=-1)\n",
    "        self.pos_emb = self.pos_emb.reshape((maxlen, dim))[None] # (1, maxlen, dim)\n",
    "\n",
    "    def sinusoidal_embeddings(self):\n",
    "        return self.pos_emb # (1, maxlen, dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Attention(nn.Layer):\n",
    "    \"\"\"```\n",
    "    Multi-head Attention\n",
    "    Args:\n",
    "        causal:bool\n",
    "        config\n",
    "    Input:\n",
    "        x: shape(B, N, d_model)\n",
    "        training: bool\n",
    "    Output:N\n",
    "        linear_att_out: shape(B, N, d_model)\n",
    "    ```\"\"\"\n",
    "    def __init__(\n",
    "            self,\n",
    "            causal:bool,\n",
    "            config:config,\n",
    "            **kwargs\n",
    "    ):\n",
    "        super().__init__(**kwargs)\n",
    "        assert config.d_model % config.num_heads == 0\n",
    "        self.flash = config.use_flash_att\n",
    "        self.causal = causal\n",
    "        self.num_heads = config.num_heads\n",
    "        self.dim = config.d_model//config.num_heads\n",
    "        \n",
    "        self.wq = nn.layers.Dense(config.d_model, use_bias=False, kernel_initializer=config.linear_init)\n",
    "        self.wk = nn.layers.Dense(config.d_model, use_bias=False, kernel_initializer=config.linear_init)\n",
    "        self.wv = nn.layers.Dense(config.d_model, use_bias=False, kernel_initializer=config.linear_init)\n",
    "        self.dropout = nn.layers.Dropout(config.dropout_rate)\n",
    "\n",
    "        self.wo = nn.layers.Dense(config.d_model, kernel_initializer=config.linear_init, bias_initializer=config.bias_init)\n",
    "        if causal and (not config.use_flash_att): # when causal and not using flash att\n",
    "            self.causal_mask = jnp.triu(jnp.full(shape=(1, 1, config.maxlen, config.maxlen), fill_value=-jnp.inf), k=1)\n",
    "\n",
    "    def call(\n",
    "            self,\n",
    "            x:Array, # (B, T, d_model)\n",
    "            training:bool\n",
    "    ):\n",
    "        B, T, d_model = x.shape\n",
    "\n",
    "        # compute q, k, v\n",
    "        q = self.wq(x) # (B, T, d_model)\n",
    "        k = self.wk(x) # (B, T, d_model)\n",
    "        v = self.wv(x) # (B, T, d_model)\n",
    "\n",
    "        # compute attention weights\n",
    "        if self.flash:\n",
    "            shape = (B, T, self.num_heads, self.dim)\n",
    "            q, k, v = (\n",
    "                q.reshape(shape).astype(jnp.bfloat16),\n",
    "                k.reshape(shape).astype(jnp.bfloat16),\n",
    "                v.reshape(shape).astype(jnp.bfloat16)\n",
    "            ) # (B, T, h, dim)\n",
    "            att_out = flash_mha(q, k, v, softmax_scale=None, is_causal=self.causal).astype(jnp.float32) # (B, T, h, dim)\n",
    "        else:\n",
    "            shape = (B, self.num_heads, T, self.dim)\n",
    "            q, k, v = q.reshape(shape), k.reshape(shape), v.reshape(shape) # (B, h, T, dim)\n",
    "            att_wei = (q @ jnp.matrix_transpose(k))/self.dim**0.5 # (B, h, T, T) <= (B, h, T, dim) @ (B, h, T, dim).transpose(-1, -2)\n",
    "            # causal mask\n",
    "            if self.causal:\n",
    "                att_wei += self.causal_mask[:, :, :T, :T] # (B, h, T, T)\n",
    "            att_wei = jax.nn.softmax(att_wei, axis=-1) # (B, h, T, T)\n",
    "            # apply attention weights to v\n",
    "            att_out = att_wei @ v # (B, h, T, T) @ (B, h, T, dv) => (B, h, T, dv)\n",
    "\n",
    "        # combine heads\n",
    "        att_out = att_out.reshape((B, T, d_model)) # (B, T, h*dim) ==> (B, T, d_model)\n",
    "\n",
    "        # linear of att_out\n",
    "        linear_att_out = self.wo(att_out)\n",
    "        linear_att_out = self.dropout(linear_att_out, training=training) # (B, T, d_model)\n",
    "        return linear_att_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FFN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FFN(nn.Layer):\n",
    "    \"\"\"https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/mlp.py#L13\"\"\"\n",
    "    def __init__(self, config:config):\n",
    "        super().__init__()\n",
    "        hidden_dim = config.d_model*4\n",
    "        self.layers = nn.Sequential([\n",
    "            nn.layers.Dense(hidden_dim, kernel_initializer=config.linear_init, bias_initializer=config.bias_init),\n",
    "            nn.layers.Activation(lambda x: jax.nn.gelu(x, approximate=True)),\n",
    "            nn.layers.Dropout(config.dropout_rate),\n",
    "            nn.layers.Dense(config.d_model, kernel_initializer=config.linear_init, bias_initializer=config.bias_init),\n",
    "            nn.layers.Dropout(config.dropout_rate)\n",
    "        ])\n",
    "\n",
    "    def call(self, x:Array, training:bool):\n",
    "        return self.layers(x, training=training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Patch Ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PatchOps:\n",
    "    \"\"\"See https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/patch_embed.py#L112\"\"\"\n",
    "    def __init__(self, config:config, project_patches:bool=True):\n",
    "        self.proj = project_patches\n",
    "        self.p = config.patch_size\n",
    "        if project_patches:\n",
    "            self.patch_proj = nn.layers.Dense(\n",
    "                config.d_model, \n",
    "                kernel_initializer=config.linear_init,\n",
    "                bias_initializer=config.bias_init\n",
    "            )\n",
    "        self.H, self.W = config.H, config.W\n",
    "        self.c = config.out_channels\n",
    "\n",
    "    def patchify(self, images:Array):\n",
    "        B, H, W, C = images.shape\n",
    "        patches = nn.ops.image.extract_patches(images, size=self.p) # (B, H, W, C)\n",
    "        patches = patches.reshape(\n",
    "            B,\n",
    "            (H // self.p) * (W // self.p), # N = (H*W)/P**2\n",
    "            self.p * self.p * C, # dim = P*2 * C\n",
    "        ) # (B, N, dim)\n",
    "        if self.proj:\n",
    "            patches = self.patch_proj(patches) # (B, N, embed_dim)\n",
    "        return patches # (B, N = (H*W)/P**2, embed_dim or dim)\n",
    "    \n",
    "    def unpacthify(self, x:Array): # (B, N = H*W//P**2, D = (P**2)*C)\n",
    "        h, w = self.H//self.p, self.W//self.p # int(x.shape[1]**0.5) # h = H//P\n",
    "        x = x.reshape(-1, h, w, self.p, self.p, self.c) # (B, H//P, W//P, P, P, C)\n",
    "        x = jnp.einsum('bhwpqc->bhpwqc', x) # (B, H//P, P, W//P, P, C)\n",
    "        x = x.reshape(-1, h*self.p, w*self.p, self.c) # (B, H, W, C)\n",
    "        return x"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def visualize_patches(x:Array, patch_size:int):\n",
    "    test_patch = PatchOps(config(d_model=1, patch_size=patch_size), project_patches=False)\n",
    "    patches:Array = np.array(test_patch.patchify(x.astype(np.float32))).astype(np.float32)\n",
    "    n = int(jnp.sqrt(patches.shape[1]))\n",
    "    plt.figure(figsize=(5,5))\n",
    "    for i, patch in enumerate(patches[0]):\n",
    "        ax = plt.subplot(n, n, i + 1)\n",
    "        patch_img = np.reshape(patch, (patch_size, patch_size, 1))\n",
    "        plt.imshow(patch_img, cmap=\"gray\")\n",
    "        plt.axis(\"off\")\n",
    "\n",
    "visualize_patches(scale(X_train[:1]), 2)\n",
    "```\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "```python\n",
    "patchy = PatchOps(config, project_patches=False)\n",
    "plt.imshow(\n",
    "    descale(patchy.unpacthify(patchy.patchify(scale(X_train[:1]))))[0],\n",
    "    cmap=\"gray\"\n",
    ")\n",
    "plt.show()\n",
    "del patchy\n",
    "```\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LabelEmbedding(nn.Layer):\n",
    "    def __init__(self, config:config):\n",
    "        super().__init__()\n",
    "        self.use_cfg = config.cfg_dropout_rate > 0\n",
    "        self.label_embed = nn.layers.Embedding(\n",
    "            input_dim=config.num_classes + int(self.use_cfg),\n",
    "            output_dim=config.d_model,\n",
    "            embeddings_initializer=config.label_emb_init\n",
    "        )\n",
    "        self.cfg_dropout_rate = config.cfg_dropout_rate\n",
    "        self.num_classes = config.num_classes\n",
    "    \n",
    "    def drop_labels(self, labels:Array, key:Array):\n",
    "        ids_to_drop = jrand.uniform(key, shape=(labels.shape[0],)) < self.cfg_dropout_rate\n",
    "        labels = jnp.where(ids_to_drop, self.num_classes, labels)\n",
    "        return labels\n",
    "\n",
    "    def call(self, labels:Array, key:Array, training:bool):\n",
    "        if self.use_cfg and training:\n",
    "            labels = self.drop_labels(labels, key)\n",
    "        return self.label_embed(labels, training=training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DiT Block and Comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_scale(x:Array, gamma:Array, beta:Array):\n",
    "    \"\"\" https://github.com/facebookresearch/DiT/blob/main/models.py#L19\n",
    "    ```\n",
    "    def modulate(x, shift, scale):\n",
    "        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n",
    "    ```\n",
    "    why +1 while scaling?\n",
    "    \"\"\"\n",
    "    return x*(1 + gamma) + beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Block(nn.Layer):\n",
    "    def __init__(self, config:config):\n",
    "        super().__init__()\n",
    "        self.scale_shift_scale = nn.Sequential([\n",
    "            nn.layers.Activation(jax.nn.silu),\n",
    "            nn.layers.Dense(config.d_model*6, kernel_initializer=config.adaLN_linear_bias_init, bias_initializer=config.adaLN_linear_bias_init),\n",
    "        ])\n",
    "        self.norm1 = nn.layers.LayerNormalization(epsilon=1e-6, center=False, scale=False)\n",
    "        self.att = Attention(causal=False, config=config)\n",
    "\n",
    "        self.norm2 = nn.layers.LayerNormalization(epsilon=1e-6, center=False, scale=False)\n",
    "        self.pointwise_ffn = FFN(config)\n",
    "\n",
    "    def call(self, x:Array, cond:Array, training:bool): # x: (B, T, d_model), cond: (B, d_model)\n",
    "        gamma1, beta1, alpha1, gamma2, beta2, alpha2 = jnp.array_split(self.scale_shift_scale(cond)[:, None], 6, axis=-1) # 6*(B, 1, d_model) <= (B, 1, 6*d_model) <= (B, d_model)\n",
    "        x = x + alpha1*self.att(shift_scale(self.norm1(x), gamma1, beta1), training=training) # (B, T, d_model)\n",
    "        x = x + alpha2*self.pointwise_ffn(shift_scale(self.norm2(x), gamma2, beta2), training=training) # (B, T, d_model)\n",
    "        return x # (B, T, d_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NormLinear(nn.Layer):\n",
    "    def __init__(self, config:config):\n",
    "        super().__init__()\n",
    "        self.shift_scale = nn.Sequential([\n",
    "            nn.layers.Activation(jax.nn.silu),\n",
    "            nn.layers.Dense(config.d_model*2, kernel_initializer=config.adaLN_linear_bias_init, bias_initializer=config.adaLN_linear_bias_init),\n",
    "        ])\n",
    "        self.norm = nn.layers.LayerNormalization(epsilon=1e-6, center=False, scale=False)\n",
    "        self.linear = nn.layers.Dense((config.patch_size**2)*config.out_channels, kernel_initializer=config.final_layer_linear_bias_init, bias_initializer=config.final_layer_linear_bias_init)\n",
    "\n",
    "    def call(self, x:Array, c:Array): # (B, T, D), # (B, D)\n",
    "        gamma, beta = jnp.array_split(self.shift_scale(c)[:, None], 2, axis=-1) # (B, 1, D) <= (B, 1, 2*D) <= (B, D)\n",
    "        x = shift_scale(self.norm(x), gamma, beta)\n",
    "        x = self.linear(x)\n",
    "        return x # (B, T, (P**2)*C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DiT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DiT(nn.Model):\n",
    "    def __init__(self, config:config):\n",
    "        super().__init__()\n",
    "        self.patch_ops = PatchOps(config)\n",
    "        self.positional_embeddings = PositionalEmbedding(config.maxlen, config.d_model).sinusoidal_embeddings() # (1, maxlen, d_model)\n",
    "        self.time_embed = TimeEmbedding(config, dim=config.d_model//2)\n",
    "        self.label_embed = LabelEmbedding(config)\n",
    "        self.blocks = [Block(config) for _ in range(config.num_layers)]\n",
    "        self.norm_linear = NormLinear(config)\n",
    "\n",
    "    def call(\n",
    "        self,\n",
    "        x:Array, # (B, H, W, C = 3 or 1)\n",
    "        t:Array, # (B,)\n",
    "        y:tp.Optional[Array], # (B,)\n",
    "        key:tp.Optional[Array],\n",
    "        training:bool\n",
    "    ):\n",
    "        x = self.patch_ops.patchify(x) # (B, N = H*W//P**2, d_model)\n",
    "        x += self.positional_embeddings\n",
    "\n",
    "        cond = self.time_embed(t, training=training) # (B, d_model)\n",
    "        if y is not None:\n",
    "            cond += self.label_embed(y, key, training=training) # (B, d_model)\n",
    "        \n",
    "        for block in self.blocks:\n",
    "            x = block(x, cond, training=training) # (B, N, d_model)\n",
    "        \n",
    "        x = self.norm_linear(x, cond) # (B, N = H*W//P**2, D = (P**2)*C)\n",
    "        x = self.patch_ops.unpacthify(x) # (B, H, W, C)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(model:nn.Model, input_shape:tuple, label_intervals:tuple[int, int]):\n",
    "    blabla = model(\n",
    "        jrand.normal(jrand.PRNGKey(342212), input_shape),\n",
    "        jnp.arange(input_shape[0]), \n",
    "        jrand.randint(jrand.PRNGKey(32344), shape=(input_shape[0],), minval=label_intervals[0], maxval=label_intervals[-1]), \n",
    "        jrand.PRNGKey(342212),\n",
    "        training=True\n",
    "    )\n",
    "    assert (blabla == 0).all()\n",
    "    assert (blabla.shape \n",
    "            == (input_shape[0], config.H, config.W, config.out_channels))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_object(dir_suffix_ftype:str, obj:tp.Any):\n",
    "    \"\"\"\n",
    "    dir_suffix_ftype: directory suffix and file type separated by \"|\"\\n\n",
    "    obj: Anything which is to be stored\n",
    "    \"\"\"\n",
    "    dir, suffix, ftype = dir_suffix_ftype.split(\"|\"); path = os.path.join(dir, \"\".join([suffix, f\".{ftype}\"]))\n",
    "    os.makedirs(name=dir, exist_ok=True)\n",
    "    with open(path, \"wb\") as file:\n",
    "        pickle.dump(obj=obj, file=file, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    return path\n",
    "\n",
    "def load_object(path:str):\n",
    "    with open(path, \"rb\") as file:\n",
    "        obj = pickle.load(file)\n",
    "    return obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iterator = iter(DataLoader(trainset).iter_batches(config.batch_size))\n",
    "val_iterator = iter(DataLoader(valset).iter_batches(config.batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"di_t\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"di_t\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">    Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ time_embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeEmbedding</span>)  │ ?                         │    <span style=\"color: #00af00; text-decoration-color: #00af00\">124,992</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ label_embedding                 │ ?                         │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,880</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LabelEmbedding</span>)                │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                   │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                 │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                 │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                 │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                 │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Block</span>)                 │ ?                         │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,496,448</span> │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ norm_linear (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NormLinear</span>)        │ ?                         │    <span style=\"color: #00af00; text-decoration-color: #00af00\">167,620</span> │\n",
       "└─────────────────────────────────┴───────────────────────────┴────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ time_embedding (\u001b[38;5;33mTimeEmbedding\u001b[0m)  │ ?                         │    \u001b[38;5;34m124,992\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ label_embedding                 │ ?                         │      \u001b[38;5;34m2,880\u001b[0m │\n",
       "│ (\u001b[38;5;33mLabelEmbedding\u001b[0m)                │                           │            │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block (\u001b[38;5;33mBlock\u001b[0m)                   │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_1 (\u001b[38;5;33mBlock\u001b[0m)                 │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_2 (\u001b[38;5;33mBlock\u001b[0m)                 │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_3 (\u001b[38;5;33mBlock\u001b[0m)                 │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_4 (\u001b[38;5;33mBlock\u001b[0m)                 │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ block_5 (\u001b[38;5;33mBlock\u001b[0m)                 │ ?                         │  \u001b[38;5;34m1,496,448\u001b[0m │\n",
       "├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
       "│ norm_linear (\u001b[38;5;33mNormLinear\u001b[0m)        │ ?                         │    \u001b[38;5;34m167,620\u001b[0m │\n",
       "└─────────────────────────────────┴───────────────────────────┴────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,274,180</span> (35.38 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,274,180\u001b[0m (35.38 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,274,180</span> (35.38 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,274,180\u001b[0m (35.38 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model(\n",
    "    model=DiT(config),\n",
    "    input_shape=(2, config.H, config.W, config.in_channels),\n",
    "    label_intervals=(0, 10)\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rate = nn.optimizers.schedules.CosineDecay(\n",
    "    initial_learning_rate=config.min_lr,\n",
    "    decay_steps=config.decay_steps,\n",
    "    warmup_steps=config.warmup_steps,\n",
    "    warmup_target=config.max_lr,\n",
    "    alpha=config.min_lr/config.max_lr\n",
    ") if not config.no_decay else lambda _: config.max_lr\n",
    "\n",
    "steps = jnp.arange(0, config.num_steps)\n",
    "lrs = jax.vmap(learning_rate)(steps)\n",
    "\n",
    "plt.plot(steps, lrs); del lrs, steps\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Learning Rate\")\n",
    "plt.title(\"Learning Rate vs Steps\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ParamGradManager:\n",
    "    \"\"\"dont decay parameters with dim less than 2\"\"\"\n",
    "    def __init__(self, trainable_vars:list):\n",
    "        order_before_segregate = [v.path for v in trainable_vars]\n",
    "        order_after_segregate = (\n",
    "            [v.path for v in trainable_vars if len(v.shape)!=1] +\n",
    "            [v.path for v in trainable_vars if len(v.shape)==1]\n",
    "        )\n",
    "        self.idx = [order_after_segregate.index(b) for b in order_before_segregate]\n",
    "\n",
    "    def filter_obj(self, trainable_obj:list):\n",
    "        \"\"\"can be grads or params\"\"\"\n",
    "        decay_obj = [v for v in trainable_obj if len(v.shape)!=1]\n",
    "        nodecay_obj = [v for v in trainable_obj if len(v.shape)==1]\n",
    "        return decay_obj, nodecay_obj\n",
    "    \n",
    "    def combine_obj(self, decay_obj:list, nodecay_obj:list):\n",
    "        obj = decay_obj + nodecay_obj\n",
    "        return [obj[i] for i in self.idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "adamw = lambda weight_decay: nn.optimizers.AdamW(\n",
    "        learning_rate=learning_rate,\n",
    "        beta_1=config.beta1,\n",
    "        beta_2=config.beta2,\n",
    "        clipnorm=config.clipnorm,\n",
    "        weight_decay=weight_decay,\n",
    "        # use_ema=int(config.ema!=0),\n",
    "        # ema_momentum=config.ema\n",
    ")\n",
    "decay_optimizer = adamw(weight_decay=config.weight_decay)\n",
    "nodecay_optimizer = adamw(weight_decay=0.0)\n",
    "loss_fn = nn.losses.MeanSquaredError()\n",
    "\n",
    "diff_utils = DiffusionUtils(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainable_vars = model.trainable_variables\n",
    "non_trainable_vars = model.non_trainable_variables\n",
    "\n",
    "param_grad_manager = ParamGradManager(trainable_vars)\n",
    "for param, opt in zip(param_grad_manager.filter_obj(trainable_vars), [decay_optimizer, nodecay_optimizer]):\n",
    "    opt.build(param)\n",
    "\n",
    "decay_opt_vars, nodecay_opt_vars = decay_optimizer.variables, nodecay_optimizer.variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "@partial(jax.jit, static_argnums=-1)\n",
    "def compute_loss(\n",
    "    trainable_vars:list,\n",
    "    non_trainable_vars:list,\n",
    "    X_batch:Array, # (B, H, W, C)\n",
    "    timestep_batch:Array, # (B,)\n",
    "    labels:tp.Optional[Array], # (B,)\n",
    "    noise_true:Array, # (B, H, W, C)\n",
    "    key:Array,\n",
    "    num_grad_accumalation_steps:int\n",
    "):\n",
    "    noise_pred, non_trainable_vars = model.stateless_call(\n",
    "        trainable_vars, non_trainable_vars, X_batch,\n",
    "        timestep_batch, labels, key, True\n",
    "    )\n",
    "    loss = loss_fn(noise_true, noise_pred)\n",
    "    loss /= num_grad_accumalation_steps\n",
    "    return loss, (non_trainable_vars, noise_pred)\n",
    "grad_fn = jax.value_and_grad(compute_loss, has_aux=True)\n",
    "\n",
    "@partial(jax.jit, static_argnums=-1)\n",
    "def mini_step(\n",
    "    train_state:tp.Sequence[list],\n",
    "    X_batch:Array, # (B, H, W, C)\n",
    "    timestep_batch:Array, # (B,)\n",
    "    labels:tp.Optional[Array], # (B,)\n",
    "    noise_true, # (B, H, W, C)\n",
    "    key:Array,\n",
    "    num_grad_accumalation_steps:int\n",
    "):\n",
    "    trainable_vars, non_trainable_vars = train_state\n",
    "\n",
    "    (loss, aux), grad = grad_fn(\n",
    "        trainable_vars, non_trainable_vars,\n",
    "        X_batch, timestep_batch, labels, noise_true,\n",
    "        key, num_grad_accumalation_steps\n",
    "    )\n",
    "    non_trainable_vars, noise_pred = aux\n",
    "    return grad, loss, (trainable_vars, non_trainable_vars), noise_pred\n",
    "\n",
    "decay_opt_apply = jax.jit(\n",
    "    fun=lambda opt_vars, grads, trainable_vars: decay_optimizer.stateless_apply(\n",
    "        opt_vars, grads, trainable_vars)\n",
    ")\n",
    "nodecay_opt_apply = jax.jit(\n",
    "    fun=lambda opt_vars, grads, trainable_vars: nodecay_optimizer.stateless_apply(\n",
    "        opt_vars, grads, trainable_vars)\n",
    ")\n",
    "\n",
    "def update_params(\n",
    "    grads:list,\n",
    "    trainable_vars:list,\n",
    "    optimizer_vars:tuple[list, list],\n",
    "):\n",
    "    decay_grads, nodecay_grads = param_grad_manager.filter_obj(grads)\n",
    "    decay_trainable_vars, nodecay_trainable_vars = param_grad_manager.filter_obj(trainable_vars)\n",
    "    decay_opt_vars, nodecay_opt_vars = optimizer_vars\n",
    "    \n",
    "    decay_trainable_vars, decay_opt_vars = decay_opt_apply(\n",
    "        decay_opt_vars, decay_grads, decay_trainable_vars\n",
    "    )\n",
    "    nodecay_trainable_vars, nodecay_opt_vars = nodecay_opt_apply(\n",
    "        nodecay_opt_vars, nodecay_grads, nodecay_trainable_vars\n",
    "    )\n",
    "    trainable_vars1 = param_grad_manager.combine_obj(decay_trainable_vars, nodecay_trainable_vars)\n",
    "    assert (\n",
    "        [v.shape for v in trainable_vars1] ==\n",
    "        [v.shape for v in trainable_vars]), (\n",
    "            f\"train vars aft: {[v.shape for v in trainable_vars1]}\\n\\ntrain vars bef: {[v.shape for v in trainable_vars]}\"\n",
    "    )\n",
    "    return trainable_vars1, (decay_opt_vars, nodecay_opt_vars)\n",
    "\n",
    "def eval(key:Array):\n",
    "    gen_image = diff_utils.generate(\n",
    "        model=model,\n",
    "        trainable_vars=trainable_vars,\n",
    "        non_trainable_vars=non_trainable_vars,\n",
    "        labels=[r.randint(0, config.num_classes-1)],\n",
    "        key=key\n",
    "    )\n",
    "    plt.imshow(gen_image.squeeze(), cmap=\"gray\")\n",
    "    plt.show() # debug_input = (scale(X_train)[:2], Y_train[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wait = 0\n",
    "step = 1\n",
    "training_losses = {\"train\": []}\n",
    "key = jrand.PRNGKey(config.seed)\n",
    "\n",
    "t0 = time.time()\n",
    "print(\"Training about to start...\")\n",
    "X_batch, labels = next(train_iterator)\n",
    "\n",
    "while True:\n",
    "    # condition to terminate\n",
    "    if step > config.num_steps or wait > config.patience:\n",
    "        print(f\"Early Stopping at Step {step}.\" if wait > config.patience else \"Training Ended.\")\n",
    "        break\n",
    "\n",
    "    key, key2, key3, key4 = jrand.split(key, num=4)\n",
    "    \n",
    "    # noise it\n",
    "    timesteps = jrand.randint(\n",
    "        key2, (X_batch.shape[0],), minval=0, maxval=config.num_timesteps\n",
    "    )\n",
    "    noisy_image_timestep, noise_true = diff_utils.noisy_it(X_batch, timesteps, key3)\n",
    "\n",
    "    # train model\n",
    "    grads = jax.tree_util.tree_map(jnp.zeros_like, trainable_vars)\n",
    "    for _ in range(config.num_grad_accumalation_steps):\n",
    "        grad, loss, (trainable_vars, non_trainable_vars), noise_pred = mini_step(\n",
    "            (trainable_vars, non_trainable_vars),\n",
    "            noisy_image_timestep[\"noisy_images\"], noisy_image_timestep[\"timesteps\"],\n",
    "            labels, noise_true, key4,\n",
    "            config.num_grad_accumalation_steps\n",
    "        )\n",
    "        grads = jax.tree_util.tree_map(\n",
    "            lambda g1, g2: jnp.add(g1, g2), grads, grad\n",
    "        ) # sum grads for grad accumation\n",
    "        X_batch, labels = next(train_iterator)\n",
    "    grad = None # save memory\n",
    "\n",
    "    loss = loss*config.num_grad_accumalation_steps # loss from last mini-step\n",
    "    \n",
    "    trainable_vars, (decay_opt_vars, nodecay_opt_vars) = update_params(\n",
    "        grads, trainable_vars, (decay_opt_vars, nodecay_opt_vars)\n",
    "    )\n",
    "    grads = None # save memory\n",
    "\n",
    "    if step % config.ckpt_freq == 0 or step == config.num_steps:\n",
    "        eval(key)\n",
    "        ckpt = (\n",
    "                trainable_vars,\n",
    "                non_trainable_vars,\n",
    "                (decay_opt_vars, nodecay_opt_vars),\n",
    "                step,\n",
    "            )\n",
    "        \n",
    "        _ = save_object(\n",
    "            config.checkpoint_dir+f\"|checkpoint|diffusion_transformer\",\n",
    "            obj=ckpt\n",
    "        )\n",
    "        print(f\"Saved checkpoint of step {step}.\")\n",
    "\n",
    "\n",
    "    # time\n",
    "    t1 = time.time()\n",
    "    dt = t1-t0; t0 = t1\n",
    "\n",
    "    # print the essentials\n",
    "    print(\n",
    "        f\"| Step: {step} || Loss: {loss:.4f} |\"\n",
    "        f\"| LR: {learning_rate(step):e} || dt: {dt*1000:.2f}ms |\"\n",
    "    )\n",
    "    training_losses[\"train\"].append(loss.tolist())\n",
    "    step += 1\n",
    "\n",
    "train_state = (trainable_vars, non_trainable_vars)\n",
    "save_object(\n",
    "    config.checkpoint_dir+f\"|mnist_train_state|diffusion_transformer\",\n",
    "    obj=train_state\n",
    ") # clear logs very long..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVtUozzrE++r76V3Exsbi9u3bqFmzplSWUzJ5+/ZttGvXDidPnlQ7FJ0TbZ+vKlWqAADu3r0rlT179gzr1q3DunXrYGlpiePHj8PHx4fJjQHjYSkyeFn/DWf/79fU1BSjRo2Sq0tqMjIyEBgYiB49eqidkVOxYkV07txZJ89x9uxZREVFYcSIEWqHjzp16oTq1atj3759ADLPSDI3N1fb9vbt24iLi5PKtc3FyRoZeHXb7AIDA5GSkqJxQbxPP/0UxYsXl/qQZcuWLbCwsICXlxc6deqErVu3qq0/dOgQnj9/jqlTp2qd+2Nvb59jX97GnTt38PPPP+Ozzz7TmHsTFxeHJ0+eoGXLlmrl+nyPDRo0CMWKFZMe9+7dG6VLl5bmaYWEhODWrVv48ssv1eYuZXmX/bN//340btxY7Qy5okWLYvjw4QgLC8OVK1fy3Gbt2rVhZ2enUV6uXDlUr15d7TBY1ujgq4fVtm7dChMTE60jPMbGxhr1nZ2d1U79trKywqBBg3D+/HlERUUBgNpZjVnPfevWrde+16ng48gNGbxTp04hJiYG69evx/fffw8hBAYOHFigDgv5+PigQ4cOOHnyJFasWAFjY2N88cUXuHz5MurVq5erNkxNTfH1119rlMfExGDFihWYNGkS1q1bh2PHjmHTpk1wdHTEmDFjEBYWhoULFwLI/K/1999/x9atW3HlyhW8fPkSPXv2hJOTEzZv3gwg85TyUaNGYdeuXbh9+zasrKwwbNgwPH/+HPv378+xf9HR0fDz84OPjw8OHjyIPXv2oGrVqhg1ahTOnDmDn3/+Wa3++fPncfPmTcyePRsWFhZqh6SAzIRi5MiR2LBhA86dO4fNmzfjyZMnKFeuHN577z2cPHkSo0ePztW+y63Zs2dj4MCBcHNzw+XLl9XW/fjjj9Ip9mfPnkXLli2lUQB9yLoWTUBAABwdHTF27FjcvHlTOoVbCIGhQ4fiwIED+OeffxAQEIDw8HA4OzujTZs2ePHihTSnKa/mzp2LDz/8EAcOHMD333+PmJgYeHl5wdXVFb169Xqrw3/t27fHjBkzsGfPHgQHByM+Ph4VKlTAkCFDYG5uDh8fH6luSEgIAOD777/HoUOHkJ6eji1btuD48eNYuXIlpk6dirp16+Lw4cNIS0tD5cqV0adPH4wZM0btEgDXr1/HmjVr0LBhQ0RFRWHIkCFwdHTE4MGDpTpXrlzBn3/+iZCQEMTExKBBgwbo3bs3li5d+lb7jgoO2U/Z4sLl1SWnU8Gzn56bffHw8BCnTp0SCQkJ4uHDh2Lu3Lmiffv2QgghWrVqJdXL6VRwbadG53SK76t1tJ0GGxYWpnZ6LwDRpk0bERISIpKTk8XNmzfFkCFDxLfffisSExPfuD+yTnXW5ubNm1K9Pn36iJCQEJGUlCSio6PFhg0bROnSpaX1JUqUEEuWLBFXrlwRcXFx4tmzZyIoKEj07t1bqlO3bl2xceNGcffuXZGUlCQiIyPFnj17hLu7e65eu1GjRokrV66IlJQUERERIZYtWyZsbGy01p05c6YQQogbN27k2F6rVq3EgQMHxLNnz0RiYqK4efOmWLt2rVp/3nSq/KtL9lPBc9rXr77XLCwsxOrVq8WzZ8/E8+fPxebNm4W9vX2O7xM7OzuNdrX18dX3ddap4P369ROzZ88WkZGRIiEhQezdu1eULVtWY/s6deqI7du3iydPnoikpCQRFhYmNm/eLNq0afPGPr1ucXV1FVu3bhUxMTEiMTFRBAcHiy5dumj9nOTmVPDy5csLHx8fcerUKREZGSlSU1NFVFSU2Lt3r2jdurVaXSMjI7F48WIRFRUl0tPTNT53Q4cOFX///bdISEgQz58/FxcvXhRz584VTk5Oap/BvXv3ivbt24sLFy6IpKQkceXKFdGrVy+1tqZOnSqCg4NFTEyMSEhIEFeuXBFTpkwRJiYmud5XXAreovr3FyKSwa5du1CjRg29jgAQFUZhYWG4fPkyunbtKndXSAacc0OUT7JfWRbInOzYpUsX/Pnnn/J0iIhIoTjnhiif3LlzB+vWrZOu+TJy5EikpqZKFzUjIiLdYHJDlE8OHjyIDz/8EE5OTkhJSUFQUBCmTp2qswv5ERFRJs65ISIiIkXhnBsiIiJSFCY3REREpCiFcs5N6dKlERcXJ3c3iIiIKA+srKzw6NGjN9YrdMlN6dKlER4eLnc3iIiI6C04Ozu/McEpdMlN1oiNs7OzzkdvrKyspMufK3FkSOnxAcqPkfEZPqXHyPgMn75izGo3N20WuuQmS1xcnN7eWPpsuyBQenyA8mNkfIZP6TEyPsMnZ4ycUExERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFFmTmxYtWmDPnj0IDw+HEALdu3d/4zatWrVCSEgIkpOTcfPmTXh5eeVDT3PH2MQEGULI3Q0iIqJCTdbkxtLSEhcvXsTnn3+eq/rly5fHvn37cPToUdStWxeLFi3Cjz/+iA4dOui5p29mbGKCb47vx6J/TsvdFSIiokLNRM4nP3jwIA4ePJjr+iNGjEBYWBi+/PJLAMC1a9fQvHlzjBs3DocPH9ZXN3Oldoe2sj4/ERERZZI1uckrDw8PBAYGqpUdOnQIixYtynEbMzMzmJubS4+trKzUfuqKfelSGs+hNPradwWJ0mNkfIZP6TEyPsOnrxjz0p5BJTdOTk6IiopSK4uKioKNjQ0sLCyQnJyssc2UKVPg4+OjUR4eHq7Tvh2LuIeQpxEAgIcPH0KlUum0/YJE1/uuIFJ6jIzP8Ck9RsZn+OSM0aCSm7fh5+cHf39/6bGVlRXCw8Ph7OyMuLg4nT3PuF0bYePoAAAoX7ECnkU/1VnbBYW+9l1BovQYGZ/hU3qMjM/w6SvGrHZzw6CSm8jISDg6OqqVOTo64vnz51pHbQAgNTUVqampGuVxcXE63em/r/kJH0ydAAB4CaHYNy2g+31XECk9RsZn+JQeI+MzfHLGaFDXuQkKCoKnp6daWfv27REUFCRTj/5zM/jvbI+Ue0iKiIiooJP9VPA6deqgTp06AABXV1fUqVMHZcuWBQDMmTMH69evl+qvXLkSFSpUwLx581C1alWMHDkSffv2xcKFC2Xpf3Yi2/VtlDzfhoiIqKCTNblp0KABLly4gAsXLgAAFi5ciAsXLsDX1xcAUKpUKZQrV06qf/fuXbz33nto3749Ll68iAkTJmDo0KGynwYOqCc3YHJDREQkG1nn3Bw7duy1oxyDBw/Wuo27u7s+u/V21EZuZOwHERFRIWdQc24KsphHEdLvqUnaJzcTERGR/jG50RGRniH9zjk3RERE8mFyoyNqE4qNuFuJiIjkwm9hIiIiUhQmN3rAw1JERETyYXKjQxnp6Zm/MLkhIiKSDZMbXfp32g1HboiIiOTD5EaHxL/ZDZMbIiIi+TC50SUhDd3I2w8iIqJCjMmNDomMrJEbmTtCRERUiDG50aH/DktxtxIREcmF38K6xMNSREREsmNyo0PMbYiIiOTH5EaHRMa/95didkNERCQbJjc69e+cGzC5ISIikguTGx2SDksZMbkhIiKSC5MbXeKkGyIiItkxudEhITLn3PAKxURERPJhcqNDWQM3REREJB8mN7r0b3ajMuJuJSIikgu/hXVICN44k4iISG5MbnRI8LgUERGR7Jjc6BJHboiIiGTH5EaHpMNSvM4NERGRbJjc6FLWUSmO3BAREcmGyY0OSde54e0XiIiIZMPkRocER26IiIhkx+RGlzjnhoiISHZMbnSIh6WIiIjkx+RGh3hYioiISH5MbnRJus6NzP0gIiIqxJjc6JBAVnLD3UpERCQXfgvrkMj497gUR26IiIhkw+RGl3j7BSIiItkxudEh6caZTG6IiIhkw+RGDzhyQ0REJB8mNzokMv69zg2TGyIiItkwudEhHpYiIiKSH5MbPeDIDRERkXyY3OiQ4NlSREREsmNyo0NZc254WIqIiEg+TG50SZpyw+SGiIhILkxudEiAVygmIiKSG5MbXRK8txQREZHc+C2sQ1n3luJRKSIiIvkwudGh/w5LMbshIiKSC5MbXco6LMVJN0RERLJhcqNDWRco5sgNERGRfJjc6JAQ/95byojJDRERkVyY3OgSr1BMREQkOyY3OiQdluKcGyIiItkwudElwVPBiYiI5MbkRoekG2cacbcSERHJhd/COiT+Oy5FREREMmFyo0ucUExERCQ7Jjc6JI3cMLkhIiKSDZMbHfpvzg2TGyIiIrnIntyMGjUKYWFhSEpKQnBwMBo2bPja+mPGjMG1a9eQmJiI+/fvw9/fH+bm5vnU2zfg7ReIiIhkJ2ty07dvX/j7+2PGjBlwd3fHxYsXcejQITg4OGit/+GHH2Lu3LmYMWMGqlWrhk8//RT9+vXDnDlz8rnn2vH2C0RERPKTNbkZP348Vq9ejXXr1uHq1asYMWIEEhMTMWTIEK31mzZtipMnT2LTpk24d+8ejhw5gk2bNqFRo0b53POccEIxERGR3EzkemJTU1PUr18ffn5+UpkQAoGBgfDw8NC6zalTpzBgwAA0bNgQf//9N1xdXdGlSxds2LAhx+cxMzNTO2xlZWWl9lOXjP69vk2RokX00r7c9LnvCgqlx8j4DJ/SY2R8hk9fMealPdmSG3t7e5iYmCAqKkqtPCoqCm5ublq32bRpE+zt7XHixAmoVCqYmppixYoVagnSq6ZMmQIfHx+N8vDw8HfqvzY7717F3fjnWLRoMWqs/0Xn7RcU+th3BY3SY2R8hk/pMTI+wydnjLIlN2+jVatWmDp1KkaNGoXTp0+jUqVKWLx4MaZNm4ZZs2Zp3cbPzw/+/v7SYysrK4SHh8PZ2RlxcXE67d+gRXNRoZE7xo0bh+Cde3TadkGgz31XUCg9RsZn+JQeI+MzfPqKMavd3JAtuYmOjsbLly/h6OioVu7o6IjIyEit28ycORMbNmzAmjVrAACXL1+GpaUlfvjhB8yePVvrFYJTU1ORmpqqUR4XF6fzN9bLl2kAgOSUZMW+aQH97LuCRukxMj7Dp/QYGZ/hkzNG2SYUp6WlISQkBJ6enlKZSqWCp6cngoKCtG5TtGhRZGRkqJWlp6dL28pNZGRNKJb9DHsiIqJCS9bDUv7+/li/fj3Onj2LM2fOYOzYsbC0tERAQAAAYP369QgPD8fUqVMBAHv37sX48eNx/vx56bDUzJkzsXfvXo2kRxa8KzgREZHsZE1utm7dCgcHB/j6+sLJyQkXLlxAp06d8PjxYwBAuXLl1JKWWbNmQQiBWbNmwdnZGU+ePMHevXvx9ddfyxWCGgHefoGIiEhusk8oXrZsGZYtW6Z1XZs2bdQep6enw9fXF76+vvnRtbyTchsmN0RERHLh5BAdEv+OMjG5ISIikg+TGx3iXcGJiIjkx+RGDzhyQ0REJB8mNzqk7To7RERElL+Y3OiQNOfGiLuViIhILvwW1gMeliIiIpIPkxsd+m9Csbz9ICIiKsyY3OhS1hWKmd0QERHJhsmNDmWN3HDODRERkXz4LaxDPCxFREQkPyY3usTDUkRERLJjcqND0mVueLYUERGRbJjc6JAQWde5YXJDREQkFyY3upR1WIojN0RERLJhcqND/919gckNERGRXJjc6BLvLUVERCQ7JjdERESkKExuiIiISFGY3OgB5xMTERHJh8mNLv2b1RibmsrcESIiosKLyY0OuXftBABo/elAmXtCRERUeDG5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFIXJDRERESmK7MnNqFGjEBYWhqSkJAQHB6Nhw4avrW9jY4OlS5fi0aNHSE5OxvXr19G5c+d86i0REREVdCZ53cDCwgIqlQpJSUkAgHLlyqFnz564cuUKjhw5kqe2+vbtC39/f4wYMQKnT5/G2LFjcejQIVStWhVPnjzRqG9qaoojR47g8ePH6N27N8LDw+Hi4oLY2Ni8hkFEREQKJvKyHDp0SHz22WcCgLCxsRERERHi/v37IjExUYwYMSJPbQUHB4slS5ZIj1UqlXj48KGYNGmS1vqfffaZuHXrljAxMcnT82RfrKyshBBCWFlZvXUbOS0LQoOkRddtF4RFn/uuoCxKj5HxGf6i9BgZn+Ev+ooxL+3meeTG3d0d48aNAwD07t0bUVFRqFevHnr16gVfX1+sXLkyV+2Ympqifv368PPzk8qEEAgMDISHh4fWbbp164agoCAsW7YM3bt3x5MnT/DLL79g3rx5yMjI0LqNmZkZzM3NpcdWVlZqP/VF3+3LIb/2nZyUHiPjM3xKj5HxGT59xZiX9vKc3BQtWhRxcXEAgA4dOmDnzp0QQiA4OBguLi65bsfe3h4mJiaIiopSK4+KioKbm5vWbSpUqIC2bdti48aN6NKlCypVqoTly5fD1NQUvr6+WreZMmUKfHx8NMrDw8Nz3dfc8r8cLP3+4sULnbdfUOhj3xU0So+R8Rk+pcfI+AyfnDHmObm5desWevTogV27dqFjx45YuHAhAKBkyZJ6/0I3MjLC48ePMXz4cGRkZODcuXNwdnbGV199lWNy4+fnB39/f+mxlZUVwsPD4ezsLCVpuuJz6rD0u7W1tU7bLgj0ue8KCqXHyPgMn9JjZHyGT18xZrWbG3lObnx9ffHLL79g4cKF+P333xEcnDla0aFDB5w/fz7X7URHR+Ply5dwdHRUK3d0dERkZKTWbSIiIpCWlqZ2COrq1asoVaoUTE1NkZaWprFNamoqUlNTNcrj4uL0+sZS6psW0P++KwiUHiPjM3xKj5HxGT45Y8zzqeA7duxAuXLl0KBBA3Tq1Ekq//3336W5OLmRlpaGkJAQeHp6SmUqlQqenp4ICgrSus3JkydRqVIlqFQqqaxKlSp49OiR1sSGiIiICp+3us5NVFQULly4ACEErKys0L17d8TFxeH69et5asff3x/Dhg3DoEGD4ObmhhUrVsDS0hIBAQEAgPXr12POnDlS/RUrVqBEiRJYvHgxKleujC5dumDq1KlYtmzZ24RBRERECpTnw1JbtmzB8ePHsWzZMlhYWODs2bMoX748VCoV+vfvj507d+a6ra1bt8LBwQG+vr5wcnLChQsX0KlTJzx+/BhA5jV0sh+CevjwoTTP59KlSwgPD8fixYsxb968vIZBRERECpXn5KZly5aYPXs2AKBnz55QqVQoXrw4vLy8MG3atDwlNwCwbNmyHEde2rRpo1EWHByc46niRERERHk+LGVjY4OYmBgAQKdOnbBjxw4kJSVh3759qFy5ss47SERERJQXeU5uHjx4AA8PDxQtWhSdOnXC4cOZpz/b2toiOTlZ5x0kIiIiyos8H5ZatGgRNm7ciPj4eNy7dw9//vkngMzDVaGhobruHxEREVGe5Dm5WbFiBc6cOYOyZcviyJEjEEIAAO7cuYNp06bpvINEREREeZHn5AYAQkJCEBISola2f/9+nXSIiIiI6F281XVuBg4ciEuXLiEpKQlJSUm4ePEiBgwYoOu+EREREeVZnkduxo0bh5kzZ2Lp0qU4efIkAKB58+ZYuXIl7O3tsWjRIl33kYiIiCjX8pzcjB49GiNHjsSGDRuksr179+Kff/6Bj48PkxsiIiKSVZ4PS5UqVQqnTp3SKD916hRKlSqlk04RERERva08Jze3bt1C3759Ncr79euHmzdv6qRTRERERG8rz4elvL29sWXLFrRs2VKac9OsWTN4enpqTXqIiIiI8lOeR2527tyJxo0bIzo6Gj169ECPHj0QHR2NRo0aYffu3XroIhEREVHuvdV1bs6dO4eBAweqlTk4OGDKlCnw8/PTSceIiIiI3sZbXedGm1KlSmHmzJm6ao6IiIjoregsuSEiIiIqCJjcEBERkaIwuSEiIiJFyfWE4gULFrx2vYODwzt3hoiIiOhd5Tq5qVev3hvrHD9+/J06Q0RERPSucp3ctG3bVp/9ICIiItIJzrkhIiIiRWFyQ0RERIrC5IaIiIgUhckNERERKQqTGyIiIlKUt7pxpo2NDRo1aoSSJUvCyEg9P9qwYYNOOkZERET0NvKc3Lz//vvYuHEjihUrhhcvXkAIIa0TQjC5ISIiIlnl+bDUggULsHbtWhQrVgy2trYoUaKEtNjZ2emjj0RERES5lufkxtnZGd9//z2SkpL00R8iIiKid5Ln5ObQoUNo0KCBPvpCRERE9M7yPOdm3759+Pbbb1G9enWEhoYiLS1Nbf3evXt11jkiIiKivMpzcrN69WoAwPTp0zXWCSFgYvJWJ2ARERER6USeMxFjY2N99IOIiIhIJ3gRPyIiIlKUXI3cjB49Gj/88ANSUlIwevTo19ZdsmSJTjpGRERE9DZyldyMGzcOGzduREpKCsaNG5djPSEEkxsiIiKSVa6SmwoVKmj9nYiIiKig4ZwbIiIiUpS3Om/b2dkZ3bp1Q7ly5WBmZqa2bsKECTrpmCFKT0uDsamp3N0gIiIq1PKc3LRt2xZ79uzBnTt34ObmhsuXL6N8+fJQqVQ4d+6cPvpoMA4vXY3O40bh2l+n5O4KERFRoZXnw1J+fn747rvvULt2bSQnJ6NXr14oW7Ysjh07hm3btumjjwYjIz098+fLdJl7QkREVHjlObmpVq0afvrpJwDAy5cvUaRIESQkJGD69OmYNGmSzjtIRERElBd5Tm4SEhKkeTYRERGoWLGitM7e3l53PTNAQgi5u0BERFTo5XnOTXBwMJo3b45r165h//79WLBgAWrVqoUPPvgAwcHB+ugjERERUa7lObkZP348ihUrBgDw9vZGsWLF0K9fP9y8eRPjx4/XeQcNiX25MgCA6m1ayNwTIiKiwitPyY2RkRHKlCmDS5cuAQASExMxcuRIvXTMEDXp94HcXSAiIir08jTnJiMjA4cPH4atra2++kNERET0TvI8ofjy5cu8BQMREREVWHlObqZNm4bvvvsO7733HpycnGBlZaW2EBEREckp13NuvvnmGyxYsAD79+8HAOzZs0ft1GeVSgUhBExM3uqODoqQlpICU3NzubtBRERUqOU6E/H29sbKlSvRpk0bffbHoB1ZuhpdJnyBZ+ERcneFiIio0Mp1cqNSqQAAx48f11tnDF1KQiIA4OmDhzL3hIiIqPDK05wbXoH39QS4f4iIiOSWpwkyN27ceGOCY2dn904dUoR/R7mIiIgo/+UpufH29sbz58/11ReDV8Q682yxSo0byNwTIiKiwitPyc3mzZvx5MkTffXF4DXq1V3uLhARERV6uZ5zw/k2REREZAhyndyo9DiPZNSoUQgLC0NSUhKCg4PRsGHDXG3Xr18/CCGwa9cuvfUtT5gAEhERyS7XyY2xsbFeDkn17dsX/v7+mDFjBtzd3XHx4kUcOnQIDg4Or93OxcUF3333XYE6NZ2jW0RERPLL8+0XdG38+PFYvXo11q1bh6tXr2LEiBFITEzEkCFDctzGyMgIGzduhLe3N+7cuZOPvSUiIqKCTtZ7JZiamqJ+/frw8/OTyoQQCAwMhIeHR47bTZ8+HY8fP8batWvRokWL1z6HmZkZzLPdEiHr/lf6uA9W9kN3SrzPlj73XUGh9BgZn+FTeoyMz/DpK8a8tCdrcmNvbw8TExNERUWplUdFRcHNzU3rNs2aNcOnn36KunXr5uo5pkyZAh8fH43y8PDwvHb3jdbdvIiYlCQAwIsXL3TefkGhj31X0Cg9RsZn+JQeI+MzfHLGaFB3uSxWrBg2bNiAYcOG4enTp7naxs/PD/7+/tJjKysrhIeHw9nZGXFxcTrt3xeb1sDepSwAwNraWqdtFwT63HcFhdJjZHyGT+kxMj7Dp68Ys9rNDVmTm+joaLx8+RKOjo5q5Y6OjoiMjNSoX7FiRbi6umLv3r1SmZFR5rShtLQ0VK1aVWMOTmpqKlJTUzXaiouL0/kbK/3lS7X2lUof+66gUXqMjM/wKT1Gxmf45IxR1gnFaWlpCAkJgaenp1SmUqng6emJoKAgjfrXrl1DzZo1UbduXWnZs2cPjh49irp16+LBgwf52X1NPFuKiIhIdrIflvL398f69etx9uxZnDlzBmPHjoWlpSUCAgIAAOvXr0d4eDimTp2KlJQU/PPPP2rbx8bGAoBGORERERVOsic3W7duhYODA3x9feHk5IQLFy6gU6dOePz4MQCgXLlyyMjIkLmXucPr3BAREclP9uQGAJYtW4Zly5ZpXdemTZvXbjt48GB9dOmtMLkhIiKSn+wX8VMUJjdERESyY3JDREREisLkRoc4cENERCQ/Jjc6JIRhTHwmIiJSMiY3usShGyIiItkxudGlbDfOJCIiInkwuSEiIiJFYXKjQy+insjdBSIiokKPyY0Ond62W+4uEBERFXpMbnSoRJnScneBiIio0GNyQ0RERIrC5EaXeLYUERGR7Jjc6JCKyQ0REZHsmNzoEO8KTkREJD8mN0RERKQoTG50KdvIDQ9RERERyYPJjZ5YligudxeIiIgKJSY3elKzTUu5u0BERFQoMbnRIbX5xDwsRUREJAsmN0RERKQoTG50KfuEYnDkhoiISA5MbvSFuQ0REZEsmNwQERGRojC50SEBXueGiIhIbkxuiIiISFGY3OgSr1BMREQkOyY3+sLkhoiISBZMbnTo0bWbcneBiIio0GNyo0OxEZHS7xy4ISIikgeTGx1Su/0CL3RDREQkCyY3REREpChMbnSJZ0sRERHJjskNERERKQqTGx0S2SfdcOSGiIhIFkxudOq/5KblwH4y9oOIiKjwYnKjJyVKl5K7C0RERIUSkxsdUj8VnIiIiOTA5IaIiIgUhcmNLnHohoiISHZMbnRIMLkhIiKSHZMbIiIiUhQmNzrFkRsiIiK5MbkhIiIiRWFyo0OcckNERCQ/Jje6xOyGiIhIdkxuiIiISFGY3OgQTwUnIiKSH5MbIiIiUhQmN0RERKQoTG6IiIhIUZjcEBERkaIwuSEiIiJFYXJDREREisLkhoiIiBSFyQ0REREpCpMbIiIiUhQmN0RERKQoBSK5GTVqFMLCwpCUlITg4GA0bNgwx7pDhw7F8ePHERMTg5iYGBw5cuS19YmIiKhwkT256du3L/z9/TFjxgy4u7vj4sWLOHToEBwcHLTWb926NTZt2oQ2bdrAw8MDDx48wOHDh1G6dOl87jkREREVRLInN+PHj8fq1auxbt06XL16FSNGjEBiYiKGDBmitf6AAQOwYsUKXLx4EdevX8fQoUNhZGQET0/PfO45ERERFUSyJjempqaoX78+AgMDpTIhBAIDA+Hh4ZGrNooWLQpTU1PExMToq5tERERkQEzkfHJ7e3uYmJggKipKrTwqKgpubm65amPevHl49OiRWoKUnZmZGczNzaXHVlZWaj916dU29fEcctLnvisolB4j4zN8So+R8Rk+fcWYl/ZkTW7e1aRJk9C/f3+0bt0aKSkpWutMmTIFPj4+GuXh4eF66ZP/5WDp9xcvXujlOeSmr31XkCg9RsZn+JQeI+MzfHLGKGtyEx0djZcvX8LR0VGt3NHREZGRka/ddsKECZg8eTLatWuH0NDQHOv5+fnB399femxlZYXw8HA4OzsjLi7u3QJ4hZWVFSYc2iE9tra21mn7ctPnvisolB4j4zN8So+R8Rk+fcWY1W5uyJrcpKWlISQkBJ6envj1118BACqVCp6enli6dGmO23311Vf4+uuv0bFjR4SEhLz2OVJTU5GamqpRHhcXp/c3VvX2rXF65169Pocc8mPfyU3pMTI+w6f0GBmf4ZMzRtnPlvL398ewYcMwaNAguLm5YcWKFbC0tERAQAAAYP369ZgzZ45Uf+LEiZg5cyaGDBmCu3fvwtHREY6OjrC0tJQrhBz1nTFV7i4QEREVOrLPudm6dSscHBzg6+sLJycnXLhwAZ06dcLjx48BAOXKlUNGRoZUf+TIkTA3N8eOHTvU2vHx8cGMGTPyte9ERERU8Mie3ADAsmXLsGzZMq3r2rRpo/bY1dU1P7pEREREBkr2w1JEREREusTkhoiIiBSFyQ0REREpCpMbIiIiUhQmN0RERKQoTG6IiIhIUZjcEBERkaIwuSEiIiJFYXKjYxWsbOXuAhERUaHG5EbH2pYqL3cXiIiICjUmNzpmbmwsdxeIiIgKNSY3REREpChMbvTM0ra43F0gIiIqVJjc6Jm1g53cXSAiIipUmNzo2Zc7fpa7C0RERIUKkxsdE3J3gIiIqJBjckNERESKwuQmHxR3cpS7C0RERIUGk5t88NkPi+XuAhERUaHB5CYflHR1kbsLREREhQaTm3xSoX5dAIBZEQs06dMDtqWd5O0QERGRQpnI3YHCYsB8X8xs1x1+Z45KZRNqecjYIyIiImXiyE0+sSnpAFMLC7m7QUREpHhMbnTMWKXKcZ11Sft87AkREVHhxORGx0yNcr4r+JTftuZjT4iIiAonJjdERESkKExu9CAlIUHuLhARERVaTG70Iud5N0RERKRfTG70IDH2+TttX6N1cxR3LKmj3hARERUuvM6NHqSlpOSpfr+ZX0OlUmHztFmo3aEtvBbMBsDr4BAREb0NjtzowcN/ruWqnnnRoihibYVGPd5Hw+7vwdK2OCo3bqDn3hUsFRvUQxFra7m7QURECsLkRg/O7Pg1V/XGbV0HVbbr4hgZGak9btTjfbT9dJDaNsYmrx9sMytSBMNXLYJH35556LE86nXpgFEBy/HVrp/l7goRESkID0vpQeSN27mq5+BSFkLkvL7fzK8BAFeOnYCphQU6jx6O8nVrY2b77kh6ESfVsyhmieT4zDO0mn/UB1WbNkbVpo1x++9zeBx27+0D+VcRayskx8W/czuvqt2+DYDMqzeTYanWoiksrIrh/P7DcneFiEgDR25kNnH3Run3frOmaa3Tc8p4jN20BlWbNoZ50SKo5dlaWlehfl3MDgpEH+/JAABzy6LSukl7NsOyuM079c+5WhXMOnkYgxfPe6d2tFG95mrOpDsVG7qjfJ1aOm1z6PIFGDBvBqyZmBLJrmGP99CkTw+5u1GgMLmRmbXDf7dkqNbcAyVdXTTqVGpUX+1xP9+p0u8jVi8BADTp3T2z4JWhIHuXsmoJDwCUKFMadTp6So8bf9AVXv5zYGxqqvHczT/qAwCo0aZFbsLJk9zmNiqVCr29J6Fxr25vrFvUxhotPu6LwYvnvvEQXmFQ1MYao9Yuw+iff4DK6N0+7lU8GkrvhyxFrIq9U5tAZh/NiujvvmvFStjCo09Pjc8BkRKYmJmh/8xp6DN9EoracP5iFv7115P4mGcoVsI2z9tVbFAvT/WNTdVfQgH15OZ/P68GAMxo2xUvnkQDAL4+sAMA8LOxMc7vP4y+MzKTpe5PY7Bz9ndq21dt2ljjOVVGRmg3/BPcCbmAOyEX8tRfIDNZMTI2RrlaNdTK7co4w8TMFFF37qqV12jTAh69e8Cjdw+c3rEHRW2s8cHXXyLx+Qupv2VrVkflxg3w3tiR0nbu73fE37v35bl/eWVetCg8h3nh4uHfEX71ht6fL0uT3t3x5N4D3P77XI51LG2LS79bO9ghIzE5c9t+H8DFvTYCxkxGelparp7vsx++BwBE3rrzX2G2ZLpe5/ao2MgdO2d9h4z09Fy1aWFVDDNPHEJ62ktMdNd9Ag0Aw1cugnO1KqjUuD42fKl9dDQ3KtSvC2e3KvhrY+G7jYrq3/mAuX1dKf9k/w4ws7BA4vMXMvam4ODIjZ5smjZTr+1X8WiEj+fN0FyRwxye6q2bo1SVSrAr4yyVVXCvo1anWf9eqNelg/TYyt5O63yYOp080Xn0Z/h83QqprF7n9mjxcV8AgI2jA3pPnwTHiq5a+zJ2cwDmn/9LbdQKAKYe2I6Jv26CxSujAUWznU1Vo00L+Py5D/U6t0ez/r3wv40/YkFoEMZuWqOW2ABQi1WfOn0xHJ5DB2H81vUwMTeHbWmnHOs2/6g3Oo/+7LXtqVQqOFerAiPjnO9T5lKnJvp4T8aotcvgOdRLbZ2JmZm0rciWfEwP3PNfn8eMQLUWTdGga6fX9qVai6awLumA6q2aS2UlnEtLv2dvf8B8X3j07oH6ObTpWKE8ph3ehcYfdJXKSletDCDzD3SXMSNhVqTIa/vzqooN6qHXtK9Q1MYa5evWhpGx5p8052pVAAC1PFvlqe1Xfb5uBXpMHge3Frm7RIONowOKOzm+03MWFBN3/4Jph3e99j35OkVtrGU/DG1iZobqrZrDsUJ5mBfV/Sjeq3+38kvTbCePvPrPbWHGkRs9ufZXECa6t8T8c8f10v5nPyzWKPvIzxupScla6xcrYYsvd2xQK2v8QTc8un5LrWzAvBlIjk9AXPRTjNsSoLbO1NwcN1/EwKlyRalMZWQEkZGBAfN9AQAxjyIw5Pv5AACPPj1weMUaHF65FiIjQ9qmTPWqGv0b8eMS6Xebkg45TmDOajuLS+0aWusBQPvPBuPg0h+kxy0H9Uf3r8Zg9chxMCtSBM8eReL5k2jYlnJE9P2HqNaiKe6cOoN0kQGzokWAuLgc286uVJX/9sekPZtQonQp+PfxQvg1zVGcnlMmAABCfjuY42TvTqM/Q7thXgje/iu2zZirsd7VvQ5GrlkqPe4yZgTCLlzCnbPnYWJujtlBR/A86jHmdO79xr6bveaPfN1O7TDwW80kPfth0azkJvsIkZWdHYDMP/bZX8e+M6bCtpQT+s6YitM790KlUsHRtby03nPoIBgZG+M3/6WwLG6DlgP7o16X9lj80VAkPIvV2sdRAcsBAE37fQAAeB75GD/fugRb51KIuxaHsjWrS3Xz8uVqYm6O9LQ0tfdtllKVKyI2Ikp9BOsVRibGUjI5qUFrvMzDta+KWFujdrtWuHj4DyTHJ0BlZIT+s6bhweWrOPHLtly3AwC12rWGsYkJLhwMzNN22RmZGEuHy0s4l0L0/YeZv5cpjWfhEWoJrjalq1bGhO0/4UbQGawaPiZXz/mmNt9G90ljpUQgNSkZUxq10VnbNdu2xODF8/Dnul+wd8GSN2+gIw26dcH747+QHr9ptw1aMBtWdiWwfPAovezjgoQjN3qUnpb2Vodt3lb99zvBI4dJZZ2/GK5RZmxqgt7TJ2qUD132nUZiAwBfH92LvfdvoMm/XyQAMPOvg+g/6xvp8avJR4eRn6LDiCHS45y+YLJf3yf7F4pjRVe0GTJA6zZ5YVfGGd2/yvzDOmzFQnj5z8HYzWvh/fse/O/n1fA9fgAfzv4GXx/di8X/nMHUwF9RqVF9vD/+CywIDYKzW5Uc287e9xKlSwEAxm9bjxJlSqOItbXWQ40lnEvByj4zCShibY2P/LxRxaMhAKDdsMyRmKx5VLXatUbt9m1gbGqKzqM/wxfrV2rMJ7L+t63SVSvBxNT0taNWjxK1J22d/zcC//t5NUzMzABoPyT5KpGRAedqVeB7/IBUZmRshFrtWmP2qSPoMmYkbEs5oZ/v19IICgDYlnZCl7EjNd5/pSpXRElXF/j+dRDthn8CuzLOGL913Rv7kcXGqSQeJyei66SxAICxm9Zk69ebRx3sXcrCc6gX5p39E98c3q21zvvjPsdXuzaiRuvmWtcDUBsZ+GDqBI31KpUqx3lGXv6z0XfGVMwOCkSHkZ+iRusWaNC1M3pOGa+1ftma1fHVro2o2qwJgMxEs1SVSjA2NcUnC/0w8NuZ0lwMi2KWOfb5VUYmxqjXuT1sHLKN3v77+W3Y4z18fWAHPvLzfmM7WX+Tqng0ApD5ma7VrrXWumWqV8XYnRuw817urhWWXbESthgw31ft8+hY0RVuLTxQ3MlRbYTDrIgFrB3s0ahnV5iYm+f5uV7V9cvRAIDWn3yEcrWqw3OoV67ebyqVKs9XozcyMcbHc33Q+IOu+HD2N1rrGJuYqD2/ZXEb9JwyHnU6tEWF+nXV/kF9lbNbFdiXK4Om/T5At4nak1GVSoUFoUGYf+6vPPU9P3HkRs+2zZiLSXs2y90NvSlibYWG3bu8tk6HkZ+idvs2CPntEDr/7/WHZABI++v4hi1oObDfO/fR1MIcUw9sz/N22UdHxm9bj6/qNUfGS/U5B6+bwPf1gR14HvUENo4O2DjZG+f2/Xfa9LAVCwEAUxt7ouuEL1D//U6o/34njatSmxWxwCcL/QAAf/+6P8d9nZU0ioz//hsb8v18nNt3SK3elCO7sPnOPxrbAf8lVe5dOuDM7t9ydYs0IYAmvXuo98XICD0nZ34Rew4dhKpNG2uM1k07tCvHNut1bq/2uLiTIyo3boCbp88CyHw9VSpVjqOUQOb1nirUr/vmAJD5pVu3Uzv8On8Rpvz233waG0cHTNixATtnfYuw85c0tqvftTP++fMEgMxDX5G3wuDWrDFiIx/jafgjqV7jD7pim48fLEsUR/zTZwCAz9evhGu92pjd6QPEhEeotZv9y7njqKEI2r5betxmyAAU/zeRHb5mKVKSk+Far3bm45ULMaGWh5RoLh3032fNvGhRlCzvgtE//4Czew9g01TfN+6XnpPHSyNir2o3/BMAgPt7HZH+Mh3Pox7jwJJVMDI21piX8+oIwcTdvwAAlg0ehTtnz0vlZWtUw9jNawEA9+Kf4+PvZuHO+Ys4vHLtG/sKZI7M1OvcHvU6t5c+R1nPpY33H3sBZI5EBoyZhMt//DfKbmphjpep2kfusnOqVCHzn4FsIY75JTOhTnzxAkFbc36fA8BHc33g3qUDHl65jmJ2tlgx5HOYFy2qddQ3i3uXjnB/L3PRIAQ8+vSU/mmYUMsDZkUs4PvXQbVqr/6TqVKpIIRAsRK2GL9tvdq6iBu3NOYuNv73Hy9jUxNYl3SAtX0JmJia4e7F0NfGm5+Y3OiZLq4zowROlSpozIl5E10kNuXr1MLon394c8VcmHZwF3zbdYNtaScMmO+L0lUqY//iFa/dxsYx87/eJr17wMTUTGO999G9av/lv3pmnN+Zo9Lvr0siKzZyx6XAP9XGpWu0aaFxlpu5pfp/7t0njsGprbvgmO0sPSNTE5hamEsjS69jbV9C7T9iAOj0+TCkJCZJj7UdhsyJW/MmcGveRKO8VJVKuHn6LIraWGPmicyEbV63/jk3JITanLAstdq1RuStO3hy9z6AzEMmWYd4k+M1D4WWrlIJX/y0SuutULK+tJt/1Bs9p0xA3NMYWNmVAABMa9ZBra7XQj/U8mwlfaFnJSRfH9wptV2noyc8X7loJwB4ZEse3x/3OQDgwtNIlK6W82giAFTM9l6q6dkKPf4dzWrQtbPW5KbFgH5o0qsbflu4HFWaNNSa2Ggbec16X6anpcFzmBcWfTgEEbm41pdbsyZqyU2Vpo3U1ldu2giVmzZ6bXJjZGyMsjWr4cE/V2H7DvObBi+ehwm1PGBlb4cek8aibqd2CL96A/59vWDtYI+kuDikJasfWlSpVPhqV+alPLRN4u39zUTEPHyE66dO5/i87v/Occz6jEzZl3nYcfvM+WqJUUlXFwz8bhaOb9iM0lUq59ieykilNhqqUqlgV1ZzFLdiQ3e0/XQgXOvVRnEnR8SER2BR/8Gw/XfkObv+M6fBys4Of6z5SSqzLfXfvEITUxOM27IOADCtWUckvSgYE5qZ3OSD0zv25Oo0ZtI9XSU2QGaisiA0SK2sx+Rxudq2YoN6Wg9PvTqxMftoUV5knU32z9G8DxNP3bdNSsIAoM/0SegzfVKutv3ip1Vay82L5m1i8JvU9GyJ7q8Mkb9uRNS+fDmt5VmjYLGRUbh+6gzKVPsv8Wo16MMc29OWmJd0dUGdjp7SPKqsxAaA2ggQ8N9k5s8DlmudC1W1WRMM+m5Wjs//qojEN19U08GlrPR7VmKTXetPPsb90H9wJ+QCek4ZL53mP3TZdxp1s2QdDrUvW0ZjXcfPhwEAuo7/Aj+MePPnwnPooDf+c5DFoXw5PLl7H55DvdC4VzfsnP0trp0IxvTf98DKrgSu/nUKrq+cIGFkkvfJzx/O/kY6HOtcrQpsSzlh2uFdSHgWi/1LVsHY2Bi3zoSgYY/34ez2X5KR0wju8FWLMKVRG7VRRmNTU1yKiUKr1xxub/5hb7Xk5sPZ01G6SiX0n/n6s/26Zpt/AwBQqaBSac4+efX9UMK5lMboTnadvhimltyoXVk/2yFyKzvbApPcqJDj+TXKZGVlhRcvXsDa2hpxuZwwqou2rezt4HP0N50+HxEZvrSUFJjqYN7H23r6MDxPZxZePPwH6nRom+P6jPR0XDl+En/v3o/Bi9UnxD+8cl1tJO/sngNo0K0zdvn5o1qLplpH7Y5t2IxWAzVH6VYOHa12IkJ2ujqZY5eff45znfLi0LLVOLxyLUzMzDB8xUJUbOT+2vqPw+5hu+88tB78Mf5YswFfrF/5Vs/7Xa8BsHZwwPCVC99q++ySXsRhycDhiLpzV+2fvHnd+kv/aMzr1h+Pw+7p7Xs2L+0yucnHtis2qCed3UFERIXH37/uQ612rWFhmftJ3QXRlEZt1A6X7563SBoJWtjvEyTEPkc5t6oICfwDtjbFmdzkFzmTGwAahzWIiIiUpr59KXStUku25IanghMREZFOhURHvLmSHjG5yWe+7TixmIiISJ+Y3OSz51FP5O4CERGRojG5kcGlwD8BANt95+Nm8Fns8luA/d+/3Wx4IiIiUsfr3Mhg/bgp0u9B2/67lkGX/40AAIRfvaF2qXoiIiLKPSY3BciURm2gMjJCSkIijE1NUb1lU9w+e166Iqt/Xy+UdC2PAdruBp5N2LmLGhe0IiIiKiyY3BQg2a9imZ6WhtDfjwEAvvZoByNjYyQ+f4Hwqzdw7UQwWg7oi7N7D+Lpg4cwMjHGe2NGISP9JfYtyrziZ4sB/WBiZoqzv+5HiTKlYVfWGR/7+SAmPAJPH4bDrEiRHO+oHRsZheI5XMq8VJFiiEh689VRiYiI5MLr3BhI2/pg71IW5WpWw7l9h2FkbAz7cmXwLCISackpaNK7O57cewCRkYHP163A5T+O4f6Fy9j7w1pUqlIZro0b4OyeA0hJTIRFMUt8fXAnXqalYcfMb9WuSrrLzx/OblVw60wI2g4dBKeKrsjIyICRkRFiox5r3BF3ycDPULJ8OfSb+TUA4J+jf+H+P1fhVKE86nVRv19P3NMYxEZGIfr+Q2yc7IP5545Ld8I9v/+wRn19ObVlJ+p0aAtL2+L58nxERIbAp2kHXsQvvzC5ybusO8a+Lj5jU1OIjAxkpKfDsrgNkuMTYOPooHHH41cZmRijWIkS6OszGSd+2YZrJ4JzrGtsYgKPvj1x7WQwou890FjvWKE8Jv66CY9u3MKifoNh41QS//t5tdo9f84fOIKfJ06Hc7UqiI2IQkLscwCAz5/7YGVXAsFbdmLrzPmwtbNDbEwMbBwd8OLJU3yycA5unTmHq3+dQsPu7+HayWCUqVYVJ7fskO4Uble2DF48eYLKjRvCc+ggXDl2Eklxceg17Svp+bf6+OH0jj2o17k9nty7j4dXrsPYxATpL1/CsaIrhi5fgCMr1uLM7t9gZGIMazs7NPuwF05t3YVnjyJR0tUFRsbGGL3hBzyLiESpyhUReesOvu35MTz69kQVj0a4cDBQ632KLhwMRN1O7aTHQdt2w8KyKM7+dhDDlvsj7mkMvus1ACVdyyPpxQu0GTwA9d/vhDmde8PBtRySX8Qj+sFDxMc8k9poObA/uk8cg5jwCCzoPRAtBvRDUWtrtBzYD3+s+QkvnjxVu//WiV+24daZEFw/dQb13+8Ep8oVcGz9L3CpU0vjcOuMtl3R/KM+8Bw6CL//+BMsi9vA0rY4Nk72xty//wQA6a7r2iTFxePnSdMxbLl/ju+pV6UkJqrd7ys97SVunz2HKh6NNOpunjYTyfEJEAKo37UTardrLa27eiIIaz7/EiPXLEXFBvWQlpyCuxdDpTt+b/XxQ0p8AlKTU2BiZgov/zmv7de3HwxA2RpuavcWun/xMoK2/yr9I6BN1j2RcnOvsNDfj0n3wNKVk5t3oFn/XjppK2DMZI3bOeRW9puavir6/kPYl1O/V9aVYydRvVWzt3quvDryQwDaDx+c6/o7Z3+HD77+8o31XqalwcTU9F269s7kTG6AzOSm0CxWVlZCCCGsrKwMqu2CsBhqfFb2dqJYCVvRpHd3YW5ZVGud4o4lhUffnqKEvb3OYzQ2NRU1WjcXLT7uq7M2VUZGAoAoYm2tfb1KJSo1qi8srIoJlUollTtXrCD23b8hKtSqoVFfWzsmZmZv7IuFVbHXrq/WsplYEBokFoQGSf3O8bWyK5Gr+Eu6uojKjRsIlUolnCpVkGKwK+kgPVetdq2l8lJVKgprB3vRZsgAUdyxpNRv9/c6iPJ1aqm1Xb5OLfHB118KI2NjtfKWg/qLel065Li/LG2LC6fKFUVJV5cc962xqelrY6rZtpXoPnGs8OjTU1gUsxTdJo4RZWtUk+oYmRirfQ6ts8VrZW8nAAizIkWEs1sV8fFcH2Fb2ikz1mKW4pvAX0X/WdMEAGHj6CBae30kxvyyRqoDQDhWKC9c3esI65IOokmfHuKLn1ZpfY+51qstuowZKbpNHCM8+vYUZkUshLllUdG03wfC0ra4KFGmtHB/v6NQGRmJtp8OFKMClgsTc3MBQK3P1Vs1FxUb1BMudWr++zpVErZ2JUREQpxo2b+3GLx4rvhk0Vy1ffiRn7dYEBokPp43Q5SrVV3YlXEWJcqUFpa2xaX97TnUSywIDRI127bS6Htv70liQWiQ6PXNRLXXqP2IIWJBaJAYtGB25nvf3FwMX7lQeP+xV5Sp7ibtf6dKFYTKyEjYlnYSfbwni1JVKgnXerXF/HN/CZ8/94luE8eIqk0bi+JOjmLqgR2i+6Sx0mfW0ra4qN26hTj08Ja0DwAII2NjMWC+r1TWx3uyqNq0sfT4fz+vFnU7tRPmRTP/flk72IueUyeIsjWqCbMiFtJr+s2R3cKteRP1z1+LpqJqsyai59QJYkFokGgzZIAAIJr17yW133n0Z6J6q+aiSe/uYtbJw6J2+zbi/XGfS+tNzM2F5zAvUaJMaeHWwkN0HDVUtBkyQIzdEiC+PrRTWFgVE5a2xUXLQf3F++O/EAtCg0RQ1AOdf1fk8TtId09sCAuTG8ZXmGOUKz7bUk7CxtEhX+JLepkmqns0ln1f59draO9SVhQrYfvG7d6UWObnolKphLWDfa7iy7c+GRmJcrWqvzYB1dXrl/wyTfSdPkkjsW416EMx8ddNUpKvUqlE+Tq1pMRQFzFm/W5kbCw8+vYUjhXKa61rVqSIeG/cKFG2ZvW3ilEfryGTGxl2ur7bLgiL0uMrDDEyPsNflB4j4zP8pSAkNwXiIn6jRo1CWFgYkpKSEBwcjIYNG762fu/evXH16lUkJSXh0qVL6Ny5cz71lIiIiAo62ZObvn37wt/fHzNmzIC7uzsuXryIQ4cOwcFB+yRBDw8PbNq0CWvWrEG9evWwe/du7N69GzVqaD+tmYiIiAofWYevgoODxZIlS6THKpVKPHz4UEyaNElr/c2bN4u9e/eqlQUFBYkVK1bIOlym77YLwqL0+ApDjIzP8Belx8j4DH8p9IelTE1NUb9+fQQGBkplQggEBgbCw8ND6zYeHh5q9QHg0KFDOdYnIiKiwkXWKxTb29vDxMQEUVFRauVRUVFwc3PTuo2Tk5PW+k5OTlrrm5mZwdzcXHpsZWWl9lOX9Nl2QaD0+ADlx8j4DJ/SY2R8hk9fMealPcXffmHKlCnw8fHRKA8PD9fbc+qz7YJA6fEByo+R8Rk+pcfI+AyfnDHKmtxER0fj5cuXcHRUv4+Ro6MjIiMjtW4TGRmZp/p+fn7w9//vCqVWVlYIDw+Hs7OzXq5QrK+2CwKlxwcoP0bGZ/iUHiPjM3z6ijGr3dyQNblJS0tDSEgIPD098euvvwLIvNS/p6cnli5dqnWboKAgeHp6YvHixVJZ+/btERQUpLV+amoqUlNTNcrj4uL09sbSZ9sFgdLjA5QfI+MzfEqPkfEZPjljlP2wlL+/P9avX4+zZ8/izJkzGDt2LCwtLREQEAAAWL9+PcLDwzF16lQAwOLFi3Hs2DGMHz8e+/btQ//+/dGgQQMMHz5czjCIiIiogJA9udm6dSscHBzg6+sLJycnXLhwAZ06dcLjx48BAOXKlUNGRoZUPygoCB999BFmzZqFOXPm4ObNm+jRowf++ecfuUIgIiKiAkT25AYAli1bhmXLlmld16ZNG42y7du3Y/v27fruFhERERkg2a9QTERERKRLTG6IiIhIUZjcEBERkaIUiDk3cuAVivNO6fEByo+R8Rk+pcfI+AxfQbhCsQqZN5kqNEqXLl0orgxJRESkRM7Oznj06NFr6xS65AbITHD0cWEhpV95UunxAcqPkfEZPqXHyPgMn77vBPCmxAYopIelcrNj3oXSrzyp9PgA5cfI+Ayf0mNkfIZPHzHmtj1OKCYiIiJFYXJDREREisLkRodSUlLg4+ODlJQUubuiF0qPD1B+jIzP8Ck9RsZn+ApCjIVyQjEREREpF0duiIiISFGY3BAREZGiMLkhIiIiRWFyQ0RERIrC5EZHRo0ahbCwMCQlJSE4OBgNGzaUu0taTZ48GWfOnMGLFy8QFRWFXbt2oUqVKmp1jh49CiGE2rJixQq1OmXLlsVvv/2GhIQEREVFYf78+TA2Nlar06pVK4SEhCA5ORk3b96El5eX3uPz9vbW6PvVq1el9ebm5li6dCmio6MRFxeH7du3o2TJkgYRW5awsDCNGIUQWLp0KQDDe/1atGiBPXv2IDw8HEIIdO/eXaPOjBkz8OjRIyQmJuLIkSOoVKmS2npbW1v8/PPPeP78OZ49e4Yff/wRlpaWanVq1aqF48ePIykpCffv38dXX32l8Ty9e/fG1atXkZSUhEuXLqFz5856jc/ExARz587FpUuXEB8fj/DwcKxfvx6lSpVSa0Pbaz5p0qQCEd+bYgSAgIAAjf4fOHBArY6hvoYAtH4ehRD48ssvpToF+TXMzfdCfv7t1NX3qeDybkvfvn1FcnKy+OSTT0S1atXEqlWrRExMjHBwcJC9b68uBw4cEF5eXqJ69eqidu3a4rfffhN3794VRYsWleocPXpUrFq1Sjg6OkqLlZWVtN7IyEhcunRJHD58WNSpU0d06tRJPH78WMyePVuqU758eREfHy++++474ebmJj7//HORlpYmOnTooNf4vL29RWhoqFrf7ezspPXLly8X9+7dE23atBHu7u7i1KlT4sSJEwYRW9Zib2+vFp+np6cQQohWrVoZ5OvXqVMnMXPmTNGjRw8hhBDdu3dXWz9x4kTx7Nkz0a1bN1GrVi2xe/ducfv2bWFubi7V2b9/vzh//rxo1KiRaNasmbhx44bYuHGjtN7KykpERESIDRs2iOrVq4t+/fqJhIQEMWzYMKmOh4eHSEtLE19++aVwc3MTvr6+IiUlRdSoUUNv8VlbW4vDhw+LPn36iCpVqojGjRuL4OBg8ffff6u1ERYWJqZNm6b2mmb/zMoZX25ew4CAALF//361/hcvXlytjqG+hgDU4nJ0dBSffPKJSE9PF66urgbxGubmeyG//nbq8PtUt3+oCuMSHBwslixZIj1WqVTi4cOHYtKkSbL37U2Lvb29EEKIFi1aSGVHjx4VCxcuzHGbTp06iZcvX4qSJUtKZZ999pmIjY0VpqamAoCYO3euCA0NVdtu06ZN4sCBA3qNx9vbW5w/f17rOmtra5GSkiJ69eollVWtWlUIIUTjxo0LfGw5LQsXLhQ3b95UxOun7Yvj0aNHYsKECWqvY1JSkujXr58AINzc3IQQQtSvX1+q07FjR5Geni5KlSolAIgRI0aIp0+fSvEBEH5+fuLq1avS482bN4u9e/eqPXdQUJBYsWKFXuN7dWnQoIEQQoiyZctKZWFhYWLMmDE5blNQ4sspxoCAALFr164ct1Haa7hr1y4RGBioVmZIr+Gr3wv5+bdTV9+nPCz1jkxNTVG/fn0EBgZKZUIIBAYGwsPDQ8ae5Y6NjQ0AICYmRq38448/xpMnTxAaGoo5c+agSJEi0joPDw+Ehobi8ePHUtmhQ4dgY2ODGjVqSHWy75OsOvmxTypXrozw8HDcvn0bP//8M8qWLQsAqF+/PszMzNT6df36ddy7d0/qV0GP7VWmpqYYMGAA1q5dq1ZuyK9fdq6urihVqpRaX168eIHTp0+rvWbPnj1DSEiIVCcwMBAZGRlo3LixVOf48eNIS0uT6hw6dAhubm4oXry4VKcgxGxjY4OMjAzExsaqlU+ePBnR0dE4d+4cvvzyS7XhfkOIr3Xr1oiKisK1a9ewfPlylChRQq3/SnkNS5Ysiffeew9r1qzRWGcor+Gr3wv59bdTl9+nhfLGmbpkb28PExMTREVFqZVHRUXBzc1Npl7ljkqlwqJFi3DixAn8888/Uvkvv/yCe/fu4dGjR6hduzbmzZuHqlWrolevXgAAJycnrfFmrXtdHRsbG1hYWCA5OVkvMZ0+fRqffPIJrl+/jlKlSsHb2xt//fUXatasCScnJ6SkpOD58+ca/XpTvwtCbNr06NEDxYsXx7p166QyQ379XpXVH219yd7X7H9QASA9PR0xMTFqdcLCwjTayFoXGxubY8xZbeQHc3NzzJs3D5s2bVK7QeD333+Pc+fOISYmBk2bNoWfnx9KlSqFCRMmSDEU5PgOHjyInTt3IiwsDBUrVsScOXNw4MABeHh4ICMjQ1GvoZeXF+Li4rBz5061ckN5DbV9L+TX305bW1udfZ8yuSnEli1bhpo1a6J58+Zq5atXr5Z+v3z5MiIiIvDHH3+gQoUKuHPnTn53M08OHjwo/R4aGorTp0/j3r176Nu3L5KSkmTsmX58+umnOHDgACIiIqQyQ379CjMTExNs3boVKpUKI0eOVFu3cOFC6ffQ0FCkpqZi1apVmDJlClJTU/O7q3m2ZcsW6ffLly/j0qVLuHPnDlq3bo0//vhDxp7p3pAhQ7Bx40aNWw8YymuY0/eCoeFhqXcUHR2Nly9fwtHRUa3c0dERkZGRMvXqzZYsWYL3338fbdq0QXh4+Gvrnj59GgCkM1QiIyO1xpu17nV1nj9/nq8jG8+fP8eNGzdQqVIlREZGwtzcXBpyzd6vN/U7a93r6uR3bOXKlUO7du3w448/vraeIb9+Wf153ecrMjJS46wNY2NjlChRQieva358jrMSGxcXF7Rv315t1Eab06dPw9TUFOXLlwdQ8ON7VVhYGJ48eaL2njT01xAAmjdvDjc3tzd+JoGC+Rrm9L2QX387dfl9yuTmHaWlpSEkJASenp5SmUqlgqenJ4KCgmTsWc6WLFmCnj17om3btrh79+4b69etWxcApNGBoKAg1KpVCw4ODlKd9u3b4/nz57hy5YpUJ/s+yaqT3/vE0tISFStWREREBEJCQpCamqrWrypVqsDFxUXqlyHFNnjwYDx+/Bj79u17bT1Dfv3CwsIQERGh1hcrKys0btxY7TWztbWFu7u7VKdt27YwMjKSErugoCC0bNkSJib/DVa3b98e165dk+a2yBVzVmJTuXJltGvXTmP+mzZ169ZFenq6dCinIMenjbOzM+zs7NTek4b8Gmb59NNPcfbsWVy6dOmNdQvaa/i674X8+tup6+9Tnc6yLoxL3759RVJSkhg0aJBwc3MTK1euFDExMWqzxgvKsmzZMvHs2TPRsmVLtVMSLSwsBABRoUIFMW3aNOHu7i5cXFxE165dxa1bt8Sff/4ptZF1yt/BgwdF7dq1RYcOHURUVJTWU/7mzZsnqlatKkaOHJkvp0t/++23omXLlsLFxUV4eHiIw4cPi8ePHwt7e3sBZJ7OePfuXdG6dWvh7u4uTp48KU6ePGkQsWVfVCqVuHv3rvDz81MrN8TXz9LSUtSpU0fUqVNHCCHE2LFjRZ06daSzhSZOnChiYmJE165dRc2aNcWuXbu0ngoeEhIiGjZsKJo2bSquX7+udhqxtbW1iIiIEOvXrxfVq1cXffv2FfHx8Rqn2aamporx48eLqlWrCm9vb52cZvu6+ExMTMTu3bvF/fv3Re3atdU+k1lnmDRp0kSMGTNG1K5dW7i6uoqPPvpIREVFiXXr1hWI+N4Uo6WlpZg/f75o3LixcHFxEW3bthVnz54V169fF2ZmZgb/GmbVsbKyEvHx8eKzzz7T2L6gv4Zv+l4A8u9vpw6/T3X7h6qwLp9//rm4e/euSE5OFsHBwaJRo0ay90nbkhMvLy8BQJQpU0b8+eefIjo6WiQlJYkbN26IefPmqV0nBYAoV66c2Ldvn0hISBCPHz8W3377rTA2Nlar06pVK3Hu3DmRnJwsbt26JT2HPpdNmzaJ8PBwkZycLB48eCA2bdokKlSoIK03NzcXS5cuFU+fPhXx8fFix44dwtHR0SBiy760b99eCCFE5cqV1coN8fVr1aqV1vdkQECAVGfGjBkiIiJCJCUliSNHjmjEbWtrKzZu3ChevHghYmNjxZo1a4SlpaVanVq1aonjx4+LpKQk8eDBAzFx4kSNvvTu3Vtcu3ZNJCcni9DQUNG5c2e9xufi4pLjZzLrukX16tUTQUFB4tmzZyIxMVH8888/YvLkyWqJgZzxvSlGCwsLcfDgQREVFSVSUlJEWFiYWLVqlcaXlaG+hll1hg0bJhISEoS1tbXG9gX9NcxJ9s98fv7t1MX3qerfX4iIiIgUgXNuiIiISFGY3BAREZGiMLkhIiIiRWFyQ0RERIrC5IaIiIgUhckNERERKQqTGyIiIlIUJjdERESkKExuiKjAsLe3x/Lly3Hv3j0kJycjIiICBw8eRNOmTQEAQgh0795d5l4SUUFn8uYqRET5Y8eOHTAzM4OXlxfu3LkDR0dHeHp6ws7OTu6uEZGB0cm9Rbhw4cLlXRYbGxshhBAtW7bUuj4sLEztvjdhYWHSum7duomQkBCRlJQkbt++LaZPn652TxshhBgxYoTYv3+/SExMFLdv3xa9evWS1puamoolS5aIR48eiaSkJHH37l0xefJk2fcJFy5c3nqRvQNcuHDhIoyNjcWLFy+Ev7+/xg0FAQh7e3vpZn6Ojo7Snd6bN28uYmNjxaBBg4Srq6to166duHPnjpg+fbq0rRBCPHnyRHz66aeicuXKwtfXV6SlpQk3NzcBQEyYMEHcu3dPNG/eXJQrV040a9ZM9O/fX/Z9woULl7deZO8AFy5cuAgA4oMPPhBPnz4ViYmJ4sSJE2L27NmiVq1a0nohhOjevbvaNkeOHNEYZfn4449FeHi42nbLly9XqxMUFCSWLVsmAIjFixeLwMBA2ePnwoWLzhbZO8CFCxcu0mJubi7atWsnpk2bJk6ePCnS0tKEl5eXALQnN48fPxaJiYkiLi5OWhITE4UQQhQpUkTabuDAgWrb+fv7iz/++EMAEPXq1RPR0dHi+vXrYvHixaJ9+/ay7wcuXLi8/cKzpYioQElJSUFgYCBmzZqFZs2aYd26dZgxY0aO9YsVKwZvb2/UrVtXWmrVqoVKlSohOTk5V895/vx5uLq64ptvvkGRIkWwdetWbNu2TVchEVE+Y3JDRAXalStXYGlpCQBITU2FsbGx2vpz586hatWquH37tsYihJDqNWnSRG27Jk2a4OrVq9LjuLg4bN26FcOHD0e/fv3Qu3dv2Nra6jEyItIXngpORAVCiRIlsG3bNqxduxaXLl1CXFwcGjRogIkTJ+LXX38FANy9exeenp44efIkUlJSEBsbC19fX/z222+4f/8+tm/fjoyMDNSpUwc1a9bEN998I7Xfp08fnD17FidOnMDHH3+MRo0a4dNPPwUAjBs3DhERETh//jwyMjLQp08fREREIDY2Vo5dQUQ6IPuxMS5cuHAxMzMTc+bMEWfPnhXPnj0T8fHx4urVq8LX11dYWFgIAOL9998XN27cEKmpqWqngnfo0EGcOHFCJCQkiNjYWBEcHCyGDh0qrRdCiJEjR4pDhw6JpKQkcefOHdGnTx9p/dChQ8W5c+dEXFyciI2NFUeOHBF169aVfZ9w4cLl7RbVv78QESmWEAI9evSQRoCISNk454aIiIgUhckNERERKQoPSxEREZGicOSGiIiIFIXJDRERESkKkxsiIiJSFCY3REREpChMboiIiEhRmNwQERGRojC5ISIiIkVhckNERESKwuSGiIiIFOX/Js6jYqgXGtMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_losses[\"train\"])\n",
    "plt.title(\"Training Loss over Number of Steps\")\n",
    "plt.xlabel(\"Steps\")\n",
    "plt.ylabel(\"Train Loss\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.14it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.71it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [2]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:13<00:00, 30.35it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.25it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [4]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.28it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [5]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.42it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [6]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:13<00:00, 30.61it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [7]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 30.78it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [8]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.35it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating images of [9]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 399/399 [00:12<00:00, 31.40it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    key, subkey = jrand.split(key)\n",
    "    gen_image = diff_utils.generate(\n",
    "        model=model, num_samples=1, labels=[i], \n",
    "        trainable_vars=trainable_vars, non_trainable_vars=non_trainable_vars, \n",
    "        key=subkey\n",
    "    )\n",
    "    plt.imshow(gen_image.squeeze(), cmap=\"gray\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}