.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

@@ -0,0 +1 @@
+3.11

demo.py

@@ -0,0 +1,22 @@
+import torch
+
+# 创建一个 2*3 的全 0 张量
+a = torch.zeros(2, 3)
+print(a)
+
+# 创建一个 2*3 的全 1 张量
+b = torch.ones(2, 3)
+print(b)
+# 创建一个 2*3 的随机张量
+c = torch.randn(2, 3)
+print(c)
+
+# 从 NumPy 数组创建张量
+import numpy as np
+numpy_array = np.array([[1,2],[3,4]])
+tensor_from_numpy = torch.from_numpy(numpy_array)
+print(tensor_from_numpy)
+
+device  = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+d = torch.randn(2,3, device=device)
+print(d)

demo/demo1.ipynb

@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb4ff0cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[0., 0., 0.],\n",
+      "        [0., 0., 0.]])\n",
+      "tensor([[1., 1., 1.],\n",
+      "        [1., 1., 1.]])\n",
+      "tensor([[-0.6469,  0.8159, -0.0863],\n",
+      "        [ 0.4979, -0.2686, -0.6490]])\n",
+      "tensor([[1, 2],\n",
+      "        [3, 4]])\n",
+      "tensor([[-0.5335,  0.5290, -0.9839],\n",
+      "        [ 0.7704,  0.4273,  1.2004]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "# 创建一个 2*3 的全 0 张量\n",
+    "a = torch.zeros(2, 3)\n",
+    "print(a)\n",
+    "\n",
+    "# 创建一个 2*3 的全 1 张量\n",
+    "b = torch.ones(2, 3)\n",
+    "print(b)\n",
+    "\n",
+    "# 创建一个 2*3 的随机张量\n",
+    "c = torch.randn(2, 3)\n",
+    "print(c)\n",
+    "\n",
+    "# 从 NumPy 数组创建张量\n",
+    "import numpy as np\n",
+    "numpy_array = np.array([[1,2],[3,4]])\n",
+    "tensor_from_numpy = torch.from_numpy(numpy_array)\n",
+    "print(tensor_from_numpy)\n",
+    "\n",
+    "device  = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "d = torch.randn(2,3, device=device)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4bca6c60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.6426,  1.6234, -0.6121],\n",
+      "        [ 0.6447, -0.8871, -0.9659]])\n",
+      "tensor([[ 0.8695, -0.4598, -0.3521],\n",
+      "        [-0.3847,  0.4315,  0.2879]])\n",
+      "tensor([[ 1.5121,  1.1636, -0.9642],\n",
+      "        [ 0.2600, -0.4556, -0.6781]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 张量相加\n",
+    "e = torch.randn(2, 3)\n",
+    "f = torch.randn(2, 3)\n",
+    "print(e)\n",
+    "print(f)\n",
+    "print(e + f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1bfa2d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.5588, -0.7465,  0.2155],\n",
+      "        [-0.2480, -0.3828, -0.2780]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 逐元素乘法\n",
+    "print(e * f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1e201604",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-0.6453, -0.7084, -0.4454],\n",
+      "        [-0.8929,  0.6103,  1.3243]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 张量的转置\n",
+    "g = torch.randn(3, 2)\n",
+    "print(g.t())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2d42353d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 2])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 张量的形状\n",
+    "print(g.shape)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "shuhe_xwdev (3.11.14)",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

demo/mlp_mse/2.神经网络从入门到实战(训练优化篇).md

@@ -0,0 +1,892 @@
+# 神经网络从入门到实战（下）——训练优化篇
+---
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1785739828049-74276e53-6bc4-4361-b541-f719632d1b27.png)
+
+## 二、全连接神经网络
+### 2.9 模型参数初始化
+<font style="color:rgb(13, 13, 13);">在 PyTorch 中，模型参数的初始化主要分为两种情况：一是依赖框架的</font>**<font style="color:rgb(13, 13, 13);">默认初始化</font>**<font style="color:rgb(13, 13, 13);">，二是通过 </font>`<font style="color:rgb(13, 13, 13);">torch.nn.init</font>`<font style="color:rgb(13, 13, 13);"> 模块进行</font>**<font style="color:rgb(13, 13, 13);">自定义初始化</font>**<font style="color:rgb(13, 13, 13);">。</font>
+
+```python
+import torch.nn as nn
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # 当执行这行代码时，自动调用调用reset_parameters方法
+        #nn.Linear 的 __init__ 就自动初始化了 weight 和 bias
+        self.fc = nn.Linear(10, 5)
+    
+    def forward(self, x):
+        return self.fc(x)
+
+# 实例化模型时，fc 就已经被初始化好了
+model = MyModel()
+print(model.fc.weight)  # 已经是有具体数值的 Tensor
+print(model.fc.bias)
+```
+
+```python
+import torch.nn as nn
+
+# 创建一个线性层
+layer = nn.Linear(10, 5)
+
+# 最简单的用法：直接用 init 覆盖默认初始化
+nn.init.xavier_uniform_(layer.weight)  # 均匀分布初始化权重
+nn.init.zeros_(layer.bias)             # 偏置设为0
+
+print(layer.weight)
+print(layer.bias)
+```
+
+<font style="color:rgb(13, 13, 13);">关于常见的初始化方法</font>
+
+1. <font style="color:rgb(13, 13, 13);">均匀分布初始化torch.nn.init.uniform_()</font>
+2. <font style="color:rgb(13, 13, 13);">正态分布初始化torch.nn.init.normal_()</font>
+3. <font style="color:rgb(13, 13, 13);">常量初始化torch.nn.init.constant_()</font>
+4. <font style="color:rgb(13, 13, 13);">Xavier均匀分布</font>
+5. <font style="color:rgb(13, 13, 13);">Xavier正态分布</font>
+6. <font style="color:rgb(13, 13, 13);">kaiming均匀分布初始化</font>
+7. <font style="color:rgb(13, 13, 13);">kaiming正态分布初始化</font>
+
+**<font style="color:rgb(13, 13, 13);">全连接层 (</font>**`**<font style="color:rgb(13, 13, 13);">nn.Linear</font>**`**<font style="color:rgb(13, 13, 13);">) </font>**<font style="color:rgb(13, 13, 13);">：权重通常采用 kaiming正态分布初始化(适合</font><font style="color:rgb(15, 17, 21);">ReLU 及其变体 (Leaky ReLU)</font><font style="color:rgb(13, 13, 13);">)，</font><font style="color:rgb(15, 17, 21);">Kaiming正态分布初始化的核心是从均值为0、标准差动态计算为</font>`<font style="color:rgb(15, 17, 21);background-color:rgb(235, 238, 242);">√(2/fan_in)(</font><font style="color:rgb(15, 17, 21);">输入数据的维度</font><font style="color:rgb(15, 17, 21);background-color:rgb(235, 238, 242);">)</font>`<font style="color:rgb(15, 17, 21);">的正态分布中随机采样，这样设计的目的是让数据在网络中稳定传播、避免梯度消失或爆炸，让模型更容易收敛。</font><font style="color:rgb(13, 13, 13);">偏置（bias）通常初始化为 0。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![若随机变量X服从一个数学期望为μ、方差为σ^2的正态分布，记作X~N（μ ,σ2）。其中， μ是正态分布的数学期望（均值）， σ2是正态分布的方差。μ = 0,σ = 1的正态分布被称为标准正态分布。-1σ到 1σ之间的概率 = 68.2%，-2σ 到 2σ之间的概率 = 95.4%。标准正态分布：当μ = 0，σ = 1时的正态分布是标准正态分布](https://cdn.nlark.com/yuque/0/2026/png/21571931/1785746766841-a3b942b3-af8a-4381-8f22-2dcbd6c0859d.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787207678875-302626ca-342a-4143-8fd6-8ba573f46605.png)
+
+| **<font style="color:rgb(15, 17, 21);">均值（μ）</font>** | **<font style="color:rgb(15, 17, 21);">标准差（σ）</font>** |
+| --- | --- |
+| <font style="color:rgb(15, 17, 21);">所有数据的平均值，表示数据</font>**<font style="color:rgb(15, 17, 21);">集中在哪个位置</font>** | <font style="color:rgb(15, 17, 21);">数据偏离均值的平均距离，表示数据</font>**<font style="color:rgb(15, 17, 21);">有多分散</font>** |
+| <font style="color:rgb(15, 17, 21);">μ = (x₁+x₂+...+xₙ) / n</font> | <font style="color:rgb(15, 17, 21);">σ = √[((x₁-μ)²+...+(xₙ-μ)²) / n]</font> |
+| <font style="color:rgb(15, 17, 21);">数据的"中心点"</font> | <font style="color:rgb(15, 17, 21);">数据的"平均误差"或"波动幅度"</font> |
+| <font style="color:rgb(15, 17, 21);">决定正态分布曲线</font>**<font style="color:rgb(15, 17, 21);">在哪儿</font>** | <font style="color:rgb(15, 17, 21);">决定正态分布曲线</font>**<font style="color:rgb(15, 17, 21);">是胖是瘦(</font>**<font style="color:rgb(15, 17, 21);">数据越分散，曲线越矮胖.数据越集中，曲线越高瘦</font>**<font style="color:rgb(15, 17, 21);">)</font>** |
+
+
+### 2.10 损失函数——模型怎么知道自已错了
+前向传播输出了模型的预测值。那这个预测值到底好不好？得有一个量化的打分标准——这就是损失函数。
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1784600475031-3ab68113-ff0d-489f-aafe-6bda14ebd15a.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1784600783707-1e0367b5-d60d-4e81-a00a-81d75b62d302.png)
+
+#### 均方差损失函数（MSE）——万能损失函数
+思路非常朴素：预测值和真实值一一相减，把差值平方（消除正负号的影响，同时让大误差被加倍惩罚），再取平均。
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787329478117-06ab7842-fdfc-4b09-b46f-9f94b97c3dae.png)
+
+**<font style="color:rgb(15, 17, 21);">为什么是“平方”而不是“绝对值”</font>**
+
+1. **<font style="color:rgb(15, 17, 21);">消去正负号</font>**<font style="color:rgb(15, 17, 21);">——直接求和，正负误差会互相抵消。</font>
+2. **<font style="color:rgb(15, 17, 21);">放大大误差</font>**<font style="color:rgb(15, 17, 21);">——误差 3 平方变 9，误差 0.4 平方变 0.16，差异被显著拉开，让模型对大错误更敏感。</font>
+
+想象你在预测奶茶店接下来三天的销量。真实销量是 100、150、80 杯，你预测的是 110、140、90 杯。MSE 的计算就是：(10² + 10² + 10²) / 3 = 300 / 3 =100。这个100 就是你当前模型的"错误程度"--**模型的损失值**。
+
+> MSE 对离群点（异常值）特别敏感——因为误差被平方了。如果你不小心预测了一个离谱的值（比如把 100 预测成 500），这一个点的贡献就是 160000，会严重拉高整体损失。这既是优点（绝不姑息大错误）也是缺点（可能被个别噪声数据带偏整条训练方向）。
+>
+
+---
+
+### 2.11 梯度下降——模型训练的实质
+有了损失函数这个"成绩单"，目标就明确了——**让损失值越来越小**。<font style="color:rgb(217, 33, 66);">模型到底怎么知道参数w，b应该往哪个方向改，后续损失计算才会变小？</font>
+
+---
+
+#### 导数：函数变化的快慢
+**导数的直观理解**：自变量x动一点点，函数值跟着动多少。
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787279165314-4797b5fb-3faf-45ce-b46e-a3f22315b987.png)
+
+在函数曲线上取 A、B 两点。AB 连线（割线）的斜率 = "从 A 到 B 这段，函数平均变化了多快"。当你让 B 无限逼近 A，割线就变成了 A 点的**切线**。切线的斜率，就是 A 点处的**导数**。
+
+> **一句话**：导数 = 切线斜率 = 函数在这一点的"瞬时变化率"。如果导数是 +3，意味着自变量往右走1小步，函数值大概会上升 3 小步。
+>
+
+<font style="color:rgb(0, 0, 0);">导数度量的是函数曲线在某一点的陡峭程度。曲线陡峭处，导数绝对值很大；越靠近波谷（最低点），曲线越平缓；到达波谷正中心时，切线完全水平，导数等于零。</font>
+
+<font style="color:rgb(15, 17, 21);">对于一元函数 </font><font style="color:rgb(15, 17, 21);">f(x)</font><font style="color:rgb(15, 17, 21);">，梯度就是</font><font style="color:rgb(15, 17, 21);">导数</font><font style="color:rgb(15, 17, 21);">，此时梯度和导数是一回事。</font>
+
++ **<font style="color:rgb(15, 17, 21);">导数为正：</font>**<font style="color:rgb(15, 17, 21);">函数在这一点向右走是上升的。</font>
++ **<font style="color:rgb(15, 17, 21);">导数为负：</font>**<font style="color:rgb(15, 17, 21);">函数在这一点向右走是下降的。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1784713493301-4b6ed893-497a-4d99-a270-3bdf28c55a81.png)
+
+**<font style="color:rgb(15, 17, 21);">几何结论：</font>**<font style="color:rgb(15, 17, 21);"> 负导数 </font><font style="color:rgb(15, 17, 21);">−f′(w)</font><font style="color:rgb(15, 17, 21);"> 这个符号，就像一个精准的“方向盘”，无论你在谷底的哪一侧，它都强行把你的 </font><font style="color:rgb(15, 17, 21);">w</font><font style="color:rgb(15, 17, 21);">往谷底的方向推。</font>**<font style="color:rgb(15, 17, 21);">梯度下降的方向</font>**<font style="color:rgb(15, 17, 21);">，是</font>**<font style="color:rgb(15, 17, 21);">负导数</font>**<font style="color:rgb(15, 17, 21);">的方向，它告诉你 w该往左还是往右。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1784708088353-018941e3-8714-4390-a905-3819423b0bf9.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786613571200-052f7bbd-5004-4325-b1c6-4487b018d591.png)
+
+| **<font style="color:rgb(13, 13, 13);">迭代轮数</font>** | **<font style="color:rgb(13, 13, 13);">权重</font>****<font style="color:rgb(13, 13, 13);">W</font>**_**<font style="color:rgb(13, 13, 13);"></font>**_ | **<font style="color:rgb(13, 13, 13);">预测值</font>****<font style="color:rgb(13, 13, 13);">y</font>**_**<font style="color:rgb(13, 13, 13);"></font>**_ | **<font style="color:rgb(13, 13, 13);">损失</font>****<font style="color:rgb(13, 13, 13);">L</font>**_**<font style="color:rgb(13, 13, 13);"></font>**_ |
+| --- | --- | --- | --- |
+| <font style="color:rgb(13, 13, 13);">初始</font> | <font style="color:rgb(13, 13, 13);">1.0</font> | <font style="color:rgb(13, 13, 13);">2.0</font> | <font style="color:rgb(13, 13, 13);">16.0</font> |
+| <font style="color:rgb(13, 13, 13);">第 1 轮</font> | <font style="color:rgb(13, 13, 13);">2.6</font> | <font style="color:rgb(13, 13, 13);">5.2</font> | <font style="color:rgb(13, 13, 13);">0.64</font> |
+| <font style="color:rgb(13, 13, 13);">第 2 轮</font> | <font style="color:rgb(13, 13, 13);">2.92</font> | <font style="color:rgb(13, 13, 13);">5.84</font> | <font style="color:rgb(13, 13, 13);">0.0256</font> |
+| <font style="color:rgb(13, 13, 13);">第 3 轮</font> | <font style="color:rgb(13, 13, 13);">2.984</font> | <font style="color:rgb(13, 13, 13);">5.968</font> | |
+
+
+#### 学习率——最容易被低估的超参数
+学习率控制每次参数更新的步长，是训练神经网络时最重要的超参数，没有之一。
+
+| 学习率 | 会发生什么 | 类比 |
+| --- | --- | --- |
+| 太大 | 参数震荡甚至发散，Loss 不降反升 | 下山时步子太大，一脚踩到对面山上去了 |
+| 适中 | 稳定快速地收敛到最优解附近 | 步伐稳健，一步步走到谷底 |
+| 太小 | 收敛慢到让人想提前下班 | 穿着高跟鞋小碎步下山，太阳落山了还没走到 |
+
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1784712510184-53cc16de-909a-41d5-8b65-22833a674c5e.png)
+
+**实践中怎么办**：从 0.01 或 0.001 开始试。如果 Loss 忽高忽低就是不降——减半；如果 Loss 平稳下降但太慢——翻倍。现代优化器（Adam、AdamW）自带自适应学习率机制，比传统的手动调参容错率高得多，是大多数任务的首选。
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786085525101-124b5713-f822-4ee4-87ab-de127c46a72e.png)
+
+#### 偏导数
+<font style="color:rgb(15, 17, 21);">“如果一个函数有多个变量，比如损失函数 </font><font style="color:rgb(15, 17, 21);">L(w,b)</font><font style="color:rgb(15, 17, 21);">，我们想调整 </font><font style="color:rgb(15, 17, 21);">w</font>_<font style="color:rgb(15, 17, 21);"></font>_<font style="color:rgb(15, 17, 21);"> 来降低 </font><font style="color:rgb(15, 17, 21);">L</font><font style="color:rgb(15, 17, 21);">，但调整 </font><font style="color:rgb(15, 17, 21);">w</font><font style="color:rgb(15, 17, 21);"> 时，</font><font style="color:rgb(15, 17, 21);">b</font><font style="color:rgb(15, 17, 21);"> 也在变，怎么办？</font>
+
+**<font style="color:rgb(15, 17, 21);">偏导数:一个多元函数（有多个自变量）在固定其他变量不变时，只随其中一个变量变化函数值的变化率.(把其他所有变量当成常数，然后按普通导数的规则来求就可以了)。</font>**
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786605280328-3f4b1961-d166-461e-8c84-59911101f00b.png)
+
+**<font style="color:rgb(26, 26, 26);">∂f/∂x 告诉你函数值在 x 方向的变化率，∂f/∂y 告诉你函数值在 y 方向的变化率。</font>**
+
+---
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786088295956-baaa1eb9-c7b6-4a45-9bab-28ca0cb36862.png)
+
+#### 梯度：把函数关于所有参数的偏导数打包成一个向量。梯度是一个向量，指向函数增长最快的方向。所以负梯度就是下降最快的方向。
+<!-- 这是一张图片，ocr 内容为： -->
+![梯度即代表了方向，梯度代表的其实是函数值增大最快的方向；在实际应用中，我们需要寻找损失函数的最小值，所以选择负梯度向量，这就是梯度下降法的底层。负梯度代表的是函数值减小最快的方向，并不一定直接指向函数图像的最低点。 模型是借助损失函数计算了各个偏导（反向传播），然后组合每个偏导成向量（梯度），再通过梯度下降法调整每一个偏导的值，从而进行参数 w 与 b 的更新（参数更新）。最开始的初始化 w 和 b 大概率会导致损失值很大，但经过这样的神经网络训练流程，多轮训练，不断微小调整 w 和 b 的值，直至损失值趋于平缓，从而完成任务。](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786606128122-4050d325-f2cf-4fa7-9e03-c3431b294f99.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786675588682-b1b581db-36fc-4dd8-a3e9-7caf2096ceb4.png)
+
+**<font style="color:rgb(15, 17, 21);">重要数学结论:沿着梯度方向走，函数值增加得最快；如果你沿着梯度的反方向走，函数值下降得最快.</font>**
+
+> <font style="color:rgb(24, 25, 28);">梯度就是对模型损失函数的每个参数求偏导数，然后组成的向量，梯度下降就是让参数沿梯度反方向减小一点点</font><font style="color:rgb(24, 25, 28);">🤏</font><font style="color:rgb(24, 25, 28);">，至于多小一点由学习率这个超参数决定</font>
+>
+
+---
+
+#### 梯度下降算法本身
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786614670448-e6c6bf14-2552-406f-b0d6-f0036a6fd512.png)
+
+**更新参数的规则**：新参数 = 旧参数 - 学习率 × 梯度。
+
++ 如果梯度是正数（参数增大 → 损失变大），那就**减小参数**
++ 如果梯度是负数（参数增大 → 损失变小），那就**增大参数**
++ **学习率**决定了每一步迈多大——太大容易"跳过头"来回震荡，太小训练慢到地老天荒
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786615292454-7d2fa173-36c1-4791-a430-04302e1f75e5.png)
+
+<font style="color:rgb(15, 17, 21);">想象你是一个盲人，站在一个起伏的山坡曲线 y=f(x)上，任务是</font>**<font style="color:rgb(15, 17, 21);">走到最低的那个谷底</font>**<font style="color:rgb(15, 17, 21);">（即找到函数的最小值）。</font>
+
++ <font style="color:rgb(15, 17, 21);">因为你是盲人，你看不到整个山的形状，只能摸脚下的坡度。</font>
++ **<font style="color:rgb(15, 17, 21);">关键动作</font>**<font style="color:rgb(15, 17, 21);">：你用脚跺一下地面，感觉坡是</font>**<font style="color:rgb(15, 17, 21);">朝左斜</font>**<font style="color:rgb(15, 17, 21);">还是</font>**<font style="color:rgb(15, 17, 21);">朝右斜</font>**<font style="color:rgb(15, 17, 21);">。</font>
+    - <font style="color:rgb(15, 17, 21);">如果脚下的坡</font>**<font style="color:rgb(15, 17, 21);">朝左低</font>**<font style="color:rgb(15, 17, 21);">（即向右走是上坡），那你应该</font>**<font style="color:rgb(15, 17, 21);">向左迈一步</font>**<font style="color:rgb(15, 17, 21);">。</font>
+    - <font style="color:rgb(15, 17, 21);">如果脚下的坡</font>**<font style="color:rgb(15, 17, 21);">朝右低</font>**<font style="color:rgb(15, 17, 21);">（即向右走是下坡），那你应该</font>**<font style="color:rgb(15, 17, 21);">向右迈一步</font>**<font style="color:rgb(15, 17, 21);">。</font>
+
+```python
+import random
+
+import matplotlib.pyplot as plt
+
+# 场景：房价预测。已知 100 套房的面积和成交价，要找出估价公式：价格 = w * 面积 + b
+w = random.random()  # 随便猜一个"每平米单价"
+b = random.random()  # 随便猜一个"基础价"
+
+_x = [i/100 for i in range(100)]  # 100 套房，面积 0~0.99（按百平米计，即 0~99 平米）
+# 真实行情是 价格 = 3 * 面积 + 10（面积按百平米、价格按十万计）
+# random.random() 是 0~1 的正数，模拟装修、楼层带来的溢价，所以每套房都比基准价略高
+_y = [3*e + 10 + random.random() for e in _x]
+
+# 接下来一遍一遍地复习这 100 套房，慢慢把估价公式修正到最准
+while True:
+    # 首先迭代x, y  因为x,y是不相关的数组 zip打包成元组
+    # 内部循环100次，每次拿出一套房的面积 x 和真实成交价 y
+    for x, y in zip(_x, _y):
+        # 用当前公式给这套房估价
+        # h 是估计的价格
+        h = w * x + b
+        # y是真实成交价（标准答案）
+        # 根据当前w b 值计算这套房估得有多离谱(损失函数)，平方让误差永远为正
+        loss = (y-h)**2
+        # 现在有损失我们需要更新 权重 w 和 b
+        # 更新权重使用梯度下降法 当前x 和 y是确定的 需要确定w 和 b
+        # y = w * x + b
+        # f(w,b) 二元函数求导：估低了(y-h为正)就把 w b 调大，估高了就调小
+        dw = -2 * x * (y-h)
+        db = -2 * (y-h)
+
+
+        # 求 dw 和 db目的是修正估价公式的 w 和 b
+        # 0.01 是学习率，即每次修正的幅度：太大来回震荡，太小学得太慢
+        w -= dw * 0.01
+        b -= db * 0.01
+
+        print(f'loss:{loss}---w:{w}---b:{b}')
+        # 开启交互模式
+        plt.ion()
+        # 清屏
+        plt.cla()
+        # _x, _y是100套真实成交的房子
+        plt.plot(_x, _y, '.')
+        # 间隔0.01s
+        # 用修正后的 w, b 画出当前的估价直线
+        plt.plot(_x, [w * e + b for e in _x])
+        plt.pause(0.01)
+
+    plt.show()
+
+```
+
+---
+
+### 2.12 反向传播与链式法则
+**<font style="color:rgb(0, 0, 0);">反向传播</font>**<font style="color:rgb(0, 0, 0);">：比较预测结果和真实答案，算出误差，然后逐层回传，算出每个权重该怎么调(</font><font style="color:rgba(0, 0, 0, 0.9);">根据预测误差计算梯度，更新模型权重（训练过程）</font><font style="color:rgb(0, 0, 0);">)。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786948447986-5a49e715-7a42-41ee-8914-038bc710571f.png)
+
+#### 复合函数求导(链式法则)
+**<font style="color:rgb(15, 17, 21);">复合函数</font>**<font style="color:rgb(15, 17, 21);">，就是</font>**<font style="color:rgb(15, 17, 21);">把一个函数的输出，当做另一个函数的输入</font>**<font style="color:rgb(15, 17, 21);">。</font>
+
+<font style="color:rgb(15, 17, 21);">如果有两个函数 </font><font style="color:rgb(15, 17, 21);">f</font><font style="color:rgb(15, 17, 21);"> 和 </font>_<font style="color:rgb(15, 17, 21);">g</font>_<font style="color:rgb(15, 17, 21);">，把 </font>_<font style="color:rgb(15, 17, 21);">g</font>_<font style="color:rgb(15, 17, 21);"> 的输出放进 </font><font style="color:rgb(15, 17, 21);">f</font><font style="color:rgb(15, 17, 21);"> 里，得到的复合函数记作：</font><font style="color:rgb(15, 17, 21);">(</font>_<font style="color:rgb(15, 17, 21);">f</font>_<font style="color:rgb(15, 17, 21);">∘</font>_<font style="color:rgb(15, 17, 21);">g</font>_<font style="color:rgb(15, 17, 21);">)(</font>_<font style="color:rgb(15, 17, 21);">x</font>_<font style="color:rgb(15, 17, 21);">)=</font>_<font style="color:rgb(15, 17, 21);">f</font>_<font style="color:rgb(15, 17, 21);">(</font>_<font style="color:rgb(15, 17, 21);">g</font>_<font style="color:rgb(15, 17, 21);">(</font>_<font style="color:rgb(15, 17, 21);">x</font>_<font style="color:rgb(15, 17, 21);">))</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786949318080-45489ba6-8ad4-4b89-9dfe-a33e62e0b267.png)
+
+#### <font style="color:rgb(15, 17, 21);">链式法则的核心思想:</font>**<font style="color:rgb(15, 17, 21);">外层函数求导，乘以内层函数的导数。</font>**
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786949410584-1fcc7805-04d4-441d-9386-54c47f726999.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786949574183-c8f7396a-ea09-4fad-bd36-04448248cf13.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787105913186-51704840-bc02-4835-9e7a-8ac2cebfafad.png)
+
+**<font style="color:rgb(15, 17, 21);background-color:rgb(237, 243, 254);">神经网络是一个由大量简单函数（主要是线性变换与非线性激活函数）复合而成的、可导的、带有可学习参数的巨大复合函数。</font>**
+
+#### <font style="color:rgb(51, 51, 51);">反向传播的完整分步示例:</font>
+<font style="color:rgb(51, 51, 51);">下面这组图把一个具体的数值例子从头算到尾：先做前向传播算出预测值和损失，再从损失出发逐层回传梯度，最后完成一次参数更新。跟着图把每一步的数对一遍，链式法则"从后往前算导数"的感觉就建立起来了。</font>
+
+
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787330567007-bf074b6c-c6d8-4319-834c-70dbb4977c0c.png)
+
+```python
+import torch
+
+# 1. 普通张量：默认不记录梯度
+x = torch.tensor([2.0, 3.0])
+print(x.requires_grad)  # False
+
+#PyTorch最核心的优势就是Autograd自动微分引擎，不用手动求导，代码一行开启自动计算梯度。
+# 2. 开启梯度记录（模型参数专用）只有设置requires_grad=True的张量，才会记录运算流程、支持求梯度。
+w = torch.tensor([1.5], requires_grad=True)
+print(w.requires_grad)  # True
+```
+
+```python
+import torch
+# 权重参数，开启梯度
+w = torch.tensor([2.0], requires_grad=True)
+# 正向运算
+y = w * 3
+# 模拟损失函数
+loss = (y - 9) ** 2
+# 反向传播，自动求导
+loss.backward()
+# 打印w的梯度
+print("w的梯度：", w.grad)
+```
+
+### 2.13 优化器
+神经网络训练流程可以简化成：
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787122730029-a0c2e149-e380-44cd-91a2-653d59bc4d32.png)
+
+**<font style="color:rgb(15, 17, 21);">优化器（Optimizer）：</font>**<font style="color:rgb(15, 17, 21);">它负责根据当前得到的损失和梯度，计算出下一步该</font>**<font style="color:rgb(15, 17, 21);">如何调整</font>**<font style="color:rgb(15, 17, 21);">模型内部的参数，让模型的表现越来越好。</font>
+
++ <font style="color:rgb(15, 17, 21);">损失函数：负责打分</font>
++ <font style="color:rgb(15, 17, 21);">反向传播：负责算梯度</font>
++ <font style="color:rgb(15, 17, 21);">优化器：负责真正更新参数</font>
+
+```python
+# 这是 optimizer 的超级简化版伪代码
+for 每一个参数 in 模型的所有参数:
+    参数.数值 = 参数.旧数值 - 学习率 × 参数的梯度
+```
+
+**<font style="color:rgb(15, 17, 21);">默认首选优化器：Adam</font>**
+
+```python
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+```
+
+1. <font style="color:rgb(15, 17, 21);">极度“省心”，默认参数就能跑</font>
+2. <font style="color:rgb(15, 17, 21);">对“学习率”不敏感（它给每个参数都“自适应”调节步长，就算你的 lr=0.01 设得稍大，它内部会自动把“梯度大的参数”的步长缩小，防止震荡。）</font>
+3. <font style="color:rgb(15, 17, 21);">收敛速度快(在训练初期，Adam 的“自适应学习率”会让模型像坐火箭一样快速下降 Loss)</font>
+
+
+
+### 2.14 完整训练循环
+#### 2.14.1数据预处理
+##### 2.14.1.1归一化
+**<font style="color:rgb(51, 51, 51);">归一化</font>**<font style="color:rgb(51, 51, 51);">：</font><font style="color:rgb(13, 13, 13);">归一化本质上是把不同尺度的数据统一到</font><font style="color:rgb(51, 51, 51);"> [0, 1]。</font>
+
+**<font style="color:rgb(13, 13, 13);">核心目的：让模型更容易、更稳定地学习，它是训练</font>**
+
+<font style="color:rgba(0, 0, 0, 0.9);">不同特征的数据差异很大，</font><font style="color:rgb(51, 51, 51);">比如"面积"是几十到几百，"房间数"只是个位数，"到最近地铁站的距离"可能是几千米。如果不做归一化：</font>
+
++ <font style="color:rgb(51, 51, 51);">出现计算溢出，</font><font style="color:rgb(15, 17, 21);">前向传播涉及大量矩阵乘法，</font><font style="color:rgba(0, 0, 0, 0.9);">容易产生极大或极小的数值</font><font style="color:rgb(15, 17, 21);">。如果输入数据量级过大（如 > 100），多层累乘后数值会迅速膨胀至</font>`<font style="color:rgb(15, 17, 21);background-color:rgb(235, 238, 242);">inf</font>`<font style="color:rgb(15, 17, 21);">（无穷大）；如果过小，会下溢至0。归一化将数据控制在合理的浮点数运算范围内（如标准差为1），保证计算过程的稳定性。</font>
++ <font style="color:rgb(51, 51, 51);">Sigmoid/Tanh 等激活函数会直接饱和，梯度消失</font>
+
+**<font style="color:rgba(0, 0, 0, 0.9);">消除数据量纲的影响</font>**<font style="color:rgba(0, 0, 0, 0.9);">:</font><font style="color:rgba(0, 0, 0, 0.9);">不同特征可能有完全不同的单位和量级（如"年龄"和"收入"）。如果不归一化，模型会错误地认为量级大的特征更重要，</font>**<font style="color:rgba(0, 0, 0, 0.9);">权重更新被大尺度特征主导</font>**<font style="color:rgba(0, 0, 0, 0.9);">，小尺度特征的信息被淹没(</font><font style="color:rgb(51, 51, 51);">数量级大的特征会在训练中"碾压"数量级小的特征</font><font style="color:rgba(0, 0, 0, 0.9);">)。</font>
+
+**<font style="color:rgb(13, 13, 13);">Min-Max 归一化</font>****<font style="color:rgb(51, 51, 51);">公式</font>**<font style="color:rgb(51, 51, 51);">:x_norm = (x - x_min) / (x_max - x_min)</font>
+
+```python
+import numpy as np
+
+# 假设这是"视频时长"和"粉丝数"两个特征，数量级差异巨大
+data = np.array([[60, 500000],    # 60秒，50万粉丝
+                 [180, 20000],    # 3分钟，2万粉丝
+                 [15, 1200000]])  # 15秒，120万粉丝
+
+# Min-Max 归一化：每个特征独立压缩到 [0, 1]
+data_norm = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))
+print(data_norm)
+# [[0.273, 0.407],
+#  [1.000, 0.000],
+#  [0.000, 1.000]]
+# 现在两个特征在同一个尺度上了
+```
+
+##### 2.14.1.2one-hot编码：把类别变成向量
+<font style="color:rgb(51, 51, 51);">计算机不认识"猫"、"狗"、"鸟"这些文字类别，需要把它们翻译成数字。但直接标 1、2、3 会让模型误以为"狗(2)是猫(1)的两倍"，所以要用 One-Hot 编码。</font>
+
+**<font style="color:rgb(15, 17, 21);">绝对不要</font>**<font style="color:rgb(15, 17, 21);">直接把“红色=1，绿色=2，蓝色=3”喂给神经网络，理由有两条：</font>
+
++ **<font style="color:rgb(15, 17, 21);">强加“大小顺序”</font>**<font style="color:rgb(15, 17, 21);">。神经网络会认为 3 > 2 > 1，即“蓝色 > 绿色 > 红色”。但颜色根本没有大小之分，这种无中生有的排序会彻底误导模型的权重更新。</font>
++ **<font style="color:rgb(15, 17, 21);">制造“距离假象”</font>**<font style="color:rgb(15, 17, 21);">。在数学计算中，(绿色 - 红色) = 1，而 (蓝色 - 红色) = 2。模型会误以为“蓝红差异”是“绿红差异”的两倍，这完全不符合事实。</font>
+
+**<font style="color:rgb(51, 51, 51);">核心做法</font>**<font style="color:rgb(51, 51, 51);">：N 个类别就用 N 维向量，每个类别独占一位"1"，其余全是"0"。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787122816055-ee865ad7-54e7-47e6-a77c-0776697adb3d.png)
+
+**<font style="color:rgba(0, 0, 0, 0.9);">规则</font>**<font style="color:rgba(0, 0, 0, 0.9);">：向量的长度 = 类别总数，属于哪一类，对应位置就为 1，其余为 0。</font>
+
+```python
+import torch.nn.functional as F
+import torch
+
+labels = torch.tensor([0, 1,2,3])
+one_hot = F.one_hot(labels, num_classes=4)
+
+print(one_hot)
+# tensor([[1, 0, 0, 0],
+#         [0, 1, 0, 0],
+#         [0, 0, 1, 0],
+#         [0, 0, 0, 1]])
+```
+
+#### 2.14.2 训练流程
+把前面拆开讲的各个零件组装起来，神经网络的训练就是一个不断重复的循环：
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786676332642-af52be2d-3018-4336-9a12-d67d3fe8c54d.png)
+
+**<font style="color:rgb(15, 17, 21);">早停法</font>**<font style="color:rgb(15, 17, 21);">就是在训练神经网络时，不把预设的轮次全部跑完，而是每训练完一轮就在</font>**<font style="color:rgb(15, 17, 21);">测试集</font>**<font style="color:rgb(15, 17, 21);">上测试一次效果；一旦发现</font>**<font style="color:rgb(15, 17, 21);">测试集</font>**<font style="color:rgb(15, 17, 21);">上的表现（如损失值）连续多个轮次不再变好，就</font>**<font style="color:rgb(15, 17, 21);">立即终止训练</font>**<font style="color:rgb(15, 17, 21);">。</font>
+
+<font style="color:rgb(15, 17, 21);"></font>
+
+```python
+#安装tensorboard
+uv pip install tensorboard -i https://pypi.tuna.tsinghua.edu.cn/simple
+#本地启动TensorBoard
+tensorboard --logdir=./mlp_mse/logs
+```
+
+```python
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from torchvision import datasets, transforms
+from net import FullyConnectedNet
+import torch.nn.functional as F
+import tqdm #让循环在运行时，自动在控制台显示一个动态更新的进度条
+
+# 指定日志目录
+writer = SummaryWriter(log_dir='./mlp/logs')
+
+# 1. 判断是否使用CUDA
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f'Using device: {device}')
+# 2. 准备数据,Dataset 读数据
+train_set = datasets.MNIST(root='./mlp/data', 
+                           train=True, download=True, transform=transforms.ToTensor())
+test_set = datasets.MNIST(root='./mlp/data', 
+                          train=False, download=True, transform=transforms.ToTensor())
+#DataLoader 负责打包、分批、迭代、打乱数据
+train_loader = DataLoader(dataset=train_set, batch_size=100, shuffle=True)
+test_loader = DataLoader(dataset=test_set, batch_size=100, shuffle=False)
+# 3. 创建模型
+model = FullyConnectedNet()
+model = model.to(device)
+# 4. 确定损失函数
+loss_fn = nn.MSELoss()
+# 5. 创建优化器 使用梯度下降算法 参数的更新
+opt = torch.optim.Adam(model.parameters())
+
+# 早停法参数：当测试集损失连续 patience 轮不再变好时，提前终止训练
+patience = 5
+best_test_loss = float('inf')  # 记录到目前为止最好的测试损失
+no_improve_count = 0           # 记录测试损失连续没有变好的轮数
+
+max_epochs = 1000
+for epoch in range(max_epochs):
+    # 6. 训练模型
+    model.train()
+    train_total_loss = 0
+    for images, labels in tqdm.tqdm(train_loader, desc="train", total=len(train_loader)):
+        # 将数据移动到设备
+        images, labels = images.to(device), labels.to(device)
+        labels = F.one_hot(labels, num_classes=10).float()
+        outputs = model(images) # 前向传播
+        loss = loss_fn(outputs, labels) # 计算损失
+
+        opt.zero_grad() # 清空梯度
+        loss.backward() # 反向传播 计算梯度
+        opt.step() # 更新参数
+        train_total_loss += loss.item()
+    train_avg_loss = train_total_loss / len(train_loader)
+    print(f'Epoch {epoch+1}/{max_epochs}, Loss: {train_avg_loss:.4f}')
+    # 7. 测试模型
+    model.eval()
+    test_total_loss = 0
+    test_total_acc = 0
+    #禁用梯度计算
+    #在推理时，我们不需要反向传播，因此不需要计算损失函数对参数的梯度,节省内存、加快推理速度
+    with torch.inference_mode(): 
+        for images, labels in tqdm.tqdm(test_loader, desc="test", total=len(test_loader)):
+            images, labels = images.to(device), labels.to(device)
+            labels = F.one_hot(labels, num_classes=10).float()
+            outputs = model(images)
+            # 计算损失
+            loss = loss_fn(outputs, labels)
+            test_total_loss += loss.item()
+
+            pred = torch.argmax(outputs, dim=1)
+            target = torch.argmax(labels, dim=1)
+            acc = torch.eq(pred, target).float().mean()
+            test_total_acc += acc.item()
+
+    test_avg_acc = test_total_acc / len(test_loader)
+    print(f'epoch:{epoch+1}, Test Accuracy: {test_avg_acc:.4f}')
+
+    test_avg_loss = test_total_loss / len(test_loader)
+    print(f'epoch:{epoch+1},Test Loss: {test_avg_loss:.4f}')
+    # 记录数据
+    writer.add_scalars('Loss/train', {'train_avg_loss': train_avg_loss,
+    'test_avg_loss': test_avg_loss}, epoch)
+    writer.add_scalar('Accuracy/test', test_avg_acc, epoch)
+    # 8. 早停判断与模型保存
+    if test_avg_loss < best_test_loss:
+        # 测试损失变好了：更新最好成绩，重置计数器，保存当前最优模型
+        best_test_loss = test_avg_loss
+        no_improve_count = 0
+        torch.save(model.state_dict(), './mlp/model/mnist_net_best.pth')
+        print(f'测试损失改善，保存最优模型 (best loss: {best_test_loss:.4f})')
+    else:
+        # 测试损失没有变好：计数器加一
+        no_improve_count += 1
+        print(f'测试损失未改善 ({no_improve_count}/{patience})')
+        if no_improve_count >= patience:
+            # 连续 patience 轮没有变好，提前终止训练
+            print(f'早停：测试损失连续 {patience} 轮未改善，在第 {epoch+1} 轮终止训练')
+            break
+
+```
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787202356469-d6a6c69e-081a-4b3e-b33d-38818701a9b9.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787202376264-73e7794f-4bb9-4d79-8d0e-7c823c556f16.png)
+
+| 代码 | 对应过程 | 解决的问题 |
+| --- | --- | --- |
+| `logits = model(features)` | 前向传播 | 模型根据当前参数做出了什么预测？ |
+| loss = loss_fn(outputs, labels) | 计算损失 | 预测结果和真实答案相差多少？ |
+| `optimizer.zero_grad()` | 清空梯度 | 如何避免上一轮梯度影响当前计算？ |
+| `loss.backward()` | 反向传播 | 每个参数应该承担多少错误？ |
+| `optimizer.step()` | 参数更新 | 参数应该如何变化才能降低损失？ |
+
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787126142349-371932e2-906a-4462-9b3d-7be83ae88090.png)
+
+
+
+| 步骤 | 做什么 | 关键点 |
+| --- | --- | --- |
+| ① 初始化 | 给所有参数赋随机初始值 | 不能让所有参数一样，否则梯度也会一样，网络就废了 |
+| ② 前向传播 | 输入数据从第一层流到最后一层 | 矩阵乘法 + 激活函数，逐层计算 |
+| ③ 算损失 | 比较预测值和真实值 | 用 MSE |
+| ④ 反向传播 | 从输出层往回，逐层算梯度 | 链式法则连接每一层，自动求导引擎包办 |
+| ⑤ 更新参数 | 沿梯度的反方向调整每个参数 | 步长 = 学习率 × 梯度 |
+| ⑥ 判断收敛 | Loss 不再明显下降或达到预设轮数 | 可以用"早停法"避免过度训练 |
+
+
+#### 2.14.3 模型预测
+```python
+import torch  
+from net import FullyConnectedNet  
+from PIL import Image  
+import numpy as np  
+from torchvision import transforms  
+import torch.nn.functional as F 
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = FullyConnectedNet()  
+model_path = "./mlp/model/mnist_net_best.pth"  
+
+# 加载训练好的权重到模型中：
+# 1. torch.load(model_path, map_location=device) 从磁盘读取权重字典（state_dict），
+#    map_location=device 保证权重被映射到当前使用的设备上（防止在 GPU 上训练的模型在纯 CPU 机器上加载失败）
+# 2. model.load_state_dict(...) 把读取到的权重参数逐个复制进模型的对应层中
+model.load_state_dict(torch.load(model_path, map_location=device))
+
+model.to(device)  # 把模型的所有参数和缓冲区移动到指定设备上（GPU 或 CPU），之后前向计算就在该设备上进行
+model.eval()  
+# 读取图片并进行预处理
+image_path = "D:/code/xwdev/mlp/mnist_pytorch_samples/image_0003_label_0.png"  
+image = Image.open(image_path)  # 用 Pillow 打开图片文件，得到一个 PIL Image 对象
+
+# 定义图像预处理流水线，transforms.Compose 会把多个变换按顺序串联起来依次执行
+transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),  # 转为灰度图：无论原图是 RGB 还是 RGBA，都强制变成单通道灰度图（MNIST 是灰度图，通道数必须为 1）
+        transforms.Resize((28, 28)),                  # 调整大小为 28x28：MNIST 模型训练时输入就是 28x28，推理时尺寸必须一致
+        transforms.ToTensor(),                        # 转为 tensor：把 PIL 图片转成 PyTorch 张量，形状为 [C, H, W]（这里是 [1, 28, 28]），
+                                                      # 同时把像素值从 0~255 的整数缩放到 0.0~1.0 的浮点数
+    ])
+
+input_tensor = transform(image)  # 对图片执行上面定义的预处理流水线，得到形状为 [1, 28, 28] 的张量（通道、高、宽）
+input_tensor = input_tensor.unsqueeze(0)  # 添加 batch 维度：在第 0 维插入一个大小为 1 的维度，
+                                          # 形状从 [1, 28, 28] 变为 [1, 1, 28, 28]，即 NCHW 格式（批量数、通道、高、宽），
+                                          # 因为模型要求输入是一“批”图片，即使只有一张也要包成 batch
+input_tensor = input_tensor.to(device)  # 把输入张量移动到与模型相同的设备上，设备和模型必须一致，否则计算时会报错
+
+# 使用模型进行预测
+with torch.no_grad():  # 上下文管理器：在其内部所有操作都不会记录梯度信息。
+                       # 推理阶段不需要反向传播，关闭梯度可以节省内存、加快计算
+    output = model(input_tensor)  # 前向传播：把图片张量送入模型，得到输出。
+                                  # 由于网络最后一层是 Softmax(dim=1)，output 是形状 [1, 10] 的概率分布，
+                                  # 每一维表示该图片属于数字 0~9 的概率，10 个概率之和为 1
+
+    predicted_class = torch.argmax(output, dim=1).item()  # 取概率最大的那一维的索引作为预测类别：
+                                                          # argmax(output, dim=1) 在第 1 维（类别维）上找最大值的下标，返回形状 [1] 的张量
+                                                          # .item() 把只含一个元素的张量转成普通的 Python 整数
+    print(f"Predicted class: {predicted_class}")  # 打印预测结果，例如 "Predicted class: 0"
+
+    # 下面三行是把送入模型的图片显示出来，方便人工核对预处理结果
+    img_display = input_tensor.squeeze().cpu().numpy()  # 把张量变回 NumPy 数组以便转成图片：
+                                                        # squeeze() 去掉所有大小为 1 的维度，[1,1,28,28] -> [28,28]
+                                                        # .cpu() 把张量从 GPU 移回 CPU（NumPy 只能处理 CPU 上的数据）
+                                                        # .numpy() 把 PyTorch 张量转成 NumPy 数组，此时像素值仍是 0.0~1.0 的浮点数
+    image = Image.fromarray((img_display * 255).astype(np.uint8))  # 把 NumPy 数组转回 PIL 图片：
+                                                                   # *255 把像素值从 0~1 还原回 0~255
+                                                                   # astype(np.uint8) 转成 8 位无符号整数（图片标准格式）
+                                                                   # Image.fromarray 根据数组创建灰度图对象
+    image.show()  # 调用系统默认的图片查看器显示这张图片
+
+```
+
+### <font style="color:rgb(15, 17, 21);">2.15 交叉熵损失函数</font>
+**<font style="color:rgb(51,51,51);">概率分布：</font>****<font style="color:rgb(13, 13, 13);">把一个随机事件所有可能出现的结果列出来，并告诉每个结果发生的可能性有多大。</font>****<font style="color:rgb(51,51,51);">表述随机变量取值的概率规律。 </font>**
+
++ **<font style="color:rgb(51,51,51);">随机变量的概率取值范围[0,1]： </font>**<font style="color:rgb(51,51,51);">这意味着任何随机变量取某个特定值的概率都在0和1之间。概率 </font>
+
+<font style="color:rgb(51,51,51);">不能为负，也不能超过</font><font style="color:rgb(51,51,51);">1</font><font style="color:rgb(51,51,51);">。 </font>
+
++ **<font style="color:rgb(51,51,51);">所有取值概率的和必须为1： </font>**<font style="color:rgb(51,51,51);">对于一个随机变量的所有可能取值，它们的概率之和必须等于1。这 </font>
+
+<font style="color:rgb(51,51,51);">表示随机变量取任何可能值的概率分配是完备的。 </font>
+
++ **<font style="color:rgb(51,51,51);">离散分布： </font>**<font style="color:rgb(51,51,51);">当随机变量的取值是有限或可数的时候，我们称其为离散随机变量。概率分布描述了这 种情况下每个可能取值的概率。</font>
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787215810689-0021f24c-9049-4f7b-8093-e62313040b8b.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787215998332-12befe72-e745-4650-a8bf-4a92df609d81.png)
+
+**<font style="color:rgb(15, 17, 21);">分类任务的本质，是让模型输出的概率分布 尽量接近 真实标签的概率分布。</font>**
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786438146354-81e7f89a-f345-4391-a304-6b0e4b9d50df.png)
+
+**<font style="color:rgb(15, 17, 21);">交叉熵损失函数:</font>****<font style="color:rgb(0, 0, 0);">用来衡量模型输出的概率分布和真实世界概率分布之间的</font>****<font style="color:rgb(51,51,51);">差异性</font>****<font style="color:rgb(0, 0, 0);">。</font>**<font style="color:rgb(51,51,51);">神经网络所预测的类别分布概率与实际类别分布概率之间的差距越小越好，即交叉熵越小越好。</font>
+
+**<font style="color:rgb(0, 0, 0);">公式：</font>**<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786940525179-50023833-0baf-43dd-8172-0aefde140bed.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786940619653-51c7d9a3-efe2-427f-9dbb-e58956461128.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786940766163-1f65b2d4-f397-4377-a203-d37f7139963f.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1786940977562-1841feff-5ca7-4f32-bb83-3ecb22a4c7bd.png)
+
+
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# 假设 3 分类，batch_size = 2
+# 模型原始输出（logits）
+logits = torch.tensor([[2.0, 1.0, 0.1],   # 样本1：偏向第0类
+                       [0.5, 2.5, 0.3]])  # 样本2：偏向第1类
+
+# 真实标签（类别索引，不是 one-hot向量）
+labels = torch.tensor([0, 1])
+
+#直接用 CrossEntropyLoss
+criterion = nn.CrossEntropyLoss()
+loss = criterion(logits, labels)
+print(f"交叉熵损失: {loss.item():.4f}")
+```
+
+```python
+from torch import nn
+import torch
+
+class FullyConnectedNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # nn.Sequential 会按顺序执行每一层。
+        self.layer = nn.Sequential(
+            nn.Flatten(),             # [batch, 1, 28, 28] -> [batch, 784]
+            nn.Linear(28 * 28, 512),  # 784 个像素点映射到 512 个隐藏特征
+            nn.ReLU(),                # 增加非线性表达能力
+            nn.Linear(512, 256),      # 继续提取更紧凑的隐藏特征
+            nn.ReLU(),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Linear(128, 10),       # 输出 10 个数字类别的分数
+        )
+
+    def forward(self, x):
+        # forward 定义“数据如何从输入流到输出”。
+        # 输入 x 是一批图片，输出是每张图片对应 10 个数字类别的 logits。
+        return self.layer(x)
+
+
+if __name__ == '__main__':
+    data = torch.randn(1,1,28,28)
+    net = FullyConnectedNet()
+    output = net(data)
+    print(output)
+
+```
+
+```python
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from torchvision import datasets, transforms
+from net import FullyConnectedNet
+import torch.nn.functional as F
+import tqdm #让循环在运行时，自动在控制台显示一个动态更新的进度条
+
+# 指定日志目录
+writer = SummaryWriter(log_dir='D:/code/xwdev/mlp_cro/logs')
+
+# 1. 判断是否使用CUDA
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f'Using device: {device}')
+# 2. 准备数据
+train_set = datasets.MNIST(root='D:/code/xwdev/mlp_cro/data', 
+                           train=True, download=True, transform=transforms.ToTensor())
+test_set = datasets.MNIST(root='D:/code/xwdev/mlp_cro/data', 
+                          train=False, download=True, transform=transforms.ToTensor())
+train_loader = DataLoader(dataset=train_set, batch_size=100, shuffle=True)
+test_loader = DataLoader(dataset=test_set, batch_size=100, shuffle=False)
+# 3. 创建模型
+model = FullyConnectedNet()
+model = model.to(device)
+# 4. 确定损失函数
+loss_fn = nn.CrossEntropyLoss()
+# 5. 创建优化器 使用梯度下降算法 参数的更新
+opt = torch.optim.Adam(model.parameters())
+
+# 早停法参数：当测试集损失连续 patience 轮不再变好时，提前终止训练
+patience = 3
+best_test_loss = float('inf')  # 记录到目前为止最好的测试损失
+no_improve_count = 0           # 记录测试损失连续没有变好的轮数
+
+max_epochs = 1000
+for epoch in range(max_epochs):
+    # 6. 训练模型
+    model.train()
+    train_total_loss = 0
+    for images, labels in tqdm.tqdm(train_loader, desc="train", total=len(train_loader)):
+        # 将数据移动到设备
+        images, labels = images.to(device), labels.to(device)
+        outputs = model(images) # 前向传播
+        loss = loss_fn(outputs, labels) # 计算损失
+
+        opt.zero_grad() # 清空梯度
+        loss.backward() # 反向传播 计算梯度
+        opt.step() # 更新参数
+        train_total_loss += loss.item()
+    train_avg_loss = train_total_loss / len(train_loader)
+    print(f'Epoch {epoch+1}/{max_epochs}, Loss: {train_avg_loss:.4f}')
+    # 7. 测试模型
+    model.eval()
+    test_total_loss = 0
+    test_total_acc = 0
+    #禁用梯度计算
+    #在推理时，我们不需要反向传播，因此不需要计算损失函数对参数的梯度
+    with torch.inference_mode(): 
+        for images, labels in tqdm.tqdm(test_loader, desc="test", total=len(test_loader)):
+            images, labels = images.to(device), labels.to(device)
+            outputs = model(images)
+            # 计算损失
+            loss = loss_fn(outputs, labels)
+            test_total_loss += loss.item()
+
+            pred = torch.argmax(outputs, dim=1)
+            acc = torch.eq(pred, labels).float().mean()
+            test_total_acc += acc.item()
+
+    test_avg_acc = test_total_acc / len(test_loader)
+    print(f'epoch:{epoch+1}, Test Accuracy: {test_avg_acc:.4f}')
+
+    test_avg_loss = test_total_loss / len(test_loader)
+    print(f'epoch:{epoch+1},Test Loss: {test_avg_loss:.4f}')
+    # 记录数据
+    writer.add_scalars('Loss/train', {'train_avg_loss': train_avg_loss,
+    'test_avg_loss': test_avg_loss}, epoch)
+    writer.add_scalar('Accuracy/test', test_avg_acc, epoch)
+    # 8. 早停判断与模型保存
+    if test_avg_loss < best_test_loss:
+        # 测试损失变好了：更新最好成绩，重置计数器，保存当前最优模型
+        best_test_loss = test_avg_loss
+        no_improve_count = 0
+        torch.save(model.state_dict(), 'D:/code/xwdev/mlp_cro/model/mnist_net_best.pth')
+        print(f'测试损失改善，保存最优模型 (best loss: {best_test_loss:.4f})')
+    else:
+        # 测试损失没有变好：计数器加一
+        no_improve_count += 1
+        print(f'测试损失未改善 ({no_improve_count}/{patience})')
+        if no_improve_count >= patience:
+            # 连续 patience 轮没有变好，提前终止训练
+            print(f'早停：测试损失连续 {patience} 轮未改善，在第 {epoch+1} 轮终止训练')
+            break
+
+```
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787203904167-111aa07e-1421-4c80-ad09-84e5f5df8015.png)
+
+<!-- 这是一张图片，ocr 内容为： -->
+![](https://cdn.nlark.com/yuque/0/2026/png/21571931/1787203923212-198e8b05-84a1-4700-a9ac-8c4dcfadf5b6.png)
+
+```python
+import torch  
+from net import FullyConnectedNet  
+from PIL import Image  
+import numpy as np  
+from torchvision import transforms  
+import torch.nn.functional as F 
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = FullyConnectedNet()  
+model_path = "D:/code/xwdev/mlp_cro/model/mnist_net_best.pth"  
+
+# 加载训练好的权重到模型中：
+# 1. torch.load(model_path, map_location=device) 从磁盘读取权重字典（state_dict），
+#    map_location=device 保证权重被映射到当前使用的设备上（防止在 GPU 上训练的模型在纯 CPU 机器上加载失败）
+# 2. model.load_state_dict(...) 把读取到的权重参数逐个复制进模型的对应层中
+model.load_state_dict(torch.load(model_path, map_location=device))
+
+model.to(device)  # 把模型的所有参数和缓冲区移动到指定设备上（GPU 或 CPU），之后前向计算就在该设备上进行
+model.eval()  
+# 读取图片并进行预处理
+image_path = "D:/code/xwdev/mlp_cro/mnist_pytorch_samples/image_0000_label_7.png"  
+image = Image.open(image_path)  # 用 Pillow 打开图片文件，得到一个 PIL Image 对象
+
+# 定义图像预处理流水线，transforms.Compose 会把多个变换按顺序串联起来依次执行
+transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),  # 转为灰度图：无论原图是 RGB 还是 RGBA，都强制变成单通道灰度图（MNIST 是灰度图，通道数必须为 1）
+        transforms.Resize((28, 28)),                  # 调整大小为 28x28：MNIST 模型训练时输入就是 28x28，推理时尺寸必须一致
+        transforms.ToTensor(),                        # 转为 tensor：把 PIL 图片转成 PyTorch 张量，形状为 [C, H, W]（这里是 [1, 28, 28]），
+                                                      # 同时把像素值从 0~255 的整数缩放到 0.0~1.0 的浮点数
+    ])
+
+input_tensor = transform(image)  # 对图片执行上面定义的预处理流水线，得到形状为 [1, 28, 28] 的张量（通道、高、宽）
+input_tensor = input_tensor.unsqueeze(0)  # 添加 batch 维度：在第 0 维插入一个大小为 1 的维度，
+                                          # 形状从 [1, 28, 28] 变为 [1, 1, 28, 28]，即 NCHW 格式（批量数、通道、高、宽），
+                                          # 因为模型要求输入是一“批”图片，即使只有一张也要包成 batch
+input_tensor = input_tensor.to(device)  # 把输入张量移动到与模型相同的设备上，设备和模型必须一致，否则计算时会报错
+
+# 使用模型进行预测
+with torch.no_grad():  # 上下文管理器：在其内部所有操作都不会记录梯度信息。
+                       # 推理阶段不需要反向传播，关闭梯度可以节省内存、加快计算
+    output = model(input_tensor)  # 前向传播：把图片张量送入模型，得到输出。
+    output = F.softmax(output, dim=1)  #  Softmax(dim=1)，output 是形状 [1, 10] 的概率分布，
+                                  # 每一维表示该图片属于数字 0~9 的概率，10 个概率之和为 1
+    
+
+    predicted_class = torch.argmax(output, dim=1).item()  # 取概率最大的那一维的索引作为预测类别：
+                                                          # argmax(output, dim=1) 在第 1 维（类别维）上找最大值的下标，返回形状 [1] 的张量
+                                                          # .item() 把只含一个元素的张量转成普通的 Python 整数
+    print(f"Predicted class: {predicted_class}")  # 打印预测结果，例如 "Predicted class: 0"
+
+    # 下面三行是把送入模型的图片显示出来，方便人工核对预处理结果
+    img_display = input_tensor.squeeze().cpu().numpy()  # 把张量变回 NumPy 数组以便转成图片：
+                                                        # squeeze() 去掉所有大小为 1 的维度，[1,1,28,28] -> [28,28]
+                                                        # .cpu() 把张量从 GPU 移回 CPU（NumPy 只能处理 CPU 上的数据）
+                                                        # .numpy() 把 PyTorch 张量转成 NumPy 数组，此时像素值仍是 0.0~1.0 的浮点数
+    image = Image.fromarray((img_display * 255).astype(np.uint8))  # 把 NumPy 数组转回 PIL 图片：
+                                                                   # *255 把像素值从 0~1 还原回 0~255
+                                                                   # astype(np.uint8) 转成 8 位无符号整数（图片标准格式）
+                                                                   # Image.fromarray 根据数组创建灰度图对象
+    image.show()  # 调用系统默认的图片查看器显示这张图片
+
+```
+

demo/mlp_mse/demo2.ipynb

@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a93bbf66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter containing:\n",
+      "tensor([[-0.1953,  0.0349,  0.2066,  0.3050,  0.0570, -0.1622, -0.1848, -0.1546,\n",
+      "         -0.1174,  0.1902],\n",
+      "        [-0.2510,  0.1219, -0.1402, -0.0613, -0.0890,  0.0510, -0.0082, -0.2142,\n",
+      "          0.1112,  0.0488],\n",
+      "        [-0.1486, -0.2754, -0.0817,  0.2407, -0.2888,  0.3057,  0.1441,  0.1292,\n",
+      "          0.0201, -0.1883],\n",
+      "        [-0.1317, -0.2687, -0.2205,  0.1629,  0.0945, -0.0216, -0.0291, -0.2245,\n",
+      "          0.2112,  0.2221],\n",
+      "        [-0.1276,  0.2041,  0.2358, -0.0377, -0.2722, -0.0177, -0.0213, -0.0972,\n",
+      "         -0.0971,  0.0606]], requires_grad=True)\n",
+      "Parameter containing:\n",
+      "tensor([ 0.2709,  0.0198, -0.1025, -0.0345,  0.1773], requires_grad=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "class MyModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.fc = nn.Linear(10, 5)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.fc(x)\n",
+    "\n",
+    "# 实例化模型时，fc 就已经被初始化了\n",
+    "model = MyModel()\n",
+    "print(model.fc.weight)\n",
+    "print(model.fc.bias)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b11a6f0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameter containing:\n",
+      "tensor([[ 0.4859,  0.4045, -0.3092,  0.0537, -0.3359, -0.1761,  0.4106, -0.6187,\n",
+      "         -0.2196, -0.0027],\n",
+      "        [ 0.3794,  0.5629, -0.4486, -0.1120, -0.3996,  0.0114, -0.4191, -0.1235,\n",
+      "          0.2704, -0.4082],\n",
+      "        [-0.6163,  0.3071, -0.2106, -0.4641, -0.2065, -0.5145,  0.0373, -0.2939,\n",
+      "          0.5346,  0.2736],\n",
+      "        [ 0.5658, -0.4418, -0.3231,  0.2414, -0.1687, -0.1383,  0.0326,  0.1102,\n",
+      "          0.4646, -0.4835],\n",
+      "        [-0.3686,  0.5512, -0.0530,  0.4921, -0.5184, -0.4362,  0.1822,  0.4276,\n",
+      "         -0.4707, -0.2698]], requires_grad=True)\n",
+      "Parameter containing:\n",
+      "tensor([0., 0., 0., 0., 0.], requires_grad=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "\n",
+    "# 创建一个线性层\n",
+    "layer = nn.Linear(10, 5)\n",
+    "\n",
+    "# 最简单的用法：直接用 init 覆盖默认初始化\n",
+    "nn.init.xavier_uniform_(layer.weight)  # 均匀分布初始化权重\n",
+    "nn.init.zeros_(layer.bias)             # 偏置设为0\n",
+    "\n",
+    "print(layer.weight)\n",
+    "print(layer.bias)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74cdfef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# 1. 普通张量：默认不记录梯度\n",
+    "x = torch.tensor([2.0, 3.0])\n",
+    "print(x.requires_grad)  # False\n",
+    "\n",
+    "#PyTorch最核心的优势就是Autograd自动微分引擎，不用手动求导，代码一行开启自动计算梯度。\n",
+    "# 2. 开启梯度记录（模型参数专用）只有设置requires_grad=True的张量，才会记录运算流程、支持求梯度。\n",
+    "w = torch.tensor([1.5], requires_grad=True)\n",
+    "print(w.requires_grad)  # True\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b0cd775e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "w的梯度： tensor([-18.])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "# 权重参数，开启梯度\n",
+    "w = torch.tensor([2.0], requires_grad=True)\n",
+    "# 正向运算\n",
+    "y = w * 3\n",
+    "# 模拟损失函数\n",
+    "loss = (y - 9) ** 2\n",
+    "# 反向传播，自动求导\n",
+    "loss.backward()\n",
+    "# 打印w的梯度\n",
+    "print(\"w的梯度：\", w.grad)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7ac1ab87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.27272727 0.40677966]\n",
+      " [1.         0.        ]\n",
+      " [0.         1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# 假设这是\"视频时长\"和\"粉丝数\"两个特征，数量级差异巨大\n",
+    "data = np.array([[60, 500000],    # 60秒，50万粉丝\n",
+    "                 [180, 20000],    # 3分钟，2万粉丝\n",
+    "                 [15, 1200000]])  # 15秒，120万粉丝\n",
+    "\n",
+    "# Min-Max 归一化：每个特征独立压缩到 [0, 1]\n",
+    "data_norm = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))\n",
+    "print(data_norm)\n",
+    "# [[0.273, 0.407],\n",
+    "#  [1.000, 0.000],\n",
+    "#  [0.000, 1.000]]\n",
+    "# 现在两个特征在同一个尺度上了\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c538338",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1, 0, 0, 0],\n",
+      "        [0, 1, 0, 0],\n",
+      "        [0, 0, 1, 0],\n",
+      "        [0, 0, 0, 1]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "import torch\n",
+    "# 4个类别 \n",
+    "labels = torch.tensor([0, 1,2,3])\n",
+    "one_hot = F.one_hot(labels, num_classes=4)\n",
+    "\n",
+    "print(one_hot)\n",
+    "# tensor([[1, 0, 0, 0],\n",
+    "#         [0, 1, 0, 0],\n",
+    "#         [0, 0, 1, 0],\n",
+    "#         [0, 0, 0, 1]])\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "shuhe_xwdev (3.11.14)",
+   "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": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

demo/mlp_mse/net.py

@@ -0,0 +1,32 @@
+from torch import nn
+import torch
+
+class FullyConnectedNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # nn.Sequential() 会按顺序执行每一层
+        self.net = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Linear(128, 10),
+
+            # 输出函数
+            nn.Softmax(dim=1)   #NV结构，激活V
+        )
+    def forward(self, x):
+        # forward() 函数定义函数如何从输入流到输出
+        # 输入 x 是一批图片，输出是每张图片对应 10 个数字类别的logits
+        return self.net(x)
+
+
+
+if __name__ == '__main__':
+    data = torch.randn(1,1,28,28)
+    net = FullyConnectedNet()
+    output = net(data)
+    print(output)

demo/mlp_mse/train.py

@@ -0,0 +1,103 @@
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from torchvision import datasets, transforms
+from net import FullyConnectedNet
+import torch.nn.functional as F
+import tqdm #让循环在运行时，自动在控制台显示一个动态更新的进度条
+import os
+
+# 指定日志目录
+writer = SummaryWriter(log_dir='./mlp_mse/logs')
+
+# 1. 判断是否使用CUDA
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f'Using device: {device}')
+# 2. 准备数据,Dataset 读数据,数据预处理
+train_set = datasets.MNIST(root='./mlp_mse/data', 
+                           train=True, download=True, transform=transforms.ToTensor())
+test_set = datasets.MNIST(root='./mlp_mse/data', 
+                          train=False, download=True, transform=transforms.ToTensor())
+#DataLoader 负责打包、分批、迭代、打乱数据
+#batch_size 每个批次多少个样本,根据显存大小设置
+train_loader = DataLoader(dataset=train_set, batch_size=100, shuffle=True)
+test_loader = DataLoader(dataset=test_set, batch_size=100, shuffle=False)
+# 3. 创建模型
+model = FullyConnectedNet()
+model = model.to(device)
+# 4. 确定损失函数
+loss_fn = nn.MSELoss()
+# 5. 创建优化器 使用梯度下降算法 参数的更新
+opt = torch.optim.Adam(model.parameters())
+
+# 早停法参数：当测试集损失连续 patience 轮不再变好时，提前终止训练
+patience = 5
+best_test_loss = float('inf')  # 记录到目前为止最好的测试损失
+no_improve_count = 0           # 记录测试损失连续没有变好的轮数
+
+max_epochs = 1000
+os.makedirs('./mlp/model', exist_ok=True)  # 确保保存模型的目录存在
+for epoch in range(max_epochs):
+    # 6. 训练模型
+    model.train()
+    train_total_loss = 0
+    for images, labels in tqdm.tqdm(train_loader, desc="train", total=len(train_loader)):
+        # 将数据移动到设备
+        images, labels = images.to(device), labels.to(device)
+        labels = F.one_hot(labels, num_classes=10).float()
+        outputs = model(images) # 前向传播
+        loss = loss_fn(outputs, labels) # 计算损失
+
+        opt.zero_grad() # 清空梯度
+        loss.backward() # 反向传播 计算梯度
+        opt.step() # 更新参数
+        train_total_loss += loss.item()
+    train_avg_loss = train_total_loss / len(train_loader)
+    print(f'Epoch {epoch+1}/{max_epochs}, Loss: {train_avg_loss:.4f}')
+    # 7. 测试模型
+    model.eval()
+    test_total_loss = 0 #一轮测试的总损失
+    test_total_acc = 0 #一轮测试的总得分
+    #禁用梯度计算
+    #在推理时，我们不需要反向传播，因此不需要计算损失函数对参数的梯度,节省内存、加快推理速度
+    with torch.inference_mode(): 
+        for images, labels in tqdm.tqdm(test_loader, desc="test", total=len(test_loader)):
+            images, labels = images.to(device), labels.to(device)
+            labels = F.one_hot(labels, num_classes=10).float()
+            outputs = model(images)
+            # 计算损失
+            loss = loss_fn(outputs, labels)
+            test_total_loss += loss.item()
+
+            pred = torch.argmax(outputs, dim=1)
+            target = torch.argmax(labels, dim=1)
+            acc = torch.eq(pred, target).float().mean()
+            test_total_acc += acc.item()
+
+    test_avg_acc = test_total_acc / len(test_loader)
+    print(f'epoch:{epoch+1}, Test Accuracy: {test_avg_acc:.4f}')
+
+    test_avg_loss = test_total_loss / len(test_loader)
+    print(f'epoch:{epoch+1},Test Loss: {test_avg_loss:.4f}')
+    # 记录数据
+    writer.add_scalars('Loss/train', {'train_avg_loss': train_avg_loss,
+    'test_avg_loss': test_avg_loss}, epoch)
+    writer.add_scalar('Accuracy/test', test_avg_acc, epoch)
+    # 8. 早停判断与模型保存
+    if test_avg_loss < best_test_loss:
+        # 测试损失变好了：更新最好成绩，重置计数器，保存当前最优模型
+        best_test_loss = test_avg_loss
+        no_improve_count = 0
+        torch.save(model.state_dict(), './mlp/model/mnist_net_best.pth')
+        print(f'测试损失改善，保存最优模型 (best loss: {best_test_loss:.4f})')
+    else:
+        # 测试损失没有变好：计数器加一
+        no_improve_count += 1
+        print(f'测试损失未改善 ({no_improve_count}/{patience})')
+        if no_improve_count >= patience:
+            # 连续 patience 轮没有变好，提前终止训练
+            print(f'早停：测试损失连续 {patience} 轮未改善，在第 {epoch+1} 轮终止训练')
+            break
+

demo/test01.py

@@ -0,0 +1,87 @@
+# =====================================================================
+# 用「梯度下降」拟合一条直线 —— 神经网络最基础的原理演示
+# ---------------------------------------------------------------------
+# 场景：已知 100 套房子的「面积」和「成交价」，想自动学出一个估价公式：
+#           价格 = w * 面积 + b
+#   其中 w 是「每平米单价」（直线的斜率），b 是「基价」（直线的截距）。
+#
+# 神经网络学习一件事，永远只有三步，循环往复：
+#   ① 预测：用当前的 w、b 算一个预测值 h
+#   ② 算误差：拿预测值 h 和真实值 y 比较，得到一个「损失 loss」
+#   ③ 调参：沿「让 loss 变小的方向」微调 w 和 b
+# 下面每一行代码，你都能对应到这三步中的某一步。
+# =====================================================================
+
+import random
+import matplotlib.pyplot as plt
+
+
+# ---------- ① 初始化参数 ----------
+# 一开始我们「随便猜」w 和 b，random.random() 会得到 [0,1) 之间的随机数。
+# 注意：真实值是 w=3、b=10，我们故意从很远的 0~1 开始猜，
+# 这样才能看出「梯度下降」是如何一步步把错误答案纠正过来的。
+w = random.random()  # 猜一个「每平米单价」
+b = random.random()  # 猜一个「基价」
+
+
+# ---------- ② 造一批「带噪声」的训练数据 ----------
+_x = [i / 100 for i in range(100)]                          # 面积：0.00 ~ 0.99，共 100 个点
+_y = [3 * e + 10 + (random.random() - 0.5) for e in _x]     # 真实价格 = 3*面积 + 10 + 噪声
+
+# 上面 _y 为什么要加 (random.random() - 0.5)？
+#   现实数据永远有误差（同面积房子价格不会一模一样）。这个式子 = 噪声，范围 [-0.5, 0.5]，
+#   均值恰好是 0，所以「整体上」数据仍然服从 y = 3x + 10，最后学出来 w≈3、b≈10。
+#   如果只加 random.random()（均值 0.5），那真实截距会变成 10.5，初学者容易困惑。
+
+
+# ---------- ③ 开始训练 ----------
+# 原来的代码是 `while True:` —— 这是个「死循环」，程序永远停不下来，所以跑不动。
+# 我们改成「固定轮数」：一轮(epoch) = 把 100 个数据点全部看一遍。
+# 300 轮足够让 w、b 收敛了。
+EPOCHS = 300        # 训练轮数
+LR = 0.01           # 学习率（learning rate）：每次调参数的「步子大小」
+
+plt.ion()           # 打开 matplotlib 的「交互模式」：画图时程序不卡住，能继续往下跑
+                    # （这个只需要开一次，所以放在循环外面）
+
+for epoch in range(EPOCHS):
+    # 内层 for：把 100 个样本逐个过一遍，每看一个样本就微调一次 w 和 b。
+    # （这种做法叫「随机梯度下降 SGD」，是训练神经网络的常见方式）
+    for x, y in zip(_x, _y):
+        # —— 第 1 步：预测 ——
+        h = w * x + b                      # 用当前的 w、b 算出「预测价格」
+
+        # —— 第 2 步：算误差（损失函数）——
+        loss = (y - h) ** 2                # 差的平方：预测偏离真实越远，loss 越大
+                                           # 平方有两点好处：① 正负误差都算数；② 好求导
+
+        # —— 第 3 步：求「梯度」，也就是 loss 对 w、b 的偏导数 ——
+        # 为什么是这两个式子？用链式法则对 loss=(y-h)^2 求导：
+        #   h = w*x + b，所以 dh/dw = x，dh/db = 1
+        #   d(loss)/dw = 2*(y-h) * d(y-h)/dw = 2*(y-h) * (-x) = -2*x*(y-h)
+        #   d(loss)/db = 2*(y-h) * d(y-h)/db = 2*(y-h) * (-1) = -2*(y-h)
+        # 梯度告诉我们：w、b 往哪个方向挪，loss 会「上升」。所以我们要「反着」挪。
+        dw = -2 * x * (y - h)              # loss 对 w 的偏导数
+        db = -2 * (y - h)                  # loss 对 b 的偏导数
+
+        # —— 第 4 步：沿「让 loss 变小」的方向更新参数 ——
+        # w -= dw * LR 的意思是：w 朝 dw 的反方向走一小步，步长 = LR * |dw|。
+        # 直观理解（只看 b）：
+        #   预测偏低时 h<y，(y-h)>0，于是 db<0，b -= 负数 → b 变大 → 预测抬高 ✅
+        #   预测偏高时 h>y，(y-h)<0，于是 db>0，b -= 正数 → b 变小 → 预测压低 ✅
+        w -= dw * LR                       # 学习率控制每步走多大
+        b -= db * LR
+
+    # —— 每结束一轮，画一次图、打印一次进度 ——
+    # plt.cla() 清空上一帧，避免线条越叠越多；plt.pause() 让画面刷新出来。
+    plt.cla()
+    plt.plot(_x, _y, 'r', label='真实数据')                       # 红点：真实房价
+    plt.plot(_x, [w * e + b for e in _x], label='拟合直线')        # 蓝线：我们学到的直线
+    plt.legend()
+    plt.pause(0.01)
+    print(f'epoch:{epoch:3d}  w:{w:.3f}  b:{b:.3f}  loss:{loss:.4f}')
+
+# 训练结束后：关掉交互模式，让窗口保留，方便观察最终结果
+plt.ioff()
+plt.show()
+print(f'\n训练完成！真实值 w=3, b=10；学到的 w≈{w:.3f}, b≈{b:.3f}')

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from shuhe-xwdev!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,12 @@
+[project]
+name = "shuhe-xwdev"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "matplotlib>=3.11.1",
+    "torch>=2.13.0",
+    "torchaudio>=2.11.0",
+    "torchvision>=0.28.0",
+]

uv.lock

@@ -0,0 +1,1125 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773, upload-time = "2025-07-26T12:01:02.277Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149, upload-time = "2025-07-26T12:01:04.072Z" },
+    { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222, upload-time = "2025-07-26T12:01:05.688Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234, upload-time = "2025-07-26T12:01:07.054Z" },
+    { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555, upload-time = "2025-07-26T12:01:08.801Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238, upload-time = "2025-07-26T12:01:10.319Z" },
+    { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218, upload-time = "2025-07-26T12:01:12.659Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867, upload-time = "2025-07-26T12:01:15.533Z" },
+    { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677, upload-time = "2025-07-26T12:01:17.088Z" },
+    { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234, upload-time = "2025-07-26T12:01:18.256Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123, upload-time = "2025-07-26T12:01:19.848Z" },
+    { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" },
+    { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979, upload-time = "2025-07-26T12:01:22.448Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" },
+    { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" },
+    { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" },
+    { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" },
+    { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018, upload-time = "2025-07-26T12:01:35.64Z" },
+    { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567, upload-time = "2025-07-26T12:01:36.804Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655, upload-time = "2025-07-26T12:01:37.999Z" },
+    { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" },
+    { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" },
+    { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" },
+    { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" },
+    { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" },
+    { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" },
+    { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" },
+    { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" },
+    { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" },
+    { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" },
+    { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" },
+    { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" },
+    { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" },
+    { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" },
+    { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" },
+    { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" },
+    { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" },
+    { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" },
+    { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809, upload-time = "2025-07-26T12:02:52.74Z" },
+    { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593, upload-time = "2025-07-26T12:02:54.037Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202, upload-time = "2025-07-26T12:02:55.947Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207, upload-time = "2025-07-26T12:02:57.468Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" },
+]
+
+[[package]]
+name = "cuda-bindings"
+version = "13.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/51/6b/457ca12dad3ee9bfcc9a545cfd6b64b359ba49de40f776f6e028e678f262/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c5879712accf6e14bb01aa5e67440eb84998b8d104b509cc7a6dc0b8f656a474", size = 6053539, upload-time = "2026-05-29T23:11:43.19Z" },
+    { url = "https://files.pythonhosted.org/packages/95/7a/c5e3c34a409b148f5c0f5a4ea374158f95d488862c1dffedf9aa5c639df9/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:04436a9364059c84b8f9636f359eccda1cf814341f5b670c71d80d2f79dbc708", size = 6674166, upload-time = "2026-05-29T23:11:45.478Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/67/5e7dba1ba576dd73da5dee894ca076ca5e959450dfff66d6d510a255d1f7/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c7855c4868aabc0cfae28abbe83d56734bdfbd08f08fc234ac1912a12858bf49", size = 6025351, upload-time = "2026-05-29T23:11:49.685Z" },
+    { url = "https://files.pythonhosted.org/packages/39/2a/6d2e9047d1fb243dbaa364b01e0297534b9ed7fd27dba1c9f361519cf69b/cuda_bindings-13.3.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e32d08f71ebcdf00f0f41eab2eb37e8da94c8ed411cc9f7f7a019ce6b34abe3a", size = 6657965, upload-time = "2026-05-29T23:11:52.227Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/6e/2394f8163360f8391f8f1b7e72d300a82724edb81a7b7084c799fbd4c91f/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9efb21c1ee64981e184b9e0ba5eb3179e5ba3d4b51665a6cb52b8ef3d01a7cbf", size = 5920504, upload-time = "2026-05-29T23:11:56.883Z" },
+    { url = "https://files.pythonhosted.org/packages/34/c2/ef9b6a63f7dc432712a462c816662e662e00d38caa9b861c8c2588195d03/cuda_bindings-13.3.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2732904099e0a4d4db774a5fc6d91ee95fae065b4d2ecabb4968c5fe2406c9d7", size = 6476660, upload-time = "2026-05-29T23:11:59.188Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/81/bff68ce829999c1e4209c761bbf903b1c06ec570416ddb25020864ad5907/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1ab2f74ed65bfef4163ba07a8db16f1085e0729291db12a2423aff84ee8278b8", size = 6013639, upload-time = "2026-05-29T23:12:03.509Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/e0/c8a1f0c8f9ffdea4f5fe6dbab89b326cef4d85caf489dad39e209da89416/cuda_bindings-13.3.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd4c814d311ec08c981f6dded1dbe7d4b371067ee4f6c14cccec4bde9590f80", size = 6534419, upload-time = "2026-05-29T23:12:05.633Z" },
+    { url = "https://files.pythonhosted.org/packages/52/b8/83b1f563925b290f2d11a01a77a84013ba56052fe3653a5bef3ccfbb43d6/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c3c772dfff49681541d59630c90f858e173ac926b9c593a2b7123f2a1043cc76", size = 5809771, upload-time = "2026-05-29T23:12:10.422Z" },
+    { url = "https://files.pythonhosted.org/packages/12/20/e79b4bfe98f075195afb6343d41c498f9dbd2d161d7021d4d28bceb83581/cuda_bindings-13.3.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:36febb7c1079d68a981dbbd8d5a67235b399802b82075c9388624719607e52b9", size = 6358584, upload-time = "2026-05-29T23:12:12.767Z" },
+]
+
+[[package]]
+name = "cuda-pathfinder"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/b4/d088047afe39827556df21118cac9ffd20cc3f968c99a7681494d1eb333c/cuda_pathfinder-1.6.0-py3-none-any.whl", hash = "sha256:1503af579d8379c24bdd65528379bc57039b0455be9f5f9686cf8e473a1fce51", size = 54591, upload-time = "2026-07-21T15:03:56.224Z" },
+]
+
+[[package]]
+name = "cuda-toolkit"
+version = "13.0.3.0"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/c7/a79086a62c98befcdb8349656c6f114e2db3b8b2422f6e25c97a7f2a9a3c/cuda_toolkit-13.0.3.0-py2.py3-none-any.whl", hash = "sha256:d693caaa261214ddd7dbb60d68e71cbed884e68c2be7509778f3051da0b91c3f", size = 2512, upload-time = "2026-04-14T00:50:08.173Z" },
+]
+
+[package.optional-dependencies]
+cublas = [
+    { name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+cudart = [
+    { name = "nvidia-cuda-runtime", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+cufft = [
+    { name = "nvidia-cufft", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+cufile = [
+    { name = "nvidia-cufile", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+]
+cupti = [
+    { name = "nvidia-cuda-cupti", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+curand = [
+    { name = "nvidia-curand", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+cusolver = [
+    { name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-cusolver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+cusparse = [
+    { name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+nvjitlink = [
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+nvrtc = [
+    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+nvtx = [
+    { name = "nvidia-nvtx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine == 'AMD64' and sys_platform == 'win32')" },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
+]
+
+[[package]]
+name = "filelock"
+version = "3.32.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/64/a02e6765de08964ed371eca577870593245afc9dfac16d037de7c10d18e6/filelock-3.32.3.tar.gz", hash = "sha256:0ffa185a3540854c95caa7fa76b76cb219d907415e2c5dc9af25fd970563487f", size = 218135, upload-time = "2026-08-13T16:00:05.577Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/8e/50f46a9c0ce8d2861a394c1347caae037ea0431d2f67d7feb151cbc4649a/filelock-3.32.3-py3-none-any.whl", hash = "sha256:7f0ca4bcc0e181c60dbbd8aa9ab5b120ebb99e4e064e83636340056f833a1f09", size = 98901, upload-time = "2026-08-13T16:00:03.974Z" },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.63.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/75/2b/a7f1545bdf5da69c4bda0cea2a5781f0ad2a6623e0277267672db43c5fe6/fonttools-4.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f", size = 2881793, upload-time = "2026-05-14T12:02:56.645Z" },
+    { url = "https://files.pythonhosted.org/packages/49/50/965308c703f085f225db2886813b27e015b8b3438c350b22dd65b52c2a2c/fonttools-4.63.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:79cdc9f567aec74a72918fd060283911406750cbc9fd28c1316023deb6ce31a9", size = 2428130, upload-time = "2026-05-14T12:02:58.891Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/38/6937fbd7f2dc3a6b48725851bc2c15ec949b9af14d9bbcb5fe83cdf9bdf9/fonttools-4.63.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c14b4fd138c4bafcca294765c547914e1aa431ae1ca94ab99d8db08c958bd3b", size = 5111952, upload-time = "2026-05-14T12:03:01.263Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/43/a81f20050a3115b57d62c8e781446949512eac36690dc384ccea65ff4cc1/fonttools-4.63.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76ac49f929aecaf82d83250b8347e099d7aecba0f4726c1d9b6df3b8bb5fe18", size = 5082308, upload-time = "2026-05-14T12:03:03.211Z" },
+    { url = "https://files.pythonhosted.org/packages/67/00/cdd9d4944ca6ae280d01e69cc37bde3bf663630b837a6fc6d2cd65d80e0e/fonttools-4.63.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dcf076a4474fe0d7367e5bbf5b052c7284fa1feca729c04176ce513521afd8a0", size = 5087932, upload-time = "2026-05-14T12:03:05.147Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/f1/0aa0dbea778c75adbef223c42019fd47d22262b905974d62d829545d485f/fonttools-4.63.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:7dd683fef0663e9f0f45cf541d788d24caa3ec9db50796b588e1757d8b3bc007", size = 5213271, upload-time = "2026-05-14T12:03:07.238Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/99/253e4056e1f0e67b9390125a154b73b5eb73ad521bece95c004858fdeec2/fonttools-4.63.0-cp311-cp311-win32.whl", hash = "sha256:afefc1ed0a59785a7fb06ea7e1678e849c193e1e387db783579bc7b3056fcfcb", size = 2304473, upload-time = "2026-05-14T12:03:09.271Z" },
+    { url = "https://files.pythonhosted.org/packages/08/60/defa5e69641db890a63be281f41345f4c33b157824eaf0b9fad3e08b0dcb/fonttools-4.63.0-cp311-cp311-win_amd64.whl", hash = "sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c", size = 2356389, upload-time = "2026-05-14T12:03:11.53Z" },
+    { url = "https://files.pythonhosted.org/packages/08/ef/b3c6b9b5be2f82416d73fe2ed2e96e2793cd80e7510bd6a17ca79cdd88ec/fonttools-4.63.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:37dd23e621e3b0aef1baa70a303b80aaf38449632cfc8fd2a55fb285bbccfc02", size = 2881131, upload-time = "2026-05-14T12:03:13.386Z" },
+    { url = "https://files.pythonhosted.org/packages/44/a0/c815bea63117fa63e4e1c01f8a1110d2112fa003f838e6467094ec2432ce/fonttools-4.63.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0", size = 2426704, upload-time = "2026-05-14T12:03:15.801Z" },
+    { url = "https://files.pythonhosted.org/packages/44/04/0b91d8e916e92ad1fac9e4624760baf0fd5ff2ead614c2f68fb21373f03f/fonttools-4.63.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af", size = 5044298, upload-time = "2026-05-14T12:03:18.085Z" },
+    { url = "https://files.pythonhosted.org/packages/77/c7/2342da9830e3e9d4870305ca5d2091d2a83284f2953079b7bdd3b5e029d8/fonttools-4.63.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:58dc6bb86a78d782f00f9190ca02c119cf5bbe2807536e361e18d42019f877d8", size = 4999800, upload-time = "2026-05-14T12:03:20.161Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/6d/67fe16c48d7ce050979b33f47e0d28a318f02da030602e944c34f7a16ef3/fonttools-4.63.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b", size = 4982666, upload-time = "2026-05-14T12:03:22.87Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/00/3bbab338c07c71fa56269953845e92c951a61457bbbb0f1022551ea266d9/fonttools-4.63.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78", size = 5133598, upload-time = "2026-05-14T12:03:25.168Z" },
+    { url = "https://files.pythonhosted.org/packages/62/f2/aa27c7f98db5b064883dadcc5283947e81e034de42e22a33675878d98b54/fonttools-4.63.0-cp312-cp312-win32.whl", hash = "sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263", size = 2292575, upload-time = "2026-05-14T12:03:27.496Z" },
+    { url = "https://files.pythonhosted.org/packages/87/36/cccb9bc2a6ab63d1b2980374f0dca72ce95ae267c9b4cfe77455bb70d0d4/fonttools-4.63.0-cp312-cp312-win_amd64.whl", hash = "sha256:59ac449f8cca9b4ffa08d2e7bbadad87ce710d69d1eda5c3c1ce579baa987272", size = 2343211, upload-time = "2026-05-14T12:03:30.057Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/8d/d8fec3dcde2963f8c908fb315e5ff2cd0ac34f82394bbbf73a2aa5145ce3/fonttools-4.63.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd", size = 2876062, upload-time = "2026-05-14T12:03:32.554Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/71/d935dc54e4ff121bfdd11e08702db63a7e6f25af21d8a3d7b7212df53641/fonttools-4.63.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c2a2a42198b696a6f48fad91709afb55176e66a5e566131219dba372fb7f8c59", size = 2424594, upload-time = "2026-05-14T12:03:34.86Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/40/e76320afa1df918e146155ef239b1719ee266092e96f5423bfd075affba1/fonttools-4.63.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d", size = 5024840, upload-time = "2026-05-14T12:03:36.745Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/36/0b805d8c485f872f65a509cbe3b58a5d0d17bee855333b54a150c79d3061/fonttools-4.63.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68", size = 4975801, upload-time = "2026-05-14T12:03:38.833Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/26/2cee03d0aa083ab022da5c07aff9ed3f689da1defb81ad6917c9627896da/fonttools-4.63.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be", size = 4965009, upload-time = "2026-05-14T12:03:41.494Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/48/cc4b66d9058c0d0982c833fad10127c4b0e9324606aafa41382295ca4102/fonttools-4.63.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27", size = 5105892, upload-time = "2026-05-14T12:03:43.525Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/1f/a98a30a814b9ddef3a2e706025f90b9e0bc94890e6cb15254bc86547d11a/fonttools-4.63.0-cp313-cp313-win32.whl", hash = "sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380", size = 2291313, upload-time = "2026-05-14T12:03:45.594Z" },
+    { url = "https://files.pythonhosted.org/packages/92/46/5177b01f3b4abfdd4409f31cca4ab279c9343a26efbe9ec78c97fc612e02/fonttools-4.63.0-cp313-cp313-win_amd64.whl", hash = "sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b", size = 2342299, upload-time = "2026-05-14T12:03:47.414Z" },
+    { url = "https://files.pythonhosted.org/packages/27/d2/23d25e3f247b328be58d04a4c9f894178a0d1eda7d42867cfb388adaf416/fonttools-4.63.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745", size = 2875338, upload-time = "2026-05-14T12:03:50.052Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/58/7dfa0c761cb3b2964e2a84c4dc986c926a87de0cb9fb60d5b28ded3f2914/fonttools-4.63.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6e528da43bc3791085f8cb6141b1d13e459226790240340fcbb4625649238b03", size = 2422661, upload-time = "2026-05-14T12:03:52.154Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/87/64cfa18a7a1621d17b7f4502b2b0ed8a135a90c3db51ea590ee99043e76b/fonttools-4.63.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b2248c5decb223562f7902ff6325077a073f608ee8e33e88ad88db734eb9f49", size = 5010526, upload-time = "2026-05-14T12:03:54.647Z" },
+    { url = "https://files.pythonhosted.org/packages/36/e1/a8933a72c45a87177fbde2696e0d0755c8c9062f8c077a961c6215fa27b1/fonttools-4.63.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b", size = 4923946, upload-time = "2026-05-14T12:03:56.984Z" },
+    { url = "https://files.pythonhosted.org/packages/27/60/872e6e233b8c5e8b41413796ff18b7fe479661bd40147e071b450dfad7a1/fonttools-4.63.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6", size = 4962489, upload-time = "2026-05-14T12:03:59.443Z" },
+    { url = "https://files.pythonhosted.org/packages/30/c4/83c24f2ec38b90cfda84bf4b1a1f49df80e84a1db4e7ac6e0d41bf23bc39/fonttools-4.63.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4", size = 5071870, upload-time = "2026-05-14T12:04:02.122Z" },
+    { url = "https://files.pythonhosted.org/packages/de/40/3ae22b60ff1d41ce0bd044b31238cdc72cef99f28b976f1e128ebd618c9b/fonttools-4.63.0-cp314-cp314-win32.whl", hash = "sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616", size = 2295026, upload-time = "2026-05-14T12:04:04.47Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/d4/98078064ccc76b45cb0f6c002452011e93c4bd26f6850344f0951cc1fe89/fonttools-4.63.0-cp314-cp314-win_amd64.whl", hash = "sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5", size = 2347454, upload-time = "2026-05-14T12:04:06.752Z" },
+    { url = "https://files.pythonhosted.org/packages/49/4e/652d1580c5f4e39f7d103b0c793e4773129ad633dce4addd0cf4dfebde02/fonttools-4.63.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:6db5140a60a5d731d21ec076745b40a310607731b0a565b50776393188649001", size = 2958152, upload-time = "2026-05-14T12:04:08.706Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/55/ad864c9a9b219f552eb46b32cd7906c466e5a578ba0c3abfcc0fe7413eb6/fonttools-4.63.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e", size = 2460809, upload-time = "2026-05-14T12:04:10.783Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/2b/0aa8db70f18cf52e49b4ed5ecec68547f981160bf5ded3b5aed6faa0a6f9/fonttools-4.63.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096", size = 5148649, upload-time = "2026-05-14T12:04:12.747Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/63/18e4369c25043096f1048e0c9915951adc4f842bd81c6b18155824d6fa99/fonttools-4.63.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:51394295f1a51de8b5f30bdb1e1b9a4231536c7064ef5c6e211eec19fa36036f", size = 4932147, upload-time = "2026-05-14T12:04:14.806Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/3f/67f3eac2ffd8a98446c5022f8ed3864eac878a5ff7af8df4c8286dba16cc/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40", size = 5027237, upload-time = "2026-05-14T12:04:17.675Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/ba/4e6214cb38a7b04779e97bb7636de9a5c7f20af7018d03dee0b64c08510a/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196", size = 5053933, upload-time = "2026-05-14T12:04:20.818Z" },
+    { url = "https://files.pythonhosted.org/packages/34/3b/214dcc19ee31d3d38fb5ad2755c11ef0514e5dc300bbaf41c0b69f393799/fonttools-4.63.0-cp314-cp314t-win32.whl", hash = "sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8", size = 2359326, upload-time = "2026-05-14T12:04:24.22Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/1e/3ff1a9b523058c2eeb6a9d50f5574e2a738200d0d94107d5bc4105e8da3f/fonttools-4.63.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419", size = 2425829, upload-time = "2026-05-14T12:04:26.829Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" },
+]
+
+[[package]]
+name = "fsspec"
+version = "2026.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/00/78/f34251dadb8f3921264a1d9b8946f5e542014ee2614b285261b4e40e6775/fsspec-2026.7.0.tar.gz", hash = "sha256:c803c40f4cf860b49dea58ee3e1c33cb9c790520e233537e1340049f89b82a88", size = 317040, upload-time = "2026-07-28T16:34:51.052Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/3c/6a2bf344106328fd04963664a60b9bb6496fc25df8e962fcdc1367285fb9/fsspec-2026.7.0-py3-none-any.whl", hash = "sha256:b57ddbafedfaef7018c1ecab32aa200a9d7ca26b77965f64e48b70061249d279", size = 206583, upload-time = "2026-07-28T16:34:49.538Z" },
+]
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/67/9c61eccb13f0bdca9307614e782fec49ffdde0f7a2314935d489fa93cd9c/kiwisolver-1.5.0.tar.gz", hash = "sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a", size = 103482, upload-time = "2026-03-09T13:15:53.382Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/dd/a495a9c104be1c476f0386e714252caf2b7eca883915422a64c50b88c6f5/kiwisolver-1.5.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9eed0f7edbb274413b6ee781cca50541c8c0facd3d6fd289779e494340a2b85c", size = 122798, upload-time = "2026-03-09T13:12:58.963Z" },
+    { url = "https://files.pythonhosted.org/packages/11/60/37b4047a2af0cf5ef6d8b4b26e91829ae6fc6a2d1f74524bcb0e7cd28a32/kiwisolver-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3c4923e404d6bcd91b6779c009542e5647fef32e4a5d75e115e3bbac6f2335eb", size = 66216, upload-time = "2026-03-09T13:13:00.155Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/aa/510dc933d87767584abfe03efa445889996c70c2990f6f87c3ebaa0a18c5/kiwisolver-1.5.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0df54df7e686afa55e6f21fb86195224a6d9beb71d637e8d7920c95cf0f89aac", size = 63911, upload-time = "2026-03-09T13:13:01.671Z" },
+    { url = "https://files.pythonhosted.org/packages/80/46/bddc13df6c2a40741e0cc7865bb1c9ed4796b6760bd04ce5fae3928ef917/kiwisolver-1.5.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2517e24d7315eb51c10664cdb865195df38ab74456c677df67bb47f12d088a27", size = 1438209, upload-time = "2026-03-09T13:13:03.385Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/d6/76621246f5165e5372f02f5e6f3f48ea336a8f9e96e43997d45b240ed8cd/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ff710414307fefa903e0d9bdf300972f892c23477829f49504e59834f4195398", size = 1248888, upload-time = "2026-03-09T13:13:05.231Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/c1/31559ec6fb39a5b48035ce29bb63ade628f321785f38c384dee3e2c08bc1/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6176c1811d9d5a04fa391c490cc44f451e240697a16977f11c6f722efb9041db", size = 1266304, upload-time = "2026-03-09T13:13:06.743Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/ef/1cb8276f2d29cc6a41e0a042f27946ca347d3a4a75acf85d0a16aa6dcc82/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50847dca5d197fcbd389c805aa1a1cf32f25d2e7273dc47ab181a517666b68cc", size = 1319650, upload-time = "2026-03-09T13:13:08.607Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/e4/5ba3cecd7ce6236ae4a80f67e5d5531287337d0e1f076ca87a5abe4cd5d0/kiwisolver-1.5.0-cp311-cp311-manylinux_2_39_riscv64.whl", hash = "sha256:01808c6d15f4c3e8559595d6d1fe6411c68e4a3822b4b9972b44473b24f4e679", size = 970949, upload-time = "2026-03-09T13:13:10.299Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/69/dc61f7ae9a2f071f26004ced87f078235b5507ab6e5acd78f40365655034/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:f1f9f4121ec58628c96baa3de1a55a4e3a333c5102c8e94b64e23bf7b2083309", size = 2199125, upload-time = "2026-03-09T13:13:11.841Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/7b/abbe0f1b5afa85f8d084b73e90e5f801c0939eba16ac2e49af7c61a6c28d/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b7d335370ae48a780c6e6a6bbfa97342f563744c39c35562f3f367665f5c1de2", size = 2293783, upload-time = "2026-03-09T13:13:14.399Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/80/5908ae149d96d81580d604c7f8aefd0e98f4fd728cf172f477e9f2a81744/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:800ee55980c18545af444d93fdd60c56b580db5cc54867d8cbf8a1dc0829938c", size = 1960726, upload-time = "2026-03-09T13:13:16.047Z" },
+    { url = "https://files.pythonhosted.org/packages/84/08/a78cb776f8c085b7143142ce479859cfec086bd09ee638a317040b6ef420/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:c438f6ca858697c9ab67eb28246c92508af972e114cac34e57a6d4ba17a3ac08", size = 2464738, upload-time = "2026-03-09T13:13:17.897Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/e1/65584da5356ed6cb12c63791a10b208860ac40a83de165cb6a6751a686e3/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:8c63c91f95173f9c2a67c7c526b2cea976828a0e7fced9cdcead2802dc10f8a4", size = 2270718, upload-time = "2026-03-09T13:13:19.421Z" },
+    { url = "https://files.pythonhosted.org/packages/be/6c/28f17390b62b8f2f520e2915095b3c94d88681ecf0041e75389d9667f202/kiwisolver-1.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:beb7f344487cdcb9e1efe4b7a29681b74d34c08f0043a327a74da852a6749e7b", size = 73480, upload-time = "2026-03-09T13:13:20.818Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/0e/2ee5debc4f77a625778fec5501ff3e8036fe361b7ee28ae402a485bb9694/kiwisolver-1.5.0-cp311-cp311-win_arm64.whl", hash = "sha256:ad4ae4ffd1ee9cd11357b4c66b612da9888f4f4daf2f36995eda64bd45370cac", size = 64930, upload-time = "2026-03-09T13:13:21.997Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/b2/818b74ebea34dabe6d0c51cb1c572e046730e64844da6ed646d5298c40ce/kiwisolver-1.5.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9", size = 123158, upload-time = "2026-03-09T13:13:23.127Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/d9/405320f8077e8e1c5c4bd6adc45e1e6edf6d727b6da7f2e2533cf58bff71/kiwisolver-1.5.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:72ec46b7eba5b395e0a7b63025490d3214c11013f4aacb4f5e8d6c3041829588", size = 66388, upload-time = "2026-03-09T13:13:24.765Z" },
+    { url = "https://files.pythonhosted.org/packages/99/9f/795fedf35634f746151ca8839d05681ceb6287fbed6cc1c9bf235f7887c2/kiwisolver-1.5.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819", size = 64068, upload-time = "2026-03-09T13:13:25.878Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/13/680c54afe3e65767bed7ec1a15571e1a2f1257128733851ade24abcefbcc/kiwisolver-1.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f", size = 1477934, upload-time = "2026-03-09T13:13:27.166Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/2f/cebfcdb60fd6a9b0f6b47a9337198bcbad6fbe15e68189b7011fd914911f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf", size = 1278537, upload-time = "2026-03-09T13:13:28.707Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/0d/9b782923aada3fafb1d6b84e13121954515c669b18af0c26e7d21f579855/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d", size = 1296685, upload-time = "2026-03-09T13:13:30.528Z" },
+    { url = "https://files.pythonhosted.org/packages/27/70/83241b6634b04fe44e892688d5208332bde130f38e610c0418f9ede47ded/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083", size = 1346024, upload-time = "2026-03-09T13:13:32.818Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/db/30ed226fb271ae1a6431fc0fe0edffb2efe23cadb01e798caeb9f2ceae8f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6", size = 987241, upload-time = "2026-03-09T13:13:34.435Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/bd/c314595208e4c9587652d50959ead9e461995389664e490f4dce7ff0f782/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1", size = 2227742, upload-time = "2026-03-09T13:13:36.4Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/43/0499cec932d935229b5543d073c2b87c9c22846aab48881e9d8d6e742a2d/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0", size = 2323966, upload-time = "2026-03-09T13:13:38.204Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/6f/79b0d760907965acfd9d61826a3d41f8f093c538f55cd2633d3f0db269f6/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15", size = 1977417, upload-time = "2026-03-09T13:13:39.966Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/31/01d0537c41cb75a551a438c3c7a80d0c60d60b81f694dac83dd436aec0d0/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314", size = 2491238, upload-time = "2026-03-09T13:13:41.698Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/34/8aefdd0be9cfd00a44509251ba864f5caf2991e36772e61c408007e7f417/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9", size = 2294947, upload-time = "2026-03-09T13:13:43.343Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/cf/0348374369ca588f8fe9c338fae49fa4e16eeb10ffb3d012f23a54578a9e/kiwisolver-1.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384", size = 73569, upload-time = "2026-03-09T13:13:45.792Z" },
+    { url = "https://files.pythonhosted.org/packages/28/26/192b26196e2316e2bd29deef67e37cdf9870d9af8e085e521afff0fed526/kiwisolver-1.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7", size = 64997, upload-time = "2026-03-09T13:13:46.878Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/69/024d6711d5ba575aa65d5538042e99964104e97fa153a9f10bc369182bc2/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09", size = 123166, upload-time = "2026-03-09T13:13:48.032Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/48/adbb40df306f587054a348831220812b9b1d787aff714cfbc8556e38fccd/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3", size = 66395, upload-time = "2026-03-09T13:13:49.365Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/3a/d0a972b34e1c63e2409413104216cd1caa02c5a37cb668d1687d466c1c45/kiwisolver-1.5.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd", size = 64065, upload-time = "2026-03-09T13:13:50.562Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/0a/7b98e1e119878a27ba8618ca1e18b14f992ff1eda40f47bccccf4de44121/kiwisolver-1.5.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3", size = 1477903, upload-time = "2026-03-09T13:13:52.084Z" },
+    { url = "https://files.pythonhosted.org/packages/18/d8/55638d89ffd27799d5cc3d8aa28e12f4ce7a64d67b285114dbedc8ea4136/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96", size = 1278751, upload-time = "2026-03-09T13:13:54.673Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/97/b4c8d0d18421ecceba20ad8701358453b88e32414e6f6950b5a4bad54e65/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099", size = 1296793, upload-time = "2026-03-09T13:13:56.287Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/10/f862f94b6389d8957448ec9df59450b81bec4abb318805375c401a1e6892/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8", size = 1346041, upload-time = "2026-03-09T13:13:58.269Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/6a/f1650af35821eaf09de398ec0bc2aefc8f211f0cda50204c9f1673741ba9/kiwisolver-1.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:d36ca54cb4c6c4686f7cbb7b817f66f5911c12ddb519450bbe86707155028f87", size = 987292, upload-time = "2026-03-09T13:13:59.871Z" },
+    { url = "https://files.pythonhosted.org/packages/de/19/d7fb82984b9238115fe629c915007be608ebd23dc8629703d917dbfaffd4/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23", size = 2227865, upload-time = "2026-03-09T13:14:01.401Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/b9/46b7f386589fd222dac9e9de9c956ce5bcefe2ee73b4e79891381dda8654/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859", size = 2324369, upload-time = "2026-03-09T13:14:02.972Z" },
+    { url = "https://files.pythonhosted.org/packages/92/8b/95e237cf3d9c642960153c769ddcbe278f182c8affb20cecc1cc983e7cc5/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902", size = 1977989, upload-time = "2026-03-09T13:14:04.503Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/95/980c9df53501892784997820136c01f62bc1865e31b82b9560f980c0e649/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167", size = 2491645, upload-time = "2026-03-09T13:14:06.106Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/32/900647fd0840abebe1561792c6b31e6a7c0e278fc3973d30572a965ca14c/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0", size = 2295237, upload-time = "2026-03-09T13:14:08.891Z" },
+    { url = "https://files.pythonhosted.org/packages/be/8a/be60e3bbcf513cc5a50f4a3e88e1dcecebb79c1ad607a7222877becaa101/kiwisolver-1.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276", size = 73573, upload-time = "2026-03-09T13:14:12.327Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/d2/64be2e429eb4fca7f7e1c52a91b12663aeaf25de3895e5cca0f47ef2a8d0/kiwisolver-1.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c", size = 64998, upload-time = "2026-03-09T13:14:13.469Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/69/ce68dd0c85755ae2de490bf015b62f2cea5f6b14ff00a463f9d0774449ff/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1", size = 125700, upload-time = "2026-03-09T13:14:14.636Z" },
+    { url = "https://files.pythonhosted.org/packages/74/aa/937aac021cf9d4349990d47eb319309a51355ed1dbdc9c077cdc9224cb11/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e", size = 67537, upload-time = "2026-03-09T13:14:15.808Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/20/3a87fbece2c40ad0f6f0aefa93542559159c5f99831d596050e8afae7a9f/kiwisolver-1.5.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7", size = 65514, upload-time = "2026-03-09T13:14:18.035Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/7f/f943879cda9007c45e1f7dba216d705c3a18d6b35830e488b6c6a4e7cdf0/kiwisolver-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c", size = 1584848, upload-time = "2026-03-09T13:14:19.745Z" },
+    { url = "https://files.pythonhosted.org/packages/37/f8/4d4f85cc1870c127c88d950913370dd76138482161cd07eabbc450deff01/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368", size = 1391542, upload-time = "2026-03-09T13:14:21.54Z" },
+    { url = "https://files.pythonhosted.org/packages/04/0b/65dd2916c84d252b244bd405303220f729e7c17c9d7d33dca6feeff9ffc4/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489", size = 1404447, upload-time = "2026-03-09T13:14:23.205Z" },
+    { url = "https://files.pythonhosted.org/packages/39/5c/2606a373247babce9b1d056c03a04b65f3cf5290a8eac5d7bdead0a17e21/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1", size = 1455918, upload-time = "2026-03-09T13:14:24.74Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/d1/c6078b5756670658e9192a2ef11e939c92918833d2745f85cd14a6004bdf/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_39_riscv64.whl", hash = "sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3", size = 1072856, upload-time = "2026-03-09T13:14:26.597Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/c8/7def6ddf16eb2b3741d8b172bdaa9af882b03c78e9b0772975408801fa63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18", size = 2333580, upload-time = "2026-03-09T13:14:28.237Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/87/2ac1fce0eb1e616fcd3c35caa23e665e9b1948bb984f4764790924594128/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021", size = 2423018, upload-time = "2026-03-09T13:14:30.018Z" },
+    { url = "https://files.pythonhosted.org/packages/67/13/c6700ccc6cc218716bfcda4935e4b2997039869b4ad8a94f364c5a3b8e63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310", size = 2062804, upload-time = "2026-03-09T13:14:32.888Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/bd/877056304626943ff0f1f44c08f584300c199b887cb3176cd7e34f1515f1/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3", size = 2597482, upload-time = "2026-03-09T13:14:34.971Z" },
+    { url = "https://files.pythonhosted.org/packages/75/19/c60626c47bf0f8ac5dcf72c6c98e266d714f2fbbfd50cf6dab5ede3aaa50/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2", size = 2394328, upload-time = "2026-03-09T13:14:36.816Z" },
+    { url = "https://files.pythonhosted.org/packages/47/84/6a6d5e5bb8273756c27b7d810d47f7ef2f1f9b9fd23c9ee9a3f8c75c9cef/kiwisolver-1.5.0-cp313-cp313t-win_arm64.whl", hash = "sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53", size = 68410, upload-time = "2026-03-09T13:14:38.695Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/d7/060f45052f2a01ad5762c8fdecd6d7a752b43400dc29ff75cd47225a40fd/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615", size = 123231, upload-time = "2026-03-09T13:14:41.323Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/a7/78da680eadd06ff35edef6ef68a1ad273bad3e2a0936c9a885103230aece/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:1d49a49ac4cbfb7c1375301cd1ec90169dfeae55ff84710d782260ce77a75a02", size = 66489, upload-time = "2026-03-09T13:14:42.534Z" },
+    { url = "https://files.pythonhosted.org/packages/49/b2/97980f3ad4fae37dd7fe31626e2bf75fbf8bdf5d303950ec1fab39a12da8/kiwisolver-1.5.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e", size = 64063, upload-time = "2026-03-09T13:14:44.759Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/f9/b06c934a6aa8bc91f566bd2a214fd04c30506c2d9e2b6b171953216a65b6/kiwisolver-1.5.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac", size = 1475913, upload-time = "2026-03-09T13:14:46.247Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/f0/f768ae564a710135630672981231320bc403cf9152b5596ec5289de0f106/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05", size = 1282782, upload-time = "2026-03-09T13:14:48.458Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/9f/1de7aad00697325f05238a5f2eafbd487fb637cc27a558b5367a5f37fb7f/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd", size = 1300815, upload-time = "2026-03-09T13:14:50.721Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/c2/297f25141d2e468e0ce7f7a7b92e0cf8918143a0cbd3422c1ad627e85a06/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a", size = 1347925, upload-time = "2026-03-09T13:14:52.304Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/d3/f4c73a02eb41520c47610207b21afa8cdd18fdbf64ffd94674ae21c4812d/kiwisolver-1.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:d168fda2dbff7b9b5f38e693182d792a938c31db4dac3a80a4888de603c99554", size = 991322, upload-time = "2026-03-09T13:14:54.637Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/46/d3f2efef7732fcda98d22bf4ad5d3d71d545167a852ca710a494f4c15343/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581", size = 2232857, upload-time = "2026-03-09T13:14:56.471Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/ec/2d9756bf2b6d26ae4349b8d3662fb3993f16d80c1f971c179ce862b9dbae/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303", size = 2329376, upload-time = "2026-03-09T13:14:58.072Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/9f/876a0a0f2260f1bde92e002b3019a5fabc35e0939c7d945e0fa66185eb20/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9", size = 1982549, upload-time = "2026-03-09T13:14:59.668Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/4f/ba3624dfac23a64d54ac4179832860cb537c1b0af06024936e82ca4154a0/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79", size = 2494680, upload-time = "2026-03-09T13:15:01.364Z" },
+    { url = "https://files.pythonhosted.org/packages/39/b7/97716b190ab98911b20d10bf92eca469121ec483b8ce0edd314f51bc85af/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796", size = 2297905, upload-time = "2026-03-09T13:15:03.925Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/36/4e551e8aa55c9188bca9abb5096805edbf7431072b76e2298e34fd3a3008/kiwisolver-1.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:d76e2d8c75051d58177e762164d2e9ab92886534e3a12e795f103524f221dd8e", size = 75086, upload-time = "2026-03-09T13:15:07.775Z" },
+    { url = "https://files.pythonhosted.org/packages/70/15/9b90f7df0e31a003c71649cf66ef61c3c1b862f48c81007fa2383c8bd8d7/kiwisolver-1.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:fa6248cd194edff41d7ea9425ced8ca3a6f838bfb295f6f1d6e6bb694a8518df", size = 66577, upload-time = "2026-03-09T13:15:09.139Z" },
+    { url = "https://files.pythonhosted.org/packages/17/01/7dc8c5443ff42b38e72731643ed7cf1ed9bf01691ae5cdca98501999ed83/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:d1ffeb80b5676463d7a7d56acbe8e37a20ce725570e09549fe738e02ca6b7e1e", size = 125794, upload-time = "2026-03-09T13:15:10.525Z" },
+    { url = "https://files.pythonhosted.org/packages/46/8a/b4ebe46ebaac6a303417fab10c2e165c557ddaff558f9699d302b256bc53/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4", size = 67646, upload-time = "2026-03-09T13:15:12.016Z" },
+    { url = "https://files.pythonhosted.org/packages/60/35/10a844afc5f19d6f567359bf4789e26661755a2f36200d5d1ed8ad0126e5/kiwisolver-1.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028", size = 65511, upload-time = "2026-03-09T13:15:13.311Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/8a/685b297052dd041dcebce8e8787b58923b6e78acc6115a0dc9189011c44b/kiwisolver-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657", size = 1584858, upload-time = "2026-03-09T13:15:15.103Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/80/04865e3d4638ac5bddec28908916df4a3075b8c6cc101786a96803188b96/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920", size = 1392539, upload-time = "2026-03-09T13:15:16.661Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/01/77a19cacc0893fa13fafa46d1bba06fb4dc2360b3292baf4b56d8e067b24/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9", size = 1405310, upload-time = "2026-03-09T13:15:18.229Z" },
+    { url = "https://files.pythonhosted.org/packages/53/39/bcaf5d0cca50e604cfa9b4e3ae1d64b50ca1ae5b754122396084599ef903/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d", size = 1456244, upload-time = "2026-03-09T13:15:20.444Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/7a/72c187abc6975f6978c3e39b7cf67aeb8b3c0a8f9790aa7fd412855e9e1f/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65", size = 1073154, upload-time = "2026-03-09T13:15:22.039Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/ca/cf5b25783ebbd59143b4371ed0c8428a278abe68d6d0104b01865b1bbd0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa", size = 2334377, upload-time = "2026-03-09T13:15:23.741Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/e5/b1f492adc516796e88751282276745340e2a72dcd0d36cf7173e0daf3210/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0", size = 2425288, upload-time = "2026-03-09T13:15:25.789Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/e5/9b21fbe91a61b8f409d74a26498706e97a48008bfcd1864373d32a6ba31c/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9", size = 2063158, upload-time = "2026-03-09T13:15:27.63Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/02/83f47986138310f95ea95531f851b2a62227c11cbc3e690ae1374fe49f0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f", size = 2597260, upload-time = "2026-03-09T13:15:29.421Z" },
+    { url = "https://files.pythonhosted.org/packages/07/18/43a5f24608d8c313dd189cf838c8e68d75b115567c6279de7796197cfb6a/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646", size = 2394403, upload-time = "2026-03-09T13:15:31.517Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/b5/98222136d839b8afabcaa943b09bd05888c2d36355b7e448550211d1fca4/kiwisolver-1.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681", size = 79687, upload-time = "2026-03-09T13:15:33.204Z" },
+    { url = "https://files.pythonhosted.org/packages/99/a2/ca7dc962848040befed12732dff6acae7fb3c4f6fc4272b3f6c9a30b8713/kiwisolver-1.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57", size = 70032, upload-time = "2026-03-09T13:15:34.411Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/fa/2910df836372d8761bb6eff7d8bdcb1613b5c2e03f260efe7abe34d388a7/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-macosx_10_13_x86_64.whl", hash = "sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797", size = 130262, upload-time = "2026-03-09T13:15:35.629Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/41/c5f71f9f00aabcc71fee8b7475e3f64747282580c2fe748961ba29b18385/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203", size = 138036, upload-time = "2026-03-09T13:15:36.894Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/06/7399a607f434119c6e1fdc8ec89a8d51ccccadf3341dee4ead6bd14caaf5/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7", size = 194295, upload-time = "2026-03-09T13:15:38.22Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/91/53255615acd2a1eaca307ede3c90eb550bae9c94581f8c00081b6b1c8f44/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-win_amd64.whl", hash = "sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57", size = 75987, upload-time = "2026-03-09T13:15:39.65Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/eb/5fcbbbf9a0e2c3a35effb88831a483345326bbc3a030a3b5b69aee647f84/kiwisolver-1.5.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:ec4c85dc4b687c7f7f15f553ff26a98bfe8c58f5f7f0ac8905f0ba4c7be60232", size = 59532, upload-time = "2026-03-09T13:15:47.047Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/9b/e17104555bb4db148fd52327feea1e96be4b88e8e008b029002c281a21ab/kiwisolver-1.5.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:12e91c215a96e39f57989c8912ae761286ac5a9584d04030ceb3368a357f017a", size = 57420, upload-time = "2026-03-09T13:15:48.199Z" },
+    { url = "https://files.pythonhosted.org/packages/48/44/2b5b95b7aa39fb2d8d9d956e0f3d5d45aef2ae1d942d4c3ffac2f9cfed1a/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737", size = 79892, upload-time = "2026-03-09T13:15:49.694Z" },
+    { url = "https://files.pythonhosted.org/packages/52/7d/7157f9bba6b455cfb4632ed411e199fc8b8977642c2b12082e1bd9e6d173/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16", size = 77603, upload-time = "2026-03-09T13:15:50.945Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/dd/8050c947d435c8d4bc94e3252f4d8bb8a76cfb424f043a8680be637a57f1/kiwisolver-1.5.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1", size = 73558, upload-time = "2026-03-09T13:15:52.112Z" },
+]
+
+[[package]]
+name = "markupsafe"
+version = "3.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/08/db/fefacb2136439fc8dd20e797950e749aa1f4997ed584c62cfb8ef7c2be0e/markupsafe-3.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad", size = 11631, upload-time = "2025-09-27T18:36:18.185Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/2e/5898933336b61975ce9dc04decbc0a7f2fee78c30353c5efba7f2d6ff27a/markupsafe-3.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a", size = 12058, upload-time = "2025-09-27T18:36:19.444Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/09/adf2df3699d87d1d8184038df46a9c80d78c0148492323f4693df54e17bb/markupsafe-3.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50", size = 24287, upload-time = "2025-09-27T18:36:20.768Z" },
+    { url = "https://files.pythonhosted.org/packages/30/ac/0273f6fcb5f42e314c6d8cd99effae6a5354604d461b8d392b5ec9530a54/markupsafe-3.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf", size = 22940, upload-time = "2025-09-27T18:36:22.249Z" },
+    { url = "https://files.pythonhosted.org/packages/19/ae/31c1be199ef767124c042c6c3e904da327a2f7f0cd63a0337e1eca2967a8/markupsafe-3.0.3-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f", size = 21887, upload-time = "2025-09-27T18:36:23.535Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/76/7edcab99d5349a4532a459e1fe64f0b0467a3365056ae550d3bcf3f79e1e/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a", size = 23692, upload-time = "2025-09-27T18:36:24.823Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/28/6e74cdd26d7514849143d69f0bf2399f929c37dc2b31e6829fd2045b2765/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115", size = 21471, upload-time = "2025-09-27T18:36:25.95Z" },
+    { url = "https://files.pythonhosted.org/packages/62/7e/a145f36a5c2945673e590850a6f8014318d5577ed7e5920a4b3448e0865d/markupsafe-3.0.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a", size = 22923, upload-time = "2025-09-27T18:36:27.109Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/62/d9c46a7f5c9adbeeeda52f5b8d802e1094e9717705a645efc71b0913a0a8/markupsafe-3.0.3-cp311-cp311-win32.whl", hash = "sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19", size = 14572, upload-time = "2025-09-27T18:36:28.045Z" },
+    { url = "https://files.pythonhosted.org/packages/83/8a/4414c03d3f891739326e1783338e48fb49781cc915b2e0ee052aa490d586/markupsafe-3.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01", size = 15077, upload-time = "2025-09-27T18:36:29.025Z" },
+    { url = "https://files.pythonhosted.org/packages/35/73/893072b42e6862f319b5207adc9ae06070f095b358655f077f69a35601f0/markupsafe-3.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c", size = 13876, upload-time = "2025-09-27T18:36:29.954Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" },
+    { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" },
+    { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" },
+    { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" },
+    { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" },
+    { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" },
+    { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" },
+    { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" },
+    { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" },
+    { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" },
+    { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" },
+    { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" },
+    { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" },
+    { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" },
+    { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" },
+    { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" },
+    { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" },
+    { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" },
+    { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" },
+    { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" },
+    { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" },
+    { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" },
+    { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" },
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.11.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/49/64/f9a391af28f518b11ad45a8a712353c94a0aefce09d3703200e5c54b610a/matplotlib-3.11.1.tar.gz", hash = "sha256:69647db5746941c793d6e445a4cd349323ffb87d9cc958c2ad84a659b4832d30", size = 32612045, upload-time = "2026-07-18T03:39:46.63Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6e/d0/791aa183dd88491555cf7d4be0b52b0bcf6c3c2a2c22c815a2e819bf53e2/matplotlib-3.11.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b7cf158e7add54a8d51ac9b5a84abd6d4e13ed4951b4f25f1c5139f41c2addb2", size = 9440302, upload-time = "2026-07-18T03:38:03.844Z" },
+    { url = "https://files.pythonhosted.org/packages/35/74/82bbdf683a301f4478384c8aaba6903631a2ca18294b2d7655c9a542bffb/matplotlib-3.11.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d2ace7273b9a5061a3b420918a16fae1f2dc5dfee1abcc13aba71b5d94b1820c", size = 9268549, upload-time = "2026-07-18T03:38:06.144Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/f0/9b4298911303f74e6d83e64a81d996c0616405ec95046fac7f17e4258b9e/matplotlib-3.11.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aee55e9041211bf84302ab55ec3965df18dd90ae19f8b58332a7feaf208bfe83", size = 10024922, upload-time = "2026-07-18T03:38:08.236Z" },
+    { url = "https://files.pythonhosted.org/packages/84/6f/0bc3c3d05b021db44c14bc379a7c0df7d57302aa15380c16fd4e63fd6a9b/matplotlib-3.11.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f4bdeea33a8d15a071dbfe6d119451b1d719c733ac666d65357082901a9099", size = 10832170, upload-time = "2026-07-18T03:38:10.276Z" },
+    { url = "https://files.pythonhosted.org/packages/db/4d/e375f39acdb2af5a9342730618608e39790ec842e6f1b392863028781459/matplotlib-3.11.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b4c78ceb2f11bcac7389d305cda17aeb1f4586a857854ab5780bd3dd8dbfc407", size = 10916701, upload-time = "2026-07-18T03:38:12.512Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/be/fa26ed085b41298f64a8f9b7592c671bbf1acc8b0df124c1c5de96b859f8/matplotlib-3.11.1-cp311-cp311-win_amd64.whl", hash = "sha256:7f33a781e12b1e53b278deb2f5373c2e55ec4f10727be3440c0cfb5cda9f944f", size = 9315331, upload-time = "2026-07-18T03:38:14.949Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/f3/eb5bdf3b6e191b200db298b08bbc1638b7f3c82cdc8680f9d88bf72559ae/matplotlib-3.11.1-cp311-cp311-win_arm64.whl", hash = "sha256:67e4c3cd578c65ebd81bdc09a1b6592ceafee6dfafe116dc85dfcb647b5bbb18", size = 9003475, upload-time = "2026-07-18T03:38:17.205Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/6c/7ef7ebcb2bd9739b2b66b18b076e077f44bb46fdbe28ca0506edb3c62c79/matplotlib-3.11.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:e15ef41507f3d525f46154ac9e3ae785dacde9f20e593a25de8986267892ef74", size = 9453849, upload-time = "2026-07-18T03:38:19.593Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/f8/6d0c312c8d9738e7d9677f09fe5c986b3239e651a7b73a2deb38b65e4a71/matplotlib-3.11.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:21a67b961a6d597bca54fae826cd20695ba4a6e4d05424a08da6e13e3176fd6b", size = 9283113, upload-time = "2026-07-18T03:38:21.95Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/cf/b4ad2cc81b6672ea29ea04e64e350a9f9b493b0908ccd884c67eeff8f7b2/matplotlib-3.11.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ba8f811b8ddfac493734d6af0b2dff96919d0c28ca0d641858dab4262777c6ea", size = 10035615, upload-time = "2026-07-18T03:38:24.315Z" },
+    { url = "https://files.pythonhosted.org/packages/88/90/4e10e033d9b66589d8ed98b84c95cdbb57033d57c1f41339d7393dbd2f2e/matplotlib-3.11.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c52f7ad20ef476806ed212380b1d54d20310c8b86bdc2c9a68b51f0024a44472", size = 10842559, upload-time = "2026-07-18T03:38:26.285Z" },
+    { url = "https://files.pythonhosted.org/packages/88/eb/799612d0f8cd3e816a10fec59329fca52cd2353264df80378dfc541ae855/matplotlib-3.11.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8b14eb22961fe865efb0e4ff167e333e428908b00115a8d800ccb65ee108e481", size = 10927532, upload-time = "2026-07-18T03:38:28.532Z" },
+    { url = "https://files.pythonhosted.org/packages/88/89/56649bbaa2fd12e20f3be03dbcc135b0c8676d88bac17977599e3eb442a0/matplotlib-3.11.1-cp312-cp312-win_amd64.whl", hash = "sha256:88a2a27dd9691ae448dfae4b26f59036be90c3c28757edd3553a29559d00859f", size = 9333886, upload-time = "2026-07-18T03:38:30.477Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/11/4d124efbbad677b7b7552f6f85a3bd432d4232f95400cea98fcd2ae36ef3/matplotlib-3.11.1-cp312-cp312-win_arm64.whl", hash = "sha256:480194afceca4df2f137c2721227d3cba67121fbf4397b69cee7f83714b0a58a", size = 9007545, upload-time = "2026-07-18T03:38:32.833Z" },
+    { url = "https://files.pythonhosted.org/packages/04/6c/4798363b7fb5644e309fe1fac30216e9146c9f70859d80d588c18caf5317/matplotlib-3.11.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6771b0cd7838c6a857a7209814158c0ad09bfef878db3033dd82d70ad101f191", size = 9454341, upload-time = "2026-07-18T03:38:35.001Z" },
+    { url = "https://files.pythonhosted.org/packages/59/98/6acadbe7f98df19d274bc107ac58bb439fa75df82c33dc110d71a4a8501f/matplotlib-3.11.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2abdee5ffa2fe11b2d19f7a5c63b785fb7c28cc46c7bc1814156341d9d1a33e1", size = 9283627, upload-time = "2026-07-18T03:38:37.061Z" },
+    { url = "https://files.pythonhosted.org/packages/24/ea/65cec46fe241390ccea1b1754207ee28eb71c5ab866bd5f22fe47e538fa4/matplotlib-3.11.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b0a19dcf73406d3746d25a5ed42d713604c9a3e024d129b102852b0d941cb9f3", size = 10035860, upload-time = "2026-07-18T03:38:39.663Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/10/63fdccccbabe002fb0960876baabc5e3f24d9c1bb4cfb25651457f74b3a0/matplotlib-3.11.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7389b77ed2ab0552f46d9a90b81b7b8e6dfcdc42adc36c37a0865799843e0e3e", size = 10843594, upload-time = "2026-07-18T03:38:42.144Z" },
+    { url = "https://files.pythonhosted.org/packages/98/51/a1155945bff7b91381875022ac1522c5dfdac0d006be8e7df389b3134eae/matplotlib-3.11.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c90be0b73568da4f662afac580956a76e308437e641b4a45aa08925eeb67d95f", size = 10927962, upload-time = "2026-07-18T03:38:44.302Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/3a/3d5e1f42dc761bf53401a62a83ff93389b37de9d2c093b2a3aa49ac34f1b/matplotlib-3.11.1-cp313-cp313-win_amd64.whl", hash = "sha256:68408341f2312836fbbdf6b3c78047f65b2d8752f5fd221c3e72d348f5b34f8b", size = 9334074, upload-time = "2026-07-18T03:38:46.616Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/db/3f5ea5a5b64060ef5e1ff60a19170423e41ce21b8497a6fe15a36e0b43e3/matplotlib-3.11.1-cp313-cp313-win_arm64.whl", hash = "sha256:0c1f44890d435c1b4ef52f701ad5828cb450ea97bcc83918fda6be74965d6cd2", size = 9007662, upload-time = "2026-07-18T03:38:49.112Z" },
+    { url = "https://files.pythonhosted.org/packages/98/6e/c7ae5e0531425b69c0826b00ebbc264c85cab853f1cd6e096c9983c2cdc1/matplotlib-3.11.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:5e510088c27a89d53580a752f959146893563e63c330e161d159b0fee652af6f", size = 9503790, upload-time = "2026-07-18T03:38:51.527Z" },
+    { url = "https://files.pythonhosted.org/packages/92/79/15be162e0a2ed546939674e2e97d0e33ec2447d86d4d4e611fa295bb178c/matplotlib-3.11.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:1524e2bdd48a93557aa47ddcfe9c225dfdd57d5a01a5c49128c20f0632980ee1", size = 9336148, upload-time = "2026-07-18T03:38:53.564Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/7f/36ffe144fc4aacfe0e3ed2318f72b6755d1e73b041d619b4d393e60f5a66/matplotlib-3.11.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:11664c551345553db92e61cae6cf1376f138f8c47cafdf13b64b18f3e3e9e464", size = 10049244, upload-time = "2026-07-18T03:38:55.911Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/5f/55812d68c0a840d3a463638f48c00ab1fe338518ec49a640cb6473b444af/matplotlib-3.11.1-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e1f8922ba31959cf6a9dfb51be64b7f7bc582801a3957dc0c2f3afcd3537adf", size = 10860798, upload-time = "2026-07-18T03:38:58.282Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/64/cca444b4eb5e6c768c44fc5e1f0b5211f20ca2b282778051996e996a2bdf/matplotlib-3.11.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83235693abde86e5e0129998f80ee39fc7f58e6d56a88fafb28a9278833e9d5f", size = 10943282, upload-time = "2026-07-18T03:39:00.465Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/0f/a49c329d394f2e9ef38506982107e8b04ecf94dd41a9d8423ff82cc737c7/matplotlib-3.11.1-cp313-cp313t-win_amd64.whl", hash = "sha256:9a076f4fc5cdc43fdf510f5981418d25c2db4973418d9f22d8bb3dc8045ada78", size = 9383532, upload-time = "2026-07-18T03:39:02.468Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/50/103e86afb806d8f64d04ede14e4cfc09dbfc25f512421ff85fdd6ebd59cf/matplotlib-3.11.1-cp313-cp313t-win_arm64.whl", hash = "sha256:216fbb93a74add02ddb4cb38ef5348f59ac00b3e84567eaf16598772d40e150a", size = 9059665, upload-time = "2026-07-18T03:39:04.607Z" },
+    { url = "https://files.pythonhosted.org/packages/35/04/3079499fa8cb661ea66d13d6439d5a3ae6710a7afd5c7f72e08914f275f8/matplotlib-3.11.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:30c492d4ba9448595b6fd8708c6725963f8148e25c0d8842948da5b05f0ee8d3", size = 9456022, upload-time = "2026-07-18T03:39:07.041Z" },
+    { url = "https://files.pythonhosted.org/packages/53/a2/69acfe84ec1f32930e801a5782a07fc5c79c8c6599a507b806d859d5da8e/matplotlib-3.11.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ac104be2768ffdd8655db9e71b768cbb45f2b9aa7b450cf1595e8f65d3822319", size = 9285475, upload-time = "2026-07-18T03:39:09.562Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/b3/31b15a2ca56d4ddd6aaa1c884c2f51cf9a61cfaf5ca6f6fbd6343d38e6df/matplotlib-3.11.1-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6be943cb68bc6660ead58c55b3aa6366cba2ef7feb06460fbcce32360376f19f", size = 10847102, upload-time = "2026-07-18T03:39:11.532Z" },
+    { url = "https://files.pythonhosted.org/packages/64/0d/a17e966e620545c1548125af0b29ac812dd17b197a18a7462ac12fa859ee/matplotlib-3.11.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5af0dcda57d471440a7b5b623e70e0a61003518443d9098f211a96ecfbbc25be", size = 11131087, upload-time = "2026-07-18T03:39:13.764Z" },
+    { url = "https://files.pythonhosted.org/packages/97/c5/5e100efdd67abb7de20befaa333612ef9bfc63417fb71398f904f25d083c/matplotlib-3.11.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:3d3fd84082b1afbd9398466c81309e20045be20d48fe0fb18c43504d164cbbb2", size = 10929036, upload-time = "2026-07-18T03:39:16.888Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/04/d719a0a36930ecc8dfc801ff340f9dcfc4223f8ca5d39d06b4020032fff8/matplotlib-3.11.1-cp314-cp314-win_amd64.whl", hash = "sha256:9601a1e90be21e4884c53b4f3dc3ee0544654946f9975258d691f1c2e2f119c6", size = 9489571, upload-time = "2026-07-18T03:39:19.449Z" },
+    { url = "https://files.pythonhosted.org/packages/48/65/facabdc2f1f6caba7e856db64dfedddca25f7608df07d96a1c8fd114fd3b/matplotlib-3.11.1-cp314-cp314-win_arm64.whl", hash = "sha256:ae30c6109848ac0f9fa36c5d6270938487614c47ba31860bd5361266dabc5685", size = 9164486, upload-time = "2026-07-18T03:39:21.424Z" },
+    { url = "https://files.pythonhosted.org/packages/88/dd/18da6cd01cf96354534f98c468a25380c68ce582a2c9dd0cae12b04af4f2/matplotlib-3.11.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:dadfe80797174e2984aae3be0b77594a3c72d2c0a40fbd4a0de48d2728caf3ae", size = 9504876, upload-time = "2026-07-18T03:39:23.633Z" },
+    { url = "https://files.pythonhosted.org/packages/79/b0/f0b63555a18b79d038c81fd6126f35fc4dfce0eaff48d96103348c7cf935/matplotlib-3.11.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:89b193b255f4f6f7948dbcee3691f4f341ab05d9a8874a67b45ddb4182922eda", size = 9336120, upload-time = "2026-07-18T03:39:25.797Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/dd/f210ec7c4a6f198d5567237048a93d0811fb5a1f1691f13320e592f95b41/matplotlib-3.11.1-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:191163532cdefcb1571ca38a6d7e6474baccde64495783e6ba47aa07ec4b9bbb", size = 10858033, upload-time = "2026-07-18T03:39:27.999Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/d2/d6d5324507c5fbb316db48e258c09c2807f3de03d9af47017e120070926f/matplotlib-3.11.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9fdf1c818ab05d0e74002091ddaf414478a3a449ec9d51c8976d45be7e3a01e2", size = 11141827, upload-time = "2026-07-18T03:39:30.092Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/68/3c22e9320bdce2c4d2f1320643ef706db7a24cb7420eea28b97a2d67f5a8/matplotlib-3.11.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b937b9dba5f5f6c1e31c47abe2186c865c0914fd18f2ce0dfc39c9adcef5951d", size = 10943061, upload-time = "2026-07-18T03:39:32.356Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/4a/907ed190ee81a9df581e0ed5456134fc0f7cb55ffcfda2f9e54ca900761c/matplotlib-3.11.1-cp314-cp314t-win_amd64.whl", hash = "sha256:f2912f647f3fbe1ccf085f91e213936f9101bead81a5e670565b1f1b3712f4fb", size = 9540074, upload-time = "2026-07-18T03:39:34.789Z" },
+    { url = "https://files.pythonhosted.org/packages/23/d4/97c19b77e0a6e3b48581185bb65088f431cd20186076cc0f650a1757ea46/matplotlib-3.11.1-cp314-cp314t-win_arm64.whl", hash = "sha256:54d47b8ae8b579633a3902ca5b4ad6c1e132a5626d64447b2e22a66394e79987", size = 9213472, upload-time = "2026-07-18T03:39:37.141Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/38/ceb1d637c4db6d06141f3739e93af3321e7caaabe69b57ae48ffe3ee95b1/matplotlib-3.11.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:427258425f9a3fc4ed79a91f9e9b9aaf5a82cb6571e85dc14063cc6fbb993741", size = 9438045, upload-time = "2026-07-18T03:39:39.491Z" },
+    { url = "https://files.pythonhosted.org/packages/89/25/72ad8b58602d3a6ef1dfc4b65ecd01634ab65a2bdf494c9fe0e966dbf081/matplotlib-3.11.1-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:1ac697e591c11b6ad04679a73c2d2f9980fe9d9f0311fb414a2e329706343dfb", size = 9266127, upload-time = "2026-07-18T03:39:41.597Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/6d/69552382fcc8e93d1f2763ef2665980a900a48b7f3a4c57ed290726d1cbc/matplotlib-3.11.1-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e4b9ac2f1f607ecda2af90a5232beee2af7582fce1cc30c4b6a1b012dc21ee99", size = 10019439, upload-time = "2026-07-18T03:39:43.78Z" },
+]
+
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" },
+]
+
+[[package]]
+name = "networkx"
+version = "3.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.4.6"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.12'",
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/49/ec46835a70be8fa6446c495126ac84fdb28cb2558e1620ffb87a10c8b64c/numpy-2.4.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4", size = 16969194, upload-time = "2026-05-18T23:33:13.503Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/0d/f5957185c0ee2f3e12f78715aa9e3b353fd83633316c8532b38faa37e3f6/numpy-2.4.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d", size = 14964111, upload-time = "2026-05-18T23:33:17.795Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/40/40a40ee0ddf7ceb782c49af278894b686e586d65d8c1889c8b5da01a3d7d/numpy-2.4.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8", size = 5469159, upload-time = "2026-05-18T23:33:20.654Z" },
+    { url = "https://files.pythonhosted.org/packages/63/13/f9a8046535cb21deae82f8d03de9617e08882d274fad2539630761888228/numpy-2.4.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538", size = 6798936, upload-time = "2026-05-18T23:33:22.987Z" },
+    { url = "https://files.pythonhosted.org/packages/33/a8/6fa8c1a345a8c85dbb21932c447bee07c30a2c2a3f31e369c0a84b300147/numpy-2.4.6-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47", size = 15966692, upload-time = "2026-05-18T23:33:26.62Z" },
+    { url = "https://files.pythonhosted.org/packages/02/03/74fe2a4cb3817d94d86402f2506554130a2f01414e299b5a843e5a8a957f/numpy-2.4.6-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93", size = 16918164, upload-time = "2026-05-18T23:33:29.955Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/80/3615be3313f7e7696609bc194b9f0101da809df79e859bdb84e0cd043f46/numpy-2.4.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8", size = 17322877, upload-time = "2026-05-18T23:33:34.724Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/ac/a691e0fe2675e370d0e08ff905adc49a1c8830e8cae03efe4477e92cd55d/numpy-2.4.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6", size = 18651487, upload-time = "2026-05-18T23:33:38.217Z" },
+    { url = "https://files.pythonhosted.org/packages/15/a7/9bc1cd626d7bf6869bfedf27b91b6ab5dd607758bf8e959d6fa80c6a59cb/numpy-2.4.6-cp311-cp311-win32.whl", hash = "sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8", size = 6233945, upload-time = "2026-05-18T23:33:41.331Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/31/7fc6239c12bce7e931463251cca4426c465e1876ba3cc785402ef4dd8f4e/numpy-2.4.6-cp311-cp311-win_amd64.whl", hash = "sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147", size = 12608406, upload-time = "2026-05-18T23:33:44.131Z" },
+    { url = "https://files.pythonhosted.org/packages/27/83/140f85a466595a16382996a1bf06b2b54bcd597488921b0c9daaeeda72af/numpy-2.4.6-cp311-cp311-win_arm64.whl", hash = "sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577", size = 10479528, upload-time = "2026-05-18T23:33:50.725Z" },
+    { url = "https://files.pythonhosted.org/packages/95/2a/3d7b5ac8aac24feaf9ad7ed58f45b0bbc06d37e4338ae84c9f2298b570f9/numpy-2.4.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1", size = 16689119, upload-time = "2026-05-18T23:33:54.065Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/12/92c4c131527599e8288d6918e888d88726f84d805d784b771f32408aeaef/numpy-2.4.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb", size = 14699246, upload-time = "2026-05-18T23:33:57.621Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/fe/c0a6b7b2ca128a8fb228575147073b660656734b8ebe4d76c8fd748dcc79/numpy-2.4.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41", size = 5204410, upload-time = "2026-05-18T23:34:00.302Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/d4/9770d14ba719432bb90a421bfd443872ed0f70f7264b64bec12ea363d5fd/numpy-2.4.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698", size = 6551240, upload-time = "2026-05-18T23:34:02.852Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/c6/50a46a6205feba2343f1d6d17438107c5dc491ed1c736e6ea68689fd906b/numpy-2.4.6-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f", size = 15671012, upload-time = "2026-05-18T23:34:05.485Z" },
+    { url = "https://files.pythonhosted.org/packages/99/60/14115e6364fa676c5397c2ad3004e527e9aa487abf5d0706ec81bbd08529/numpy-2.4.6-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853", size = 16645538, upload-time = "2026-05-18T23:34:09.265Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/c5/693cbe59e57db94d2231fa519ca3978dc9e19da5a8f088588f5c6e947ff2/numpy-2.4.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a", size = 17020706, upload-time = "2026-05-18T23:34:13.053Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/fc/85b7c4eff9b4966ade25c2273cf7e7012e92366c032058653934b37de044/numpy-2.4.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2", size = 18368541, upload-time = "2026-05-18T23:34:17.024Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/81/e1b27545deedce7f4a0b348618c6b62d74e36a4dc9ccd42f3eb2f85eee32/numpy-2.4.6-cp312-cp312-win32.whl", hash = "sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45", size = 5962825, upload-time = "2026-05-18T23:34:20.3Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/ca/feab00bd44aa5fe1ad2c18f08b4d3bb92e26484b0b1d1443897809ed528c/numpy-2.4.6-cp312-cp312-win_amd64.whl", hash = "sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751", size = 12321687, upload-time = "2026-05-18T23:34:23.095Z" },
+    { url = "https://files.pythonhosted.org/packages/63/cf/5a6d34850a39d1093558564f77ee8e8e0bee5061151b8f05a55711001ec7/numpy-2.4.6-cp312-cp312-win_arm64.whl", hash = "sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8", size = 10221482, upload-time = "2026-05-18T23:34:25.876Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/82/bdab26d7438c6791ca31b7c024ca37c1eab8b726ba236129005cd4a06e45/numpy-2.4.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0", size = 16684648, upload-time = "2026-05-18T23:34:29.41Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/30/a80189bcc7f5e4258b3fbc3968d909d1756f54d023299ecc39ad6fdb9ef8/numpy-2.4.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb", size = 14693902, upload-time = "2026-05-18T23:34:33.013Z" },
+    { url = "https://files.pythonhosted.org/packages/97/12/70b5d0d7c15e1ebb8a6a84a8caa1d19e181d84fb58bb6d70aca29099dec1/numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f", size = 5198992, upload-time = "2026-05-18T23:34:36.132Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/8c/ebd2a8f8a83541f8d38cc5667e8c2b69cecfd30da6e45693e8158857d44b/numpy-2.4.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3", size = 6546944, upload-time = "2026-05-18T23:34:38.484Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/c5/7b863a97a91671a0338f4253bd3b5a3d3852f0692dae91711c9f4a10e787/numpy-2.4.6-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b", size = 15669392, upload-time = "2026-05-18T23:34:41.257Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/9d/3584b9984ca4c047aea75214ce1a4c4c73d849bd71b604264b7f5653f8a8/numpy-2.4.6-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089", size = 16633220, upload-time = "2026-05-18T23:34:45.075Z" },
+    { url = "https://files.pythonhosted.org/packages/05/ae/7c67fba23bd98caec7c99261f3a16072ade14813486b0282cb29846de832/numpy-2.4.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a", size = 17020800, upload-time = "2026-05-18T23:34:49.065Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/5d/3b6725cb31d983c5e66916f5d36f6d7e5521129e4c4404d64f918292a5b6/numpy-2.4.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605", size = 18357600, upload-time = "2026-05-18T23:34:52.709Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/da/2ccc6c2fe8898dee01d90c75c5f5f914a23daf99e3e0f59516a08760c8b5/numpy-2.4.6-cp313-cp313-win32.whl", hash = "sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91", size = 5961134, upload-time = "2026-05-18T23:34:55.618Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/cd/9cc4dc876fb065d5c220aae4d5e14826b2715331bb7618ce1fb07a679d99/numpy-2.4.6-cp313-cp313-win_amd64.whl", hash = "sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359", size = 12318598, upload-time = "2026-05-18T23:34:58.928Z" },
+    { url = "https://files.pythonhosted.org/packages/39/1e/c0bcba1f8694116485fe28fd1be698c278fcda4141c5b0e53a2aed8b12a8/numpy-2.4.6-cp313-cp313-win_arm64.whl", hash = "sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778", size = 10222272, upload-time = "2026-05-18T23:35:02.167Z" },
+    { url = "https://files.pythonhosted.org/packages/63/6d/cc5619247c8f4204e507f5883528372e4ac4bb189e579fb859a12e480b1f/numpy-2.4.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1", size = 14821197, upload-time = "2026-05-18T23:35:05.468Z" },
+    { url = "https://files.pythonhosted.org/packages/00/58/f1c39161c87d9e9bed660f1ed4bafc0e403d5ec9650b6dd77aead07d489b/numpy-2.4.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe", size = 5326287, upload-time = "2026-05-18T23:35:08.693Z" },
+    { url = "https://files.pythonhosted.org/packages/af/57/3917ab0fd97f271a8694513581b8a36c655f111c446852c302f04ccdb6fc/numpy-2.4.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997", size = 6646763, upload-time = "2026-05-18T23:35:11.459Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/0f/037e64c494b67581ae18193d770adef354c41f3f2c8ebf865602d949bf8f/numpy-2.4.6-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20", size = 15728070, upload-time = "2026-05-18T23:35:14.79Z" },
+    { url = "https://files.pythonhosted.org/packages/21/a6/5d2bae9c9542eb4df16dc9c46dc79c186e9bad53805dfa5399a6023c6db0/numpy-2.4.6-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d", size = 16681752, upload-time = "2026-05-18T23:35:18.836Z" },
+    { url = "https://files.pythonhosted.org/packages/92/14/23d1dfb410ae362cd59ce53e936b1513d545eb40db3949ced632e19a459e/numpy-2.4.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67", size = 17086024, upload-time = "2026-05-18T23:35:22.52Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/6e/23595a2c642cdf3bc567877064bdd7f91c8b0038a4453cf2daf7248eafe9/numpy-2.4.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd", size = 18403398, upload-time = "2026-05-18T23:35:26.398Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/90/0ac3bc947217e66dec77e7cbc6a1979d1af70b6461b82f620d3bccd5e4c8/numpy-2.4.6-cp313-cp313t-win32.whl", hash = "sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab", size = 6084971, upload-time = "2026-05-18T23:35:29.387Z" },
+    { url = "https://files.pythonhosted.org/packages/77/71/5673e351671a1d2bd6063b91b44f70c0affea7d1516fa7a6572941ba4aa1/numpy-2.4.6-cp313-cp313t-win_amd64.whl", hash = "sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75", size = 12458532, upload-time = "2026-05-18T23:35:32.175Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/88/19d3503c5046e688f049274b27a3ef3d771152fa80d3ba3d01a3dff61abe/numpy-2.4.6-cp313-cp313t-win_arm64.whl", hash = "sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd", size = 10291881, upload-time = "2026-05-18T23:35:35.465Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/91/3ab2044d05fd16d343c5ac2e69b127f1b2854040dd20b193257c78028bd3/numpy-2.4.6-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079", size = 16683458, upload-time = "2026-05-18T23:35:38.353Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/62/764ce66fa4147ae6d73071a3abf804ffe606f174618697c571acdf26a7c9/numpy-2.4.6-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7", size = 14704559, upload-time = "2026-05-18T23:35:42.14Z" },
+    { url = "https://files.pythonhosted.org/packages/60/61/23f27c172f022e04025b7dc2367f4d63c1a398120607ec896228649a6f48/numpy-2.4.6-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5", size = 5209716, upload-time = "2026-05-18T23:35:45.377Z" },
+    { url = "https://files.pythonhosted.org/packages/03/71/21cf70dc6ea3e3acb95fc53a265b2fc248b981f0194ceb5b475271b8809d/numpy-2.4.6-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096", size = 6543947, upload-time = "2026-05-18T23:35:47.926Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/91/64288395ee1799bd2e0b04a305dce9666da90c961e1f3fe982a05ee1c036/numpy-2.4.6-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b", size = 15685197, upload-time = "2026-05-18T23:35:50.863Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/eb/ebffaa97dc55502df69584a8f0dcf07f69a3e0b3e2323670a2722db9aa39/numpy-2.4.6-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8", size = 16638245, upload-time = "2026-05-18T23:35:54.752Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/0b/54f9da33128d7e350fab89c7455902eeae70349ee52bddb448dc4a576f45/numpy-2.4.6-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402", size = 17036587, upload-time = "2026-05-18T23:35:58.355Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/f0/fdebc1052db1cc37c64beb22072d67cd6d1c71adca1299f53dec2b5e20d3/numpy-2.4.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb", size = 18363226, upload-time = "2026-05-18T23:36:02.845Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/b4/298628d98c72b57e57f7165ae6a481a1deaf6f3c28262a6e4c739c275930/numpy-2.4.6-cp314-cp314-win32.whl", hash = "sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1", size = 6010196, upload-time = "2026-05-18T23:36:05.92Z" },
+    { url = "https://files.pythonhosted.org/packages/df/ac/46de6dda46478f7942f839e094970be2d4a861e005c4b3bf07c92e291a09/numpy-2.4.6-cp314-cp314-win_amd64.whl", hash = "sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261", size = 12450334, upload-time = "2026-05-18T23:36:09.107Z" },
+    { url = "https://files.pythonhosted.org/packages/78/92/b8b798ac784102c0da830d2257d59358e3d3d90d1e2b3f2575dad976c5cf/numpy-2.4.6-cp314-cp314-win_arm64.whl", hash = "sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6", size = 10495678, upload-time = "2026-05-18T23:36:12.766Z" },
+    { url = "https://files.pythonhosted.org/packages/30/34/ec28d1aa8115971537c01469ab2011ee96827930f0a124de1000cc2a7ed7/numpy-2.4.6-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a", size = 14823672, upload-time = "2026-05-18T23:36:16.473Z" },
+    { url = "https://files.pythonhosted.org/packages/16/bd/f6d1fede4e54e8042a7ff97bb495510f3c220f94bcd9e8b228e87c92cc0d/numpy-2.4.6-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e", size = 5328731, upload-time = "2026-05-18T23:36:19.767Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/f0/e105b9e2fd728a9910103884decd6951d9dd73896b914a98d9a231de02ee/numpy-2.4.6-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e", size = 6649805, upload-time = "2026-05-18T23:36:22.266Z" },
+    { url = "https://files.pythonhosted.org/packages/82/dd/1206a7ca6ab15e3f02069707ca96222e202af681bb73756da7527f3cb837/numpy-2.4.6-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43", size = 15730496, upload-time = "2026-05-18T23:36:25.713Z" },
+    { url = "https://files.pythonhosted.org/packages/51/e7/38d3ea825dcab85a591734decb2f6c67caa7c8367d374df1a1c3842f9b07/numpy-2.4.6-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e", size = 16679616, upload-time = "2026-05-18T23:36:29.652Z" },
+    { url = "https://files.pythonhosted.org/packages/93/b7/caabfdf53edf663e0b4eb74d7d405d83baef09eb5e83bcd32d601d72b93e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895", size = 17085145, upload-time = "2026-05-18T23:36:33.449Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/45/68d7c33a6bcf3e5aa3bdbd57a367e6f615286dfd6482f97e8ffeb734306e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4", size = 18403813, upload-time = "2026-05-18T23:36:37.369Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/50/0753655aa844c99cd9e018aacf76f130f1bd81d881bb74bc0aef5d73a8ba/numpy-2.4.6-cp314-cp314t-win32.whl", hash = "sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063", size = 6156982, upload-time = "2026-05-18T23:36:40.817Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/d4/7c67becf668f973cb490cec3e98dfd799d866f9c989a54d355672cfa0db6/numpy-2.4.6-cp314-cp314t-win_amd64.whl", hash = "sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627", size = 12638908, upload-time = "2026-05-18T23:36:43.996Z" },
+    { url = "https://files.pythonhosted.org/packages/43/bb/e1c71a4295b1b1d1393d50dbb4f2a36283c6859d9d3892e84f00ec5a91d5/numpy-2.4.6-cp314-cp314t-win_arm64.whl", hash = "sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66", size = 10565867, upload-time = "2026-05-18T23:36:47.114Z" },
+    { url = "https://files.pythonhosted.org/packages/de/12/b422cc84439adc0d00de605bf4a308890ae5c26f2c71fbd73e5d08fbb0dd/numpy-2.4.6-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662", size = 16847511, upload-time = "2026-05-18T23:36:50.673Z" },
+    { url = "https://files.pythonhosted.org/packages/44/53/f481bef68011740f8849418d82db07230e825013f31f4eef5ba5b805316a/numpy-2.4.6-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7", size = 14889064, upload-time = "2026-05-18T23:36:53.879Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/57/42ed575c10ced8af951d426bc4e1f8aff16fd851db33f067036215a7f860/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f", size = 5394157, upload-time = "2026-05-18T23:36:57.194Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/ef/f66cc724fcc36c1e364c67f51ae9146090b8b584f27d58b97fdae3edd737/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c", size = 6708728, upload-time = "2026-05-18T23:36:59.575Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/9c/c531f2293b91265d8b48e9b329f54fdd7ffae73cb4134ea10cca4237e9cc/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0", size = 15798374, upload-time = "2026-05-18T23:37:02.674Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/b0/413077f6b1153ed3cba361401c6783bbad6114804a000cc22eb71c13e190/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02", size = 16747286, upload-time = "2026-05-18T23:37:06.327Z" },
+    { url = "https://files.pythonhosted.org/packages/15/ce/e5ec180bc41812edcd8daeb8639d205622c0e8c02259d8ab25a0201b3c2a/numpy-2.4.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73", size = 12504263, upload-time = "2026-05-18T23:37:09.715Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.5.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.12'",
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/80/db0b4559e57ec36362bedbb05530a87fafbcb6067708c946967a41d449e7/numpy-2.5.2.tar.gz", hash = "sha256:d482d171c406ae88c5b19cad3b6a1c4c5209f886ab74bc44c2c865c23f52d860", size = 20773161, upload-time = "2026-08-09T13:48:27.962Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/72/dccb0aaf40972777283303919f613964227266d0c13adebb79ac124f1c3e/numpy-2.5.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:14e373cfc6387177e8409dac3c7159be8eb05cd77096cd7c950268b86f62831c", size = 16891693, upload-time = "2026-08-09T13:44:51.702Z" },
+    { url = "https://files.pythonhosted.org/packages/60/2e/b5aee50a1f74ac815cf8331812cb8251e29024025de462e0c047641c614c/numpy-2.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4bbd96c833ecc8cc069ce518078fc8c60cb9cbfb0fea5b7a803ad65035596d03", size = 11903109, upload-time = "2026-08-09T13:44:55.501Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/f4/29e78102a80601cf034d4e9767022cffeca2c3b4c926e1754572ca95593d/numpy-2.5.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:6e8172ddfcf5cf74b811d372b570b83c60bd2de87a6fbfbebdadb4a9bd9c6cbb", size = 5350202, upload-time = "2026-08-09T13:44:58.401Z" },
+    { url = "https://files.pythonhosted.org/packages/11/4b/dcd3b7eadaf4035d2c7a4289d232523a6964f602598ef7674e4bd7291f93/numpy-2.5.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:65f188481f1669e26f62b701e8205d19e460fa4a9b52a1414ba382330e4a3414", size = 6687736, upload-time = "2026-08-09T13:45:00.813Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/21/4947e0e9d6c9fc2e2ff15b8949049ee44f63adb9cacc729ab8793f97e712/numpy-2.5.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8ee9c4eeb8454b3660a8b53493563c3e121c2fc94fbd72b848ef814ed7b676a9", size = 15612696, upload-time = "2026-08-09T13:45:04.151Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/5f/62d28cf019460c7f1394105b4d49d9911a9c444cb77ab0bd95a204c5a6de/numpy-2.5.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3cdec01fa790a186d430433fdd4d4ffb70eed6f0eeb4bf05c8dbe2dce0a9bcb8", size = 16722264, upload-time = "2026-08-09T13:45:07.714Z" },
+    { url = "https://files.pythonhosted.org/packages/14/25/3f0be4c1b9fdf5dd5e708a6806978564d7c46a055c000496309ff2a2f8af/numpy-2.5.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7999d4ddb0c4025018373fd787510d46e04c769467af22869707b3c1cfd459ab", size = 16974396, upload-time = "2026-08-09T13:45:11.316Z" },
+    { url = "https://files.pythonhosted.org/packages/22/72/6262cbdeeb45da9d971e40715f579d791603ba8ec0b5e2db1ac55454421d/numpy-2.5.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c1f017dc0875c9209d219f97feceb7d54c2661bb243deb4114478e1295808af7", size = 18476044, upload-time = "2026-08-09T13:45:14.869Z" },
+    { url = "https://files.pythonhosted.org/packages/36/33/29208b8b075bde62d26a81d14b358c42b0f69b6cabd98d4ff97f37f22b05/numpy-2.5.2-cp312-cp312-win32.whl", hash = "sha256:d6a48072864e3324e194a8fbb3c657bcc5b5c869dbc64c9537b1d5c862572c0a", size = 6072817, upload-time = "2026-08-09T13:45:17.867Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/b9/87fea2769fe1c47c1b5b01d8310772c9d1a85d485de7cf386ef7a3332b02/numpy-2.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:28ac63476ec7651484215ee7fa15a1f78b57c14621f01e392afe17b9a1390ce4", size = 12464674, upload-time = "2026-08-09T13:45:20.734Z" },
+    { url = "https://files.pythonhosted.org/packages/14/52/032b97e00461ab0809bbe4c588b035620e5a14b8cdee47ecddefc7b17d33/numpy-2.5.2-cp312-cp312-win_arm64.whl", hash = "sha256:27650bb0e7140fa3d37b9923b4803645e0b125d190f326eecfd3f4dad8e8ade1", size = 10397131, upload-time = "2026-08-09T13:45:23.73Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/d2/6b24738a0ef4557d189b150046cd07823c50e4273e8aebd651222e24306f/numpy-2.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8e4cb9a754c8a0c62eaa88273a5fba3391f4a610d1dee893c0755da31c083f15", size = 16886595, upload-time = "2026-08-09T13:45:27.323Z" },
+    { url = "https://files.pythonhosted.org/packages/65/60/f2d208d366f263f39c6e69ed309290717aab41078b6d04c9be2a84fa2a07/numpy-2.5.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:52c808f96484f5571a5cc863775ce50247c17dfb3b0361f8ed6b4b0456f80080", size = 11896845, upload-time = "2026-08-09T13:45:31.638Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/79/81e0bf24f4d020a2b1d5cd297a9f60c3f24eeb116f9bba5870443f7b6a4a/numpy-2.5.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:29d81e97f668489cba8ebfd796b9bdd453525d35dd9e162e2daec94bf3fc7740", size = 5343880, upload-time = "2026-08-09T13:45:34.373Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/cc/e3141cf06d1a8a2c7e107543fe1269c1d1af760d4d683c0794a4ee1127c2/numpy-2.5.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:afb3f0632d6b2e3ba04dbce8d1e48d321b369138b73830b5ca371a0e8d479d56", size = 6682264, upload-time = "2026-08-09T13:45:36.7Z" },
+    { url = "https://files.pythonhosted.org/packages/29/f1/2a64a307d92c5d98f5255a4014eb43bb6103ee477087b61ecae44a3aa9b9/numpy-2.5.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0aadf13b60048d501e05fa699efaf7734e2494f3498a4c2a5521d822640324f3", size = 15609566, upload-time = "2026-08-09T13:45:39.518Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/44/59a1eb68e773c4098d107ef34a0dbdeca501d72ffcfbff9a7707343921ce/numpy-2.5.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:29b86ff8a6cc556b47ec6b64b194815cc80e6bf5eedcc6cddfd65318cb0b4eee", size = 16709995, upload-time = "2026-08-09T13:45:43.661Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/4c/3e54d4ddbc359a1295f8b633e8106bcd4d7d4a206e82df051bdfb3058755/numpy-2.5.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6950c4b7dd562453090548ba7f5da7e59f57f85663f15d5dcc60e249192f7e59", size = 16972511, upload-time = "2026-08-09T13:45:47.094Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/9f/02e371638ebf19b66d46231e4be52999e87f32d1961b113bc45656608b22/numpy-2.5.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b9727f472d2f3888053b8a75ab0cb94745a9de224bb5846dbadc0092101bc71d", size = 18465609, upload-time = "2026-08-09T13:45:50.808Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/ae/ad6645abc7a3510fe48e8ea1ab4598166f500057ef4ebf38bfad4f1577de/numpy-2.5.2-cp313-cp313-win32.whl", hash = "sha256:4f9744f9fbdcea0bc552e8f19e1f141f811a3f9bc2be2cc6e86d982cab23e3f4", size = 6070204, upload-time = "2026-08-09T13:45:54.111Z" },
+    { url = "https://files.pythonhosted.org/packages/15/20/f3489f86d81ea460b2bcdceaed094142ca6579f6be0ec527b781d39afe68/numpy-2.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:85aaccb24182c25df891ad0ec333585967e115269d5f1b17f2c9ae005bc96657", size = 12460532, upload-time = "2026-08-09T13:45:57.167Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/21/35b31dde1b283b79de828b80f876afd8c94e28fe1e9c375f89e261cc4c0d/numpy-2.5.2-cp313-cp313-win_arm64.whl", hash = "sha256:bd68ece1553d2023c09a4226d9e41c586ad2d20594d1a456186c33513d2cb3f2", size = 10396725, upload-time = "2026-08-09T13:46:00.478Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/f8/c3b222bf075b50afd8e949a07a15c4b312a4a84bd8102a332bcd953cbbb4/numpy-2.5.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:d787cf769c3baeb5f6235e778edb52c08dfa923789b5958f28e6450f96107cb1", size = 16885180, upload-time = "2026-08-09T13:46:03.939Z" },
+    { url = "https://files.pythonhosted.org/packages/17/e1/2c1d4b1987795a92b5bbf7c24fe249ab96aa2573ab0d7604802c189d7b86/numpy-2.5.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:24b9dc2e3d84aa58523798805194e23e736f3f6ce2d1a5b92583ae734e6dbda8", size = 11907878, upload-time = "2026-08-09T13:46:07.045Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/ee/d08226fc858044355983a6e5b94f08ff6f3969e0a2b160a4a89f0ddb3445/numpy-2.5.2-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:9e9413326d726c2545bfa65d2c0876871e8d8386e77f992c1d426e180bbd4323", size = 5354922, upload-time = "2026-08-09T13:46:10.04Z" },
+    { url = "https://files.pythonhosted.org/packages/94/f0/6d3d933056440ebbc5e6bad92065fc6c26a48a84a36b1208580e94eea76c/numpy-2.5.2-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:60e902ac295855348a5ca2ea4c89108989a9f5fddfad3dfc0a8f36b10358567e", size = 6679168, upload-time = "2026-08-09T13:46:12.275Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/3b/ecd49dd90033cceb2704d88ca905d4d7d89b0e8c739608754ffd325fa820/numpy-2.5.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:50e500dc868e9313530ce12ba470fe50ff3afe3d62993ed6eff652dacd555b65", size = 15624501, upload-time = "2026-08-09T13:46:15.322Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/99/461bd36dbdfac6c1c53efa370bd55a83227542d0d118f1677dbf1a3dacd5/numpy-2.5.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:318b9a4c845dbea06708a29c84ee429cc3065048db34cdb799047643492050ee", size = 16713701, upload-time = "2026-08-09T13:46:18.949Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/9c/2b251df9e8a5d647b62b0cbc1b90a91850c1cf4859ecb532fd0b4eacff6c/numpy-2.5.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:34c319e2963be042673fb46570501b2f06c41924e17e3563d58646b4380dfb68", size = 16986065, upload-time = "2026-08-09T13:46:23.006Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/25/20de43f53ff1390534a124475055a19f01fe10c920a0fd11b8e18d6d6052/numpy-2.5.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f06571a052127dc1b4e8b83029b4d1b20daa2b64a31cdd181fc6bc774e9000eb", size = 18470031, upload-time = "2026-08-09T13:46:27.102Z" },
+    { url = "https://files.pythonhosted.org/packages/56/5e/0c577ca308d6da5eb79b546ba10bbe5b60148192194e2da060913b1de4f1/numpy-2.5.2-cp314-cp314-win32.whl", hash = "sha256:2cc779226e476d1e1f08c74068c419e60f41a9e0e069c92f6671d31d5c985e98", size = 6121028, upload-time = "2026-08-09T13:46:30.046Z" },
+    { url = "https://files.pythonhosted.org/packages/15/5c/7bcbd5b11f94199073320410cddcbb80cee62415bfeb540874b265c2d922/numpy-2.5.2-cp314-cp314-win_amd64.whl", hash = "sha256:7587f53dfbd5edc0f7b87c6217b4c6d2d1f2ef9c3da70bc1315e7db5f8d7ec9d", size = 12597627, upload-time = "2026-08-09T13:46:32.886Z" },
+    { url = "https://files.pythonhosted.org/packages/87/bc/4d0b06fba0da90ccc75af62823cb9dcedb6c9ea0cffa058cb2c9ee773a77/numpy-2.5.2-cp314-cp314-win_arm64.whl", hash = "sha256:3e4c367352d3747784248a227fbec218e193b56f7e6692e3b64fc805478ecfdf", size = 10680414, upload-time = "2026-08-09T13:46:36.036Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/17/f429aac9dc08833a0d0f188eba38c532a751b1a1f2ca6018a37b455cb321/numpy-2.5.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b879fb674276e331513fb136b78dbc6bd3c848309e0d841cfd63be3896c4cfc1", size = 12026967, upload-time = "2026-08-09T13:46:39.084Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/9f/d0849de96a2a4ceaa16662f18ee13eaa9c0aa418269fdc8c4857c56b11da/numpy-2.5.2-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:fd0d703772bba096843785bd38371e31bb4a0c1151497ad5739d182114a73f7f", size = 5473874, upload-time = "2026-08-09T13:46:42.075Z" },
+    { url = "https://files.pythonhosted.org/packages/89/3c/8df216d4a4a5422a3de045301cf7df8ea47286d76f5cb7160b0128ac26b7/numpy-2.5.2-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:3a2f061cebd9e3d23bdcfaaded5e2293a4c6a5b60fa42df85d410a725ce621bf", size = 6789276, upload-time = "2026-08-09T13:46:44.387Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/3a/20d7e9891c4ddfadd6ff8d95bf4b29f353d8e1770553de2099880551dfb9/numpy-2.5.2-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6df895598c0edcb41030126c89e0f353b07d93238116143b7405e937359736c4", size = 15659154, upload-time = "2026-08-09T13:46:47.538Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/d6/f3aa3d2688bf501b858835c6bd087ae9b51a56ae6fca8e2b0990abd177af/numpy-2.5.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1ab3d4a901f844ea836c3e80bf463c6a27d7f3c14e8e292fcf28d348b25b9bce", size = 16748909, upload-time = "2026-08-09T13:46:51.442Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/8f/1c5cae8d2baf86ab802ae97a00be55bc7e21ebc11b12bbc33376c5f05342/numpy-2.5.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:cebc2d6dbb605a7703d59751dea4bd6b0ab127a5a4338a6f432df1936fef8b26", size = 17027685, upload-time = "2026-08-09T13:46:55.095Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/27/71d3467404aedc1c24ce79610f91b52b0b0f466c43a701aa56fc75c145ab/numpy-2.5.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:eaca7ff36f0f52e2111ec71f169d8fd3e889e7ddc0d2592e0d703fd8d3ce8fac", size = 18501181, upload-time = "2026-08-09T13:46:59.09Z" },
+    { url = "https://files.pythonhosted.org/packages/14/2f/42921d27c40aea7e077f4a423ae509fd9220b028cd787bafefd8ab2b3a5f/numpy-2.5.2-cp314-cp314t-win32.whl", hash = "sha256:ddf47472af2e4280d79bac82304f5e80150211f1b9e614b760061d5fdfbb6eba", size = 6271085, upload-time = "2026-08-09T13:47:01.903Z" },
+    { url = "https://files.pythonhosted.org/packages/75/e6/bad5f5d56de9b1971bac959963dda276d35c40f1854475005434bbe08692/numpy-2.5.2-cp314-cp314t-win_amd64.whl", hash = "sha256:44ef9675d908e65f9953063837c3277730f3f4437615a4cdab67b366cabaf884", size = 12787971, upload-time = "2026-08-09T13:47:04.963Z" },
+    { url = "https://files.pythonhosted.org/packages/df/05/f608795cb34391acd67e38d94a3c36abd8d8576293a3a80727d7595c372c/numpy-2.5.2-cp314-cp314t-win_arm64.whl", hash = "sha256:eaa088384c46f519dacb93b7ec483a6d6b19a4a2085ae4f25ab9b1c43d387d1e", size = 10750306, upload-time = "2026-08-09T13:47:07.976Z" },
+    { url = "https://files.pythonhosted.org/packages/33/c6/28de0191c5f82b7d42a0a51390ba98587048aa93a39fafb05bdbe6e8d00c/numpy-2.5.2-cp315-cp315-macosx_10_15_x86_64.whl", hash = "sha256:078f9b027b478c9379b9677babbf0f8b8f1ecfada27636d7b9a93990c638739f", size = 16885274, upload-time = "2026-08-09T13:47:11.439Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/d1/973ca116000d244897e468ea1aff30b589e5022e3c8744b71706fe33bd57/numpy-2.5.2-cp315-cp315-macosx_11_0_arm64.whl", hash = "sha256:50a68f4bacd8a2b33d8da3d2269d0d78500f86ea582e4786dc10f5ef2c2c6842", size = 11907846, upload-time = "2026-08-09T13:47:15.128Z" },
+    { url = "https://files.pythonhosted.org/packages/78/d9/8c4b3937ef204cb2fd88d389ccd0f265a2ffb11f35a01d2064cf46714bd6/numpy-2.5.2-cp315-cp315-macosx_14_0_arm64.whl", hash = "sha256:e79aba74ffaf5f78a050d777c184cddf8fdffabab38acf5f3ef1fecbc17895d6", size = 5354892, upload-time = "2026-08-09T13:47:18.07Z" },
+    { url = "https://files.pythonhosted.org/packages/74/9b/b6ee65ea2999fdb7023935e108e6fb776ee4082aa15f159acfa857e578c8/numpy-2.5.2-cp315-cp315-macosx_14_0_x86_64.whl", hash = "sha256:9a0731745a72a184490a582fb4af2533512bd071ace67785b5fdffc0ae58dce8", size = 6679309, upload-time = "2026-08-09T13:47:20.456Z" },
+    { url = "https://files.pythonhosted.org/packages/43/f3/acb18d8b137a393c8e7803a8c994c9e64bde3930692a69d826993113a159/numpy-2.5.2-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4ec954036759bcee3aa484f8603bd9c14f3e776293b85578b8734c2d72777c69", size = 15625850, upload-time = "2026-08-09T13:47:24.365Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/bf/a8e9bb0db815a0e265b5744ebedd3af0bd5faad8604e5b50a1cd012f3c91/numpy-2.5.2-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dc649493697006bc90614a5f0bbc8cb3cb1866715c474e473694968d7e6b99ab", size = 16713664, upload-time = "2026-08-09T13:47:27.965Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/c3/6e913736b3dd6582344af32418b5fb9dab34282e8a8174ae1d54ceb0fc13/numpy-2.5.2-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:cf7de32f486e4ac9e2d93b810f9e9ac72a728dd46a32a0bb403222f27f653514", size = 16986749, upload-time = "2026-08-09T13:47:31.541Z" },
+    { url = "https://files.pythonhosted.org/packages/80/09/7d3b23eff5c7428ef6c01e6f7052bb60d504c4d33e317b36b8959c24ad97/numpy-2.5.2-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:2ffa7bacab3e2ee1b19ed31766bb60bb380b68c23f051e199c5cc598afd68710", size = 18470495, upload-time = "2026-08-09T13:47:35.364Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/a4/68a321d825374f6eb677ffe8ef8c6b9a328304e6fd2e39d9530822776607/numpy-2.5.2-cp315-cp315-win32.whl", hash = "sha256:6b588cc8f902d6bff201c19fd00c43ab8545671e3554d014e12e14139e5e8617", size = 6120696, upload-time = "2026-08-09T13:47:38.561Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/23/deafbb1700f79fae9cd1e91220f133d124cc267de1b584da3fbf6db2f6cd/numpy-2.5.2-cp315-cp315-win_amd64.whl", hash = "sha256:07d4e89f3a9ab0a9ba24264ccdb642b3dd951b2281e8883a5481a4aa79cc31a7", size = 12597324, upload-time = "2026-08-09T13:47:41.401Z" },
+    { url = "https://files.pythonhosted.org/packages/33/cd/3272ba105e3bbbdaeb11357eda31e7a6825ffe159e8171665660299a948f/numpy-2.5.2-cp315-cp315-win_arm64.whl", hash = "sha256:a610dc7e3c52edd39c2bc2375ff9c3fd59cb3ad00e4472d36f83bc1457145788", size = 10680466, upload-time = "2026-08-09T13:47:44.873Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/0e/58370637b1bb70a5c9ce2b43f4b521ccb224e36ccb76a6596b17ae4b447c/numpy-2.5.2-cp315-cp315t-macosx_10_15_x86_64.whl", hash = "sha256:40f4d451aed46a8046a1aae41c4e55fb3612273df9c502480135e1501576a34b", size = 16993947, upload-time = "2026-08-09T13:47:48.97Z" },
+    { url = "https://files.pythonhosted.org/packages/10/93/2abcb807712b289d6d60fe4cf30532f98974a8396d885650f3ba5a13026e/numpy-2.5.2-cp315-cp315t-macosx_11_0_arm64.whl", hash = "sha256:c081cbe16ba1ab53078e5ff29013621e33c509eedab055775d956427712c236e", size = 12025331, upload-time = "2026-08-09T13:47:52.646Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/3a/2898e003a5fbaf87e76c039b4ee1f5eb390471b4ffe74887c1f34c4e791e/numpy-2.5.2-cp315-cp315t-macosx_14_0_arm64.whl", hash = "sha256:0090ccdd57ec2703e9b49d0bf554767370581c1dd0a6b2bb2b2d9def317d042a", size = 5472336, upload-time = "2026-08-09T13:47:55.403Z" },
+    { url = "https://files.pythonhosted.org/packages/61/a5/23f69d07c544597b29758b31b55c27dc9d541012a2c1496189fef702aec2/numpy-2.5.2-cp315-cp315t-macosx_14_0_x86_64.whl", hash = "sha256:6a9bb119fb8dd21ba30b3f0e555b7e2b081bd9883af21ec9c1c633d161cda3a8", size = 6788387, upload-time = "2026-08-09T13:47:58.192Z" },
+    { url = "https://files.pythonhosted.org/packages/15/ea/c0dbdbcf22f43782510a3e492dd3da73c6112b69cac8929d16d127536fc4/numpy-2.5.2-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a839318485284a6fb31be4f8f2c91c8f2cb22f4543c4a8903f12b0671ffe07cc", size = 15667096, upload-time = "2026-08-09T13:48:01.562Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/5e/29c73c31748cdb0f7566642125ba17fd5b56780cddf891b085dab27e4466/numpy-2.5.2-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba0a474801b8dc67b66bf465548abc90e82b44d2611b5770f33008dcabffe8ec", size = 16751730, upload-time = "2026-08-09T13:48:05.706Z" },
+    { url = "https://files.pythonhosted.org/packages/47/95/02501e8454796bb58dadf7a99d3181e0b464bf264e1003039572f9779fac/numpy-2.5.2-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:0a4035ae1129ff8777f08bfbd44f1e5d8e9c049ce0c2dd78fc0d92c13e7251c0", size = 17038686, upload-time = "2026-08-09T13:48:09.627Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/b5/53a681d91b5c82687067d8ea5035e02d917b5509d6f334cb06484a954714/numpy-2.5.2-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:77843ca236b777e67f8d6b3660ea116e499612703a0ecd7093f316201eb9d8e2", size = 18507727, upload-time = "2026-08-09T13:48:13.744Z" },
+    { url = "https://files.pythonhosted.org/packages/42/06/6e11443f7b64ee376c860506091103bf68f92d2cab9e8d96d4501babf07c/numpy-2.5.2-cp315-cp315t-win32.whl", hash = "sha256:7354826bc6f8f69402e9b7fe28d15fcd34feebd74f856f111585c5b0c9fb0251", size = 6269775, upload-time = "2026-08-09T13:48:17.543Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/18/195d6b86cd72dbbc501edfa778005fa6b87afd34c153e46028cd3a0938f4/numpy-2.5.2-cp315-cp315t-win_amd64.whl", hash = "sha256:e5651f3f87add730ee6608d915009e19c911fba0cb000c7e3ea994b7d768eb12", size = 12782559, upload-time = "2026-08-09T13:48:21.023Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/07/458c344f0f0c178f4481dad5cca790626ffe4c34eabf9467069d06ee4999/numpy-2.5.2-cp315-cp315t-win_arm64.whl", hash = "sha256:5f8e00be2ec6f45f4e8a41a527f68d44a7d96fee92a650e4d8b1326f77f61e6e", size = 10748103, upload-time = "2026-08-09T13:48:24.21Z" },
+]
+
+[[package]]
+name = "nvidia-cublas"
+version = "13.1.1.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nvidia-cuda-nvrtc" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/a1/0bd24ee8c8d03adac032fd2909426a00c88f8c57961b1277ded97f91119f/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b7a210458267ac818974c53038fbec2e969d5c99f305ab15c72522fa9f001dd5", size = 542848918, upload-time = "2026-04-08T18:46:22.985Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/cd/154ca20c38269e05eff77c1464e6c1da89f50a6390b565e9d82e06bc11e1/nvidia_cublas-13.1.1.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:37936a16db8fe4ac1f065c2139360608a543a09275cb1a1af612e08cfa065436", size = 423138758, upload-time = "2026-04-08T18:46:58.655Z" },
+]
+
+[[package]]
+name = "nvidia-cuda-cupti"
+version = "13.0.85"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/2a/80353b103fc20ce05ef51e928daed4b6015db4aaa9162ed0997090fe2250/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_aarch64.whl", hash = "sha256:796bd679890ee55fb14a94629b698b6db54bcfd833d391d5e94017dd9d7d3151", size = 10310827, upload-time = "2025-09-04T08:26:42.012Z" },
+    { url = "https://files.pythonhosted.org/packages/33/6d/737d164b4837a9bbd202f5ae3078975f0525a55730fe871d8ed4e3b952b0/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:4eb01c08e859bf924d222250d2e8f8b8ff6d3db4721288cf35d14252a4d933c8", size = 10715597, upload-time = "2025-09-04T08:26:51.312Z" },
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc"
+version = "13.0.88"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/68/483a78f5e8f31b08fb1bb671559968c0ca3a065ac7acabfc7cee55214fd6/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:ad9b6d2ead2435f11cbb6868809d2adeeee302e9bb94bcf0539c7a40d80e8575", size = 90215200, upload-time = "2025-09-04T08:28:44.204Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/dc/6bb80850e0b7edd6588d560758f17e0550893a1feaf436807d64d2da040f/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d27f20a0ca67a4bb34268a5e951033496c5b74870b868bacd046b1b8e0c3267b", size = 43015449, upload-time = "2025-09-04T08:28:20.239Z" },
+]
+
+[[package]]
+name = "nvidia-cuda-runtime"
+version = "13.0.96"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/87/4f/17d7b9b8e285199c58ce28e31b5c5bbaa4d8271af06a89b6405258245de2/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ef9bcbe90493a2b9d810e43d249adb3d02e98dd30200d86607d8d02687c43f55", size = 2261060, upload-time = "2025-10-09T08:55:15.78Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/24/d1558f3b68b1d26e706813b1d10aa1d785e4698c425af8db8edc3dced472/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f82250d7782aa23b6cfe765ecc7db554bd3c2870c43f3d1821f1d18aebf0548", size = 2243632, upload-time = "2025-10-09T08:55:36.117Z" },
+]
+
+[[package]]
+name = "nvidia-cudnn-cu13"
+version = "9.20.0.48"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nvidia-cublas" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/56/c5/83384d846b2fd17c44bd499b36c75a45ed4f095fbbb2252294e89cea5c5c/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1", size = 444574296, upload-time = "2026-03-09T19:28:27.751Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/5e/edb9c0ae051602c3ccaffe424256463636d639e27d7f302dde9975ef9e7a/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0c45dd8eeb50b603f07995b1b300c62ffe6a1980482b82b3bcf94a4ca9d49304", size = 366173588, upload-time = "2026-03-09T19:29:34.474Z" },
+]
+
+[[package]]
+name = "nvidia-cufft"
+version = "12.0.0.61"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nvidia-nvjitlink" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" },
+]
+
+[[package]]
+name = "nvidia-cufile"
+version = "1.15.1.6"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/70/4f193de89a48b71714e74602ee14d04e4019ad36a5a9f20c425776e72cd6/nvidia_cufile-1.15.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08a3ecefae5a01c7f5117351c64f17c7c62efa5fffdbe24fc7d298da19cd0b44", size = 1223672, upload-time = "2025-09-04T08:32:22.779Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/73/cc4a14c9813a8a0d509417cf5f4bdaba76e924d58beb9864f5a7baceefbf/nvidia_cufile-1.15.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:bdc0deedc61f548bddf7733bdc216456c2fdb101d020e1ab4b88d232d5e2f6d1", size = 1136992, upload-time = "2025-09-04T08:32:14.119Z" },
+]
+
+[[package]]
+name = "nvidia-curand"
+version = "10.4.0.35"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/72/7c2ae24fb6b63a32e6ae5d241cc65263ea18d08802aaae087d9f013335a2/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:133df5a7509c3e292aaa2b477afd0194f06ce4ea24d714d616ff36439cee349a", size = 61962106, upload-time = "2025-08-04T10:21:41.128Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/9f/be0a41ca4a4917abf5cb9ae0daff1a6060cc5de950aec0396de9f3b52bc5/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:1aee33a5da6e1db083fe2b90082def8915f30f3248d5896bcec36a579d941bfc", size = 59544258, upload-time = "2025-08-04T10:22:03.992Z" },
+]
+
+[[package]]
+name = "nvidia-cusolver"
+version = "12.0.4.66"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nvidia-cublas" },
+    { name = "nvidia-cusparse" },
+    { name = "nvidia-nvjitlink" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/67/cba3777620cdacb99102da4042883709c41c709f4b6323c10781a9c3aa34/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0a759da5dea5c0ea10fd307de75cdeb59e7ea4fcb8add0924859b944babf1112", size = 200941980, upload-time = "2025-09-04T08:33:22.767Z" },
+]
+
+[[package]]
+name = "nvidia-cusparse"
+version = "12.6.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "nvidia-nvjitlink" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/18/623c77619c31d62efd55302939756966f3ecc8d724a14dab2b75f1508850/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b3c89c88d01ee0e477cb7f82ef60a11a4bcd57b6b87c33f789350b59759360b", size = 145942937, upload-time = "2025-09-04T08:33:58.029Z" },
+]
+
+[[package]]
+name = "nvidia-cusparselt-cu13"
+version = "0.8.1"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/46/e1/cdc1797eadf82d3a9a575a19b33fdc871a97edbec42c00b5b5e914f4aff4/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4dca476c50bf4780d46cd0bfbd82e2bc10a08e4fef7950917ce8d7578d22a23f", size = 221051344, upload-time = "2025-09-05T18:49:51.289Z" },
+    { url = "https://files.pythonhosted.org/packages/34/7d/2661f2fb3ac4302f3a246f5fc030213ac60c1fe0bce84f9783dbd831dbb7/nvidia_cusparselt_cu13-0.8.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:786ce87568c303fadb5afcc7102d454cd3040d75f6f8626f5db460d1871f4dd0", size = 170148586, upload-time = "2025-09-05T18:50:50.248Z" },
+]
+
+[[package]]
+name = "nvidia-nccl-cu13"
+version = "2.29.7"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/72/0d/daf50d44177ee0cbc7ff0a0c91eb5ff676c82be42f9a970bc7597f440c3a/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:674a12383e3c38a1bcccae7d4f3633b37852230b6047883cb2f4c2d1b36d9bf5", size = 206014712, upload-time = "2026-03-03T05:34:20.843Z" },
+    { url = "https://files.pythonhosted.org/packages/67/f4/58e4e91b6919367c7aafb8e36fce9aad1a3047e536bf7e2fd560927d3a4c/nvidia_nccl_cu13-2.29.7-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:edd81538446786ec3b73972543e53bb43bcaf0bfc8ef76cb679fcc390ffe136d", size = 205976000, upload-time = "2026-03-03T05:36:24.472Z" },
+]
+
+[[package]]
+name = "nvidia-nvjitlink"
+version = "13.3.33"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f0/ee/580ca6f29dcab0221db8706badca1bbbb084f1975c4d4e83329c3a7e31f0/nvidia_nvjitlink-13.3.33-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:26a6de7fb4c8fdaa7703d3dad720d6d427ddfea5c48a528fd97c11733ad830e5", size = 40742423, upload-time = "2026-05-26T16:54:51.613Z" },
+    { url = "https://files.pythonhosted.org/packages/69/30/45414e35ff2eee7db3da037e5707037ccf9d2b5218ffbdb055ea4d5aa98a/nvidia_nvjitlink-13.3.33-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ce48b37dfeb3cb1eae4cf85adacb47d7a6539ea2272870c9a3628ce275c2037e", size = 39168635, upload-time = "2026-05-26T16:54:13.906Z" },
+]
+
+[[package]]
+name = "nvidia-nvshmem-cu13"
+version = "3.4.5"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/0f/05cc9c720236dcd2db9c1ab97fff629e96821be2e63103569da0c9b72f19/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6dc2a197f38e5d0376ad52cd1a2a3617d3cdc150fd5966f4aee9bcebb1d68fe9", size = 60215947, upload-time = "2025-09-06T00:32:20.022Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/35/a9bf80a609e74e3b000fef598933235c908fcefcef9026042b8e6dfde2a9/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:290f0a2ee94c9f3687a02502f3b9299a9f9fe826e6d0287ee18482e78d495b80", size = 60412546, upload-time = "2025-09-06T00:32:41.564Z" },
+]
+
+[[package]]
+name = "nvidia-nvtx"
+version = "13.0.85"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/f3/d86c845465a2723ad7e1e5c36dcd75ddb82898b3f53be47ebd429fb2fa5d/nvidia_nvtx-13.0.85-py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4936d1d6780fbe68db454f5e72a42ff64d1fd6397df9f363ae786930fd5c1cd4", size = 148047, upload-time = "2025-09-04T08:29:01.761Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/64/3708a90d1ebe202ffdeb7185f878a3c84d15c2b2c31858da2ce0583e2def/nvidia_nvtx-13.0.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb7780edb6b14107373c835bf8b72e7a178bac7367e23da7acb108f973f157a6", size = 148878, upload-time = "2025-09-04T08:28:53.627Z" },
+]
+
+[[package]]
+name = "packaging"
+version = "26.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/fa/3944b40b07da9ce895c0e6303a5ab7d53da063554f534556b134a54d6093/packaging-26.3.tar.gz", hash = "sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79", size = 313412, upload-time = "2026-08-04T18:15:28.737Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/63/34/ba1c580383c9eada3711951fef0795c80b829a078d72188184bcab9dd527/packaging-26.3-py3-none-any.whl", hash = "sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c", size = 129956, upload-time = "2026-08-04T18:15:27.159Z" },
+]
+
+[[package]]
+name = "pillow"
+version = "12.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/3d/bb7fca845737cf9d7dbde16ed1843984665ff2e0a518f5db43e77ec540b9/pillow-12.3.0.tar.gz", hash = "sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce", size = 47025035, upload-time = "2026-07-01T11:56:38.965Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fb/c8/0a78b0e02d7ac54bc03e5321c9220da52f0c2ea83b21f7c40e7f3169c502/pillow-12.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756", size = 5392415, upload-time = "2026-07-01T11:53:47.162Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/5b/a02d30018abd97ced9f5a6c63d28597694a00d066516b9c1c6de45859fc9/pillow-12.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6", size = 4785266, upload-time = "2026-07-01T11:53:49.079Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/98/766667a4be768150a202836acd9fad19c06824ca86c4286d3cf6b274964e/pillow-12.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd", size = 6263814, upload-time = "2026-07-01T11:53:51.32Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/2d/ede717bc1144f63886c21fd349bb95860b0d1a21149ff16f2bb362b612b6/pillow-12.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd", size = 6934408, upload-time = "2026-07-01T11:53:53.487Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/48/9c58b685e69d49c31af6c8eb9012055fab7e665785165c84796e2c73ce72/pillow-12.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c", size = 6337160, upload-time = "2026-07-01T11:53:55.457Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/fa/dc2a5c0ba6df93f67c31d34b808b7ce440b40cdbf96f0b81cde1d1e6fa93/pillow-12.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5", size = 7045172, upload-time = "2026-07-01T11:53:57.736Z" },
+    { url = "https://files.pythonhosted.org/packages/86/a5/444817a4d4c4c2417df00513086ca196f388d8f9ef40c2e4ccd1ad1af54b/pillow-12.3.0-cp311-cp311-win32.whl", hash = "sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b", size = 6472232, upload-time = "2026-07-01T11:53:59.767Z" },
+    { url = "https://files.pythonhosted.org/packages/63/c6/4bad1b18d132a50b27e1365e1ab163616f7a5bb56d330f66f9d1d9d4f9d4/pillow-12.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a", size = 7233653, upload-time = "2026-07-01T11:54:02.066Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/16/00f91ab7760dc842f5aad55217e80fc4a7067a0604535249bc8a2d6d9870/pillow-12.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26", size = 2568195, upload-time = "2026-07-01T11:54:04.622Z" },
+    { url = "https://files.pythonhosted.org/packages/37/bf/fb3ebff8ddcb76aac5a01389251bbbb9519922a9b520d8247c1ca864a25d/pillow-12.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965", size = 5345969, upload-time = "2026-07-01T11:54:06.397Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/66/9a386a92561f402389a4fc70c18838bf6d35eb5eb5c6850b4b2dc64f5048/pillow-12.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7", size = 4780323, upload-time = "2026-07-01T11:54:09.351Z" },
+    { url = "https://files.pythonhosted.org/packages/25/27/ac8f99618ffd3dde21db0f4d4b1d2ab00c0880595bfd17df103f7f39fd0c/pillow-12.3.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d9c7f76c0673154f044e9d78c8655fb4213f6ca31a836df48b40fe5d187717b9", size = 6266838, upload-time = "2026-07-01T11:54:11.71Z" },
+    { url = "https://files.pythonhosted.org/packages/84/21/a35af28dcc61f37ed850a2d64c65c701321dfbf25085e469d5559360cbbf/pillow-12.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91", size = 6940830, upload-time = "2026-07-01T11:54:13.732Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/51/8b08617af3ad95e33ce6d7dd2c99ed6c8298f7fb131636303956be022e25/pillow-12.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c", size = 6344383, upload-time = "2026-07-01T11:54:15.756Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/72/cf78ac9780bb93c28328f408973845a309d4d145041665f734572ced1b52/pillow-12.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df", size = 7052934, upload-time = "2026-07-01T11:54:17.721Z" },
+    { url = "https://files.pythonhosted.org/packages/20/20/25e0f4dc178a6bc0696793720055519a0de89e7661dae886992decbd2f81/pillow-12.3.0-cp312-cp312-win32.whl", hash = "sha256:dbce0b29841537a2fa4a214c2bbf14de3587c9680caa9b4e217568472490b28f", size = 6472684, upload-time = "2026-07-01T11:54:19.839Z" },
+    { url = "https://files.pythonhosted.org/packages/45/89/da2f7971a317f83d807fdd4065c0af40208e59e692cc43d315a71a0e96d1/pillow-12.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09", size = 7227137, upload-time = "2026-07-01T11:54:22.025Z" },
+    { url = "https://files.pythonhosted.org/packages/de/47/4845a0a6c0dbf1db8456bd9fc791f13c5ced7ced20606d08a0aacfd25b49/pillow-12.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510", size = 2568267, upload-time = "2026-07-01T11:54:24.051Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/ac/31fb64e1e7efb5a4b50cd3d92049ba89ac6e4d8d3bb6a74e15048ca3353e/pillow-12.3.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89", size = 4161684, upload-time = "2026-07-01T11:54:25.934Z" },
+    { url = "https://files.pythonhosted.org/packages/87/b4/9805e23d2b4d77842b468513841fda254ee42f0289d25088340e4ff46e2d/pillow-12.3.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace", size = 4255487, upload-time = "2026-07-01T11:54:27.935Z" },
+    { url = "https://files.pythonhosted.org/packages/df/39/ecf519435a200c693fe053a6ee4d835b41cf963a4dfc2551c4e637cb2a71/pillow-12.3.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec", size = 3696433, upload-time = "2026-07-01T11:54:29.813Z" },
+    { url = "https://files.pythonhosted.org/packages/42/92/2fc3ffad878ae8dd5469ec1bc8eb83b71f48e13efdf68f02709003982a32/pillow-12.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66", size = 5345889, upload-time = "2026-07-01T11:54:31.97Z" },
+    { url = "https://files.pythonhosted.org/packages/10/76/8803c13605b763d33d156c4678fc77f8443389c0c51c8aef707bb02015f4/pillow-12.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35", size = 4780109, upload-time = "2026-07-01T11:54:34.026Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/01/e18aff37cb0b4aac47ac90f016d347a49aca667ef97f190b06ac2aabc928/pillow-12.3.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65", size = 6263736, upload-time = "2026-07-01T11:54:36.131Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/62/de5bdd77d935331f4f802edc11e4d82950f642caad6cb2f949837b8560e2/pillow-12.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3", size = 6937129, upload-time = "2026-07-01T11:54:38.216Z" },
+    { url = "https://files.pythonhosted.org/packages/70/4d/105627a13300c5e0df1d174230b32fd1273062c96f7745fd552b945d1e1d/pillow-12.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a", size = 6339562, upload-time = "2026-07-01T11:54:40.354Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/1d/f13de01a553988ab895ba1c722e06cf3144d4f57656fd5b81b6d881f1179/pillow-12.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e", size = 7049439, upload-time = "2026-07-01T11:54:42.489Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/f9/066794cca041b969964f779ee5fa66a9498bbf34248ac39c5d7954e4198f/pillow-12.3.0-cp313-cp313-win32.whl", hash = "sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f", size = 6473287, upload-time = "2026-07-01T11:54:44.9Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/9b/7a58e61d62be561da3a356fe2384d4059a6345fc130e23ef1c36a5b81d24/pillow-12.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8", size = 7239691, upload-time = "2026-07-01T11:54:47.141Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/b0/c4ed4f0ef8f8fa5ee8351537db6650bb8189f7e118842978dd6589065692/pillow-12.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b", size = 2568185, upload-time = "2026-07-01T11:54:49.137Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/01/001f65b68192f0228cc1dbbc8d2530ab5d58b61037ba0587f946fea607cd/pillow-12.3.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330", size = 4161736, upload-time = "2026-07-01T11:54:51.156Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/d2/0219746d0fd16fc8a84498e79452375be3797d3ce4044596ce565164b84f/pillow-12.3.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217", size = 4255435, upload-time = "2026-07-01T11:54:53.414Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/02/8d0bc62ef0302318c46ff2a512822d2610e81c7aa46c9b3abe6cbaca5ad0/pillow-12.3.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930", size = 3696262, upload-time = "2026-07-01T11:54:55.739Z" },
+    { url = "https://files.pythonhosted.org/packages/85/e2/73c77d218410b14f5f2d565e8a998d5317b7b9c75368d29985139f7a46f0/pillow-12.3.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8", size = 5350344, upload-time = "2026-07-01T11:54:57.657Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/da/32c752228ae345f489e3a42499d817b6c3996da7e8a3bc7a04fc806b243b/pillow-12.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0", size = 4780131, upload-time = "2026-07-01T11:54:59.713Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/9d/8b2c807dbef61a5197c047afe99823787eb66f63daf9fb2432f91d6f0462/pillow-12.3.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321", size = 6263757, upload-time = "2026-07-01T11:55:01.778Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/44/c85361f65dbe00eea8576ee467c768d25129989efb76e94f205e9ca9bb46/pillow-12.3.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b", size = 6936962, upload-time = "2026-07-01T11:55:03.93Z" },
+    { url = "https://files.pythonhosted.org/packages/18/7e/e483414b35800b86b6f08dbbc7803fb5cd52c4d6f897f47d53ea2c7e6f65/pillow-12.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198", size = 6339171, upload-time = "2026-07-01T11:55:05.989Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/f4/68c491844841ede6bed70189546b3ee9731cf9f2cbad396faff5e1ccba45/pillow-12.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130", size = 7048116, upload-time = "2026-07-01T11:55:08.131Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/34/77f3f793fed8efc7d243f21b33c5a3f0d1c97ee70346d3db855587e155ff/pillow-12.3.0-cp314-cp314-win32.whl", hash = "sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a", size = 6467209, upload-time = "2026-07-01T11:55:10.408Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/e0/492879f69d94f91f60fc8cd05ba03650e9520afebb2fb7aa12777d7c7f38/pillow-12.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d", size = 7237707, upload-time = "2026-07-01T11:55:12.745Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/ac/6b11f2875f1c2ac040d84e1bbf9cf22a88038f901ca1037898b280b38365/pillow-12.3.0-cp314-cp314-win_arm64.whl", hash = "sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838", size = 2565995, upload-time = "2026-07-01T11:55:14.736Z" },
+    { url = "https://files.pythonhosted.org/packages/52/69/c2208e56af9bfc1913afb24020297a691eb1d4ef688474c8a04913f65e04/pillow-12.3.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e", size = 5352503, upload-time = "2026-07-01T11:55:17.076Z" },
+    { url = "https://files.pythonhosted.org/packages/07/70/e5686d753e898a45d778ff1718dba8516ead6ab6b95d85fc8c4b70650cf2/pillow-12.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17", size = 4782956, upload-time = "2026-07-01T11:55:19.448Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/37/25c6692f06927ee973ff18c8d9ee98ad0b4d84ee67a09610c2dd1447958e/pillow-12.3.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385", size = 6322855, upload-time = "2026-07-01T11:55:21.613Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/91/420637fcb8f1bc11029e403b4538e6694744428d8246118e45719f944556/pillow-12.3.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c", size = 6989642, upload-time = "2026-07-01T11:55:24.006Z" },
+    { url = "https://files.pythonhosted.org/packages/10/08/b94d7811281ccf0d143a1cf768d1c49e1e54af63e7b708ab2ee3eb87face/pillow-12.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d", size = 6391281, upload-time = "2026-07-01T11:55:26.252Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/87/24233f785f55474dc02ce3e739c5528a77e3a862e9333d1dd7a25cc31f70/pillow-12.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931", size = 7096716, upload-time = "2026-07-01T11:55:28.318Z" },
+    { url = "https://files.pythonhosted.org/packages/23/26/fcb2f6e37175b04f53570b59937867e2b80ee1685e744023153028fc14f9/pillow-12.3.0-cp314-cp314t-win32.whl", hash = "sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7", size = 6474125, upload-time = "2026-07-01T11:55:30.956Z" },
+    { url = "https://files.pythonhosted.org/packages/90/de/3634abee5f1c9e13c56787b7d5517b0ba8d6de51700b95578cf338349c9f/pillow-12.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c", size = 7242939, upload-time = "2026-07-01T11:55:34.044Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/2a/fd13f8eb24de5714a6eb444a3d67e2842c6c576e159a43793adf23051351/pillow-12.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45", size = 2567506, upload-time = "2026-07-01T11:55:35.988Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/dc/8fdce34ec725a33c81c6ba122b904d6b9024e50ea9ac7bede62fab54506c/pillow-12.3.0-cp315-cp315-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139", size = 4162063, upload-time = "2026-07-01T11:55:37.941Z" },
+    { url = "https://files.pythonhosted.org/packages/76/66/2044b9a63d3b84ff048228dfcb7cd9bf0df983e8470971bf7d4c57b693de/pillow-12.3.0-cp315-cp315-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402", size = 4255549, upload-time = "2026-07-01T11:55:40.022Z" },
+    { url = "https://files.pythonhosted.org/packages/52/7e/1f67e6f4ece6b582ee4b539decbcc9f848dc245a93ed8cd7338bafef72f1/pillow-12.3.0-cp315-cp315-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c", size = 3696331, upload-time = "2026-07-01T11:55:41.98Z" },
+    { url = "https://files.pythonhosted.org/packages/12/40/d306fc2c8e4d45d7f175c77edca7063be7b86fe7fe6e68f4353bf71d808c/pillow-12.3.0-cp315-cp315-macosx_10_15_x86_64.whl", hash = "sha256:dc624f6bc473dacdf7ef7eb8678d0d08edf15cd94fad6ae5c7d6cc67a4e4902f", size = 5350370, upload-time = "2026-07-01T11:55:44.028Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/44/668fb1437e8ce420f62d6106eb66e44a5971602a4d794615bdf79315d82d/pillow-12.3.0-cp315-cp315-macosx_11_0_arm64.whl", hash = "sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701", size = 4780147, upload-time = "2026-07-01T11:55:46.073Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/08/93fa2e70e30a2d81547e481b6ee2bb9522117221fb1e0ce4b5df70967677/pillow-12.3.0-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace", size = 6273659, upload-time = "2026-07-01T11:55:48.264Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/6d/043e96ff814fc31a33077e4cba86082167db520c93632afdf2042febbb0c/pillow-12.3.0-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4", size = 6947439, upload-time = "2026-07-01T11:55:50.503Z" },
+    { url = "https://files.pythonhosted.org/packages/af/92/ba71d2ee2ac0edf3fa33bd9d5ee9ee080da70b1766f3ca3934f9938ddac9/pillow-12.3.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39", size = 6353577, upload-time = "2026-07-01T11:55:52.697Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/ce/e63064e2122923ff687c8ad792d0d736a7b3920a56a46982e81a7fdd25d6/pillow-12.3.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71", size = 7060394, upload-time = "2026-07-01T11:55:55.149Z" },
+    { url = "https://files.pythonhosted.org/packages/54/76/a09cc3ccc8d773a7283d34c38bec1708f9e3cc932093cbc4c5e71ac4060b/pillow-12.3.0-cp315-cp315-win32.whl", hash = "sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827", size = 6467375, upload-time = "2026-07-01T11:55:57.769Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/03/1846c49ba3b1d5550392a4bbd06d6fb4578e1cd91a803198b5c90f5f7d53/pillow-12.3.0-cp315-cp315-win_amd64.whl", hash = "sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5", size = 7237048, upload-time = "2026-07-01T11:55:59.975Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/bb/89f35dcc79610423f9f195504d7def7f0d1416a711541b42867e25fe3412/pillow-12.3.0-cp315-cp315-win_arm64.whl", hash = "sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658", size = 2566006, upload-time = "2026-07-01T11:56:02.143Z" },
+    { url = "https://files.pythonhosted.org/packages/30/88/707027ba09942dfa2c28759b5c222d769290a41c6d20ea60ec250801941f/pillow-12.3.0-cp315-cp315t-macosx_10_15_x86_64.whl", hash = "sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf", size = 5352509, upload-time = "2026-07-01T11:56:04.2Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/6d/00352fa25332c2569cd387851f568cc5a4b75a9adbfb37ac4fbce4c02eec/pillow-12.3.0-cp315-cp315t-macosx_11_0_arm64.whl", hash = "sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64", size = 4783167, upload-time = "2026-07-01T11:56:06.631Z" },
+    { url = "https://files.pythonhosted.org/packages/13/4f/9e049dfa21af7c22427275720e2490267ba8138120add5c4c574deb69782/pillow-12.3.0-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e", size = 6329237, upload-time = "2026-07-01T11:56:08.868Z" },
+    { url = "https://files.pythonhosted.org/packages/36/16/cf6eeaae8d0fce8dd390a33437cf68c5d5bd73834a2bc6e2f14efda0ab45/pillow-12.3.0-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777", size = 6997047, upload-time = "2026-07-01T11:56:11.379Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/69/dbf769bdd55f48bf5733cac28edc6364ffaa072ec9ba336266e4fe66be55/pillow-12.3.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1", size = 6400440, upload-time = "2026-07-01T11:56:13.908Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/e1/ffc9cfc2eea0d178da8018e18e959301ad9d6bc9f3edb7181e748a474b97/pillow-12.3.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9", size = 7105895, upload-time = "2026-07-01T11:56:16.575Z" },
+    { url = "https://files.pythonhosted.org/packages/18/f0/a5595c1e8c3ae44b9828cb2f0fa8155e5095ef04d6327b8f61cf44a3df85/pillow-12.3.0-cp315-cp315t-win32.whl", hash = "sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8", size = 6474384, upload-time = "2026-07-01T11:56:18.855Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/04/62bcd9f844984c5938d3b05264a61d797a29d3e0812341a8204af70bbdee/pillow-12.3.0-cp315-cp315t-win_amd64.whl", hash = "sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418", size = 7243537, upload-time = "2026-07-01T11:56:21.214Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/68/1f3066acedf37673694a7141381d8f811ae97f30d34413d236abe7d489f1/pillow-12.3.0-cp315-cp315t-win_arm64.whl", hash = "sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59", size = 2567491, upload-time = "2026-07-01T11:56:23.506Z" },
+    { url = "https://files.pythonhosted.org/packages/75/18/2e8b40223153ccbc60df07f9e8928dc0c76202aa4e55ae9f53962b6510d6/pillow-12.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468", size = 5302510, upload-time = "2026-07-01T11:56:25.736Z" },
+    { url = "https://files.pythonhosted.org/packages/46/3e/51fabf59d5ab801ceab709453d3ab6b180083496579549de4c45ced6528a/pillow-12.3.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94", size = 4736058, upload-time = "2026-07-01T11:56:28.041Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/20/22fe9384b7949e25fb1293bcfc84fb82590ff4ea6b37c95b24d26d793d86/pillow-12.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e", size = 5237776, upload-time = "2026-07-01T11:56:30.263Z" },
+    { url = "https://files.pythonhosted.org/packages/08/14/f6ba68107680ffa74b39985f3f30884e41318fbc4250caa423c79b4788bb/pillow-12.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3", size = 5860358, upload-time = "2026-07-01T11:56:32.68Z" },
+    { url = "https://files.pythonhosted.org/packages/36/54/0169bc772ec491108b62f644f8ecf1fe5d8ae5ebafde2ee2142210166903/pillow-12.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a", size = 7231786, upload-time = "2026-07-01T11:56:35.046Z" },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.3.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" },
+]
+
+[[package]]
+name = "setuptools"
+version = "84.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6d/44/f5da03a8ef95d369145c5bb53050e7877c9f3d312e128605fd9504829143/setuptools-84.0.0.tar.gz", hash = "sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73", size = 1168449, upload-time = "2026-08-08T18:27:58.365Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/95/9c/c510029fc6ef33a6275cd2c5d3cecd6613dfd6aa401d57c54f1c18852ccf/setuptools-84.0.0-py3-none-any.whl", hash = "sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670", size = 818216, upload-time = "2026-08-08T18:27:56.719Z" },
+]
+
+[[package]]
+name = "shuhe-xwdev"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "torch" },
+    { name = "torchaudio" },
+    { name = "torchvision" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "matplotlib", specifier = ">=3.11.1" },
+    { name = "torch", specifier = ">=2.13.0" },
+    { name = "torchaudio", specifier = ">=2.11.0" },
+    { name = "torchvision", specifier = ">=0.28.0" },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" },
+]
+
+[[package]]
+name = "sympy"
+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mpmath" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
+]
+
+[[package]]
+name = "torch"
+version = "2.13.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-bindings", marker = "python_full_version < '3.15' and sys_platform == 'linux'" },
+    { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" },
+    { name = "filelock" },
+    { name = "fsspec" },
+    { name = "jinja2" },
+    { name = "networkx" },
+    { name = "nvidia-cudnn-cu13", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparselt-cu13", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvshmem-cu13", marker = "sys_platform == 'linux'" },
+    { name = "setuptools" },
+    { name = "sympy" },
+    { name = "triton", marker = "python_full_version < '3.15' and sys_platform == 'linux'" },
+    { name = "typing-extensions" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5b/fe/cba54dc58523434919b66f13a667e36e436deddd77ca519e96553617d4ec/torch-2.13.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:e76f9bcecc52b8ff711239a2f7547d5353df95878ab232f0773c1d95928b92f8", size = 111187938, upload-time = "2026-07-08T16:05:17.065Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/59/1e3160e18e12aa3038390efab3ce02b36a9d4d6a527ecdd8520dca2e68d8/torch-2.13.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:092790c696a760c729fd5722835f50b9d81fd7c8f141571f3f3cf4081a8f664c", size = 427199369, upload-time = "2026-07-08T16:04:51.054Z" },
+    { url = "https://files.pythonhosted.org/packages/01/79/1f2d34ad7034ee1c7ffc1cf8bf0f8213af2a81df6ecdb3997ecec107c09d/torch-2.13.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:60fcdcb2f3876e21146cb4524ef06397d727ca9ad5f020818547e25075fe3cb7", size = 526574961, upload-time = "2026-07-08T16:04:07.075Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/fd/0f2ce40f58aefbdb3392f9acce3c8171940943ae2d661f70558bfa73befb/torch-2.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:a0d8b11f16a48d60e2015d8213aa0390744cbebb98e58b62b3514dddc656e330", size = 122015870, upload-time = "2026-07-08T16:05:27.59Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/3a/ed0f4d4d1dcde03bced7aac9a28e800abcdc0cbd06b6775044c9fbd877b7/torch-2.13.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:2fe228aba290d14b9f31b049be550dbd469c3fd3013d7a19705b30454da97027", size = 111213045, upload-time = "2026-07-08T16:05:22.997Z" },
+    { url = "https://files.pythonhosted.org/packages/df/a9/f6a2a4d763ff1df02e9a64c477029db614295bc9367f4131223791ccc243/torch-2.13.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:572df8be8ffb4599c88cbd6a0726f1f854f4da65d2e3c09f0e2c2283333cd6d4", size = 427210998, upload-time = "2026-07-08T16:04:37.708Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/82/fea946351658e6534db52d2cc12bc53087cbf87f9440c5f180f367c1950b/torch-2.13.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:796633c4cdf0fe2cdced72d8f88f22e73dbcfce83132763162f6d4bff13b820b", size = 526605292, upload-time = "2026-07-08T16:04:22.81Z" },
+    { url = "https://files.pythonhosted.org/packages/21/d6/e8f3c6f7e01f626f77259de9860d2a78bc84c40539e28e79b7e98b0bb659/torch-2.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:024c6cc0c1b085f2f91f20a3dc27b0471d021c31ce84b81be3afdc39f791fd9d", size = 122057313, upload-time = "2026-07-08T16:03:53.43Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/fa/c1c10b7aff4a9a3e8956d4f0a5f468fa6db7abc3208805719076772b4833/torch-2.13.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:33449899ce5496c1b84b4853179d94fd102028ae1407314d9fb956bb79e70d09", size = 111213743, upload-time = "2026-07-08T16:03:28.579Z" },
+    { url = "https://files.pythonhosted.org/packages/11/18/9ecb37b56293a0be8d80f810bf672a72fe7e02f8b475d5ef1b9bf8a0d748/torch-2.13.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:1e09d6a722504957c694faceca843acde562786df1144ebcc5a74075ec7f6005", size = 427213008, upload-time = "2026-07-08T16:03:44.106Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/5a/7c50ba1b7b713d71d34669c6d13dab0a11531a3eceb0307a5162dbfec0f7/torch-2.13.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:a3a9a21312872af8a26950b2c15680335a386a1f56ed03e780653d78b9607e9e", size = 526602329, upload-time = "2026-07-08T16:03:12.649Z" },
+    { url = "https://files.pythonhosted.org/packages/91/3d/e7adcc6aaf36961cd18f56cf8ad0f3058c3a5c84ccf391762176c94581b8/torch-2.13.0-cp313-cp313-win_amd64.whl", hash = "sha256:49b58f1e2c52440abb6f17c28f0335fe6c6d01ad1a7f55b0183b81e4b34d64e6", size = 122057920, upload-time = "2026-07-08T16:03:01.808Z" },
+    { url = "https://files.pythonhosted.org/packages/36/76/6dcc7f0c07052102dd36f83cbc5800842a909c8c3fbf1a7f8a5844954de9/torch-2.13.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:d849b390e07d8d333ce8ecaf91b273c656c598379a19c9acf1318a883f6b391c", size = 111227066, upload-time = "2026-07-08T16:03:33.6Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/09/2c10e8cd0e00fa5d23c052df6ce467eaa7182399f5e0f824f1e4ff42ccae/torch-2.13.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:a3893dc2da0a972a8ca5d698c85a9f967559ac5f8ee1797b77408aa8734d073c", size = 427226309, upload-time = "2026-07-08T16:02:53.127Z" },
+    { url = "https://files.pythonhosted.org/packages/76/c6/22c2102bbef14ca6a6cb4c20e42f088e49c5f812be4e160ae57502e325f9/torch-2.13.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:49f1ea385c754e54919408a9bb3b5a72b0b755bbe2c916c1d6f70afbec4908a2", size = 526614507, upload-time = "2026-07-08T16:02:16.441Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/0c/7d1deb6bce5bc3e6042caf39100ac768eba3b9a098e1dddd16f75bd6489b/torch-2.13.0-cp314-cp314-win_amd64.whl", hash = "sha256:4f8573e3ce9ebcd53fe922f01077a6085ccdfbe5f12fd215883a9d87d7a744fd", size = 122051871, upload-time = "2026-07-08T16:03:23.521Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/ce/aa8b7f9949d32e0f2f624f342bc3b48112c1b8a130288465938bc83bcbf9/torch-2.13.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:c28def70706c2f9ecc752574766e8ae4da9b810ab6676b611166761a78a9f1e1", size = 111537025, upload-time = "2026-07-08T16:02:44.28Z" },
+    { url = "https://files.pythonhosted.org/packages/69/d1/491e3a0389430946145888b0203f2b6a759ce2a61481b96a85c2da4f2ced/torch-2.13.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:31061ff56ed8fbf26c749806905aeb749ebeb819810fd5d52508aa5afd90dddc", size = 427219769, upload-time = "2026-07-08T16:02:31.18Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/1d/38006e045bf0a1fc28ef01e757c554e59e59a8770c284bc4f47b14e60441/torch-2.13.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:cc26eead4cf51d0b544e31e364dcf000846549c273bd148936fe9d24d29acb92", size = 526571320, upload-time = "2026-07-08T16:01:59.348Z" },
+    { url = "https://files.pythonhosted.org/packages/56/94/655c91992a882bd5071aa0b5d22a07dbb130d801e872be97c0b627a7c693/torch-2.13.0-cp314-cp314t-win_amd64.whl", hash = "sha256:a7de8a313090dc5c7d7ba4bfe5c3be222528f9a4dba1acc83bddb1157360c4b8", size = 122306773, upload-time = "2026-07-08T16:02:39.832Z" },
+]
+
+[[package]]
+name = "torchaudio"
+version = "2.11.0"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/77/0eec7f175d88f312296bd5b11c23bd58da37c1021f53da3db4df449ce3ee/torchaudio-2.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:492dd64645e9d0bb843e94f1d9a4d1e31426262ffc594fafecc1697df9df5eb9", size = 684142, upload-time = "2026-03-23T18:13:36.805Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/f9/6f7ebe071b44592c85269762b55b63ab0a091b5f479f73544738f7564a1e/torchaudio-2.11.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:73dab4841f94d888bc7c2aed7b5547c643edc974306919fe1adfb65d57cccf4b", size = 1626527, upload-time = "2026-03-23T18:13:39.011Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/70/17408e0d154d0c894537a88dcbadc48e8ad3b6e1ef4a1dabda5d40245ee0/torchaudio-2.11.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:1a07ec72fd6f26a588c39b5f029e0130d16bb40bc4221635580bf8fb18fcbc80", size = 1771930, upload-time = "2026-03-23T18:13:37.963Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/75/b6d03fc75b409bdaec597274d1bdd4213db716ed16f6801386b31d59c551/torchaudio-2.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:bb59ba4452bbbe95d75ad3ef18df9824955625f36698ce9a5998a4a9f3c1ba1d", size = 328658, upload-time = "2026-03-23T18:13:44.545Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/b1/77658817acacd01a72b714440c62f419efc4d90170e704e8e7a2c0918988/torchaudio-2.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a1cf1acc883bee9cb906a933572fed6a8a933f86ef34e9ea7d803f72317e8c1b", size = 684226, upload-time = "2026-03-23T18:13:40.023Z" },
+    { url = "https://files.pythonhosted.org/packages/78/28/c7adc053039f286c2aca0038b766cbe3294e66fec6b29a820e95128f9ede/torchaudio-2.11.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:bc653defca1c16154398517a1adc98d0fb7f1dd08e58ced217558d213c2c6e29", size = 1626670, upload-time = "2026-03-23T18:13:42.162Z" },
+    { url = "https://files.pythonhosted.org/packages/88/d8/d6d0f896e064aa67377484efef4911cdcc07bce2929474e1417cc0af18c2/torchaudio-2.11.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6503c0bdb29daf2e6281bb70ea2dfe2c3553b782b619eb5d73bdadd8a3f7cecf", size = 1771992, upload-time = "2026-03-23T18:13:33.188Z" },
+    { url = "https://files.pythonhosted.org/packages/23/a8/941277ecc39f7a0a169d554302a1f1afd87c1d94a8aec828891916cea59a/torchaudio-2.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:478110f981e5d40a8d82221732c57a56c85a1d5895fb8fe646e86ee15eded3bd", size = 328663, upload-time = "2026-03-23T18:13:19.218Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/9e/f76fcd9877c8c78f258ee34e0fb8291fdb91e6218d582d9ca66b1e4bd4ae/torchaudio-2.11.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:e3f9696a9ef1d49acc452159b052370c636406d072e9d8f10895fda87b591ea9", size = 679904, upload-time = "2026-03-23T18:13:28.329Z" },
+    { url = "https://files.pythonhosted.org/packages/85/70/249c1498ebdad3e7752866635ec0855fc0dcf898beccda5a9d2b9df8e4d0/torchaudio-2.11.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:b034d7672f1c415434f48ef17807f2cce47f29e8795338c751d4e596c9fbe8b5", size = 1618523, upload-time = "2026-03-23T18:13:15.703Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/98/be13fe35d9aa5c26381c0e453c828a789d15c007f8f7d08c95341d19974d/torchaudio-2.11.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:1c1101c1243ef0e4063ec63298977e2d3655c15cf88d9eb0a1bd4fe2db9f47ea", size = 1771992, upload-time = "2026-03-23T18:13:35.343Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/8b/2bbb3dca6ff28cba0de250874d5ef4fc2822c47a934b59b3974cff3219ef/torchaudio-2.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:986f4df5ed17b003dc52489468601720090e65f964f8bebccf90eb45bba75744", size = 328662, upload-time = "2026-03-23T18:13:18.308Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/ce/52c652d30af7d6e96c8f1735d26131e94708e3f38d852b8fa97958804dd8/torchaudio-2.11.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:bda09ea630ae7207384fb0f28c35e4f8c0d82dd6eba020b6b335ad0caa9fed49", size = 680814, upload-time = "2026-03-23T18:13:17.08Z" },
+    { url = "https://files.pythonhosted.org/packages/06/95/1ad1507482e7263e556709a3f5f87fecd375a0742cdaf238806c8e72eaad/torchaudio-2.11.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:9fe3083c62e035646483a14e180d33561bdc2eed436c9ab1259c137fb7120b4a", size = 1618546, upload-time = "2026-03-23T18:13:29.686Z" },
+    { url = "https://files.pythonhosted.org/packages/98/4c/480328ba07487eb9890406720304d0d460dd7a6a64098614f5aa53b662ca/torchaudio-2.11.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:13cff988697ccbad539987599f9dc672f40c417bed67570b365e4e5002bbd096", size = 1771991, upload-time = "2026-03-23T18:13:30.843Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/98/5d4790e2d6548768999acd34999d5aeefce8bcc23a07afaa5f03e723f557/torchaudio-2.11.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ed404c4399ad7f172c86a47c1b25293d322d1d58e26b10b0456a86cf67d37d84", size = 328661, upload-time = "2026-03-23T18:13:34.359Z" },
+    { url = "https://files.pythonhosted.org/packages/39/fe/ffa618b4f0d9732d7df7a2fa2bd48657d896599bc224e5af3c70d46c546b/torchaudio-2.11.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:cc09cd1f6015b8549e7fe255fb1be5346b57e7fee06541d3f3dbb012d8c4715f", size = 679901, upload-time = "2026-03-23T18:13:25.472Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/54/f414d7b92dd0b3094a2409c95a97bd6c49aa0620da722a0e55462f9bd9cb/torchaudio-2.11.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:79fb3cb99169fd41bd9719647261402a164da0d105a4d81f42a3260844ec5e79", size = 1618527, upload-time = "2026-03-23T18:13:26.68Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/a8/bf2e1f6ce24c990192400ae49b4acc1a0d0295b6c6a06bceecdc46ce08de/torchaudio-2.11.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:00e9f71ab9c656f0abdb40c515bd65d4658ab0ad380dee27a2efd7d51dabd3d6", size = 1771995, upload-time = "2026-03-23T18:13:23.373Z" },
+    { url = "https://files.pythonhosted.org/packages/83/6f/b0efb44e0bfe8dd4d78d76ae3be280354e1fb5c8631c782785d74cd8a7b1/torchaudio-2.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:1424638adb8bb40087bc7b6eb103e8e4fe398210f09076f33b7b5e61501b5d66", size = 328662, upload-time = "2026-03-23T18:13:32.243Z" },
+    { url = "https://files.pythonhosted.org/packages/60/84/1c792b0b700eac9a96772cfd9f96c097b17bca3234a2fde3c64b8063660d/torchaudio-2.11.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:da2725e250866da42a12934c9a6552f65a18b7187fd7a6221387f0e605fb3b96", size = 679926, upload-time = "2026-03-23T18:13:24.452Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/a0/62a5842062f739239691f2e57523e0570dd06704ad987755f7644a3afa23/torchaudio-2.11.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:1be3767064364ae82705bdf2b15c1e8b41fea82c4cd04d47428a8684b634b6ed", size = 1618552, upload-time = "2026-03-23T18:13:21.09Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/89/c293d818f9f899db93bf291b42401c05ae29acfb2e53d5341c30ea703e62/torchaudio-2.11.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:67f6edac29ed004652c11db5c19d9debb5d835695930574f564efc8bdd061bba", size = 1771986, upload-time = "2026-03-23T18:13:22.153Z" },
+    { url = "https://files.pythonhosted.org/packages/93/f7/ee5da8c03f1a3c7662c6c6a119f24a4b3e646da94be56dce3201e3a6ee9b/torchaudio-2.11.0-cp314-cp314t-win_amd64.whl", hash = "sha256:88fb5e29f670a33d9bac6aabb1d2734460cf6e461bde5cdc352826035851b16d", size = 328661, upload-time = "2026-03-23T18:13:20.1Z" },
+]
+
+[[package]]
+name = "torchvision"
+version = "0.28.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "numpy", version = "2.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "pillow" },
+    { name = "torch" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/b2/1e010052079e4c577007b789db336ea7075f1a426e84d17121fbc3745516/torchvision-0.28.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:83fe6c020866a85acd7d97deccc45ff11d66daf42916d04396a4309c66c0ccb8", size = 1856017, upload-time = "2026-07-08T16:07:55.533Z" },
+    { url = "https://files.pythonhosted.org/packages/27/be/1b9c5de9c655ca2df4a74100fa671a7b848532ff787e077ccde14a7dea2a/torchvision-0.28.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:5a38bc6da3d72621be003400b66f66a2b4c6d644fde05f680c2cb7ca8cf8dd6c", size = 7841822, upload-time = "2026-07-08T16:07:49.207Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/9b/f1e68e861d4462e3e195a642c2b448e7b7d3fad5f209487162b9a2133d9b/torchvision-0.28.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:7e80f543b22503d9415e126db5f0ff3917036925e38560ee6b9ae38c571a4002", size = 7670718, upload-time = "2026-07-08T16:07:46.525Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/de/1494610ff54cbb154beb55033cc2cd50f3de04dac132fa2dd00e4f2b2556/torchvision-0.28.0-cp311-cp311-win_amd64.whl", hash = "sha256:9a45ea67235d965ef52187130d20002a4de20c54ea3d927a24286961d268dc37", size = 3814319, upload-time = "2026-07-08T16:07:37.153Z" },
+    { url = "https://files.pythonhosted.org/packages/15/49/c1cab1ecbb3ff1a380a3f99283db1dee61b8afe354f6352c643b65937130/torchvision-0.28.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:e9f54c30cd52e3ef7fd034cc69b7bb7e0964e1c8f8743e018ab92e95b40f9eee", size = 1856020, upload-time = "2026-07-08T16:07:52.182Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/4c/95233776e2def960e5abb7a07931230a545f43717a56a1e1140162033598/torchvision-0.28.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:5cf78ebc401ce64ae19b8c55de866bb836797d559a4de9c25ccbe74cfa642d3a", size = 7842127, upload-time = "2026-07-08T16:07:53.446Z" },
+    { url = "https://files.pythonhosted.org/packages/93/e4/e9b2495d0d57b9f60d63c57d0a910410a81b4b073bf70917bef815291119/torchvision-0.28.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:028a3d481b37d785605620d7cdad897064c5a55bae2aa1f2658766333e291940", size = 7675040, upload-time = "2026-07-08T16:07:58.017Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/9c/55ed9cb6dfe3ee9c837df5cd0e758372e5829aa38b8dd71343aa632cc4e2/torchvision-0.28.0-cp312-cp312-win_amd64.whl", hash = "sha256:87dc16b2df427c1318ad335f1e2be2b3b15b2cf20f7934c83b0505a48425ee5d", size = 4085785, upload-time = "2026-07-08T16:07:50.928Z" },
+    { url = "https://files.pythonhosted.org/packages/20/55/08a726c14c67b37c8aca04b077766909f1c7ed23f76116884fe63b9bd033/torchvision-0.28.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:d483b4aa3f5237569053f749cd1a2b5bb548ca456e40461a5dd087f21149d123", size = 1856021, upload-time = "2026-07-08T16:07:45.386Z" },
+    { url = "https://files.pythonhosted.org/packages/db/8f/40beacd53809194f5259e590d1afaeaa8ad57da15f77c646e6560bcc4616/torchvision-0.28.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:bb6dd6918460ed89cc7644adcc2402991474d6933cf1ce92b390641cb233fddf", size = 7797014, upload-time = "2026-07-08T16:07:43.04Z" },
+    { url = "https://files.pythonhosted.org/packages/32/db/062cdb5a84380a60439775311fff34d89229760d2a50680393dc18699956/torchvision-0.28.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:ad7b3a439265cc3739a4ab5b4c998c0e38ea99c0ee7ca4dea35c5d0b099ec237", size = 7674669, upload-time = "2026-07-08T16:07:38.91Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/a6/b4081e2d04e1541abf82785ac9e5178a494c19330391f551356c8c18b7b3/torchvision-0.28.0-cp313-cp313-win_amd64.whl", hash = "sha256:7e9dd6f60d6e15f8dc27d4f877fdb6002fc70d70272412135f1c2ff9cfa08d3b", size = 4157380, upload-time = "2026-07-08T16:07:40.22Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/b9/da40eca5bbe9596c12ae9899ab7abaf887f5e20f29d08b924b4633714821/torchvision-0.28.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:3bd9dba55224a9db4a2d77f6feaa5651770d8c8e86d3d0ddb0fa6bec54c8712b", size = 1856014, upload-time = "2026-07-08T16:07:44.282Z" },
+    { url = "https://files.pythonhosted.org/packages/06/d6/313aafd3df4eaf5f330211bd4e75b7598bddbfee4f55580d3b58536e1b20/torchvision-0.28.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:89f90e29b0966352811b12589f3a3c61943bf2bb9487b9d7bbec10efb1096bb5", size = 7796873, upload-time = "2026-07-08T16:07:30.907Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/41/31f8e959ab8f942600b6357f8999c21d779d5fd3304b0fd204ff4b518239/torchvision-0.28.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:36beb0782976906069ca03d4c9aacaf4b6b838b06ed6c20960ea9c51cce7acdd", size = 7674634, upload-time = "2026-07-08T16:07:29.657Z" },
+    { url = "https://files.pythonhosted.org/packages/15/15/4c5115253fd470672cdac0a1cf139e06b4f3e29d041238a2b255937f63be/torchvision-0.28.0-cp314-cp314-win_amd64.whl", hash = "sha256:3557cc7b539f46dabcda2b6f2b14017ccbeef024de466d4fc5835fc3f287f769", size = 4184005, upload-time = "2026-07-08T16:07:35.805Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/80/822a6163da716f8a78141cf6678d74e26a572285d4ea866ef8aa657bb307/torchvision-0.28.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:09ce8f56e81f19b9c378ae7bb109f83f6659fd8bc3cd14241a48e4af46e9ed49", size = 1856011, upload-time = "2026-07-08T16:07:33.404Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/d1/cd3f9463b39a790ec8c0c2f6e6c8061edb1562114d04fcdfa786ed889345/torchvision-0.28.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:62c7d110f86a039245b587e4fae60278c649f3bd42ff79cfbc1178eca4e72542", size = 7796742, upload-time = "2026-07-08T16:07:28.339Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/82/3e0a7ad18e99831e2d7f4713d3be717b7159ff5a920862dd5c23c454aa71/torchvision-0.28.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:904cf89af220f8c6b2ed0296bb5065b474ce43b77558e48b2bf9de8b0ba17204", size = 7675526, upload-time = "2026-07-08T16:07:34.572Z" },
+    { url = "https://files.pythonhosted.org/packages/18/d4/23aea03b28297bc66a4461f55ae4296368a9d85fa9a454bafcb2a5348bd7/torchvision-0.28.0-cp314-cp314t-win_amd64.whl", hash = "sha256:46f581979c010ad6da6bd85ee602aa707e1ff44312670223b7a0ee517ad06d47", size = 4291452, upload-time = "2026-07-08T16:07:32.236Z" },
+]
+
+[[package]]
+name = "triton"
+version = "3.7.1"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7b/f9/19d842d06a08559534fa1eaab6ca551b1bcf40f06620bddec1babaa2772d/triton-3.7.1-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4a0e1cd4c4a76370ed74a8432a53cea28716827d19e40ffc732233e35ceb3f6", size = 184664887, upload-time = "2026-06-17T20:03:42.913Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/5e/fce69606f7f240297f163e25539906732b199530d486ce67ae319877e821/triton-3.7.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6744957e9fd610a29680ec2346057d0c86948ed3812468670719f391e94b44a5", size = 197701306, upload-time = "2026-06-17T19:53:13.673Z" },
+    { url = "https://files.pythonhosted.org/packages/94/fa/f856e24deb462d5f18bd4b5a746957862ab9b6ee5834bda60605ec348366/triton-3.7.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9497f2e696ee368862a181a90b2dcc03ca978cc4f602abd67c7d81022a6988e1", size = 184692359, upload-time = "2026-06-17T20:03:48.288Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/6f/fb96d15db6f36d6eae4cafb998c2e0353bf59d7c4ea1662d7497f269134a/triton-3.7.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7e40869937a68206ec70d7f25bb7ec6433cb083f9135e1f36dbd318dc449a728", size = 197719725, upload-time = "2026-06-17T19:53:20.419Z" },
+    { url = "https://files.pythonhosted.org/packages/00/42/c5089d4d9327fcd1e862c599cc2927f39418f84dd11a84cb2ccff9d4787a/triton-3.7.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cdbfc09d9ec58bc5e68321525653220de7515c199e7a8097a97c85e62b52cd0a", size = 184694629, upload-time = "2026-06-17T20:03:53.444Z" },
+    { url = "https://files.pythonhosted.org/packages/07/42/2c3ac59253ae8892b6f307875263dd23dc875cdf732d3aea40d6d41fb7cb/triton-3.7.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:58c0e131da05134a2a4788ccbcc0c1105cf0f54c8e98f19e34cd465396dc15eb", size = 197729241, upload-time = "2026-06-17T19:53:27.801Z" },
+    { url = "https://files.pythonhosted.org/packages/40/71/e01aa7ad573883ed9456f130226babdec70b005e098c4d6226a6238e761b/triton-3.7.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fe4ea396a06171f1f1f58cbd39c70b09294398f7dd7c620939bab54ad6f934fa", size = 184705764, upload-time = "2026-06-17T20:03:59.064Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/09/5683146fda6a2b569deb78ccfd8fbfea8bfe55f726b081c0a6bb18dd6f28/triton-3.7.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2020153b08280415ec0da6607834e79166442147e78e144df06b508c75b186d2", size = 197729537, upload-time = "2026-06-17T19:53:35.516Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/f8/448220c3092019f9fdfab39ec47985968181d67da34b44f6a7f6280a5cbb/triton-3.7.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c58e4c61f0c73b5dba3b5d19b4a7093c32f90dc18b2a7f121a7c16ccd31107b7", size = 184814760, upload-time = "2026-06-17T20:04:04.984Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/ac/229b7d4589d2e5937310e72c6d46e89599d16a4a12b479ffa1499fee8eb8/triton-3.7.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10ba85fa2cca4a2fbdeb36bf1cb082f2c252bda55bf9fccd74f65ec5bc647e68", size = 197824404, upload-time = "2026-06-17T19:53:42.772Z" },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f6/cc/6253133b5bb138fc3306cebfbda2c520f545d36b5be2c7255cc528bb45d6/typing_extensions-4.16.0.tar.gz", hash = "sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5", size = 113555, upload-time = "2026-07-02T08:40:05.92Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/49/d3/b8441a820a491ddfc024b0b0cf0393375b75ea13866d9c66727e54c2fc80/typing_extensions-4.16.0-py3-none-any.whl", hash = "sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8", size = 45571, upload-time = "2026-07-02T08:40:04.659Z" },
+]