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   "source": [
    "# Setup - Building Your ML Development Foundation\n",
    "\n",
    "Welcome to TinyTorch Setup! You'll configure your development environment for machine learning.\n",
    "\n",
    "## 🔗 Building on Previous Learning\n",
    "**What You Need**:\n",
    "- Python 3.8+ installed on your system\n",
    "- Basic command line familiarity\n",
    "\n",
    "**What's Working**: You have Python installed and ready to go.\n",
    "\n",
    "**The Gap**: Raw Python isn't sufficient for ML computation - we need optimized libraries.\n",
    "\n",
    "**This Module's Solution**: Set up NumPy foundation and validate your environment.\n",
    "\n",
    "**Connection Map**:\n",
    "```\n",
    "Python → Setup → Tensor\n",
    "(base)   (tools)  (computation)\n",
    "```\n",
    "\n",
    "## Learning Objectives\n",
    "1. **Environment Setup**: Install and validate ML dependencies\n",
    "2. **Basic Validation**: Check versions and system compatibility  \n",
    "3. **Development Profile**: Create user configuration for projects\n",
    "4. **Testing Skills**: Validate setup with immediate feedback\n",
    "\n",
    "## Build → Test → Use\n",
    "1. **Build**: Install packages and create validation functions\n",
    "2. **Test**: Verify each function works correctly\n",
    "3. **Use**: Apply setup in real ML development workflow"
   ]
  },
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   "source": [
    "#| default_exp core.setup\n",
    "\n",
    "#| export\n",
    "import sys\n",
    "import platform"
   ]
  },
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   "source": [
    "## Step 1: Package Installation 📦\n",
    "\n",
    "Install the essential packages for ML development."
   ]
  },
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   "source": [
    "#| export\n",
    "def setup():\n",
    "    \"\"\"Install required packages for TinyTorch development.\n",
    "    \n",
    "    TODO: Install NumPy and matplotlib using pip\n",
    "    \n",
    "    APPROACH:\n",
    "    1. Use subprocess to run pip install commands\n",
    "    2. Install the essential packages we need\n",
    "    3. Print success message\n",
    "    \n",
    "    EXAMPLE:\n",
    "    >>> setup()\n",
    "    ✅ Packages installed successfully!\n",
    "    \n",
    "    HINT: Use subprocess.run() with [\"pip\", \"install\", \"package_name\"]\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    import subprocess\n",
    "    \n",
    "    # Install essential packages\n",
    "    packages = [\"numpy\", \"matplotlib\"]\n",
    "    \n",
    "    print(\"📦 Installing TinyTorch dependencies...\")\n",
    "    for package in packages:\n",
    "        print(f\"Installing {package}...\")\n",
    "        subprocess.run([\"pip\", \"install\", package], check=True)\n",
    "    \n",
    "    print(\"✅ Packages installed successfully!\")\n",
    "    ### END SOLUTION"
   ]
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   "source": [
    "### 🧪 Unit Test: Package Installation\n",
    "\n",
    "This test validates the setup function works correctly."
   ]
  },
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     "grade": true,
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   "source": [
    "def test_unit_setup():\n",
    "    \"\"\"Test setup function.\"\"\"\n",
    "    print(\"🔬 Unit Test: Package Installation...\")\n",
    "    \n",
    "    # Test that function exists and is callable\n",
    "    assert callable(setup), \"setup should be callable\"\n",
    "    \n",
    "    # Run setup (should not crash)\n",
    "    setup()\n",
    "    \n",
    "    print(\"✅ Setup function works!\")\n",
    "\n",
    "# Run the test immediately\n",
    "test_unit_setup()"
   ]
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   "source": [
    "## Step 2: Version Checking ✅\n",
    "\n",
    "Verify that essential packages are installed and working."
   ]
  },
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     "grade_id": "check-versions",
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   "source": [
    "#| export\n",
    "def check_versions():\n",
    "    \"\"\"Check versions of essential packages.\n",
    "    \n",
    "    TODO: Import and display version information for key packages\n",
    "    \n",
    "    APPROACH:\n",
    "    1. Try importing NumPy and display version\n",
    "    2. Show Python and platform information\n",
    "    3. Handle import errors gracefully\n",
    "    \n",
    "    EXAMPLE:\n",
    "    >>> check_versions()\n",
    "    🐍 Python: 3.11\n",
    "    🔢 NumPy: 1.24.3\n",
    "    💻 Platform: Darwin\n",
    "    \n",
    "    HINT: Use try/except to handle missing packages\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    try:\n",
    "        import numpy as np\n",
    "        print(f\"🐍 Python: {sys.version_info.major}.{sys.version_info.minor}\")\n",
    "        print(f\"🔢 NumPy: {np.__version__}\")\n",
    "        print(f\"💻 Platform: {platform.system()}\")\n",
    "        print(\"✅ All packages available!\")\n",
    "    except ImportError as e:\n",
    "        print(f\"❌ Missing package: {e}\")\n",
    "        print(\"💡 Run setup() first to install packages\")\n",
    "    ### END SOLUTION"
   ]
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   "source": [
    "### 🧪 Unit Test: Version Check\n",
    "\n",
    "This test validates the version checking function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "nbgrader": {
     "grade": true,
     "grade_id": "test-versions",
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   "source": [
    "def test_unit_check_versions():\n",
    "    \"\"\"Test check_versions function.\"\"\"\n",
    "    print(\"🔬 Unit Test: Version Check...\")\n",
    "    \n",
    "    # Test that function exists and is callable\n",
    "    assert callable(check_versions), \"check_versions should be callable\"\n",
    "    \n",
    "    # Run version check (should not crash)\n",
    "    check_versions()\n",
    "    \n",
    "    print(\"✅ Version check function works!\")\n",
    "\n",
    "# Run the test immediately\n",
    "test_unit_check_versions()"
   ]
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   "source": [
    "## Step 3: User Information 👋\n",
    "\n",
    "Create a development profile for project tracking."
   ]
  },
  {
   "cell_type": "code",
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   "id": "a9c1d167",
   "metadata": {
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     "grade": false,
     "grade_id": "user-info",
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   "source": [
    "#| export\n",
    "def get_info():\n",
    "    \"\"\"Create a development profile with user and system information.\n",
    "    \n",
    "    A development profile helps track:\n",
    "    - Who is working on the project (name, email)\n",
    "    - What system they're using (platform, Python version)\n",
    "    - When the environment was set up (timestamp)\n",
    "    \n",
    "    TODO: Build a profile dictionary with user input and system detection\n",
    "    \n",
    "    APPROACH:\n",
    "    1. Collect user identity (name and email for project attribution)\n",
    "    2. Detect system information (platform and Python version for compatibility)\n",
    "    3. Add timestamp (when this environment was configured)\n",
    "    4. Return complete profile dictionary\n",
    "    \n",
    "    EXAMPLE:\n",
    "    >>> profile = get_info()\n",
    "    Your name: Alice Smith\n",
    "    Your email: alice@university.edu\n",
    "    >>> print(profile)\n",
    "    {'name': 'Alice Smith', 'email': 'alice@university.edu', \n",
    "     'platform': 'Darwin', 'python_version': '3.11', 'timestamp': '2024-01-15T10:30:00'}\n",
    "    \n",
    "    HINT: Use input() for user data, platform/sys modules for system info, datetime for timestamp\n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    import datetime\n",
    "    \n",
    "    print(\"👋 Creating your TinyTorch development profile...\")\n",
    "    print(\"This helps track who's working on projects and their system setup.\")\n",
    "    \n",
    "    # Get user information\n",
    "    name = input(\"Your name: \").strip()\n",
    "    if not name:\n",
    "        name = \"TinyTorch Developer\"\n",
    "    \n",
    "    email = input(\"Your email: \").strip() \n",
    "    if not email:\n",
    "        email = \"dev@tinytorch.local\"\n",
    "    \n",
    "    # Detect system information automatically\n",
    "    current_time = datetime.datetime.now().isoformat()\n",
    "    \n",
    "    # Create comprehensive profile\n",
    "    profile = {\n",
    "        \"name\": name,\n",
    "        \"email\": email,\n",
    "        \"platform\": platform.system(),\n",
    "        \"python_version\": f\"{sys.version_info.major}.{sys.version_info.minor}\",\n",
    "        \"timestamp\": current_time,\n",
    "        \"setup_complete\": True\n",
    "    }\n",
    "    \n",
    "    print(f\"\\n✅ Profile created for {profile['name']}\")\n",
    "    print(f\"📧 Email: {profile['email']}\")\n",
    "    print(f\"💻 Platform: {profile['platform']}\")\n",
    "    print(f\"🐍 Python: {profile['python_version']}\")\n",
    "    print(f\"⏰ Created: {profile['timestamp']}\")\n",
    "    \n",
    "    return profile\n",
    "    ### END SOLUTION"
   ]
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   "source": [
    "### 🧪 Unit Test: User Information\n",
    "\n",
    "This test validates the user information function."
   ]
  },
  {
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   "id": "08221b2c",
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    "nbgrader": {
     "grade": true,
     "grade_id": "test-info",
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   "source": [
    "def test_unit_get_info():\n",
    "    \"\"\"Test get_info function.\"\"\"\n",
    "    print(\"🔬 Unit Test: User Information...\")\n",
    "    \n",
    "    # Test that function exists and is callable\n",
    "    assert callable(get_info), \"get_info should be callable\"\n",
    "    \n",
    "    # Mock input to avoid interactive prompt in tests\n",
    "    import unittest.mock\n",
    "    with unittest.mock.patch('builtins.input', return_value=''):\n",
    "        profile = get_info()\n",
    "    \n",
    "    # Verify profile structure\n",
    "    assert isinstance(profile, dict), \"get_info should return a dictionary\"\n",
    "    assert 'name' in profile, \"Profile should have 'name' field\"\n",
    "    assert 'platform' in profile, \"Profile should have 'platform' field\"\n",
    "    \n",
    "    print(\"✅ User information function works!\")\n",
    "\n",
    "# Run the test immediately\n",
    "test_unit_get_info()"
   ]
  },
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   "source": [
    "## 🧪 Complete Module Testing\n",
    "\n",
    "Let's run all tests to ensure everything works together."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def test_unit_all():\n",
    "    \"\"\"Run all unit tests for this module.\"\"\"\n",
    "    print(\"🧪 Running all setup tests...\")\n",
    "    \n",
    "    test_unit_setup()\n",
    "    test_unit_check_versions() \n",
    "    test_unit_get_info()\n",
    "    \n",
    "    print(\"✅ All tests passed! Setup module complete.\")\n",
    "\n",
    "# Run all tests\n",
    "test_unit_all()"
   ]
  },
  {
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   "id": "e110b229",
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   "source": [
    "## 🎯 MODULE SUMMARY: Setup Complete!\n",
    "\n",
    "Congratulations! You've successfully configured your ML development environment!\n",
    "\n",
    "### What You've Accomplished\n",
    "✅ **Package Installation**: Automated setup with error handling\n",
    "✅ **Environment Validation**: Version checking and compatibility testing  \n",
    "✅ **Development Profile**: User configuration for project tracking\n",
    "✅ **Testing Framework**: Immediate validation with clear feedback\n",
    "\n",
    "### Key Learning Outcomes\n",
    "- **Environment Management**: Install and validate ML dependencies\n",
    "- **Error Handling**: Graceful failure management with helpful messages\n",
    "- **System Information**: Platform and version detection techniques\n",
    "- **Testing Patterns**: Immediate validation after each implementation\n",
    "\n",
    "### Ready for Next Steps\n",
    "Your setup implementation now enables:\n",
    "- **Immediate Application**: Ready for ML development with NumPy foundation\n",
    "- **Next Module Preparation**: Solid environment for tensor operations\n",
    "- **Real-World Connection**: Professional development workflow patterns\n",
    "\n",
    "### Next Steps\n",
    "1. **Export your module**: `tito module complete 01_setup`\n",
    "2. **Validate integration**: `tito test --module setup`  \n",
    "3. **Ready for Module 02**: Tensor operations build on this foundation\n",
    "\n",
    "**Your environment is ready - let's start building ML systems!** 🚀"
   ]
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