TinyTorch/modules/setup/setup_dev.ipynb

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"""
# Module 0: Setup - Tiny🔥Torch Development Workflow

Welcome to TinyTorch! This module teaches you the development workflow you'll use throughout the course.

## Learning Goals
- Understand the nbdev notebook-to-Python workflow
- Write your first TinyTorch code
- Run tests and use the CLI tools
- Get comfortable with the development rhythm

## The TinyTorch Development Cycle

1. **Write code** in this notebook using `#| export` 
2. **Export code** with `python bin/tito.py sync --module setup`
3. **Run tests** with `python bin/tito.py test --module setup`
4. **Check progress** with `python bin/tito.py info`

Let's get started!
"""

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#| default_exp core.utils

# Setup imports and environment
import sys
import platform
from datetime import datetime
import os
from pathlib import Path

print("🔥 TinyTorch Development Environment")
print(f"Python {sys.version}")
print(f"Platform: {platform.system()} {platform.release()}")
print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")

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"""
## Step 1: Understanding the Module → Package Structure

**🎓 Teaching vs. 🔧 Building**: This course has two sides:
- **Teaching side**: You work in `modules/setup/setup_dev.ipynb` (learning-focused)
- **Building side**: Your code exports to `tinytorch/core/utils.py` (production package)

**Key Concept**: The `#| default_exp core.utils` directive at the top tells nbdev to export all `#| export` cells to `tinytorch/core/utils.py`.

This separation allows us to:
- Organize learning by **concepts** (modules)  
- Organize code by **function** (package structure)
- Build a real ML framework while learning systematically

Let's write a simple "Hello World" function with the `#| export` directive:
"""

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#| export
def hello_tinytorch():
    """
    A simple hello world function for TinyTorch.
    
    TODO: Implement this function to display TinyTorch ASCII art and welcome message.
    Load the flame art from tinytorch_flame.txt file with graceful fallback.
    """
    raise NotImplementedError("Student implementation required")

def add_numbers(a, b):
    """
    Add two numbers together.
    
    TODO: Implement addition of two numbers.
    This is the foundation of all mathematical operations in ML.
    """
    raise NotImplementedError("Student implementation required")

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#| hide
#| export
def hello_tinytorch():
    """Display the TinyTorch ASCII art and welcome message."""
    try:
        # Get the directory containing this file
        current_dir = Path(__file__).parent
        art_file = current_dir / "tinytorch_flame.txt"
        
        if art_file.exists():
            with open(art_file, 'r') as f:
                ascii_art = f.read()
            print(ascii_art)
            print("Tiny🔥Torch")
            print("Build ML Systems from Scratch!")
        else:
            print("🔥 TinyTorch 🔥")
            print("Build ML Systems from Scratch!")
    except NameError:
        # Handle case when running in notebook where __file__ is not defined
        try:
            art_file = Path(os.getcwd()) / "tinytorch_flame.txt"
            if art_file.exists():
                with open(art_file, 'r') as f:
                    ascii_art = f.read()
                print(ascii_art)
                print("Tiny🔥Torch")
                print("Build ML Systems from Scratch!")
            else:
                print("🔥 TinyTorch 🔥")
                print("Build ML Systems from Scratch!")
        except:
            print("🔥 TinyTorch 🔥")
            print("Build ML Systems from Scratch!")

def add_numbers(a, b):
    """Add two numbers together."""
    return a + b

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"""
### 🧪 Test Your Implementation

Once you implement the functions above, run this cell to test them:
"""

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# Test the functions in the notebook (will fail until implemented)
try:
    print("Testing hello_tinytorch():")
    hello_tinytorch()
    print()
    print("Testing add_numbers():")
    print(f"2 + 3 = {add_numbers(2, 3)}")
except NotImplementedError as e:
    print(f"⚠️  {e}")
    print("Implement the functions above first!")

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"""
## Step 2: A Simple Class

Let's create a simple class that will help us understand system information. This is still basic, but shows how to structure classes in TinyTorch.
"""

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#| export
class SystemInfo:
    """
    Simple system information class.
    
    TODO: Implement this class to collect and display system information.
    """
    
    def __init__(self):
        """
        Initialize system information collection.
        
        TODO: Collect Python version, platform, and machine information.
        """
        raise NotImplementedError("Student implementation required")
    
    def __str__(self):
        """
        Return human-readable system information.
        
        TODO: Format system info as a readable string.
        """
        raise NotImplementedError("Student implementation required")
    
    def is_compatible(self):
        """
        Check if system meets minimum requirements.
        
        TODO: Check if Python version is >= 3.8
        """
        raise NotImplementedError("Student implementation required")

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#| hide
#| export
class SystemInfo:
    """Simple system information class."""
    
    def __init__(self):
        self.python_version = sys.version_info
        self.platform = platform.system()
        self.machine = platform.machine()
    
    def __str__(self):
        return f"Python {self.python_version.major}.{self.python_version.minor} on {self.platform} ({self.machine})"
    
    def is_compatible(self):
        """Check if system meets minimum requirements."""
        return self.python_version >= (3, 8)

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"""
### 🧪 Test Your SystemInfo Class

Once you implement the SystemInfo class above, run this cell to test it:
"""

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# Test the SystemInfo class (will fail until implemented)
try:
    print("Testing SystemInfo class:")
    info = SystemInfo()
    print(f"System: {info}")
    print(f"Compatible: {info.is_compatible()}")
except NotImplementedError as e:
    print(f"⚠️  {e}")
    print("Implement the SystemInfo class above first!")

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"""
## Step 3: Developer Personalization

Let's make TinyTorch yours! Create a developer profile that will identify you throughout your ML systems journey.
"""

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#| export
class DeveloperProfile:
    """
    Developer profile for personalizing TinyTorch experience.
    
    TODO: Implement this class to store and display developer information.
    Default to course instructor but allow students to personalize.
    """
    
    @staticmethod
    def _load_default_flame():
        """
        Load the default TinyTorch flame ASCII art from file.
        
        TODO: Implement file loading for tinytorch_flame.txt with fallback.
        """
        raise NotImplementedError("Student implementation required")
    
    def __init__(self, name="Vijay Janapa Reddi", affiliation="Harvard University", 
                 email="vj@eecs.harvard.edu", github_username="profvjreddi", ascii_art=None):
        """
        Initialize developer profile.
        
        TODO: Store developer information with sensible defaults.
        Students should be able to customize this with their own info and ASCII art.
        """
        raise NotImplementedError("Student implementation required")
    
    def __str__(self):
        """
        Return formatted developer information.
        
        TODO: Format developer info as a professional signature with optional ASCII art.
        """
        raise NotImplementedError("Student implementation required")
    
    def get_signature(self):
        """
        Get a short signature for code headers.
        
        TODO: Return a concise signature like "Built by Name (@github)"
        """
        raise NotImplementedError("Student implementation required")
    
    def get_ascii_art(self):
        """
        Get ASCII art for the profile.
        
        TODO: Return custom ASCII art or default flame loaded from file.
        """
        raise NotImplementedError("Student implementation required")

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#| hide
#| export
class DeveloperProfile:
    """Developer profile for personalizing TinyTorch experience."""
    
    @staticmethod
    def _load_default_flame():
        """Load the default TinyTorch flame ASCII art from file."""
        try:
            # Try to load from the same directory as this module
            try:
                # Try to get the directory of the current file
                current_dir = os.path.dirname(__file__)
            except NameError:
                # If __file__ is not defined (e.g., in notebook), use current directory
                current_dir = os.getcwd()
            
            flame_path = os.path.join(current_dir, 'tinytorch_flame.txt')
            
            with open(flame_path, 'r', encoding='utf-8') as f:
                flame_art = f.read()
            
            # Add the Tiny🔥Torch text below the flame
            return f"""{flame_art}
                    
                    Tiny🔥Torch
            Build ML Systems from Scratch!
            """
        except (FileNotFoundError, IOError):
            # Fallback to simple flame if file not found
            return """
    🔥 TinyTorch Developer 🔥
         .  .  .  .  .  .
        .    .  .  .  .   .
       .  .    .  .  .  .  .
      .  .  .    .  .  .  .  .
     .  .  .  .    .  .  .  .  .
    .  .  .  .  .    .  .  .  .  .
   .  .  .  .  .  .    .  .  .  .  .
  .  .  .  .  .  .  .    .  .  .  .  .
 .  .  .  .  .  .  .  .    .  .  .  .  .
.  .  .  .  .  .  .  .  .    .  .  .  .  .
 \\  \\  \\  \\  \\  \\  \\  \\  \\  /  /  /  /  /  /
  \\  \\  \\  \\  \\  \\  \\  \\  /  /  /  /  /  /
   \\  \\  \\  \\  \\  \\  \\  /  /  /  /  /  /
    \\  \\  \\  \\  \\  \\  /  /  /  /  /  /
     \\  \\  \\  \\  \\  /  /  /  /  /  /
      \\  \\  \\  \\  /  /  /  /  /  /
       \\  \\  \\  /  /  /  /  /  /
        \\  \\  /  /  /  /  /  /
         \\  /  /  /  /  /  /
          \\/  /  /  /  /  /
           \\/  /  /  /  /
            \\/  /  /  /
             \\/  /  /
              \\/  /
               \\/
                    
                    Tiny🔥Torch
            Build ML Systems from Scratch!
            """
    
    def __init__(self, name="Vijay Janapa Reddi", affiliation="Harvard University", 
                 email="vj@eecs.harvard.edu", github_username="profvjreddi", ascii_art=None):
        self.name = name
        self.affiliation = affiliation
        self.email = email
        self.github_username = github_username
        self.ascii_art = ascii_art or self._load_default_flame()
    
    def __str__(self):
        return f"👨‍💻 {self.name} | {self.affiliation} | @{self.github_username}"
    
    def get_signature(self):
        """Get a short signature for code headers."""
        return f"Built by {self.name} (@{self.github_username})"
    
    def get_ascii_art(self):
        """Get ASCII art for the profile."""
        return self.ascii_art
    
    def get_full_profile(self):
        """Get complete profile with ASCII art."""
        return f"""{self.ascii_art}
        
👨‍💻 Developer: {self.name}
🏛️  Affiliation: {self.affiliation}
📧 Email: {self.email}
🐙 GitHub: @{self.github_username}
🔥 Ready to build ML systems from scratch!
"""

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"""
### 🧪 Test Your Developer Profile

Customize your developer profile! Replace the default information with your own:
"""

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# Test the DeveloperProfile class
try:
    print("Testing DeveloperProfile (with defaults):")
    # Default profile (instructor)
    default_profile = DeveloperProfile()
    print(f"Profile: {default_profile}")
    print(f"Signature: {default_profile.get_signature()}")
    print()
    
    print("🎨 ASCII Art Preview:")
    print(default_profile.get_ascii_art())
    print()
    
    print("🔥 Full Profile Display:")
    print(default_profile.get_full_profile())
    print()
    
    # TODO: Students should customize this with their own information!
    print("🎯 YOUR TURN: Create your own profile!")
    print("Uncomment and modify the lines below:")
    print("# my_profile = DeveloperProfile(")
    print("#     name='Your Name',")
    print("#     affiliation='Your University/Company',")
    print("#     email='your.email@example.com',")
    print("#     github_username='yourgithub',")
    print("#     ascii_art='''")
    print("#     Your Custom ASCII Art Here!")
    print("#     Maybe your initials, a logo, or something fun!")
    print("#     '''")
    print("# )")
    print("# print(f'My Profile: {my_profile}')")
    print("# print(f'My Signature: {my_profile.get_signature()}')")
    print("# print(my_profile.get_full_profile())")
    
except NotImplementedError as e:
    print(f"⚠️  {e}")
    print("Implement the DeveloperProfile class above first!")

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"""
### 🎨 Personalization Challenge

**For Students**: Make TinyTorch truly yours by:

1. **Update your profile** in the cell above with your real information
2. **Create custom ASCII art** - your initials, a simple logo, or something that represents you
3. **Customize the flame file** - edit `tinytorch_flame.txt` to create your own default art
4. **Add your signature** to code you write throughout the course
5. **Show off your full profile** with the `get_full_profile()` method

This isn't just about customization - it's about taking ownership of your learning journey in ML systems!

**ASCII Art Customization Options:**

**Option 1: Custom ASCII Art Parameter**
```python
my_profile = DeveloperProfile(
    name="Your Name",
    ascii_art='''
    Your Custom ASCII Art Here!
    Maybe your initials, a logo, or something fun!
    '''
)
```

**Option 2: Edit the Default Flame File**
- Edit `tinytorch_flame.txt` in this directory
- Replace with your own ASCII art design
- All students using defaults will see your custom art!

**ASCII Art Ideas:**
- Your initials in block letters
- A simple logo or symbol that represents you
- Your university mascot in ASCII
- A coding-themed design
- Something that motivates you!

**Pro Tip**: The `tinytorch_flame.txt` file contains the beautiful default flame art. You can:
- Edit it directly for a personalized default
- Create your own `.txt` file and modify the code to load it
- Use online ASCII art generators for inspiration
"""

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"""
## Step 4: Try the Export Process

Now let's export our code! In your terminal, run:

```bash
python bin/tito.py sync --module setup
```

This will export the code marked with `#| export` to `tinytorch/core/utils.py`.

**What happens during export:**
1. nbdev scans this notebook for `#| export` cells
2. Extracts the Python code  
3. Writes it to `tinytorch/core/utils.py` (because of `#| default_exp core.utils`)
4. Handles imports and dependencies automatically

**🔍 Verification**: After export, check `tinytorch/core/utils.py` - you'll see your functions there with auto-generated headers pointing back to this notebook!

**Note**: The export process will use the instructor solutions (from `#|hide` cells) so the package will have working implementations even if you haven't completed the exercises yet.
"""

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"""
## Step 5: Run Tests

After exporting, run the tests:

```bash
python bin/tito.py test --module setup
```

This will run all tests for the setup module and verify your implementation works correctly.

## Step 6: Check Your Progress

See your overall progress:

```bash
python bin/tito.py info
```

This shows which modules are complete and which are pending.
"""

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"""
## 🎉 Congratulations!

You've learned the TinyTorch development workflow:

1. ✅ Write code in notebooks with `#| export`
2. ✅ Export with `tito sync --module setup`  
3. ✅ Test with `tito test --module setup`
4. ✅ Check progress with `tito info`

**This is the rhythm you'll use for every module in TinyTorch.**

### Next Steps

Ready for the real work? Head to **Module 1: Tensor** where you'll build the core data structures that power everything else in TinyTorch.

**Development Tips:**
- Always test your code in the notebook first
- Export frequently to catch issues early  
- Read error messages carefully - they're designed to help
- When stuck, check if your code exports cleanly first

Happy building! 🔥
"""