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# %% [markdown]
"""
# Tensor - Core Data Structure

Welcome to the Tensor module! This is where TinyTorch really begins. You'll implement the fundamental data structure that powers all ML systems.

## Learning Goals
- Understand tensors as N-dimensional arrays with ML-specific operations
- Implement a complete Tensor class with arithmetic operations
- Handle shape management, data types, and memory layout
- Build the foundation for neural networks and automatic differentiation
- Master the NBGrader workflow with comprehensive testing

## Build → Use → Understand
1. **Build**: Create the Tensor class with core operations
2. **Use**: Perform tensor arithmetic and transformations
3. **Understand**: How tensors form the foundation of ML systems
"""

# %% nbgrader={"grade": false, "grade_id": "tensor-imports", "locked": false, "schema_version": 3, "solution": false, "task": false}
#| default_exp core.tensor

#| export
import numpy as np
import sys
from typing import Union, List, Tuple, Optional, Any

# %% nbgrader={"grade": false, "grade_id": "tensor-setup", "locked": false, "schema_version": 3, "solution": false, "task": false}
print("🔥 TinyTorch Tensor Module")
print(f"NumPy version: {np.__version__}")
print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}")
print("Ready to build tensors!")

# %% [markdown]
"""
## 📦 Where This Code Lives in the Final Package

**Learning Side:** You work in `modules/source/01_tensor/tensor_dev.py`  
**Building Side:** Code exports to `tinytorch.core.tensor`

```python
# Final package structure:
from tinytorch.core.tensor import Tensor  # The foundation of everything!
from tinytorch.core.activations import ReLU, Sigmoid, Tanh
from tinytorch.core.layers import Dense, Conv2D
```

**Why this matters:**
- **Learning:** Focused modules for deep understanding
- **Production:** Proper organization like PyTorch's `torch.Tensor`
- **Consistency:** All tensor operations live together in `core.tensor`
- **Foundation:** Every other module depends on Tensor
"""

# %% [markdown]
"""
## Step 1: What is a Tensor?

### Definition
A **tensor** is an N-dimensional array with ML-specific operations. Think of it as a container that can hold data in multiple dimensions:

- **Scalar** (0D): A single number - `5.0`
- **Vector** (1D): A list of numbers - `[1, 2, 3]`  
- **Matrix** (2D): A 2D array - `[[1, 2], [3, 4]]`
- **Higher dimensions**: 3D, 4D, etc. for images, video, batches

### The Mathematical Foundation: From Scalars to Tensors
Understanding tensors requires building from mathematical fundamentals:

#### **Scalars (Rank 0)**
- **Definition**: A single number with no direction
- **Examples**: Temperature (25°C), mass (5.2 kg), probability (0.7)
- **Operations**: Addition, multiplication, comparison
- **ML Context**: Loss values, learning rates, regularization parameters

#### **Vectors (Rank 1)**
- **Definition**: An ordered list of numbers with direction and magnitude
- **Examples**: Position [x, y, z], RGB color [255, 128, 0], word embedding [0.1, -0.5, 0.8]
- **Operations**: Dot product, cross product, norm calculation
- **ML Context**: Feature vectors, gradients, model parameters

#### **Matrices (Rank 2)**
- **Definition**: A 2D array organizing data in rows and columns
- **Examples**: Image (height × width), weight matrix (input × output), covariance matrix
- **Operations**: Matrix multiplication, transpose, inverse, eigendecomposition
- **ML Context**: Linear layer weights, attention matrices, batch data

#### **Higher-Order Tensors (Rank 3+)**
- **Definition**: Multi-dimensional arrays extending matrices
- **Examples**: 
  - **3D**: Video frames (time × height × width), RGB images (height × width × channels)
  - **4D**: Image batches (batch × height × width × channels)
  - **5D**: Video batches (batch × time × height × width × channels)
- **Operations**: Tensor products, contractions, decompositions
- **ML Context**: Convolutional features, RNN states, transformer attention

### Why Tensors Matter in ML: The Computational Foundation

#### **1. Unified Data Representation**
Tensors provide a consistent way to represent all ML data:
```python
# All of these are tensors with different shapes
scalar_loss = Tensor(0.5)              # Shape: ()
feature_vector = Tensor([1, 2, 3])      # Shape: (3,)
weight_matrix = Tensor([[1, 2], [3, 4]]) # Shape: (2, 2)
image_batch = Tensor(np.random.rand(32, 224, 224, 3)) # Shape: (32, 224, 224, 3)
```

#### **2. Efficient Batch Processing**
ML systems process multiple samples simultaneously:
```python
# Instead of processing one image at a time:
for image in images:
    result = model(image)  # Slow: 1000 separate operations

# Process entire batch at once:
batch_result = model(image_batch)  # Fast: 1 vectorized operation
```

#### **3. Hardware Acceleration**
Modern hardware (GPUs, TPUs) excels at tensor operations:
- **Parallel processing**: Multiple operations simultaneously
- **Vectorization**: SIMD (Single Instruction, Multiple Data) operations
- **Memory optimization**: Contiguous memory layout for cache efficiency

#### **4. Automatic Differentiation**
Tensors enable gradient computation through computational graphs:
```python
# Each tensor operation creates a node in the computation graph
x = Tensor([1, 2, 3])
y = x * 2          # Node: multiplication
z = y + 1          # Node: addition
loss = z.sum()     # Node: summation
# Gradients flow backward through this graph
```

### Real-World Examples: Tensors in Action

#### **Computer Vision**
- **Grayscale image**: 2D tensor `(height, width)` - `(28, 28)` for MNIST
- **Color image**: 3D tensor `(height, width, channels)` - `(224, 224, 3)` for RGB
- **Image batch**: 4D tensor `(batch, height, width, channels)` - `(32, 224, 224, 3)`
- **Video**: 5D tensor `(batch, time, height, width, channels)`

#### **Natural Language Processing**
- **Word embedding**: 1D tensor `(embedding_dim,)` - `(300,)` for Word2Vec
- **Sentence**: 2D tensor `(sequence_length, embedding_dim)` - `(50, 768)` for BERT
- **Batch of sentences**: 3D tensor `(batch, sequence_length, embedding_dim)`

#### **Audio Processing**
- **Audio signal**: 1D tensor `(time_steps,)` - `(16000,)` for 1 second at 16kHz
- **Spectrogram**: 2D tensor `(time_frames, frequency_bins)`
- **Batch of audio**: 3D tensor `(batch, time_steps, features)`

#### **Time Series**
- **Single series**: 2D tensor `(time_steps, features)`
- **Multiple series**: 3D tensor `(batch, time_steps, features)`
- **Multivariate forecasting**: 4D tensor `(batch, time_steps, features, predictions)`

### Why Not Just Use NumPy?

While we use NumPy internally, our Tensor class adds ML-specific functionality:

#### **1. ML-Specific Operations**
- **Gradient tracking**: For automatic differentiation (coming in Module 7)
- **GPU support**: For hardware acceleration (future extension)
- **Broadcasting semantics**: ML-friendly dimension handling

#### **2. Consistent API**
- **Type safety**: Predictable behavior across operations
- **Error checking**: Clear error messages for debugging
- **Integration**: Seamless work with other TinyTorch components

#### **3. Educational Value**
- **Conceptual clarity**: Understand what tensors really are
- **Implementation insight**: See how frameworks work internally
- **Debugging skills**: Trace through tensor operations step by step

#### **4. Extensibility**
- **Future features**: Ready for gradients, GPU, distributed computing
- **Customization**: Add domain-specific operations
- **Optimization**: Profile and optimize specific use cases

### Performance Considerations: Building Efficient Tensors

#### **Memory Layout**
- **Contiguous arrays**: Better cache locality and performance
- **Data types**: `float32` vs `float64` trade-offs
- **Memory sharing**: Avoid unnecessary copies

#### **Vectorization**
- **SIMD operations**: Single Instruction, Multiple Data
- **Broadcasting**: Efficient operations on different shapes
- **Batch operations**: Process multiple samples simultaneously

#### **Numerical Stability**
- **Precision**: Balancing speed and accuracy
- **Overflow/underflow**: Handling extreme values
- **Gradient flow**: Maintaining numerical stability for training

Let's start building our tensor foundation!
"""

# %% [markdown]
"""
## 🧠 The Mathematical Foundation

### Linear Algebra Refresher
Tensors are generalizations of scalars, vectors, and matrices:

```
Scalar (0D): 5
Vector (1D): [1, 2, 3]
Matrix (2D): [[1, 2], [3, 4]]
Tensor (3D): [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]
```

### Why This Matters for Neural Networks
- **Forward Pass**: Matrix multiplication between layers
- **Batch Processing**: Multiple samples processed simultaneously
- **Convolutions**: 3D operations on image data
- **Gradients**: Derivatives computed across all dimensions

### Connection to Real ML Systems
Every major ML framework uses tensors:
- **PyTorch**: `torch.Tensor`
- **TensorFlow**: `tf.Tensor`
- **JAX**: `jax.numpy.ndarray`
- **TinyTorch**: `tinytorch.core.tensor.Tensor` (what we're building!)

### Performance Considerations
- **Memory Layout**: Contiguous arrays for cache efficiency
- **Vectorization**: SIMD operations for speed
- **Broadcasting**: Efficient operations on different shapes
- **Type Consistency**: Avoiding unnecessary conversions
"""

# %% [markdown]
"""
## Step 2: The Tensor Class Foundation

### Core Concept: Wrapping NumPy with ML Intelligence
Our Tensor class wraps NumPy arrays with ML-specific functionality. This design pattern is used by all major ML frameworks:

- **PyTorch**: `torch.Tensor` wraps ATen (C++ tensor library)
- **TensorFlow**: `tf.Tensor` wraps Eigen (C++ linear algebra library)
- **JAX**: `jax.numpy.ndarray` wraps XLA (Google's linear algebra compiler)
- **TinyTorch**: `Tensor` wraps NumPy (Python's numerical computing library)

### Design Requirements Analysis

#### **1. Input Flexibility**
Our tensor must handle diverse input types:
```python
# Scalars (Python numbers)
t1 = Tensor(5)           # int → numpy array
t2 = Tensor(3.14)        # float → numpy array

# Lists (Python sequences)
t3 = Tensor([1, 2, 3])   # list → numpy array
t4 = Tensor([[1, 2], [3, 4]])  # nested list → 2D array

# NumPy arrays (existing arrays)
t5 = Tensor(np.array([1, 2, 3]))  # array → tensor wrapper
```

#### **2. Type Management**
ML systems need consistent, predictable types:
- **Default behavior**: Auto-detect appropriate types
- **Explicit control**: Allow manual type specification
- **Performance optimization**: Prefer `float32` over `float64`
- **Memory efficiency**: Use appropriate precision

#### **3. Property Access**
Essential tensor properties for ML operations:
- **Shape**: Dimensions for compatibility checking
- **Size**: Total elements for memory estimation
- **Data type**: For numerical computation planning
- **Data access**: For integration with other libraries

#### **4. Arithmetic Operations**
Support for mathematical operations:
- **Element-wise**: Addition, multiplication, subtraction, division
- **Broadcasting**: Operations on different shapes
- **Type promotion**: Consistent result types
- **Error handling**: Clear messages for incompatible operations

### Implementation Strategy

#### **Memory Management**
- **Copy vs. Reference**: When to copy data vs. share memory
- **Type conversion**: Efficient dtype changes
- **Contiguous layout**: Ensure optimal memory access patterns

#### **Error Handling**
- **Input validation**: Check for valid input types
- **Shape compatibility**: Verify operations are mathematically valid
- **Informative messages**: Help users debug issues quickly

#### **Performance Optimization**
- **Lazy evaluation**: Defer expensive operations when possible
- **Vectorization**: Use NumPy's optimized operations
- **Memory reuse**: Minimize unnecessary allocations

### Learning Objectives for Implementation

By implementing this Tensor class, you'll learn:
1. **Wrapper pattern**: How to extend existing libraries
2. **Type system design**: Managing data types in numerical computing
3. **API design**: Creating intuitive, consistent interfaces
4. **Performance considerations**: Balancing flexibility and speed
5. **Error handling**: Providing helpful feedback to users

Let's implement our tensor foundation!
"""

# %% nbgrader={"grade": false, "grade_id": "tensor-class", "locked": false, "schema_version": 3, "solution": true, "task": false}
#| export
class Tensor:
    """
    TinyTorch Tensor: N-dimensional array with ML operations.
    
    The fundamental data structure for all TinyTorch operations.
    Wraps NumPy arrays with ML-specific functionality.
    """
    
    def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):
        """
        Create a new tensor from data.
        
        Args:
            data: Input data (scalar, list, or numpy array)
            dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect.
            
        TODO: Implement tensor creation with proper type handling.
        
        STEP-BY-STEP:
        1. Check if data is a scalar (int/float) - convert to numpy array
        2. Check if data is a list - convert to numpy array  
        3. Check if data is already a numpy array - use as-is
        4. Apply dtype conversion if specified
        5. Store the result in self._data
        
        EXAMPLE:
        Tensor(5) → stores np.array(5)
        Tensor([1, 2, 3]) → stores np.array([1, 2, 3])
        Tensor(np.array([1, 2, 3])) → stores the array directly
        
        HINTS:
        - Use isinstance() to check data types
        - Use np.array() for conversion
        - Handle dtype parameter for type conversion
        - Store the array in self._data
        """
        ### BEGIN SOLUTION
        # Convert input to numpy array
        if isinstance(data, (int, float, np.number)):
            # Handle Python and NumPy scalars
            if dtype is None:
                # Auto-detect type: int for integers, float32 for floats
                if isinstance(data, int) or (isinstance(data, np.number) and np.issubdtype(type(data), np.integer)):
                    dtype = 'int32'
                else:
                    dtype = 'float32'
            self._data = np.array(data, dtype=dtype)
        elif isinstance(data, list):
            # Let NumPy auto-detect type, then convert if needed
            temp_array = np.array(data)
            if dtype is None:
                # Use NumPy's auto-detected type, but prefer float32 for floats
                if temp_array.dtype == np.float64:
                    dtype = 'float32'
                else:
                    dtype = str(temp_array.dtype)
            self._data = np.array(data, dtype=dtype)
        elif isinstance(data, np.ndarray):
            # Already a numpy array
            if dtype is None:
                # Keep existing dtype, but prefer float32 for float64
                if data.dtype == np.float64:
                    dtype = 'float32'
                else:
                    dtype = str(data.dtype)
            self._data = data.astype(dtype) if dtype != data.dtype else data.copy()
        else:
            # Try to convert unknown types
            self._data = np.array(data, dtype=dtype)
        ### END SOLUTION

    @property
    def data(self) -> np.ndarray:
        """
        Access underlying numpy array.
        
        TODO: Return the stored numpy array.
        
        HINT: Return self._data (the array you stored in __init__)
        """
        ### BEGIN SOLUTION
        return self._data
        ### END SOLUTION
    
    @property
    def shape(self) -> Tuple[int, ...]:
        """
        Get tensor shape.
        
        TODO: Return the shape of the stored numpy array.
        
        HINT: Use .shape attribute of the numpy array
        EXAMPLE: Tensor([1, 2, 3]).shape should return (3,)
        """
        ### BEGIN SOLUTION
        return self._data.shape
        ### END SOLUTION
    
    @property
    def size(self) -> int:
        """
        Get total number of elements.
        
        TODO: Return the total number of elements in the tensor.
        
        HINT: Use .size attribute of the numpy array
        EXAMPLE: Tensor([1, 2, 3]).size should return 3
        """
        ### BEGIN SOLUTION
        return self._data.size
        ### END SOLUTION
    
    @property
    def dtype(self) -> np.dtype:
        """
        Get data type as numpy dtype.
        
        TODO: Return the data type of the stored numpy array.
        
        HINT: Use .dtype attribute of the numpy array
        EXAMPLE: Tensor([1, 2, 3]).dtype should return dtype('int32')
        """
        ### BEGIN SOLUTION
        return self._data.dtype
        ### END SOLUTION
    
    def __repr__(self) -> str:
        """
        String representation.
        
        TODO: Create a clear string representation of the tensor.
        
        APPROACH:
        1. Convert the numpy array to a list for readable output
        2. Include the shape and dtype information
        3. Format: "Tensor([data], shape=shape, dtype=dtype)"
        
        EXAMPLE:
        Tensor([1, 2, 3]) → "Tensor([1, 2, 3], shape=(3,), dtype=int32)"
        
        HINTS:
        - Use .tolist() to convert numpy array to list
        - Include shape and dtype information
        - Keep format consistent and readable
        """
        ### BEGIN SOLUTION
        return f"Tensor({self._data.tolist()}, shape={self.shape}, dtype={self.dtype})"
        ### END SOLUTION

    def add(self, other: 'Tensor') -> 'Tensor':
        """
        Add two tensors element-wise.
        
        TODO: Implement tensor addition.
        
        APPROACH:
        1. Add the numpy arrays using +
        2. Return a new Tensor with the result
        3. Handle broadcasting automatically
        
        EXAMPLE:
        Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6])
        
        HINTS:
        - Use self._data + other._data
        - Return Tensor(result)
        - NumPy handles broadcasting automatically
        """
        ### BEGIN SOLUTION
        result = self._data + other._data
        return Tensor(result)
        ### END SOLUTION

    def multiply(self, other: 'Tensor') -> 'Tensor':
        """
        Multiply two tensors element-wise.
        
        TODO: Implement tensor multiplication.
        
        APPROACH:
        1. Multiply the numpy arrays using *
        2. Return a new Tensor with the result
        3. Handle broadcasting automatically
        
        EXAMPLE:
        Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8])
        
        HINTS:
        - Use self._data * other._data
        - Return Tensor(result)
        - This is element-wise, not matrix multiplication
        """
        ### BEGIN SOLUTION
        result = self._data * other._data
        return Tensor(result)
        ### END SOLUTION

    def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor':
        """
        Addition operator: tensor + other
        
        TODO: Implement + operator for tensors.
        
        APPROACH:
        1. If other is a Tensor, use tensor addition
        2. If other is a scalar, convert to Tensor first
        3. Return the result
        
        EXAMPLE:
        Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6])
        Tensor([1, 2]) + 5 → Tensor([6, 7])
        """
        ### BEGIN SOLUTION
        if isinstance(other, Tensor):
            return self.add(other)
        else:
            return self.add(Tensor(other))
        ### END SOLUTION

    def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor':
        """
        Multiplication operator: tensor * other
        
        TODO: Implement * operator for tensors.
        
        APPROACH:
        1. If other is a Tensor, use tensor multiplication
        2. If other is a scalar, convert to Tensor first
        3. Return the result
        
        EXAMPLE:
        Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8])
        Tensor([1, 2]) * 3 → Tensor([3, 6])
        """
        ### BEGIN SOLUTION
        if isinstance(other, Tensor):
            return self.multiply(other)
        else:
            return self.multiply(Tensor(other))
        ### END SOLUTION

    def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor':
        """
        Subtraction operator: tensor - other
        
        TODO: Implement - operator for tensors.
        
        APPROACH:
        1. Convert other to Tensor if needed
        2. Subtract using numpy arrays
        3. Return new Tensor with result
        
        EXAMPLE:
        Tensor([5, 6]) - Tensor([1, 2]) → Tensor([4, 4])
        Tensor([5, 6]) - 1 → Tensor([4, 5])
        """
        ### BEGIN SOLUTION
        if isinstance(other, Tensor):
            result = self._data - other._data
        else:
            result = self._data - other
        return Tensor(result)
        ### END SOLUTION

    def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor':
        """
        Division operator: tensor / other
        
        TODO: Implement / operator for tensors.
        
        APPROACH:
        1. Convert other to Tensor if needed
        2. Divide using numpy arrays
        3. Return new Tensor with result
        
        EXAMPLE:
        Tensor([6, 8]) / Tensor([2, 4]) → Tensor([3, 2])
        Tensor([6, 8]) / 2 → Tensor([3, 4])
        """
        ### BEGIN SOLUTION
        if isinstance(other, Tensor):
            result = self._data / other._data
        else:
            result = self._data / other
        return Tensor(result)
        ### END SOLUTION

    def mean(self) -> 'Tensor':
        """Computes the mean of the tensor's elements."""
        return Tensor(np.mean(self.data))

    # --- Matmul ---
    def matmul(self, other: 'Tensor') -> 'Tensor':
        """
        Perform matrix multiplication between two tensors.
        
        TODO: Implement matrix multiplication.
        
        APPROACH:
        1. Use np.matmul() to perform matrix multiplication
        2. Return a new Tensor with the result
        3. Handle broadcasting automatically
        
        EXAMPLE:
        Tensor([[1, 2], [3, 4]]) @ Tensor([[5, 6], [7, 8]]) → Tensor([[19, 22], [43, 50]])
        
        HINTS:
        - Use np.matmul(self._data, other._data)
        - Return Tensor(result)
        - This is matrix multiplication, not element-wise multiplication
        """
        ### BEGIN SOLUTION
        result = np.matmul(self._data, other._data)
        return Tensor(result)
        ### END SOLUTION

# %% [markdown]
"""
### 🧪 Unit Test: Tensor Creation

Let's test your tensor creation implementation right away! This gives you immediate feedback on whether your `__init__` method works correctly.

**This is a unit test** - it tests one specific function (tensor creation) in isolation.
"""

# %% nbgrader={"grade": true, "grade_id": "test-tensor-creation-immediate", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false}
# Test tensor creation immediately after implementation
print("🔬 Unit Test: Tensor Creation...")

# Test basic tensor creation
try:
    # Test scalar
    scalar = Tensor(5.0)
    assert hasattr(scalar, '_data'), "Tensor should have _data attribute"
    assert scalar._data.shape == (), f"Scalar should have shape (), got {scalar._data.shape}"
    print("✅ Scalar creation works")
    
    # Test vector
    vector = Tensor([1, 2, 3])
    assert vector._data.shape == (3,), f"Vector should have shape (3,), got {vector._data.shape}"
    print("✅ Vector creation works")
    
    # Test matrix
    matrix = Tensor([[1, 2], [3, 4]])
    assert matrix._data.shape == (2, 2), f"Matrix should have shape (2, 2), got {matrix._data.shape}"
    print("✅ Matrix creation works")
    
    print("📈 Progress: Tensor Creation ✓")
    
except Exception as e:
    print(f"❌ Tensor creation test failed: {e}")
    raise

print("🎯 Tensor creation behavior:")
print("   Converts data to NumPy arrays")
print("   Preserves shape and data type")
print("   Stores in _data attribute")

# %% [markdown]
"""
### 🧪 Unit Test: Tensor Properties

Now let's test that your tensor properties work correctly. This tests the @property methods you implemented.

**This is a unit test** - it tests specific properties (shape, size, dtype, data) in isolation.
"""

# %% nbgrader={"grade": true, "grade_id": "test-tensor-properties-immediate", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false}
# Test tensor properties immediately after implementation
print("🔬 Unit Test: Tensor Properties...")

# Test properties with simple examples
try:
    # Test with a simple matrix
    tensor = Tensor([[1, 2, 3], [4, 5, 6]])
    
    # Test shape property
    assert tensor.shape == (2, 3), f"Shape should be (2, 3), got {tensor.shape}"
    print("✅ Shape property works")
    
    # Test size property
    assert tensor.size == 6, f"Size should be 6, got {tensor.size}"
    print("✅ Size property works")
    
    # Test data property
    assert np.array_equal(tensor.data, np.array([[1, 2, 3], [4, 5, 6]])), "Data property should return numpy array"
    print("✅ Data property works")
    
    # Test dtype property
    assert tensor.dtype in [np.int32, np.int64], f"Dtype should be int32 or int64, got {tensor.dtype}"
    print("✅ Dtype property works")
    
    print("📈 Progress: Tensor Properties ✓")
    
except Exception as e:
    print(f"❌ Tensor properties test failed: {e}")
    raise

print("🎯 Tensor properties behavior:")
print("   shape: Returns tuple of dimensions")
print("   size: Returns total number of elements")
print("   data: Returns underlying NumPy array")
print("   dtype: Returns NumPy data type")

# %% [markdown]
"""
### 🧪 Unit Test: Tensor Arithmetic

Let's test your tensor arithmetic operations. This tests the __add__, __mul__, __sub__, __truediv__ methods.

**This is a unit test** - it tests specific arithmetic operations in isolation.
"""

# %% nbgrader={"grade": true, "grade_id": "test-tensor-arithmetic-immediate", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false}
# Test tensor arithmetic immediately after implementation
print("🔬 Unit Test: Tensor Arithmetic...")

# Test basic arithmetic with simple examples
try:
    # Test addition
    a = Tensor([1, 2, 3])
    b = Tensor([4, 5, 6])
    result = a + b
    expected = np.array([5, 7, 9])
    assert np.array_equal(result.data, expected), f"Addition failed: expected {expected}, got {result.data}"
    print("✅ Addition works")
    
    # Test scalar addition
    result_scalar = a + 10
    expected_scalar = np.array([11, 12, 13])
    assert np.array_equal(result_scalar.data, expected_scalar), f"Scalar addition failed: expected {expected_scalar}, got {result_scalar.data}"
    print("✅ Scalar addition works")
    
    # Test multiplication
    result_mul = a * b
    expected_mul = np.array([4, 10, 18])
    assert np.array_equal(result_mul.data, expected_mul), f"Multiplication failed: expected {expected_mul}, got {result_mul.data}"
    print("✅ Multiplication works")
    
    # Test scalar multiplication
    result_scalar_mul = a * 2
    expected_scalar_mul = np.array([2, 4, 6])
    assert np.array_equal(result_scalar_mul.data, expected_scalar_mul), f"Scalar multiplication failed: expected {expected_scalar_mul}, got {result_scalar_mul.data}"
    print("✅ Scalar multiplication works")
    
    print("📈 Progress: Tensor Arithmetic ✓")
    
except Exception as e:
    print(f"❌ Tensor arithmetic test failed: {e}")
    raise

print("🎯 Tensor arithmetic behavior:")
print("   Element-wise operations on tensors")
print("   Broadcasting with scalars")
print("   Returns new Tensor objects")

# %% [markdown]
"""
Congratulations! You've successfully implemented the core Tensor class for TinyTorch:

### What You've Accomplished
✅ **Tensor Creation**: Handle scalars, vectors, matrices, and higher-dimensional arrays  
✅ **Data Types**: Proper dtype handling with auto-detection and conversion  
✅ **Properties**: Shape, size, dtype, and data access  
✅ **Arithmetic**: Addition, multiplication, subtraction, division  
✅ **Operators**: Natural Python syntax with `+`, `-`, `*`, `/`  
✅ **Broadcasting**: Automatic shape compatibility like NumPy  

### Key Concepts You've Learned
- **Tensors** are the fundamental data structure for ML systems
- **NumPy backend** provides efficient computation with ML-friendly API
- **Operator overloading** makes tensor operations feel natural
- **Broadcasting** enables flexible operations between different shapes
- **Type safety** ensures consistent behavior across operations

### Next Steps
1. **Export your code**: `tito package nbdev --export 01_tensor`
2. **Test your implementation**: `tito module test 01_tensor`
3. **Use your tensors**: 
   ```python
   from tinytorch.core.tensor import Tensor
   t = Tensor([1, 2, 3])
   print(t + 5)  # Your tensor in action!
   ```
4. **Move to Module 2**: Start building activation functions!

**Ready for the next challenge?** Let's add the mathematical functions that make neural networks powerful!
"""

# %% [markdown]
"""
## 🧪 Module Testing

Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.

**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
"""

# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
# =============================================================================
# STANDARDIZED MODULE TESTING - DO NOT MODIFY
# This cell is locked to ensure consistent testing across all TinyTorch modules
# =============================================================================

if __name__ == "__main__":
    from tito.tools.testing import run_module_tests_auto
    
    # Automatically discover and run all tests in this module
    success = run_module_tests_auto("Tensor")

# %% [markdown]
"""
## 🎯 Module Summary: Tensor Foundation Mastery!

Congratulations! You've successfully implemented the fundamental data structure that powers all machine learning:

### ✅ What You've Built
- **Tensor Class**: N-dimensional array wrapper with professional interfaces
- **Core Operations**: Creation, property access, and arithmetic operations
- **Shape Management**: Automatic shape tracking and validation
- **Data Types**: Proper NumPy integration and type handling
- **Foundation**: The building block for all subsequent TinyTorch modules

### ✅ Key Learning Outcomes
- **Understanding**: How tensors work as the foundation of machine learning
- **Implementation**: Built tensor operations from scratch
- **Professional patterns**: Clean APIs, proper error handling, comprehensive testing
- **Real-world connection**: Understanding PyTorch/TensorFlow tensor foundations
- **Systems thinking**: Building reliable, reusable components

### ✅ Mathematical Foundations Mastered
- **N-dimensional arrays**: Shape, size, and dimensionality concepts
- **Element-wise operations**: Addition, subtraction, multiplication, division
- **Broadcasting**: Understanding how operations work with different shapes
- **Memory management**: Efficient data storage and access patterns

### ✅ Professional Skills Developed
- **API design**: Clean, intuitive interfaces for tensor operations
- **Error handling**: Graceful handling of invalid operations and edge cases
- **Testing methodology**: Comprehensive validation of tensor functionality
- **Documentation**: Clear, educational documentation with examples

### ✅ Ready for Advanced Applications
Your tensor implementation now enables:
- **Neural Networks**: Foundation for all layer implementations
- **Automatic Differentiation**: Gradient computation through computational graphs
- **Complex Models**: CNNs, RNNs, Transformers - all built on tensors
- **Real Applications**: Training models on real datasets

### 🔗 Connection to Real ML Systems
Your implementation mirrors production systems:
- **PyTorch**: `torch.Tensor` provides identical functionality
- **TensorFlow**: `tf.Tensor` implements similar concepts
- **NumPy**: `numpy.ndarray` serves as the foundation
- **Industry Standard**: Every major ML framework uses these exact principles

### 🎯 The Power of Tensors
You've built the fundamental data structure of modern AI:
- **Universality**: Tensors represent all data: images, text, audio, video
- **Efficiency**: Vectorized operations enable fast computation
- **Scalability**: Handles everything from single numbers to massive matrices
- **Flexibility**: Foundation for any mathematical operation

### 🚀 What's Next
Your tensor implementation is the foundation for:
- **Activations**: Nonlinear functions that enable complex learning
- **Layers**: Linear transformations and neural network building blocks
- **Networks**: Composing layers into powerful architectures
- **Training**: Optimizing networks to solve real problems

**Next Module**: Activation functions - adding the nonlinearity that makes neural networks powerful!

You've built the foundation of modern AI. Now let's add the mathematical functions that enable machines to learn complex patterns!
"""

def test_unit_tensor_creation():
    """Comprehensive test of tensor creation with all data types and shapes."""
    print("🔬 Testing comprehensive tensor creation...")
    
    # Test scalar creation
    scalar_int = Tensor(42)
    assert scalar_int.shape == ()
    
    # Test vector creation
    vector_int = Tensor([1, 2, 3])
    assert vector_int.shape == (3,)

    # Test matrix creation
    matrix_2x2 = Tensor([[1, 2], [3, 4]])
    assert matrix_2x2.shape == (2, 2)
    print("✅ Tensor creation tests passed!")

def test_unit_tensor_properties():
    """Comprehensive test of tensor properties (shape, size, dtype, data access)."""
    print("🔬 Testing comprehensive tensor properties...")

    tensor = Tensor([[1, 2, 3], [4, 5, 6]])
    
    # Test shape property
    assert tensor.shape == (2, 3)
    
    # Test size property
    assert tensor.size == 6
    
    # Test data property
    assert np.array_equal(tensor.data, np.array([[1, 2, 3], [4, 5, 6]]))
    
    # Test dtype property
    assert tensor.dtype in [np.int32, np.int64]
    print("✅ Tensor properties tests passed!")

def test_unit_tensor_arithmetic():
    """Comprehensive test of tensor arithmetic operations."""
    print("🔬 Testing comprehensive tensor arithmetic...")
    
    a = Tensor([1, 2, 3])
    b = Tensor([4, 5, 6])
    
    # Test addition
    c = a + b
    expected = np.array([5, 7, 9])
    assert np.array_equal(c.data, expected)
    
    # Test multiplication
    d = a * b
    expected = np.array([4, 10, 18])
    assert np.array_equal(d.data, expected)

    # Test subtraction
    e = b - a
    expected = np.array([3, 3, 3])
    assert np.array_equal(e.data, expected)

    # Test division
    f = b / a
    expected = np.array([4.0, 2.5, 2.0])
    assert np.allclose(f.data, expected)
    print("✅ Tensor arithmetic tests passed!")