Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here's a function that creates overlapping strides of a numpy array using the strided trick: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each stride (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a shape tuple for the output array
    shape = (n_windows, window_size)
    
    # Calculate the strides tuple for the strided array
    # First stride is the stride between windows (in bytes)
    # Second stride is the stride between elements within a window (in bytes)
    strides_tuple = (tokens.strides[0] * stride, tokens.strides[0])
    
    # Create the strided array view
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_tuple,
        writeable=False
    )
    
    return windows

# Example usage:
if __name__ == "__main__":
    # Create sample array
    tokens = np.arange(100)
    
    # Get strided windows
    windows = strides(tokens)
    
    # Print results
    print(f"Original array shape: {tokens.shape}")
    print(f"Strided windows shape: {windows.shape}")
    
    # Print first few windows
    print("\nFirst few windows:")
    for i in range(min(3, len(windows))):
        print(f"Window {i}: {windows[i]}")
This code: 1. Takes a numpy array of tokens as input 2. Uses `np.lib.stride_tricks.as_strided` to create a view of the array with overlapping windows 3. Returns an array where each row is a window of the specified size Example output: 
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# Create sample array
tokens = np.arange(100)
windows = strides(tokens)

Original array shape: (100,)
Strided windows shape: (6, 50)

First few windows:
Window 0: [ 0  1  2  3  4  5  6  7  8  9 ... 49]
Window 1: [10 11 12 13 14 15 16 17 18 19 ... 59]
Window 2: [20 21 22 23 24 25 26 27 28 29 ... 69]
Key features: - Uses memory-efficient striding (creates views rather than copies) - Allows customizable window size and stride length - Returns a 2D numpy array where each row is a window - Sets `writeable=False` for safety (prevents accidental modifications) You can modify the window size and stride by passing different parameters: 
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# For different window size and stride
windows = strides(tokens, window_size=30, stride=15)
Note: This implementation assumes that: 1. The input is a 1D numpy array 2. The window_size and stride are valid for the input array length 3. You want non-writeable views (for safety) If you need different behavior, you can modify the function accordingly.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each stride (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a shape tuple for the output array
    shape = (n_windows, window_size)
    
    # Calculate the strides tuple for the strided array
    # First stride is the stride between windows (in bytes)
    # Second stride is the stride between elements within a window (in bytes)
    strides_tuple = (tokens.strides[0] * stride, tokens.strides[0])
    
    # Create the strided array view
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_tuple,
        writeable=False
    )
    
    return windows

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each stride (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a shape tuple for the output array
    shape = (n_windows, window_size)
    
    # Calculate the strides tuple for the strided array
    # First stride is the stride between windows (in bytes)
    # Second stride is the stride between elements within a window (in bytes)
    strides_tuple = (tokens.strides[0] * stride, tokens.strides[0])
    
    # Create the strided array view
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_tuple,
        writeable=False
    )
    
    return windows





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Final Answer: True

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here's a function that creates overlapping sub-arrays using NumPy's striding trick: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping sub-arrays from the input array using striding.
    
    Parameters:
    tokens (numpy.ndarray): Input array of tokens
    window_size (int): Size of each sub-array (default: 50)
    stride (int): Number of elements to move forward for each sub-array (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping sub-arrays
    """
    # Calculate the number of valid sub-arrays
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return strided

# Example usage:
if __name__ == "__main__":
    # Create a sample array
    tokens = np.arange(100)
    
    # Get the strided arrays
    result = strides(tokens)
    
    # Print the first few sub-arrays
    print("Shape of result:", result.shape)
    print("\nFirst few sub-arrays:")
    for i in range(min(3, len(result))):
        print(f"Sub-array {i}:", result[i])
This function: 1. Takes an input array `tokens` and optional parameters `window_size` (default 50) and `stride` (default 10) 2. Calculates how many valid sub-arrays can be created 3. Uses `np.lib.stride_tricks.as_strided` to create a view of the original array with overlapping windows 4. Returns the resulting array of sub-arrays Example output: 
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# If you run with a sample array of 100 elements:
tokens = np.arange(100)
result = strides(tokens)

# You'll get output like:
Shape of result: (6, 50)

First few sub-arrays:
Sub-array 0: [ 0  1  2  3  4  5  6  7  8  9 ... 49]
Sub-array 1: [10 11 12 13 14 15 16 17 18 19 ... 59]
Sub-array 2: [20 21 22 23 24 25 26 27 28 29 ... 69]
Important notes: 1. The function creates a view of the original array, not a copy, so it's memory-efficient 2. The `writeable=False` parameter prevents accidental modifications to the original array 3. Make sure the input array is large enough for the window size and desired number of strides You can modify the window_size and stride parameters when calling the function: 
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# Different window size and stride
result = strides(tokens, window_size=30, stride=15)
Error handling could be added for cases where: - Input array is too small for the window size - Window size is less than stride - Invalid input parameters Here's a version with basic error handling: 
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def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping sub-arrays from the input array using striding.
    """
    if not isinstance(tokens, np.ndarray):
        raise TypeError("Input must be a numpy array")
    
    if window_size <= 0 or stride <= 0:
        raise ValueError("Window size and stride must be positive")
        
    if window_size > len(tokens):
        raise ValueError("Window size cannot be larger than input array")
        
    if stride > window_size:
        raise ValueError("Stride cannot be larger than window size")
    
    # Rest of the function remains the same
    ...

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping sub-arrays from the input array using striding.
    
    Parameters:
    tokens (numpy.ndarray): Input array of tokens
    window_size (int): Size of each sub-array (default: 50)
    stride (int): Number of elements to move forward for each sub-array (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping sub-arrays
    """
    # Calculate the number of valid sub-arrays
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return strided

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping sub-arrays from the input array using striding.
    
    Parameters:
    tokens (numpy.ndarray): Input array of tokens
    window_size (int): Size of each sub-array (default: 50)
    stride (int): Number of elements to move forward for each sub-array (default: 10)
    
    Returns:
    numpy.ndarray: Array of overlapping sub-arrays
    """
    # Calculate the number of valid sub-arrays
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    strided = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return strided





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Final Answer: True

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here's a function that creates overlapping strides of an array using NumPy's striding trick: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using striding trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each window (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

# Example usage:
if __name__ == "__main__":
    # Create sample array
    tokens = np.arange(100)
    
    # Get strided windows
    windows = strides(tokens)
    
    # Print results
    print("Original array shape:", tokens.shape)
    print("Strided windows shape:", windows.shape)
    print("\nFirst few windows:")
    for i in range(min(3, len(windows))):
        print(f"Window {i}:", windows[i])
This implementation: 1. Takes an input array of tokens and optional parameters for window size and stride length 2. Uses `np.lib.stride_tricks.as_strided` to create a view of the original array with overlapping windows 3. Returns a 2D array where each row is a window of the specified size Example output: 
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# Create sample array
tokens = np.arange(100)
windows = strides(tokens)

Original array shape: (100,)
Strided windows shape: (6, 50)

First few windows:
Window 0: [ 0  1  2  3  4  5  6  7  8  9 ... 49]
Window 1: [10 11 12 13 14 15 16 17 18 19 ... 59]
Window 2: [20 21 22 23 24 25 26 27 28 29 ... 69]
Some important notes: 1. The function uses `writeable=False` to prevent accidental modifications to the strided array. 2. The resulting array is a view of the original data, not a copy, making it memory-efficient. 3. You can modify the window_size and stride parameters to get different window sizes and overlap patterns. 4. Be careful with the input array size and window parameters to avoid invalid configurations. You could also add error checking: 
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def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using striding trick.
    """
    if not isinstance(tokens, np.ndarray):
        tokens = np.array(tokens)
    
    if window_size > len(tokens):
        raise ValueError("Window size cannot be larger than array length")
    
    if stride <= 0:
        raise ValueError("Stride must be positive")
    
    # Rest of the function remains the same
This makes the function more robust and user-friendly.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using striding trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each window (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens) 
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using striding trick.
    
    Parameters:
    tokens (np.ndarray): Input array of tokens
    window_size (int): Size of each window (default: 50)
    stride (int): Number of elements to move forward for each window (default: 10)
    
    Returns:
    np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens) 
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create the strided array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Final Answer: True

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here's a function that creates overlapping windows of tokens using NumPy's striding trick: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of tokens using striding.
    
    Args:
        tokens: numpy array of tokens
        window_size: size of each window (default: 50)
        stride: step size between windows (default: 10)
    
    Returns:
        numpy array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

# Example usage:
if __name__ == "__main__":
    # Create sample tokens
    tokens = np.arange(100)
    
    # Get overlapping windows
    windows = strides(tokens)
    
    # Print first few windows
    print("Shape of windows:", windows.shape)
    print("\nFirst window:", windows[0])
    print("\nSecond window:", windows[1])
    print("\nThird window:", windows[2])
This implementation: 1. Takes a numpy array of tokens as input, along with optional parameters for window_size and stride 2. Uses np.lib.stride_tricks.as_strided to create a view of the array with overlapping windows 3. Returns a 2D array where each row is a window of tokens Example output: 
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tokens = np.arange(100)
windows = strides(tokens)

# Shape will be (6, 50) for a 100-element array with window_size=50 and stride=10
# First window: [0, 1, 2, ..., 49]
# Second window: [10, 11, 12, ..., 59]
# Third window: [20, 21, 22, ..., 69]
Important notes: 1. The function uses writeable=False to prevent accidental modifications to the view 2. The stride trick is memory-efficient as it creates views instead of copies 3. Be careful with very large arrays as the views still reference the original data You can modify window_size and stride parameters to get different window configurations: 
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# Different window size and stride
windows = strides(tokens, window_size=30, stride=15)
To avoid potential memory issues with very large arrays, you might want to add some input validation: 
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def strides(tokens, window_size=50, stride=10):
    if not isinstance(tokens, np.ndarray):
        tokens = np.array(tokens)
    
    if window_size > len(tokens):
        raise ValueError("Window size cannot be larger than array length")
    
    if stride <= 0:
        raise ValueError("Stride must be positive")
    
    # ... rest of the function

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of tokens using striding.
    
    Args:
        tokens: numpy array of tokens
        window_size: size of each window (default: 50)
        stride: step size between windows (default: 10)
    
    Returns:
        numpy array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of tokens using striding.
    
    Args:
        tokens: numpy array of tokens
        window_size: size of each window (default: 50)
        stride: step size between windows (default: 10)
    
    Returns:
        numpy array of overlapping windows
    """
    # Calculate the number of valid windows
    n_tokens = len(tokens)
    n_windows = ((n_tokens - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_windows, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,  # stride between windows
                    tokens.strides[0])            # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Final Answer: True

Initial Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

LLM Generation

Query

Write a function strides() that given an array of tokens T as numpy array, using strided trick, returns [T[0:50], T[10:60], T[20: 70] ... ]

Output

Here's a function that creates overlapping strides of a numpy array using the strided trick: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Args:
        tokens (np.ndarray): Input array of tokens
        window_size (int): Size of each window (default: 50)
        stride (int): Step size between windows (default: 10)
    
    Returns:
        np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,    # stride between windows
                    tokens.strides[0])              # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

# Example usage:
if __name__ == "__main__":
    # Create sample array
    tokens = np.arange(100)
    
    # Get strided windows
    windows = strides(tokens)
    
    # Print results
    print(f"Input array shape: {tokens.shape}")
    print(f"Output windows shape: {windows.shape}")
    print("\nFirst few windows:")
    for i in range(min(3, len(windows))):
        print(f"Window {i}: {windows[i]}")
This implementation: 1. Takes a numpy array as input, along with optional parameters for window_size and stride 2. Uses np.lib.stride_tricks.as_strided to create a view of the array with overlapping windows 3. Returns a 2D array where each row is a window of the specified size Example output: 
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# Example usage
tokens = np.arange(100)
windows = strides(tokens)

Input array shape: (100,)
Output windows shape: (6, 50)

First few windows:
Window 0: [ 0  1  2  3  4  5  6  7  8  9 ... 49]
Window 1: [10 11 12 13 14 15 16 17 18 19 ... 59]
Window 2: [20 21 22 23 24 25 26 27 28 29 ... 69]
Key features: - Uses memory-efficient strided views instead of creating copies - Allows customization of window size and stride - Returns a view of the original array (memory efficient) - Sets writeable=False to prevent accidental modifications Note: - The function assumes the input array is 1-dimensional - The returned view is read-only to prevent accidental modifications that could corrupt memory - Make sure the input array is large enough for at least one window You can modify the window_size and stride parameters when calling the function: 
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# Different window size and stride
windows = strides(tokens, window_size=30, stride=5)

Extract Code

I extracted the following code from that output: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Args:
        tokens (np.ndarray): Input array of tokens
        window_size (int): Size of each window (default: 50)
        stride (int): Step size between windows (default: 10)
    
    Returns:
        np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,    # stride between windows
                    tokens.strides[0])              # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows

Check if all of the following conditions are true:

Substring Evaluation

Testing if the previous output contains the string as_strided: True

Run Code Interpreter

Running the following program: 

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import numpy as np

def strides(tokens, window_size=50, stride=10):
    """
    Create overlapping windows of the input array using strided trick.
    
    Args:
        tokens (np.ndarray): Input array of tokens
        window_size (int): Size of each window (default: 50)
        stride (int): Step size between windows (default: 10)
    
    Returns:
        np.ndarray: Array of overlapping windows
    """
    # Calculate the number of valid windows
    n_samples = ((len(tokens) - window_size) // stride) + 1
    
    # Create a new shape for the strided array
    shape = (n_samples, window_size)
    
    # Calculate the strides in bytes
    strides_bytes = (tokens.strides[0] * stride,    # stride between windows
                    tokens.strides[0])              # stride within window
    
    # Create strided view of the array
    windows = np.lib.stride_tricks.as_strided(
        tokens,
        shape=shape,
        strides=strides_bytes,
        writeable=False
    )
    
    return windows





answer = strides(np.arange(100)).shape
expected = (6, 50)
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'

answer = strides(np.arange(100)).sum()
expected = 14850
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: All tests passed

Substring Evaluation

Testing if the previous output contains the string All tests passed: True

Final Answer: True