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

Okay, here's the Python function `strides` using `numpy.lib.stride_tricks.as_strided` to achieve the desired result. 

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import numpy as np
from numpy.lib.stride_tricks import as_strided
from numpy.typing import NDArray # Optional: for type hinting

def strides(T: NDArray, window_size: int = 50, step: int = 10) -> NDArray:
    """
    Creates a strided view of a 1D array T.

    Generates an array where each row is a slice of T of length `window_size`,
    with the starting index of each slice advancing by `step`.

    Equivalent to stacking [T[0:window_size], T[step:step+window_size], T[2*step:2*step+window_size], ...].

    Args:
        T: The input 1D NumPy array (e.g., array of tokens).
        window_size: The desired length of each window/slice (default: 50).
        step: The step size between the start of consecutive windows (default: 10).

    Returns:
        A 2D NumPy array (view) where each row is a window into T.
        The view is non-writeable by default for safety due to potential
        data overlap. Returns an empty array with the correct number of columns
        if T is too short to contain even one full window.

    Raises:
        ValueError: If T is not a 1D array or if window_size or step are
                    not positive integers.
    """
    # Input validation
    if not isinstance(T, np.ndarray) or T.ndim != 1:
        raise ValueError("Input T must be a 1D NumPy array.")
    if not isinstance(window_size, int) or window_size <= 0:
        raise ValueError("window_size must be a positive integer.")
    if not isinstance(step, int) or step <= 0:
        raise ValueError("step must be a positive integer.")

    n = T.shape[0]          # Total number of elements in T
    itemsize = T.itemsize   # Size in bytes of each element in T

    # Calculate the number of windows that fit completely
    if n < window_size:
        num_windows = 0
    else:
        # The last window starts at index `(num_windows - 1) * step`
        # It must end at or before index `n - 1`.
        # So, `(num_windows - 1) * step + window_size <= n`
        num_windows = (n - window_size) // step + 1

    # Handle the case where no full windows fit
    if num_windows <= 0:
        # Return an empty array with the correct shape (0 rows, window_size columns)
        # and the same dtype as T
        return np.empty((0, window_size), dtype=T.dtype)

    # Define the shape and strides for the new view
    out_shape = (num_windows, window_size)

    # Calculate strides:
    # - To move to the next element *within* a window (column): jump `itemsize` bytes
    # - To move to the start of the *next* window (row): jump `step * itemsize` bytes
    out_strides = (step * itemsize, itemsize)

    # Create the strided view using as_strided
    # Set writeable=False for safety, as the windows likely overlap in memory.
    # Modifying the view could unintentionally change multiple parts of the
    # underlying data if writeable=True.
    strided_view = as_strided(T, shape=out_shape, strides=out_strides, writeable=False)

    return strided_view

# --- Example Usage ---
# Create a sample array of tokens (e.g., integers)
tokens = np.arange(100) # Example: tokens 0 through 99

# Get the strided view
strided_tokens = strides(tokens, window_size=50, step=10)

print("Original array shape:", tokens.shape)
print("Strided array shape:", strided_tokens.shape) # Expected: (6, 50) for 100 tokens

# Verify the content of the first few windows
print("\nFirst window (T[0:50]):")
print(strided_tokens[0])
print("Matches np.arange(0, 50):", np.array_equal(strided_tokens[0], np.arange(0, 50)))

print("\nSecond window (T[10:60]):")
print(strided_tokens[1])
print("Matches np.arange(10, 60):", np.array_equal(strided_tokens[1], np.arange(10, 60)))

print("\nThird window (T[20:70]):")
print(strided_tokens[2])
print("Matches np.arange(20, 70):", np.array_equal(strided_tokens[2], np.arange(20, 70)))

print("\nLast window (T[50:100]):")
print(strided_tokens[-1])
print("Matches np.arange(50, 100):", np.array_equal(strided_tokens[-1], np.arange(50, 100)))

# Example with a shorter array
short_tokens = np.arange(65)
strided_short = strides(short_tokens, window_size=50, step=10)
print("\nShort array (len 65) strided shape:", strided_short.shape) # Expected: (2, 50)
print("Last window (T[10:60]):", strided_short[-1])
print("Matches np.arange(10, 60):", np.array_equal(strided_short[-1], np.arange(10, 60)))

# Example with an array too short for one window
too_short_tokens = np.arange(40)
strided_too_short = strides(too_short_tokens, window_size=50, step=10)
print("\nToo short array (len 40) strided shape:", strided_too_short.shape) # Expected: (0, 50)
**Explanation:** 1. **`as_strided(T, shape=..., strides=...)`**: This is the core function. It creates a new *view* of the original array `T`'s data without copying it. 2. **`shape=(num_windows, window_size)`**: We define the desired shape of the output. It should have `num_windows` rows (one for each slice) and `window_size` columns (the length of each slice). 3. **`num_windows = (n - window_size) // step + 1`**: This calculates how many full windows of size `window_size` can fit into the array `T` when advancing by `step`. * `n - window_size`: The maximum starting index for a window. * `// step`: How many steps fit into that range. * `+ 1`: To include the first window starting at index 0. 4. **`itemsize = T.itemsize`**: Gets the size (in bytes) of a single element in the array `T`. This is crucial for calculating byte strides. 5. **`strides=(step * itemsize, itemsize)`**: This is the key to the "trick". It tells NumPy how many bytes to jump in memory to get to the next element along each dimension of the *new* shape: * `itemsize`: To move to the next element *within* a window (i.e., move one column to the right), we just need to move by the size of one element in the original array `T`. * `step * itemsize`: To move to the start of the *next* window (i.e., move one row down), we need to jump forward `step` elements in the original array `T`. 6. **`writeable=False`**: This is highly recommended when using `as_strided` for overlapping views. Since the rows in the output array share underlying data, modifying an element in one row could unintentionally change the value seen in *other* rows if they happen to point to the same memory location. Setting `writeable=False` prevents accidental data corruption. If you need a writeable copy, you can explicitly call `.copy()` on the result: `strided_tokens.copy()`. 7. **Edge Case Handling**: The code checks if the input array `T` is long enough to hold even one window and returns an appropriately shaped empty array if not. It also includes basic input validation.

Extract Code

I extracted the following code from that output: 

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import numpy as np
from numpy.lib.stride_tricks import as_strided
from numpy.typing import NDArray # Optional: for type hinting

def strides(T: NDArray, window_size: int = 50, step: int = 10) -> NDArray:
    """
    Creates a strided view of a 1D array T.

    Generates an array where each row is a slice of T of length `window_size`,
    with the starting index of each slice advancing by `step`.

    Equivalent to stacking [T[0:window_size], T[step:step+window_size], T[2*step:2*step+window_size], ...].

    Args:
        T: The input 1D NumPy array (e.g., array of tokens).
        window_size: The desired length of each window/slice (default: 50).
        step: The step size between the start of consecutive windows (default: 10).

    Returns:
        A 2D NumPy array (view) where each row is a window into T.
        The view is non-writeable by default for safety due to potential
        data overlap. Returns an empty array with the correct number of columns
        if T is too short to contain even one full window.

    Raises:
        ValueError: If T is not a 1D array or if window_size or step are
                    not positive integers.
    """
    # Input validation
    if not isinstance(T, np.ndarray) or T.ndim != 1:
        raise ValueError("Input T must be a 1D NumPy array.")
    if not isinstance(window_size, int) or window_size <= 0:
        raise ValueError("window_size must be a positive integer.")
    if not isinstance(step, int) or step <= 0:
        raise ValueError("step must be a positive integer.")

    n = T.shape[0]          # Total number of elements in T
    itemsize = T.itemsize   # Size in bytes of each element in T

    # Calculate the number of windows that fit completely
    if n < window_size:
        num_windows = 0
    else:
        # The last window starts at index `(num_windows - 1) * step`
        # It must end at or before index `n - 1`.
        # So, `(num_windows - 1) * step + window_size <= n`
        num_windows = (n - window_size) // step + 1

    # Handle the case where no full windows fit
    if num_windows <= 0:
        # Return an empty array with the correct shape (0 rows, window_size columns)
        # and the same dtype as T
        return np.empty((0, window_size), dtype=T.dtype)

    # Define the shape and strides for the new view
    out_shape = (num_windows, window_size)

    # Calculate strides:
    # - To move to the next element *within* a window (column): jump `itemsize` bytes
    # - To move to the start of the *next* window (row): jump `step * itemsize` bytes
    out_strides = (step * itemsize, itemsize)

    # Create the strided view using as_strided
    # Set writeable=False for safety, as the windows likely overlap in memory.
    # Modifying the view could unintentionally change multiple parts of the
    # underlying data if writeable=True.
    strided_view = as_strided(T, shape=out_shape, strides=out_strides, writeable=False)

    return strided_view

# --- Example Usage ---
# Create a sample array of tokens (e.g., integers)
tokens = np.arange(100) # Example: tokens 0 through 99

# Get the strided view
strided_tokens = strides(tokens, window_size=50, step=10)

print("Original array shape:", tokens.shape)
print("Strided array shape:", strided_tokens.shape) # Expected: (6, 50) for 100 tokens

# Verify the content of the first few windows
print("\nFirst window (T[0:50]):")
print(strided_tokens[0])
print("Matches np.arange(0, 50):", np.array_equal(strided_tokens[0], np.arange(0, 50)))

print("\nSecond window (T[10:60]):")
print(strided_tokens[1])
print("Matches np.arange(10, 60):", np.array_equal(strided_tokens[1], np.arange(10, 60)))

print("\nThird window (T[20:70]):")
print(strided_tokens[2])
print("Matches np.arange(20, 70):", np.array_equal(strided_tokens[2], np.arange(20, 70)))

print("\nLast window (T[50:100]):")
print(strided_tokens[-1])
print("Matches np.arange(50, 100):", np.array_equal(strided_tokens[-1], np.arange(50, 100)))

# Example with a shorter array
short_tokens = np.arange(65)
strided_short = strides(short_tokens, window_size=50, step=10)
print("\nShort array (len 65) strided shape:", strided_short.shape) # Expected: (2, 50)
print("Last window (T[10:60]):", strided_short[-1])
print("Matches np.arange(10, 60):", np.array_equal(strided_short[-1], np.arange(10, 60)))

# Example with an array too short for one window
too_short_tokens = np.arange(40)
strided_too_short = strides(too_short_tokens, window_size=50, step=10)
print("\nToo short array (len 40) strided shape:", strided_too_short.shape) # Expected: (0, 50)

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
from numpy.lib.stride_tricks import as_strided
from numpy.typing import NDArray # Optional: for type hinting

def strides(T: NDArray, window_size: int = 50, step: int = 10) -> NDArray:
    """
    Creates a strided view of a 1D array T.

    Generates an array where each row is a slice of T of length `window_size`,
    with the starting index of each slice advancing by `step`.

    Equivalent to stacking [T[0:window_size], T[step:step+window_size], T[2*step:2*step+window_size], ...].

    Args:
        T: The input 1D NumPy array (e.g., array of tokens).
        window_size: The desired length of each window/slice (default: 50).
        step: The step size between the start of consecutive windows (default: 10).

    Returns:
        A 2D NumPy array (view) where each row is a window into T.
        The view is non-writeable by default for safety due to potential
        data overlap. Returns an empty array with the correct number of columns
        if T is too short to contain even one full window.

    Raises:
        ValueError: If T is not a 1D array or if window_size or step are
                    not positive integers.
    """
    # Input validation
    if not isinstance(T, np.ndarray) or T.ndim != 1:
        raise ValueError("Input T must be a 1D NumPy array.")
    if not isinstance(window_size, int) or window_size <= 0:
        raise ValueError("window_size must be a positive integer.")
    if not isinstance(step, int) or step <= 0:
        raise ValueError("step must be a positive integer.")

    n = T.shape[0]          # Total number of elements in T
    itemsize = T.itemsize   # Size in bytes of each element in T

    # Calculate the number of windows that fit completely
    if n < window_size:
        num_windows = 0
    else:
        # The last window starts at index `(num_windows - 1) * step`
        # It must end at or before index `n - 1`.
        # So, `(num_windows - 1) * step + window_size <= n`
        num_windows = (n - window_size) // step + 1

    # Handle the case where no full windows fit
    if num_windows <= 0:
        # Return an empty array with the correct shape (0 rows, window_size columns)
        # and the same dtype as T
        return np.empty((0, window_size), dtype=T.dtype)

    # Define the shape and strides for the new view
    out_shape = (num_windows, window_size)

    # Calculate strides:
    # - To move to the next element *within* a window (column): jump `itemsize` bytes
    # - To move to the start of the *next* window (row): jump `step * itemsize` bytes
    out_strides = (step * itemsize, itemsize)

    # Create the strided view using as_strided
    # Set writeable=False for safety, as the windows likely overlap in memory.
    # Modifying the view could unintentionally change multiple parts of the
    # underlying data if writeable=True.
    strided_view = as_strided(T, shape=out_shape, strides=out_strides, writeable=False)

    return strided_view

# --- Example Usage ---
# Create a sample array of tokens (e.g., integers)
tokens = np.arange(100) # Example: tokens 0 through 99

# Get the strided view
strided_tokens = strides(tokens, window_size=50, step=10)

print("Original array shape:", tokens.shape)
print("Strided array shape:", strided_tokens.shape) # Expected: (6, 50) for 100 tokens

# Verify the content of the first few windows
print("\nFirst window (T[0:50]):")
print(strided_tokens[0])
print("Matches np.arange(0, 50):", np.array_equal(strided_tokens[0], np.arange(0, 50)))

print("\nSecond window (T[10:60]):")
print(strided_tokens[1])
print("Matches np.arange(10, 60):", np.array_equal(strided_tokens[1], np.arange(10, 60)))

print("\nThird window (T[20:70]):")
print(strided_tokens[2])
print("Matches np.arange(20, 70):", np.array_equal(strided_tokens[2], np.arange(20, 70)))

print("\nLast window (T[50:100]):")
print(strided_tokens[-1])
print("Matches np.arange(50, 100):", np.array_equal(strided_tokens[-1], np.arange(50, 100)))

# Example with a shorter array
short_tokens = np.arange(65)
strided_short = strides(short_tokens, window_size=50, step=10)
print("\nShort array (len 65) strided shape:", strided_short.shape) # Expected: (2, 50)
print("Last window (T[10:60]):", strided_short[-1])
print("Matches np.arange(10, 60):", np.array_equal(strided_short[-1], np.arange(10, 60)))

# Example with an array too short for one window
too_short_tokens = np.arange(40)
strided_too_short = strides(too_short_tokens, window_size=50, step=10)
print("\nToo short array (len 40) strided shape:", strided_too_short.shape) # Expected: (0, 50)





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: Original array shape: (100,) Strided array shape: (6, 50) First window (T[0:50]): [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49] Matches np.arange(0, 50): True Second window (T[10:60]): [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Matches np.arange(10, 60): True Third window (T[20:70]): [20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69] Matches np.arange(20, 70): True Last window (T[50:100]): [50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99] Matches np.arange(50, 100): True Short array (len 65) strided shape: (2, 50) Last window (T[10:60]): [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59] Matches np.arange(10, 60): True Too short array (len 40) strided shape: (0, 50) All tests passed

Substring Evaluation

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

Final Answer: True