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

# Implementing the strides function with NumPy's stride tricks The task is to create a function that generates overlapping windows of a fixed size from a NumPy array, using stride tricks for efficiency. Let me implement this: 

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import numpy as np
from numpy.lib.stride_tricks import as_strided

def strides(T):
    """
    Given an array of tokens T as numpy array, returns a list of overlapping windows
    [T[0:50], T[10:60], T[20:70], ...] using NumPy's stride tricks for efficiency.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of views of the original array with stride 10 and window size 50
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Define window size and stride
    window_size = 50
    stride = 10
    
    # Calculate number of windows
    n = len(T)
    num_windows = max(0, (n - window_size) // stride + 1)
    
    if num_windows <= 0:
        return []
    
    # Create a strided view of the array
    # shape: (number of windows, size of each window)
    # strides: (bytes to move to next window, bytes to move to next element)
    strided_array = as_strided(
        T, 
        shape=(num_windows, window_size),
        strides=(T.itemsize * stride, T.itemsize)
    )
    
    # Convert to list of arrays as requested
    result = [strided_array[i] for i in range(num_windows)]
    
    return result
This function: 1. Takes a NumPy array as input 2. Creates a strided view with a window size of 50 and stride of 10 3. Returns a list of views into the original array 4. Uses NumPy's stride tricks to avoid unnecessary data copying The key advantage of using `as_strided` is that it creates views of the original data rather than copies, which is more memory-efficient.

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

def strides(T):
    """
    Given an array of tokens T as numpy array, returns a list of overlapping windows
    [T[0:50], T[10:60], T[20:70], ...] using NumPy's stride tricks for efficiency.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of views of the original array with stride 10 and window size 50
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Define window size and stride
    window_size = 50
    stride = 10
    
    # Calculate number of windows
    n = len(T)
    num_windows = max(0, (n - window_size) // stride + 1)
    
    if num_windows <= 0:
        return []
    
    # Create a strided view of the array
    # shape: (number of windows, size of each window)
    # strides: (bytes to move to next window, bytes to move to next element)
    strided_array = as_strided(
        T, 
        shape=(num_windows, window_size),
        strides=(T.itemsize * stride, T.itemsize)
    )
    
    # Convert to list of arrays as requested
    result = [strided_array[i] for i in range(num_windows)]
    
    return result

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

def strides(T):
    """
    Given an array of tokens T as numpy array, returns a list of overlapping windows
    [T[0:50], T[10:60], T[20:70], ...] using NumPy's stride tricks for efficiency.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of views of the original array with stride 10 and window size 50
    """
    # Ensure T is a numpy array
    T = np.asarray(T)
    
    # Define window size and stride
    window_size = 50
    stride = 10
    
    # Calculate number of windows
    n = len(T)
    num_windows = max(0, (n - window_size) // stride + 1)
    
    if num_windows <= 0:
        return []
    
    # Create a strided view of the array
    # shape: (number of windows, size of each window)
    # strides: (bytes to move to next window, bytes to move to next element)
    strided_array = as_strided(
        T, 
        shape=(num_windows, window_size),
        strides=(T.itemsize * stride, T.itemsize)
    )
    
    # Convert to list of arrays as requested
    result = [strided_array[i] for i in range(num_windows)]
    
    return result





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: Traceback (most recent call last): File "/usr/src/app/main.py", line 48, in <module> answer = strides(np.arange(100)).shape ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'list' object has no attribute 'shape'

Substring Evaluation

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

Final Answer: False

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

# Implementing a Function to Create Strided Token Windows To create sliding windows of tokens with a fixed stride, I'll use NumPy's strided trick which is memory-efficient as it creates views of the original array rather than copying data. 

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import numpy as np
from numpy.lib.stride_tricks import as_strided

def strides(T):
    """
    Given an array of tokens T, returns a list of strided windows.
    Each window is of size 50, with a stride of 10 elements.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of array slices [T[0:50], T[10:60], T[20:70], ...]
    """
    # Set window parameters
    window_size = 50
    stride = 10
    
    # Calculate number of complete windows
    n_windows = (len(T) - window_size) // stride + 1
    
    if n_windows <= 0:
        return []
    
    # Create a strided view of the original array
    windows_view = as_strided(
        T, 
        shape=(n_windows, window_size),
        strides=(stride * T.strides[0], T.strides[0])
    )
    
    # Convert to list of arrays
    return [windows_view[i] for i in range(n_windows)]
## How it works 1. The function takes a NumPy array `T` as input 2. It creates windows of size 50 with a stride of 10 elements 3. The `as_strided` function creates a view of the original array with modified strides 4. The first stride (stride * T.strides[0]) specifies the byte distance between the start of each window 5. The second stride (T.strides[0]) specifies the byte distance between consecutive elements within a window 6. The result is returned as a list of arrays, where each array is a view of the original data This implementation is memory-efficient because it doesn't copy the data, just creates different views of it.

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

def strides(T):
    """
    Given an array of tokens T, returns a list of strided windows.
    Each window is of size 50, with a stride of 10 elements.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of array slices [T[0:50], T[10:60], T[20:70], ...]
    """
    # Set window parameters
    window_size = 50
    stride = 10
    
    # Calculate number of complete windows
    n_windows = (len(T) - window_size) // stride + 1
    
    if n_windows <= 0:
        return []
    
    # Create a strided view of the original array
    windows_view = as_strided(
        T, 
        shape=(n_windows, window_size),
        strides=(stride * T.strides[0], T.strides[0])
    )
    
    # Convert to list of arrays
    return [windows_view[i] for i in range(n_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
from numpy.lib.stride_tricks import as_strided

def strides(T):
    """
    Given an array of tokens T, returns a list of strided windows.
    Each window is of size 50, with a stride of 10 elements.
    
    Args:
        T (numpy.ndarray): Input array of tokens
        
    Returns:
        list: List of array slices [T[0:50], T[10:60], T[20:70], ...]
    """
    # Set window parameters
    window_size = 50
    stride = 10
    
    # Calculate number of complete windows
    n_windows = (len(T) - window_size) // stride + 1
    
    if n_windows <= 0:
        return []
    
    # Create a strided view of the original array
    windows_view = as_strided(
        T, 
        shape=(n_windows, window_size),
        strides=(stride * T.strides[0], T.strides[0])
    )
    
    # Convert to list of arrays
    return [windows_view[i] for i in range(n_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: Traceback (most recent call last): File "/usr/src/app/main.py", line 40, in <module> answer = strides(np.arange(100)).shape ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'list' object has no attribute 'shape'

Substring Evaluation

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

Final Answer: False