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Pretty standard hashmap problem once you see it.
Clarify the input format and constraints, then propose a hash-based grouping strategy: compute a hash (or use the full content) for each file and group files by identical content. Discuss trade-offs between hashing and direct comparison, and handle edge cases like empty files and large contents.
Pro tip: Mention that you would use a cryptographic hash like SHA-256 to minimize collision risk, but also discuss the trade-off of hash collisions and the possibility of verifying with direct comparison for critical applications.
Ask about input size, file content size, whether paths are unique, and if the order of groups or files within groups matters. Confirm that only groups with at least two files should be returned.
Decide between hashing file contents (e.g., SHA-256) or using the content directly as a key. Consider memory and time trade-offs, especially for large files.
Iterate through all files, compute a key (hash or content), and use a hash map to group full paths by that key. Ensure that only groups with size >= 2 are kept.
Address empty files, duplicate paths, and very large files. Discuss potential optimizations like streaming hashing or parallel processing if needed.
Explain time and space complexity (O(N) for N files, assuming constant-time hashing) and discuss trade-offs between hashing and direct comparison, including collision risks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The flip part was trivial but the blur tripped me up a little.
Clarify the input format and operation sequence, then design modular functions for horizontal flip and box blur, ensuring the blur uses only in-bounds pixels and integer floor division. Apply operations in order, and analyze time/space complexity, discussing trade-offs between in-place and new-matrix approaches.
Pro tip: Write helper functions for each operation and test with edge cases like 1x1 matrices and empty inputs; explicitly handle boundary conditions in blur to avoid index errors.
Confirm the input matrix dimensions, operation sequence format, and that blur uses only in-bounds pixels with floor division. Ask about edge cases like empty matrices or single-row/column images.
Implement horizontal flip by reversing each row, and box blur by averaging the 3x3 neighborhood (or fewer at borders) using integer floor division. Ensure each operation returns a new matrix or modifies in-place as specified.
Iterate through the given operation sequence, applying each operation to the current matrix. Maintain the matrix state correctly between operations.
Discuss time complexity O(N*M*K) for K operations and space complexity O(N*M) if creating new matrices. Compare in-place vs. new matrix approaches for memory and simplicity.
Walk through small examples, including edge cases, to verify correctness. Mention potential optimizations like using prefix sums for blur if multiple blurs are applied.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.