5 lines
5.5 KiB
JSON
5 lines
5.5 KiB
JSON
{"run": 0, "warmup": true, "ttft_s": 0.12214227900039987, "elapsed_s": 11.675187773000289, "decode_tps": 22.072102125143978, "usage": {"prompt_tokens": 58, "total_tokens": 314, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 212}}, "content": "\n\n# Merging Overlapping Intervals\n\n## The Function\n\n```python\nfrom typing import List, Tuple\n\ndef merge_intervals(intervals: List[List[int]]) -> List[List[int]]:\n \"\"\"\n", "reasoning": "The user wants a Python function that merges overlapping intervals, along with an explanation of the algorithm, edge cases, and complexity analysis.\n\nLet me think about the best approach:\n\n1. **Algorithm**: The classic approach is to sort intervals by start time, then iterate through them, merging when there's overlap. This is the most common and efficient approach.\n\n2. **Key considerations**:\n - What counts as \"overlapping\"? Adjacent intervals like [1,2] and [2,3] \u2014 should they merge? I'll handle both cases and mention it.\n - Edge cases: empty list, single interval, fully contained intervals, non-overlapping intervals, intervals touching at endpoints.\n\n3. **Complexity**: O(n log n) for sorting, O(n) for the merge pass, so O(n log n) overall. Space is O(n) for the output (or O(1) extra if we modify in place).\n\nLet me write a clean, well-documented solution with thorough explanation.\n"}
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{"run": 1, "warmup": false, "ttft_s": 0.11141436400066596, "elapsed_s": 11.829587345000618, "decode_tps": 21.7610714924128, "usage": {"prompt_tokens": 58, "total_tokens": 314, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 212}}, "content": "\n\n# Merging Overlapping Intervals\n\n## The Function\n\n```python\nfrom typing import List, Tuple\n\ndef merge_intervals(intervals: List[List[int]]) -> List[List[int]]:\n \"\"\"\n", "reasoning": "The user wants a Python function that merges overlapping intervals, along with an explanation of the algorithm, edge cases, and complexity analysis.\n\nLet me think about the best approach:\n\n1. **Algorithm**: The classic approach is to sort intervals by start time, then iterate through them, merging when there's overlap. This is the most common and efficient approach.\n\n2. **Key considerations**:\n - What counts as \"overlapping\"? Adjacent intervals like [1,2] and [2,3] \u2014 should they merge? I'll handle both cases and mention it.\n - Edge cases: empty list, single interval, fully contained intervals, non-overlapping intervals, intervals touching at endpoints.\n\n3. **Complexity**: O(n log n) for sorting, O(n) for the merge pass, so O(n log n) overall. Space is O(n) for the output (or O(1) extra if we modify in place).\n\nLet me write a clean, well-documented solution with thorough explanation.\n"}
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{"run": 2, "warmup": false, "ttft_s": 0.11719201899995824, "elapsed_s": 11.707202442999915, "decode_tps": 22.001705837292434, "usage": {"prompt_tokens": 58, "total_tokens": 314, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 212}}, "content": "\n\n# Merging Overlapping Intervals\n\n## The Function\n\n```python\nfrom typing import List, Tuple\n\ndef merge_intervals(intervals: List[List[int]]) -> List[List[int]]:\n \"\"\"\n", "reasoning": "The user wants a Python function that merges overlapping intervals, along with an explanation of the algorithm, edge cases, and complexity analysis.\n\nLet me think about the best approach:\n\n1. **Algorithm**: The classic approach is to sort intervals by start time, then iterate through them, merging when there's overlap. This is the most common and efficient approach.\n\n2. **Key considerations**:\n - What counts as \"overlapping\"? Adjacent intervals like [1,2] and [2,3] \u2014 should they merge? I'll handle both cases and mention it.\n - Edge cases: empty list, single interval, fully contained intervals, non-overlapping intervals, intervals touching at endpoints.\n\n3. **Complexity**: O(n log n) for sorting, O(n) for the merge pass, so O(n log n) overall. Space is O(n) for the output (or O(1) extra if we modify in place).\n\nLet me write a clean, well-documented solution with thorough explanation.\n"}
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{"run": 3, "warmup": false, "ttft_s": 0.10968124400005763, "elapsed_s": 11.686878399000307, "decode_tps": 22.026056616809385, "usage": {"prompt_tokens": 58, "total_tokens": 314, "completion_tokens": 256, "completion_tokens_details": {"reasoning_tokens": 212}}, "content": "\n\n# Merging Overlapping Intervals\n\n## The Function\n\n```python\nfrom typing import List, Tuple\n\ndef merge_intervals(intervals: List[List[int]]) -> List[List[int]]:\n \"\"\"\n", "reasoning": "The user wants a Python function that merges overlapping intervals, along with an explanation of the algorithm, edge cases, and complexity analysis.\n\nLet me think about the best approach:\n\n1. **Algorithm**: The classic approach is to sort intervals by start time, then iterate through them, merging when there's overlap. This is the most common and efficient approach.\n\n2. **Key considerations**:\n - What counts as \"overlapping\"? Adjacent intervals like [1,2] and [2,3] \u2014 should they merge? I'll handle both cases and mention it.\n - Edge cases: empty list, single interval, fully contained intervals, non-overlapping intervals, intervals touching at endpoints.\n\n3. **Complexity**: O(n log n) for sorting, O(n) for the merge pass, so O(n log n) overall. Space is O(n) for the output (or O(1) extra if we modify in place).\n\nLet me write a clean, well-documented solution with thorough explanation.\n"}
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