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⚡️ Speed up function is_with_or_async_with_stmt by 37% in src/black/nodes.py #44

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@codeflash-ai codeflash-ai bot commented Dec 14, 2024

📄 is_with_or_async_with_stmt in src/black/nodes.py

✨ Performance Summary:

  • Speed Increase: 📈 37% (0.37x faster)
  • Runtime Reduction: ⏱️ From 7.97 microseconds down to 5.81 microseconds (best of 85 runs)

📝 Explanation and details

Sure, let's optimize the given Python code for a better runtime performance. Here are a few strategies to optimize this.

  1. Use tuple unpacking where possible for better readability and performance.
  2. Move from .pgen2.token import tok_name to the top to avoid repeated import within repr().
  3. Convert lists and dict copying actions to utilize more performance-efficient methods.
  4. Ensure only necessary attributes are being initialized in the constructor.

In this optimization.

  • from .pgen2.token import tok_name is moved out of the __repr__ method.
  • Some redundant class attributes are eliminated or simplified in constructors.
  • Logical expressions in the is_with_or_async_with_stmt function are made more straightforward with reduced overhead.
  • Attribute copies are now performed by slicing fixers_applied.

These changes together ensure that the overall performance, particularly during object creation and method execution, is improved while maintaining the same functionality.


Correctness verification

The new optimized code was tested for correctness. The results are listed below:

Test Status Details
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 5 Passed See below
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Coverage 100.0%

🌀 Generated Regression Tests Details

Click to view details
from typing import Any, Final, Optional

# imports
import pytest  # used for our unit tests
from black.nodes import is_with_or_async_with_stmt
from blib2to3 import pygram
from blib2to3.pgen2 import token
from blib2to3.pytree import Leaf, Node

# function to test
NAME: Final = 1
ASYNC: Final = 57
syms: Final = pygram.python_symbols
from black.nodes import is_with_or_async_with_stmt

# unit tests


def test_basic_valid_async_with_statement():
    # Create a leaf node representing 'async' with a next sibling 'with_stmt'
    next_sibling = Node(syms.with_stmt, [])
    leaf = Leaf(token.ASYNC, "async")
    leaf._next_sibling = next_sibling
    codeflash_output = is_with_or_async_with_stmt(leaf)


def test_invalid_async_with_statement():
    # Create a leaf node representing 'async' but with an invalid next sibling
    next_sibling = Node(syms.if_stmt, [])
    leaf = Leaf(token.ASYNC, "async")
    leaf._next_sibling = next_sibling
    codeflash_output = is_with_or_async_with_stmt(leaf)


def test_edge_case_with_no_parent():
    # Create a leaf node representing 'with' with no parent
    leaf = Leaf(token.NAME, "with")
    codeflash_output = is_with_or_async_with_stmt(leaf)

def test_edge_case_async_with_no_next_sibling():
    # Create a leaf node representing 'async' with no next sibling
    leaf = Leaf(token.ASYNC, "async")
    codeflash_output = is_with_or_async_with_stmt(leaf)


def test_complex_tree_structure_async_with():
    # Create a complex tree structure with a valid 'async with' statement
    next_sibling = Node(syms.with_stmt, [])
    leaf = Leaf(token.ASYNC, "async")
    leaf._next_sibling = next_sibling
    codeflash_output = is_with_or_async_with_stmt(leaf)





from typing import Any, Final, Optional

# imports
import pytest  # used for our unit tests
from black.nodes import is_with_or_async_with_stmt
from blib2to3 import pygram
from blib2to3.pgen2 import token
from blib2to3.pytree import Base, Leaf

NAME: Final = 1
ASYNC: Final = 57
syms: Final = pygram.python_symbols
from black.nodes import is_with_or_async_with_stmt

# unit tests

# Helper function to create a mock Leaf node
def create_leaf(type: int, value: str, parent: Optional[Leaf] = None, next_sibling: Optional[Leaf] = None) -> Leaf:
    leaf = Leaf(type, value)
    leaf.parent = parent
    leaf.next_sibling = next_sibling
    return leaf

📣 **Feedback**

If you have any feedback or need assistance, feel free to join our Discord community:

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Sure, let's optimize the given Python code for a better runtime performance. Here are a few strategies to optimize this.

1. Use tuple unpacking where possible for better readability and performance.
2. Move `from .pgen2.token import tok_name` to the top to avoid repeated import within repr().
3. Convert lists and dict copying actions to utilize more performance-efficient methods.
4. Ensure only necessary attributes are being initialized in the constructor.




In this optimization.
- `from .pgen2.token import tok_name` is moved out of the `__repr__` method.
- Some redundant class attributes are eliminated or simplified in constructors.
- Logical expressions in the `is_with_or_async_with_stmt` function are made more straightforward with reduced overhead.
- Attribute copies are now performed by slicing `fixers_applied`.

These changes together ensure that the overall performance, particularly during object creation and method execution, is improved while maintaining the same functionality.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Dec 14, 2024
@codeflash-ai codeflash-ai bot requested a review from misrasaurabh1 December 14, 2024 01:24
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bool was unnecessary

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