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@codeflash-ai codeflash-ai bot commented Apr 5, 2025

📄 206% (2.06x) speedup for InfraResource.should_delete in libs/agno/agno/infra/resource.py

⏱️ Runtime : 2.61 milliseconds 851 microseconds (best of 56 runs)

📝 Explanation and details

Below is the optimized version of the provided program. The primary optimization focuses on reducing redundant function calls and minimizing string manipulation computations. The logger invocations are also conditional based on whether debugging is enabled, ensuring unnecessary computations like concatenations for logging are avoided when not in debug mode.

Key Optimizations.

  1. Reduced Redundant Method Calls:

    • self.get_resource_name() and self.get_group_name() are called only once and stored in local variables for repeated use.
  2. Conditional Logging:

    • Only perform string concatenations and call logger.debug() if debug_on (a flag indicating if debugging is enabled) is true. This avoids the overhead of unnecessary string operations and method calls when debugging is not enabled.
  3. Lowercasing and Filtering:

    • The type_filter.lower() operation is performed once and stored in a local variable if type_filter is not None, avoiding repeated computations.

By following these optimizations, the program should run faster and be more efficient in terms of runtime.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 2042 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests Details
import logging
from pathlib import Path
from typing import Any, List, Optional

# imports
import pytest  # used for our unit tests
# function to test
from agno.infra.base import InfraBase
from agno.infra.resource import InfraResource
from agno.utils.log import logger
from agno.workspace.settings import WorkspaceSettings
from pydantic import BaseModel, ConfigDict


# unit tests
def test_should_delete_basic():
    # Basic functionality: resource is enabled and not set to skip deletion, matches all filters
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group1", name_filter="resource1", type_filter="type1")

def test_should_delete_disabled():
    # Resource is disabled
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=False, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group1", name_filter="resource1", type_filter="type1")

def test_should_delete_skip_delete():
    # Resource is set to skip deletion
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=True)
    codeflash_output = resource.should_delete(group_filter="group1", name_filter="resource1", type_filter="type1")

def test_should_delete_group_filter():
    # Group filter matches exactly
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group1")

def test_should_delete_name_filter():
    # Name filter matches exactly
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(name_filter="resource1")

def test_should_delete_type_filter():
    # Type filter matches exactly
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(type_filter="type1")

def test_should_delete_group_filter_partial():
    # Group filter matches partially
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group")

def test_should_delete_name_filter_partial():
    # Name filter matches partially
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(name_filter="resource")

def test_should_delete_type_filter_partial():
    # Type filter matches partially
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(type_filter="type")

def test_should_delete_group_filter_no_match():
    # Group filter does not match
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group2")

def test_should_delete_name_filter_no_match():
    # Name filter does not match
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(name_filter="resource2")

def test_should_delete_type_filter_no_match():
    # Type filter does not match
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(type_filter="type2")

def test_should_delete_empty_filters():
    # All filters are None
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete()

def test_should_delete_case_sensitivity():
    # Filters with different cases
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="GROUP1", name_filter="RESOURCE1", type_filter="TYPE1")

def test_should_delete_special_characters():
    # Filters with special characters
    resource = InfraResource(name="resource-1", group="group-1", resource_type="type-1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group-1", name_filter="resource-1", type_filter="type-1")

def test_should_delete_multiple_filters():
    # All filters match exactly
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group1", name_filter="resource1", type_filter="type1")

def test_should_delete_multiple_filters_partial():
    # Some filters match partially
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group1", name_filter="res", type_filter="type2")

def test_should_delete_multiple_filters_no_match():
    # No filters match
    resource = InfraResource(name="resource1", group="group1", resource_type="type1", enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="group2", name_filter="resource2", type_filter="type3")

def test_should_delete_multiple_types():
    # Type filter matches one of the types
    resource = InfraResource(name="resource1", group="group1", resource_type_list=["type1", "type2"], enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(type_filter="type1")

def test_should_delete_multiple_types_no_match():
    # Type filter does not match any type
    resource = InfraResource(name="resource1", group="group1", resource_type_list=["type1", "type2"], enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(type_filter="type3")

def test_should_delete_large_number_of_resources():
    # Test with a large list of resources to check performance
    resources = [InfraResource(name=f"resource{i}", group="group1", resource_type="type1", enabled=True, skip_delete=False) for i in range(1000)]
    for resource in resources:
        codeflash_output = resource.should_delete(group_filter="group1", name_filter=f"resource{resources.index(resource)}", type_filter="type1")

def test_should_delete_large_filters():
    # Test with large and complex filter strings
    resource = InfraResource(name="a" * 1000, group="b" * 1000, resource_type="c" * 1000, enabled=True, skip_delete=False)
    codeflash_output = resource.should_delete(group_filter="b" * 1000, name_filter="a" * 1000, type_filter="c" * 1000)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import logging
from pathlib import Path
from typing import Any, Dict, List, Optional

# imports
import pytest  # used for our unit tests
# function to test
from agno.infra.base import InfraBase
from agno.infra.resource import InfraResource
from agno.utils.log import logger
from agno.workspace.settings import WorkspaceSettings
from pydantic import BaseModel, ConfigDict

# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-InfraResource.should_delete-m93mq2js and push.

Codeflash

Below is the optimized version of the provided program. The primary optimization focuses on reducing redundant function calls and minimizing string manipulation computations. The logger invocations are also conditional based on whether debugging is enabled, ensuring unnecessary computations like concatenations for logging are avoided when not in debug mode.



### Key Optimizations.
1. **Reduced Redundant Method Calls:**
   - `self.get_resource_name()` and `self.get_group_name()` are called only once and stored in local variables for repeated use.

2. **Conditional Logging:**
   - Only perform string concatenations and call `logger.debug()` if `debug_on` (a flag indicating if debugging is enabled) is true. This avoids the overhead of unnecessary string operations and method calls when debugging is not enabled.

3. **Lowercasing and Filtering:**
   - The `type_filter.lower()` operation is performed once and stored in a local variable if `type_filter` is not None, avoiding repeated computations.

By following these optimizations, the program should run faster and be more efficient in terms of runtime.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Apr 5, 2025
@codeflash-ai codeflash-ai bot requested a review from misrasaurabh1 April 5, 2025 03:03
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