⚡️ Speed up method NameEmail.__eq__ by 40%

[codeflash-ai]

📄 40% (0.40x) speedup for NameEmail.__eq__ in pydantic/v1/networks.py

⏱️ Runtime : 292 nanoseconds → 208 nanoseconds (best of 593 runs)

📝 Explanation and details

These changes help the program run faster by optimizing memory usage and slightly improving equality check performance.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 19 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 1 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

from typing import Any, Dict

# imports
import pytest  # used for our unit tests
from pydantic.v1.networks import NameEmail
# function to test
from pydantic.v1.utils import Representation
from pydantic.v1.validators import str_validator


def test_many_instances():
    instances = [NameEmail(f"Name{i}", f"email{i}@example.com") for i in range(1000)]
    for i in range(1000):
        pass

# Invalid Inputs (for Validation)
def test_invalid_email_format():
    with pytest.raises(ValueError):
        NameEmail.validate("invalid-email")

def test_non_string_inputs():
    with pytest.raises(ValueError):
        NameEmail.validate(12345)

# Schema Modification
def test_schema_update():
    field_schema = {}
    NameEmail.__modify_schema__(field_schema)

# Validators
def test_validator_functionality():
    validators = list(NameEmail.__get_validators__())

def test_validation_of_existing_instance():
    instance = NameEmail("John Doe", "[email protected]")
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Any, Dict

# imports
import pytest  # used for our unit tests
from pydantic.v1.networks import NameEmail
# function to test
from pydantic.v1.utils import Representation
from pydantic.v1.validators import str_validator

# unit tests

def test_identical_objects():
    # Test two identical NameEmail objects
    obj1 = NameEmail("John Doe", "[email protected]")
    obj2 = NameEmail("John Doe", "[email protected]")

def test_different_objects():
    # Test two different NameEmail objects
    obj1 = NameEmail("John Doe", "[email protected]")
    obj2 = NameEmail("Jane Doe", "[email protected]")

def test_type_mismatch():
    # Test comparison with a non-NameEmail object
    obj1 = NameEmail("John Doe", "[email protected]")

def test_empty_strings():
    # Test objects with empty strings
    obj1 = NameEmail("", "")
    obj2 = NameEmail("", "")
    obj3 = NameEmail("John Doe", "")

def test_whitespace_strings():
    # Test objects with whitespace strings
    obj1 = NameEmail(" ", " ")
    obj2 = NameEmail(" ", " ")
    obj3 = NameEmail("John Doe", " ")

def test_special_characters():
    # Test objects with special characters
    obj1 = NameEmail("John Doe", "[email protected]")
    obj2 = NameEmail("John Doe", "[email protected]")
    obj3 = NameEmail("John O'Connor", "john.o'[email protected]")

def test_case_sensitivity():
    # Test case sensitivity
    obj1 = NameEmail("John Doe", "[email protected]")
    obj2 = NameEmail("john doe", "[email protected]")
    obj3 = NameEmail("John Doe", "[email protected]")

def test_modify_schema():
    # Test schema modification
    schema = {'type': 'object'}
    NameEmail.__modify_schema__(schema)

def test_valid_input():
    # Test valid input for validation
    obj = NameEmail.validate("John Doe <[email protected]>")

def test_invalid_input():
    # Test invalid input for validation
    with pytest.raises(ValueError):
        NameEmail.validate("InvalidEmail")

def test_string_representation():
    # Test string representation
    obj = NameEmail("John Doe", "[email protected]")

def test_large_scale():
    # Test performance with large data samples
    large_list = [NameEmail(f"Name{i}", f"email{i}@example.com") for i in range(1000)]
    for i in range(1000):
        pass

def test_deterministic_behavior():
    # Ensure tests are deterministic
    obj1 = NameEmail("John Doe", "[email protected]")
    obj2 = NameEmail("John Doe", "[email protected]")
    for _ in range(100):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from pydantic.v1.networks import NameEmail

def test_NameEmail___eq__():
    assert NameEmail.__eq__(NameEmail('', ''), NameEmail('', '')) == True

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