Created async Environment.get_id method for unique environment ID

[jamesbraza]

Resolves #165

[codeflash-ai]

⚡️ Codeflash found optimizations for this PR

📄 11% (0.11x) speedup for CalculatorEnv.from_task in packages/gsm8k/src/aviary/envs/gsm8k/env.py

⏱️ Runtime : 1.13 millisecond → 1.02 millisecond (best of 84 runs)

📝 Explanation and details

To optimize the runtime and reduce memory usage, the main approach would focus on improving the method from_task. This involves making sure we avoid unnecessary object constructions and ensure we handle only what's needed. Given that the problem_id is always None and answer is always 0.0, we can optimize the code, but there is not much room for major optimization due to the simplicity of the class and the logic.

Here is the optimized version of CalculatorEnv.

Key optimizations done.

1. Created a single CalculatorEnvConfig instance to use for all instances of CalculatorEnv created via the from_task class method to avoid creating multiple unneeded instances.
2. Ensured we provided the necessary default parameters directly to the class constructor. This utilizes class construction more efficiently by preventing multiple redundant allocations or object constructions.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 21 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
# function to test
from aviary.core import Environment
from aviary.envs.gsm8k.env import CalculatorEnv


class CalculatorEnvConfig:
    pass  # Assuming a dummy config class for testing purposes
from aviary.envs.gsm8k.env import CalculatorEnv

# unit tests

# Basic Functionality
def test_basic_functionality():
    codeflash_output = CalculatorEnv.from_task("2 + 2")

# Edge Cases
def test_empty_task_string():
    codeflash_output = CalculatorEnv.from_task("")

def test_task_with_special_characters():
    codeflash_output = CalculatorEnv.from_task("2 + @")

# Invalid Inputs


def test_long_mathematical_expression():
    codeflash_output = CalculatorEnv.from_task("1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + 10")

# Performance and Scalability
def test_very_large_task_string():
    large_task = " + ".join(["1"] * 10000)
    codeflash_output = CalculatorEnv.from_task(large_task)

# Configuration Handling
def test_default_configuration():
    codeflash_output = CalculatorEnv.from_task("3 * 3")


def test_consistent_instance_creation():
    task = "4 / 2"
    codeflash_output = CalculatorEnv.from_task(task)
    codeflash_output = CalculatorEnv.from_task(task)

# Large Scale Test Cases
def test_performance_with_large_data_samples():
    large_task = " + ".join([str(i) for i in range(1000)])
    codeflash_output = CalculatorEnv.from_task(large_task)

# Side Effects and State Verification
def test_no_unintended_side_effects():
    global_state = {}
    codeflash_output = CalculatorEnv.from_task("2 + 2")

# Miscellaneous
def test_task_with_whitespace_variations():
    codeflash_output = CalculatorEnv.from_task(" 2 + 2 ")
    codeflash_output = CalculatorEnv.from_task("\t3 * 3\n")
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
# function to test
from aviary.core import Environment
from aviary.envs.gsm8k.env import CalculatorEnv


class CalculatorEnvConfig:
    pass  # Placeholder for the actual CalculatorEnvConfig class
from aviary.envs.gsm8k.env import CalculatorEnv

# unit tests

def test_basic_functionality():
    # Test creating an instance with a simple task string
    codeflash_output = CalculatorEnv.from_task("2 + 2")

def test_empty_task_string():
    # Test creating an instance with an empty task string
    codeflash_output = CalculatorEnv.from_task("")

def test_whitespace_task_string():
    # Test creating an instance with a whitespace task string
    codeflash_output = CalculatorEnv.from_task(" ")

def test_unicode_task_string():
    # Test creating an instance with a Unicode task string
    codeflash_output = CalculatorEnv.from_task("π * r^2")

def test_very_long_task_string():
    # Test creating an instance with a very long task string
    long_task = "2 + 2" * 1000
    codeflash_output = CalculatorEnv.from_task(long_task)


def test_default_configuration():
    # Test interaction with default configuration
    codeflash_output = CalculatorEnv.from_task("2 + 2")


def test_deterministic_behavior():
    # Test consistency of returned instance
    codeflash_output = CalculatorEnv.from_task("2 + 2")
    codeflash_output = CalculatorEnv.from_task("2 + 2")

def test_no_side_effects():
    # Test no unexpected side effects
    global_state = {}
    task = "2 + 2"
    codeflash_output = CalculatorEnv.from_task(task)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

📢 Feedback on this optimization?

[jamesbraza]

@sidnarayanan and I discussed a few things on __hash__:

• __hash__ ignoring state is risky because it enables set reductions of Environments with different states.
• __hash__ being reused for problem ID is not necessarily Pythonic (it doesn't align with semantics of object.__hash__)
• To understand its semantics, it requires knowledge of each Environment.__hash__'s implementation details

So, I reverted to the awaitable get_id method, which has none of these tradeoffs.