# Context:
USER: when `subprocess` `timeout` fires, do we need to kill the subprocess? just in `run_unit_tests`
ASSISTANT: When a subprocess times out, it is not automatically terminated. So, yes, if you want to ensure that the subprocess is terminated when it times out, you should explicitly kill it.
Here's how you can modify the `run_unit_tests` function to kill the subprocess when it times out:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
try:
result = subprocess.run(
command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
timeout=timeout,
)
if result.returncode != 0:
all_tests_passed = False
print(f"Test {test_file} failed")
res = cleanup_test_output(result.stdout)
except subprocess.TimeoutExpired:
all_tests_passed = False
res = f"Test {test_file} timed out after {timeout} seconds."
=======
with subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) as proc:
try:
stdout, _ = proc.communicate(timeout=timeout)
if proc.returncode != 0:
all_tests_passed = False
print(f"Test {test_file} failed")
res = cleanup_test_output(stdout)
except subprocess.TimeoutExpired:
proc.kill()
proc.wait()
all_tests_passed = False
res = f"Test {test_file} timed out after {timeout} seconds."
>>>>>>> UPDATED
```
This change uses `subprocess.Popen` to create the subprocess and `communicate` to interact with it. If `communicate` times out, it raises `subprocess.TimeoutExpired`, and then we kill the subprocess with `proc.kill()`.
# Context:
USER: make a test for `cleanup_test_output`
ASSISTANT: To create a test for the `cleanup_test_output` function, we need to create a new test file in the `benchmark/tests` directory. Let's call it `test_benchmark_utils.py`.
In this new file, we will import the `cleanup_test_output` function from `benchmark.py` and write a test function using the `unittest` framework.
Here is the content for the new test file:
```python
benchmark/tests/test_benchmark_utils.py
<<<<<<< ORIGINAL
=======
import unittest
from benchmark.benchmark import cleanup_test_output
class TestCleanupTestOutput(unittest.TestCase):
def test_cleanup_test_output(self):
# Test case with timing info
output = "Ran 5 tests in 0.003s\nOK"
expected = "\nOK"
self.assertEqual(cleanup_test_output(output), expected)
# Test case without timing info
output = "OK"
expected = "OK"
self.assertEqual
^C KeyboardInterrupt
USER: no in test_benchmark.py
ASSISTANT: To add a test for the `cleanup_test_output` function in the `test_benchmark.py` file, we need to import the `cleanup_test_output` function from `benchmark.py` and write a test function using the `unittest` framework.
Here is the content for the updated test file:
```python
benchmark/tests/test_benchmark.py
<<<<<<< ORIGINAL
=======
import unittest
from benchmark.benchmark import cleanup_test_output
class TestCleanupTestOutput(unittest.TestCase):
def test_cleanup_test_output(self):
# Test case with timing info
output = "Ran 5 tests in 0.003s\nOK"
expected = "\nOK"
self.assertEqual(cleanup_test_output(output), expected)
# Test case without timing info
output = "OK"
expected = "OK"
self.assertEqual(cleanup_test_output(output), expected)
>>>>>>> UPDATED
```
Please note that you need to add the above code to the existing `test_benchmark.py` file. The `ORIGINAL` section is empty because it's a new addition to the file.
# Context:
USER: change the `functions` jsonschema so that `original_lines` is a list of strings and so is `update_files`
ASSISTANT: To make the requested changes, we need to modify the `functions` JSON schema in the `EditBlockFunctionCoder` class in the `aider/coders/editblock_func_coder.py` file.
Here is the change:
```python
aider/coders/editblock_func_coder.py
<<<<<<< ORIGINAL
properties=dict(
path=dict(
type="string",
description="Path of file to edit",
),
original_lines=dict(
type="string",
description=(
"Lines from the original file, including all"
" whitespace, newlines, without skipping any lines"
),
),
updated_lines=dict(
type="string",
description="New content to replace the `original_lines` with",
),
),
=======
properties=dict(
path=dict(
type="string",
description="Path of file to edit",
),
original_lines=dict(
type="array",
items=dict(
type="string",
),
description=(
"Lines from the original file, including all"
" whitespace, newlines, without skipping any lines"
),
),
updated_lines=dict(
type="array",
items=dict(
type="string",
),
description="New content to replace the `original_lines` with",
),
),
>>>>>>> UPDATED
```
This change modifies the `original_lines` and `updated_lines` properties to be an array of strings instead of a single string. This allows for multiple lines to be specified in the `original_lines` and `updated_lines` fields.
# Context:
USER: when calling `ChatCompletion`, take a sha1 hash of dict with all the args and append it to a list self.chat_completion_call_hashes
ASSISTANT: To implement this change, we need to modify the `send_with_retries` method in the `Coder` class in the `aider/coders/base_coder.py` file. We will import the `hashlib` module to generate the SHA1 hash. We will also add a new attribute `chat_completion_call_hashes` to the `Coder` class to store the hashes.
Here are the changes:
```python
aider/coders/base_coder.py
<<<<<<< ORIGINAL
import json
import os
import sys
import traceback
from json.decoder import JSONDecodeError
from pathlib import Path
=======
import hashlib
import json
import os
import sys
import traceback
from json.decoder import JSONDecodeError
from pathlib import Path
>>>>>>> UPDATED
```
```python
aider/coders/base_coder.py
<<<<<<< ORIGINAL
class Coder:
abs_fnames = None
repo = None
last_aider_commit_hash = None
last_asked_for_commit_time = 0
repo_map = None
functions = None
total_cost = 0.0
=======
class Coder:
abs_fnames = None
repo = None
last_aider_commit_hash = None
last_asked_for_commit_time = 0
repo_map = None
functions = None
total_cost = 0.0
chat_completion_call_hashes = []
>>>>>>> UPDATED
```
```python
aider/coders/base_coder.py
<<<<<<< ORIGINAL
def send_with_retries(self, model, messages, functions):
kwargs = dict(
model=model,
messages=messages,
temperature=0,
stream=self.stream,
)
if functions is not None:
kwargs["functions"] = self.functions
res = openai.ChatCompletion.create(**kwargs)
return res
=======
def send_with_retries(self, model, messages, functions):
kwargs = dict(
model=model,
messages=messages,
temperature=0,
stream=self.stream,
)
if functions is not None:
kwargs["functions"] = self.functions
# Generate SHA1 hash of kwargs and append it to chat_completion_call_hashes
hash_object = hashlib.sha1(json.dumps(kwargs, sort_keys=True).encode())
self.chat_completion_call_hashes.append(hash_object.hexdigest())
res = openai.ChatCompletion.create(**kwargs)
return res
>>>>>>> UPDATED
```
# Context:
USER: in `build_docker` check if the image is already built and return
ASSISTANT: To check if the Docker image is already built and return if it exists, we can use the `docker images` command and parse its output. Here's how to modify the `build_docker` function:
```python
benchmark/benchmark.py
<<<<<<< ORIGINAL
def build_docker():
command = [
"docker",
"build",
"--quiet",
"-t",
"benchmark",
"-f",
"benchmark/Dockerfile",
"/dev/null",
]
print(" ".join(command))
try:
result = subprocess.run(
command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
)
res = result.stdout
print(res)
except subprocess.CalledProcessError as e:
res = f"Failed to build Docker image: {e.output}"
return res
=======
def build_docker():
image_name = "benchmark"
check_command = ["docker", "images", "-q", image_name]
check_result = subprocess.run(check_command, stdout=subprocess.PIPE, text=True)
if check_result.stdout.strip():
print(f"Docker image '{image_name}' already exists, skipping build.")
return
command = [
"docker",
"build",
"--quiet",
"-t",
image_name,
"-f",
"benchmark/Dockerfile",
"/dev/null",
]
print(" ".join(command))
try:
result = subprocess.run(
command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
)
res = result.stdout
print(res)
except subprocess.CalledProcessError as e:
res = f"Failed to build Docker image: {e.output}"
return res
>>>>>>> UPDATED
```
This modification adds a check for the existence of the Docker image before attempting to build it. If the image already exists, the function prints a message and returns early.
