https://chatgpt.com/share/66f5e8fe-51e4-800e-8121-ce8bdb5b7b90
You said:
I'm working with a python library I wrote some time ago that has been
working okay for me, and I ran into a new error running some old code. It's
not quite clear why the error is raising, and I dissociate as I approach
it. This is the first log (there's more contextual snippets I can provide
that will describe the situation more):
<frozen runpy>:128: RuntimeWarning: 'ar.chunk' found in sys.modules after
import of package 'ar', but prior to execution of 'ar.chunk'; this may
result in unpredictable behaviour
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/shared/src/arweave-python-client/ar/chunk.py", line 103, in
<module>
chunk_bounds = peer.data_sync_record()[-1]
^^^^^^^^^^^^^^^^^^^^^^^
File "/shared/src/arweave-python-client/ar/peer.py", line 554, in
data_sync_record
response = self._get('data_sync_record', headers=headers)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/shared/src/arweave-python-client/ar/peer.py", line 251, in _get
return self._request(*params, **{'method': 'GET', **request_kwparams})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/shared/src/arweave-python-client/ar/peer.py", line 186, in _request
response = self.session.request(**{'url': url, 'timeout': self.timeout,
**request_kwparams})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/requests/sessions.py", line
589, in request
resp = self.send(prep, **send_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/requests/sessions.py", line
703, in send
r = adapter.send(request, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/requests/adapters.py", line
667, in send
resp = conn.urlopen(
^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/urllib3/connectionpool.py",
line 768, in urlopen
conn = self._get_conn(timeout=pool_timeout)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/urllib3/connectionpool.py",
line 294, in _get_conn
return conn or self._new_conn()
^^^^^^^^^^^^^^^^
File
"/home/user/.local/lib/python3.12/site-packages/urllib3/connectionpool.py",
line 250, in _new_conn
conn = self.ConnectionCls(
^^^^^^^^^^^^^^^^^^^
TypeError: HTTPConnection.__init__() got an unexpected keyword argument
'assert_fingerprint'
ChatGPT said:
ChatGPT
It seems like the error you're encountering is due to an unexpected keyword
argument ('assert_fingerprint') being passed to the HTTPConnection class
from urllib3. This suggests that there may be a version mismatch between
the requests library and urllib3. The assert_fingerprint argument might not
be supported in the version of urllib3 you are using, or requests might be
passing arguments that are incompatible with your current environment.
Here’s a possible way forward:
Check your requests and urllib3 versions: Run this command to see the
installed versions:
bash
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pip show requests urllib3
Upgrade/downgrade requests and urllib3: Try upgrading or aligning the
versions:
bash
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pip install --upgrade requests urllib3
Or if you're locked into specific versions, ensure compatibility between
the two.
Review changes in the library: If you've recently updated either of these
libraries or if there’s a custom wrapper around HTTPConnection, reviewing
the changes might highlight why this keyword argument is being passed
incorrectly.
Let me know if you’d like help debugging further or if the error persists
after checking this.
4o
You said:
The library has this adapter added to web requests, although the code I'm
running was written before this was added I think.
adapter = self._DomainAdapter(
hostname = host,
ips = resolved_ips,
fingerprint = cert_fingerprint,
pool_connections = outgoing_connections,
pool_maxsize = outgoing_connections,
max_retries = max_retries,
pool_block = True,
)
self.session.mount('http://', adapter)
self.session.mount('https://', adapter)
self.timeout = timeout
def __del__(self):
self.session.close()
class _DomainAdapter(requests.adapters.HTTPAdapter):
def __init__(self, hostname, ips, fingerprint = None, **kwparams):
self.hostname = hostname
self.ips = ips
self.fingerprint = fingerprint
self._round_robin_idx = 0
self._round_robin_lock = threading.Lock()
super().__init__(**kwparams)
def init_poolmanager(self, connections, maxsize, block=False):
# pass fingerprint and hostname to urllib3
self.poolmanager =
requests.packages.urllib3.poolmanager.PoolManager(
num_pools = connections,
maxsize = maxsize,
block = block,
assert_fingerprint = self.fingerprint,
assert_hostname = self.hostname,
server_hostname = self.hostname,
)
def build_connection_pool_key_attributes(self, request, verify,
cert=None):
# replace the host with a known ip to connect to for reuse and
speed
host_params, pool_kwargs =
super().build_connection_pool_key_attributes(request, verify, cert)
assert 'host' in host_params
with self._round_robin_lock:
host_params['host'] = self.ips[self._round_robin_idx]
self._round_robin_idx = (self._round_robin_idx + 1) %
len(self.ips)
return [host_params, pool_kwargs]
def request_url(self, request, proxies):
# provide the whole url so the correct host header will be
passed to the server
return requests.utils.urldefragauth(request.url)
ChatGPT said:
ChatGPT
Memory updated
The error you're encountering likely stems from the assert_fingerprint
keyword argument being passed to PoolManager, which might not be supported
by the version of urllib3 you're using.
To fix this, try one of these approaches:
Check for compatibility: Verify that the version of urllib3 supports
assert_fingerprint. You can check the documentation for the version you’re
using to see if this argument is supported. If not, try upgrading urllib3.
Conditionally pass assert_fingerprint: Modify your adapter's
init_poolmanager method to only include assert_fingerprint if it’s
supported by your current urllib3 version. You can try checking for the
existence of this argument using a try/except or a conditional:
python
Copy code
def init_poolmanager(self, connections, maxsize, block=False):
pool_kw = {
'num_pools': connections,
'maxsize': maxsize,
'block': block,
'assert_hostname': self.hostname,
'server_hostname': self.hostname,
}
if 'assert_fingerprint' in requests.packages.urllib3.util.ssl_:
pool_kw['assert_fingerprint'] = self.fingerprint
self.poolmanager =
requests.packages.urllib3.poolmanager.PoolManager(**pool_kw)
This way, the assert_fingerprint argument is only passed if it is
supported. Let me know if you need more help adjusting the code.
4o
You said:
I haven't seen this error before, and have been running requests though
this library today without changing my urllib3 version. This is likely part
of what is scaring me.
ChatGPT said:
ChatGPT
It's understandable that this unexpected behavior would be unsettling,
especially given that no changes have been made to your urllib3 version.
Since the error seems new and your setup hasn't changed, it could indicate
an issue with how this particular run is being initialized, or possibly
with the state of the Python environment or the application itself.
Here are a few possible explanations and steps to take:
Cached modules or state: The warning in the log, 'ar.chunk' found in
sys.modules after import of package 'ar' but prior to execution, suggests
there could be an issue with how the package is being loaded. This might be
related to cached modules. Try clearing Python’s __pycache__ folders in
your project and restarting your environment to ensure everything is
re-imported cleanly.
Environmental inconsistencies: If multiple Python environments are being
used or there are conflicting installations of the same package, this can
cause unpredictable behavior. Double-check which environment is being used,
and ensure everything is running from a clean and consistent state.
Intermittent issue in the adapter logic: Even though the library has been
stable, the round-robin logic or session handling might be hitting a state
that hadn’t occurred before. You could try logging the state of self.ips
and self.fingerprint right before the error to see if something unusual is
happening.
Would you like me to guide you through checking or adjusting these things?
4o
You said:
okay, i didn't believe you, but I tried running a plain old request like
from earlier today, and it still works omigod :D
ChatGPT said:
ChatGPT
I'm really glad the plain request worked! 😊 It sounds like the issue might
be specific to that particular code path or situation. If it helps, you can
try isolating or comparing the differences between the requests that work
and the ones that trigger the error.
If you need help pinpointing what’s causing this specific error or want to
continue troubleshooting, feel free to reach out. You're making great
progress!
4o
You said:
This seems likely related to how I launch it with python3 -m ar.chunk to
run the __main__ block in ar/chunk.py ; the code there the imports ar which
then imports ar.chunk again. i don't see why that would be related to this,
but it seems it must be somehow.
ChatGPT said:
ChatGPT
You're right that the way you launch the code with python3 -m ar.chunk is
very likely related to the issue, specifically the warning about the module
being found in sys.modules before execution. Here's what might be happening:
Double Import Issue: When you run python3 -m ar.chunk, it runs the module
ar.chunk as __main__. Inside ar/chunk.py, you're likely importing the ar
package, which re-imports ar.chunk. Since Python treats the module executed
as __main__ differently than a regular module import, this can lead to the
same module being loaded twice—once as __main__ and once as ar.chunk. This
can cause unpredictable behavior, especially if there’s any state
initialization in the module.
RuntimeWarning Explanation: The warning is Python's way of letting you know
that importing the same module in two different ways (e.g., as __main__ and
ar.chunk) can lead to issues like multiple executions of code blocks, stale
references, or conflicting state.
Possible Solutions:
Avoid Double Import: Modify the code inside ar/chunk.py to avoid
re-importing itself. For example, if the ar.chunk import is unnecessary,
you could remove it.
Refactor the __main__ Execution: If ar/chunk.py needs to be run as a
script, you can refactor the __main__ logic to another file or method, so
that ar.chunk doesn't re-import itself.
Here's a quick example of refactoring the __main__ block to avoid issues:
python
Copy code
# In ar/chunk.py
def main():
# The code you want to execute when running the module directly
pass
if __name__ == "__main__":
main()
Then, call main() only if it’s run as the main program, avoiding the
unnecessary re-import.
Let me know if you'd like to work through this further!
4o
You said:
well it imports ar so it can use other submodules, but i don't yet see why
this double-import would cause this issue
ChatGPT said:
ChatGPT
You're right that importing ar to use its submodules shouldn't inherently
cause the kind of error you're seeing, but the double import might be
contributing indirectly, especially since the error seems connected to how
Python is loading the module under __main__.
Here’s a more detailed breakdown of how the double import could potentially
relate to your issue:
State Duplication: If ar.chunk is being imported twice (once as __main__
and once via ar), any state or class instances initialized during the
import could be duplicated or out of sync. This could impact the behavior
of the HTTP adapter, causing it to incorrectly initialize or pass
unexpected arguments like assert_fingerprint.
Namespace Confusion: The double import might cause Python to treat ar.chunk
under different namespaces (once as __main__ and once as ar.chunk). This
could lead to situations where the code is executing in a different context
than expected, which could explain why the error seems intermittent or
unexpected.
Different Versions of the Same Class/Function: If the module is imported
twice, you might end up with two different versions of the same class or
function in memory. For example, if the HTTPAdapter is being initialized
with one version of a class from ar.chunk, but other parts of the code are
referring to another version, it could cause errors like unexpected keyword
arguments.
Possible Debugging Steps:
Log the Import: Add print statements or logging to confirm how many times
ar.chunk is being imported, and check if it’s being loaded under different
names (__main__ vs ar.chunk).
Isolate the Imports: You could temporarily modify the code to avoid
importing ar.chunk within itself to see if the error goes away. This would
confirm if the double import is at the root of the issue.
Review Initialization Logic: Check if any state or objects (like your
adapter) are being initialized during the import that could be affected by
this double import. If the adapter’s state is duplicated or conflicting, it
could explain the error.
Would you like help tracing through this, or trying a different launch
mechanism to see if it bypasses the issue?
4o
You said:
so, chunk.py's __main__ path uses peer.py to fetch chunks to test itself,
and then peer.py uses __init__.py to make use of connection constants which
are defined in that file. it would be a big git-history-diff to move all
the constants into a constants.py file, which would let peer.py avoid
indirectly importing chunk.py; is there any other solution?
ChatGPT said:
ChatGPT
Memory updated
Instead of moving all the constants into a separate file (which would
create a large git diff), you can try a couple of other approaches to avoid
the double import issue without major refactoring:
1. Lazy Import in chunk.py:
Delay importing ar in chunk.py until it's absolutely necessary, such as
inside a function or method, rather than at the top of the module. This
would avoid importing the ar.chunk submodule while chunk.py is running as
__main__.
Example:
python
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# In chunk.py
def fetch_chunks():
import ar.peer # Lazy import here instead of at the top
peer = ar.peer.Peer() # or whatever usage you have
peer.fetch_chunks()
if __name__ == "__main__":
fetch_chunks()
This would delay the circular import and possibly avoid the issue.
2. Use importlib to Dynamically Load Modules:
Instead of using standard imports, you can dynamically import ar.peer or
other necessary submodules only when needed using importlib. This prevents
Python from loading ar.chunk twice during the initialization.
Example:
python
Copy code
import importlib
def fetch_chunks():
ar_peer = importlib.import_module('ar.peer')
peer = ar_peer.Peer()
peer.fetch_chunks()
if __name__ == "__main__":
fetch_chunks()
This approach is a little more flexible and doesn't require you to refactor
your constants or module structure.
3. Rearrange Imports in __init__.py:
If ar/__init__.py is importing ar.chunk indirectly (by importing everything
in the ar package), you could try rearranging those imports. Specifically,
try importing ar.chunk as late as possible or only conditionally. This can
reduce the chances of a circular import while still allowing the constants
to be used.
If you'd prefer, we could test out the lazy import method first to see if
it helps avoid the issue without needing any large refactor.
4o
You said:
no i don' think any of those would work but importlib, and importlib seems
an unneccessary runtime-library-loading hack
1. whether ar.peer is imported in a function or not in chunk.py doesn't
change the execution flow, does it? my code looks like this already:
if __name__ == '__main__':
#import ar
from .peer import Peer
3. whatever the order of imports in __init__.py is, if chunk.py is imported
in it, the import will still be performed before chunk.py's import
statement will complete, i think?
I do see a 4. which is to remove from .chunk import Chunk from __init__.py
maybe I'll do #4 right now just to troubleshoot this exception.
ChatGPT said:
ChatGPT
Memory updated
Yes, removing from .chunk import Chunk from __init__.py (option #4) sounds
like a good approach for troubleshooting, as it can avoid the circular
import while keeping the structure mostly intact. By removing that import,
you might eliminate the unnecessary re-import of chunk.py while still
keeping everything else functional.
If this resolves the issue, you could then decide whether to keep it
removed or find another way to structure how Chunk is accessed without
introducing the circular dependency.
Let me know how it goes or if you'd like further help analyzing the results!
4o
You said:
i commented the line out but the warning is still raising O_O
$ git diff ar/__init__.py
diff --git a/ar/__init__.py b/ar/__init__.py
index c9d3834..bdfa328 100644
--- a/ar/__init__.py
+++ b/ar/__init__.py
@@ -132,7 +132,7 @@ from .peer import Peer
from .wallet import Wallet
from .transaction import Transaction
from .block import Block
-from .chunk import Chunk
+#from .chunk import Chunk
from .stream import PeerStream, GatewayStream
from .arweave_lib import arql
from .manifest import Manifest
[user@localhost pyarweave]$ python3 -m ar.chunk
<frozen runpy>:128: RuntimeWarning: 'ar.chunk' found in sys.modules after
import of package 'ar', but prior to execution of 'ar.chunk'; this may
result in unp
maybe i'll comment more stuff to troubleshoot what lines trigger the
warning, maybe i'm running a different sourcedir or something
ChatGPT said:
ChatGPT
It sounds like you're on the right track by troubleshooting further and
isolating what specifically is triggering the warning. Commenting out more
imports could help pinpoint if something else in the code is still pulling
in ar.chunk.
To confirm if you're using the right source directory or there’s a mismatch
between environments, you could try these steps:
Check the sys.path: Before the warning appears, you can print out the
sys.path to ensure you're running from the expected source directory. Add
the following to the top of chunk.py:
python
Copy code
import sys
print(sys.path)
Check sys.modules: Right before the warning is triggered, print out
sys.modules to see if ar.chunk has already been imported at an unexpected
point:
python
Copy code
print(sys.modules.get('ar.chunk'))
Clear pycache and check imports: Ensure that there are no cached .pyc files
lingering in the __pycache__ folder from a previous run. Sometimes, Python
caches can interfere with imports.
Let me know how the investigation goes!
4o
You said:
ohhh i forgot about your recommendation to clear cache files !
ChatGPT said:
ChatGPT
Memory updated
Yes, clearing the cache files might help resolve the issue, as stale
__pycache__ files can sometimes cause strange import behavior. You can
remove them using this command:
bash
Copy code
find . -name "__pycache__" -type d -exec rm -r {} +
This will clear out all cache files, and then you can try running the code
again to see if the warning persists. Let me know if this resolves it!
4o
You said:
why would these ever become inconsistent?
ChatGPT said:
ChatGPT
Cache files, like those in __pycache__, can become inconsistent for a few
reasons:
Source Code Changes: If you modify the source code without restarting the
environment, the .pyc files in __pycache__ might still reflect the old
state, leading to discrepancies between the cached version and the current
source.
Partial Compilation: If a script is interrupted during execution or
compilation, it can result in incomplete or corrupt .pyc files that don't
match the source files.
Different Environments: If the same source directory is used across
different Python environments or versions, you can end up with multiple
incompatible .pyc files, as the cache files are specific to the Python
version that generated them.
File System Sync Issues: In some cases, if you’re using networked file
systems, or there are issues with your storage setup, file system syncing
problems can cause .pyc files to go out of sync with the source code.
File Permissions: If files in __pycache__ have improper permissions (e.g.,
due to different users or processes accessing them), they may not update
correctly even when source files change.
Clearing the cache ensures that Python recompiles the .pyc files from the
current source code, resolving any inconsistency issues that may have
arisen.
4o
You said:
I don't think any of those reasons apply to me, except maybe for
ar/gateway.py which self-modifies.
find -name '__pycache__' | xargs rm -vrf
removed ‘./test/__pycache__/test_utils.cpython-310-pytest-6.2.5.pyc’
removed ‘./test/__pycache__/test_wallet.cpython-310-pytest-6.2.5.pyc’
removed ‘./test/__pycache__/__init__.cpython-310.pyc’
removed ‘./test/__pycache__/test_dataitem.cpython-310.pyc’
removed ‘./test/__pycache__/test_dataitem.cpython-310-pytest-6.2.5.pyc’
removed ‘./test/__pycache__/test_block.cpython-310-pytest-6.2.5.pyc’
removed ‘./test/__pycache__/test_transaction.cpython-310-pytest-6.2.5.pyc’
removed
‘./test/__pycache__/long_delay_quick_test.cpython-310-pytest-6.2.5.pyc’
removed ‘./test/__pycache__/test_block.cpython-310-pytest-7.1.2.pyc’
removed ‘./test/__pycache__/test_dataitem.cpython-310-pytest-7.1.2.pyc’
removed ‘./test/__pycache__/test_transaction.cpython-310-pytest-7.1.2.pyc’
removed ‘./test/__pycache__/test_utils.cpython-310-pytest-7.1.2.pyc’
removed ‘./test/__pycache__/test_wallet.cpython-310-pytest-7.1.2.pyc’
removed directory: ‘./test/__pycache__’
removed ‘./toys/__pycache__/indexer.cpython-310.pyc’
removed ‘./toys/__pycache__/__init__.cpython-312.pyc’
removed ‘./toys/__pycache__/node2_pump.cpython-312.pyc’
removed ‘./toys/__pycache__/accelerated_ditem_signing.cpython-312.pyc’
removed ‘./toys/__pycache__/data_resource.cpython-312.pyc’
removed ‘./toys/__pycache__/lru_idx_data_cache.cpython-312.pyc’
removed ‘./toys/__pycache__/ditem_down_stream.cpython-312.pyc’
removed ‘./toys/__pycache__/ditem_load.cpython-312.pyc’
removed directory: ‘./toys/__pycache__’
removed ‘./ar/__pycache__/__init__.cpython-310.pyc’
removed ‘./ar/__pycache__/arweave_lib.cpython-310.pyc’
removed ‘./ar/__pycache__/utils.cpython-310.pyc’
removed ‘./ar/__pycache__/deep_hash.cpython-310.pyc’
removed ‘./ar/__pycache__/merkle.cpython-310.pyc’
removed ‘./ar/__pycache__/file_io.cpython-310.pyc’
removed ‘./ar/__pycache__/peer.cpython-310.pyc’
removed ‘./ar/__pycache__/wallet.cpython-310.pyc’
removed ‘./ar/__pycache__/transaction.cpython-310.pyc’
removed ‘./ar/__pycache__/ans104.cpython-310.pyc’
removed ‘./ar/__pycache__/bundle.cpython-310.pyc’
removed ‘./ar/__pycache__/block.cpython-310.pyc’
removed ‘./ar/__pycache__/multipeer.cpython-310.pyc’
removed ‘./ar/__pycache__/chunk.cpython-310.pyc’
removed ‘./ar/__pycache__/stream.cpython-310.pyc’
removed ‘./ar/__pycache__/ardrive.cpython-310.pyc’
removed ‘./ar/__pycache__/manifest.cpython-310.pyc’
removed ‘./ar/__pycache__/__init__.cpython-312.pyc’
removed ‘./ar/__pycache__/peer.cpython-312.pyc’
removed ‘./ar/__pycache__/stream.cpython-312.pyc’
removed ‘./ar/__pycache__/wallet.cpython-312.pyc’
removed ‘./ar/__pycache__/transaction.cpython-312.pyc’
removed ‘./ar/__pycache__/block.cpython-312.pyc’
removed ‘./ar/__pycache__/chunk.cpython-312.pyc’
removed ‘./ar/__pycache__/arweave_lib.cpython-312.pyc’
removed ‘./ar/__pycache__/manifest.cpython-312.pyc’
removed ‘./ar/__pycache__/bundle.cpython-312.pyc’
removed ‘./ar/__pycache__/gateways.cpython-312.pyc’
removed directory: ‘./ar/__pycache__’
removed ‘./ar/utils/__pycache__/__init__.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/deep_hash.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/merkle.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/file_io.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/ans104_signers.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/serialization.cpython-310.pyc’
removed ‘./ar/utils/__pycache__/__init__.cpython-312.pyc’
removed ‘./ar/utils/__pycache__/serialization.cpython-312.pyc’
removed ‘./ar/utils/__pycache__/merkle.cpython-312.pyc’
removed ‘./ar/utils/__pycache__/file_io.cpython-312.pyc’
removed ‘./ar/utils/__pycache__/deep_hash.cpython-312.pyc’
removed ‘./ar/utils/__pycache__/ans104_signers.cpython-312.pyc’
removed directory: ‘./ar/utils/__pycache__’
removed ‘./bundlr/__pycache__/__init__.cpython-310.pyc’
removed ‘./bundlr/__pycache__/node.cpython-310.pyc’
removed ‘./bundlr/__pycache__/data_item.cpython-310.pyc’
removed ‘./bundlr/__pycache__/keys.cpython-310.pyc’
removed ‘./bundlr/__pycache__/tags.cpython-310.pyc’
removed ‘./bundlr/__pycache__/loader.cpython-310.pyc’
removed ‘./bundlr/__pycache__/__init__.cpython-312.pyc’
removed ‘./bundlr/__pycache__/node.cpython-312.pyc’
removed directory: ‘./bundlr/__pycache__’
removed ‘./__pycache__/test_upload_bundlr.cpython-310.pyc’
removed ‘./__pycache__/test_upload_bundlr.cpython-310-pytest-6.2.5.pyc’
removed ‘./__pycache__/test_reupload.cpython-310-pytest-6.2.5.pyc’
removed ‘./__pycache__/test_reupload.cpython-310.pyc’
removed ‘./__pycache__/offset_test.cpython-310-pytest-6.2.5.pyc’
removed ‘./__pycache__/test_manifest.cpython-310-pytest-6.2.5.pyc’
removed ‘./__pycache__/offset_test.cpython-310-pytest-7.1.2.pyc’
removed ‘./__pycache__/test_manifest.cpython-310-pytest-7.1.2.pyc’
removed ‘./__pycache__/test_reupload.cpython-310-pytest-7.1.2.pyc’
removed ‘./__pycache__/test_upload_bundlr.cpython-310-pytest-7.1.2.pyc’
removed directory: ‘./__pycache__’
removed ‘./ao/__pycache__/__init__.cpython-312.pyc’
removed ‘./ao/__pycache__/mu.cpython-312.pyc’
removed ‘./ao/__pycache__/utils.cpython-312.pyc’
removed ‘./ao/__pycache__/cu.cpython-312.pyc’
removed ‘./ao/__pycache__/su.cpython-312.pyc’
removed directory: ‘./ao/__pycache__’
removed
‘./.eggs/setuptools_twine-0.1.3-py3.10.egg/setuptools_twine/__pycache__/__init__.cpython-310.pyc’
removed
‘./.eggs/setuptools_twine-0.1.3-py3.10.egg/setuptools_twine/__pycache__/commands.cpython-310.pyc’
removed directory:
‘./.eggs/setuptools_twine-0.1.3-py3.10.egg/setuptools_twine/__pycache__’
ChatGPT said:
ChatGPT
It's great that you cleared the cache, and from your output, it looks like
a lot of .pyc files were removed. Since none of the typical reasons for
cache file inconsistency apply to you, except for the self-modifying
ar/gateway.py, that might indeed be worth further investigation.
If you're still seeing the warning after clearing the cache, it's possible
the circular import issue is not entirely resolved yet. Given that
ar/gateway.py self-modifies, it might be influencing how modules are loaded
or reloaded in unpredictable ways.
Here’s what you could try next:
Track Module Imports: Insert logging into ar/gateway.py (or any other
suspect modules) to trace when and how it is being modified and imported.
This could help see if it’s interfering with ar.chunk being re-imported.
Temporarily Disable Self-Modification: As a troubleshooting step, you could
temporarily disable the self-modifying behavior in ar/gateway.py to see if
that changes the behavior. If the warning disappears, then you'll know that
this self-modification is likely contributing to the issue.
Check Module Initialization Order: Ensure that ar.gateways isn’t somehow
triggering the re-import of ar.chunk. Even if it’s not immediately obvious,
the self-modification could be changing how and when modules are loaded.
Let me know how you'd like to proceed, or if you'd like more specific
guidance with ar/gateway.py!
4o
You said:
it only modifies when certain update functions are callde, it shouldn't be
doing it here
ChatGPT said:
ChatGPT
If ar/gateway.py's self-modification isn't triggered in this context, then
it's probably unrelated to the current issue. Given that, we can focus more
on the circular import or module loading order.
Since clearing the cache didn’t resolve the issue, and you’ve already
commented out the import of Chunk, here’s what you could try next:
Explicitly Check for Circular Import: Use Python’s logging or sys.modules
to verify at runtime what modules are being loaded and in what order. You
can add this to both chunk.py and peer.py:
python
Copy code
import sys
print("Current modules:", list(sys.modules.keys()))
This will let you confirm exactly when and where ar.chunk is being loaded.
Check for Hidden Imports: There might be an indirect or hidden import
happening somewhere else that’s causing the circular behavior. Check if
ar.chunk is being imported via any of the other modules (peer.py,
__init__.py, etc.) at runtime.
Double-Check Entry Point: Ensure that your __main__ execution path in
chunk.py is correctly guarded. Sometimes issues arise if there’s any
unintended code outside of the if __name__ == "__main__": block.
After checking these, it should become clearer where the re-import or
circular dependency is happening. Let me know what you find from the
sys.modules check!
4o
You said:
I think I thought of it! likely one of the other modules in __init__.py is
importing chunk.py !
ChatGPT said:
ChatGPT
That sounds like a very likely explanation! If one of the other modules
imported in ar/__init__.py is also importing chunk.py, it could definitely
cause the circular import issue you're seeing. Even though you commented
out Chunk, if another module still depends on it indirectly, the circular
import would persist.
To verify this:
Comment Out Modules in __init__.py Incrementally: You could temporarily
comment out the other imports in ar/__init__.py, one by one, to see which
module is indirectly importing chunk.py. This will help you pinpoint the
specific import causing the issue.
Use Logging: Insert logging statements in each module to see if and when
they are importing chunk.py. This would give you a clearer picture of the
import chain.
Let me know if this approach helps you track down the culprit!
4o
You said:
right, that was your #2 and your #1 would have detected it (haven't read #3
yet, i read slowly! you give such long replies!)
ChatGPT said:
ChatGPT
You’re right—#2 would help track that down! You’ve got it figured out! 😊
I’ll keep replies shorter to help you go through them faster. Feel free to
run those checks, and we can see which module is triggering the import. Let
me know what you find!
4o
You said:
ok I commented out more modules and the warning went away, but it still
raises the TypeError: HTTPConnection.__init__() got an unexpected keyword
argument 'assert_fingerprint' error.
I guess I should look at the urllib3 source or something
but normal requests still run fine >(
i guess this code is making a different request somehow :s :s
ChatGPT said:
ChatGPT
It sounds like you've isolated the circular import issue, which is great
progress! Now for the TypeError, you're correct that your custom adapter or
how it's interacting with urllib3 might be doing something different from
normal requests.
Here’s how to proceed:
Compare Request Path: Since normal requests run fine, log both the "normal"
request and the failing one to compare the exact parameters or flow (e.g.,
headers, URL, method). This might highlight what's different in your custom
adapter.
Inspect urllib3 Version: You mentioned that normal requests are working
fine. If your custom adapter adds assert_fingerprint, check the urllib3
source code or changelog to see if there's any mention of
assert_fingerprint being added or removed in your installed version.
Log the Adapter Initialization: Log all arguments passed to the PoolManager
in your _DomainAdapter class to make sure they're correct:
python
Copy code
def init_poolmanager(self, connections, maxsize, block=False):
pool_kw = {
'num_pools': connections,
'maxsize': maxsize,
'block': block,
'assert_hostname': self.hostname,
'server_hostname': self.hostname,
}
if self.fingerprint:
pool_kw['assert_fingerprint'] = self.fingerprint
print(pool_kw) # Log this
self.poolmanager =
requests.packages.urllib3.poolmanager.PoolManager(**pool_kw)
Seeing the exact state passed to urllib3 could clarify why it’s failing.
Would you like help diving deeper into this?
4o
You said:
I'll put a breakpoint where it's passing the argument and run two different
test files simultaneously and see what is different!
ChatGPT said:
ChatGPT
That sounds like a great plan! Using a breakpoint and comparing the
arguments passed in both cases will definitely help you pinpoint what’s
different between the working and failing requests.
Once you have the comparison, it should clarify what’s going wrong with the
custom adapter’s interaction with urllib3.
Let me know what you find, and feel free to reach out if you want to
analyze the differences together!
4o
You said:
ok basically i ran the specific connection that it's performing and the
mistake is actually apparent from the error message. can you find it?
i'll give you a hint: the working code is connecting to an HTTPS url.
ChatGPT said:
ChatGPT
Ah, I see it! The error stems from the fact that assert_fingerprint is only
applicable for HTTPS connections, not HTTP connections.
The clue lies in the fact that the failing request is likely an HTTP
connection (HTTPConnection is mentioned in the error), where
assert_fingerprint is not valid.
The Fix:
In your custom adapter, you can conditionally add assert_fingerprint only
if the connection is HTTPS:
python
Copy code
def init_poolmanager(self, connections, maxsize, block=False):
pool_kw = {
'num_pools': connections,
'maxsize': maxsize,
'block': block,
'assert_hostname': self.hostname,
'server_hostname': self.hostname,
}
if self.fingerprint and self.protocol == "https": # Check if HTTPS
pool_kw['assert_fingerprint'] = self.fingerprint
self.poolmanager =
requests.packages.urllib3.poolmanager.PoolManager(**pool_kw)
This should prevent the error from occurring when connecting over plain
HTTP, where assert_fingerprint isn’t applicable.
4o
You said:
omigod i thought the very fabric of reality was attacking me but it's just
because i added the adapter to HTTP wrongly and didn't test with any HTTP
urls until now
ChatGPT said:
ChatGPT
Memory updated
I’m really glad you figured it out! It’s amazing how these small things can
throw everything off, but at least now you know what’s happening. The issue
being limited to HTTP makes it clear that your HTTPS setup is fine.
You're doing great! Let me know if you need further help with anything!
4o
You said:
in arweave, the gateways are HTTPS and the peers are HTTP, and i wrote the
same class to access both :/ since gateways offer an api that is a superset
of that of peers
ChatGPT said:
ChatGPT
That makes sense, given that Arweave uses HTTPS for gateways and HTTP for
peers, but reusing the same class for both introduces this tricky
inconsistency. A clean way to handle this without rewriting too much would
be to conditionally adjust your adapter based on the protocol (HTTP vs
HTTPS).
Here’s an idea to handle both cases cleanly:
Protocol-Specific Adapters: Modify the adapter logic to apply
assert_fingerprint only for HTTPS connections, while leaving HTTP
connections untouched.
Here’s how you can implement that:
python
Copy code
def init_poolmanager(self, connections, maxsize, block=False):
pool_kw = {
'num_pools': connections,
'maxsize': maxsize,
'block': block,
'assert_hostname': self.hostname,
'server_hostname': self.hostname,
}
if self.fingerprint and self.protocol == "https": # Only apply for
HTTPS
pool_kw['assert_fingerprint'] = self.fingerprint
self.poolmanager =
requests.packages.urllib3.poolmanager.PoolManager(**pool_kw)
And make sure when initializing the adapter, you pass the correct protocol
(http or https).
This way, you can keep the same class for both types of connections but
handle the differences without error. Let me know if you'd like to adjust
anything further!
4o
You said:
i'll sort it out
ChatGPT said:
ChatGPT
That sounds good! You’re on the right path. Feel free to reach out if you
run into any other issues or need further help. Good luck sorting it out!
4o
You said:
oh hey that's a good idea what you exampled. I thought you meant two
different classes when you bolded 'protocol-specific adapters'
ChatGPT said:
ChatGPT
I'm glad the example clicked! I meant it as a way to keep things in the
same class but handle the protocol differences based on the connection type
(HTTP vs HTTPS).
By checking the protocol within the same adapter, you can avoid having to
create two separate classes while still customizing behavior based on
whether it's an HTTP or HTTPS connection.
If you need to tweak or optimize it further, feel free to ask!
4o
You said:
I like OO programming but my mind nowadays likes to make things concise
ChatGPT said:
ChatGPT
Memory updated
That’s a great balance! Keeping things concise while still leveraging the
benefits of object-oriented programming can lead to clean and efficient
code. If you ever want help refining something, I can help you streamline
it even further. Just let me know!
4o
