[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: Attachable flag brakes compatibility mode and requires local swarm cluster Hello; When using compatibility mode, if there is an attachable flag declared in networks It is expected to run as a regular docker-compose stack. Example as follows: ``` networks: foonetwork: - driver: overlay - attachable: true ``` However, this config asks for local swarm cluster. When I run the ```docker-compose --compatibility config``` command, I can still see the ```attachable: true``` flag, which requires a version 3.2v compose file. It seems like a conversion bug in ```docker-compose config``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: "conan install .. " broken for Wrong package recipe reference (1.12.0) - [x] I've specified the Conan version, operating system version and any tool that can be relevant. - [x] I've explained the steps to reproduce the error or the motivation/use case of the question/suggestion. If a launch 'conan install ..' in my build director, where .. has a conanfile.py file. I got an exeption "Wrong package recipe reference". I have add a display and it seems that conan tries to parse ".." as a recipe. the same happens if I use "conan install ../conanfile.py" Regards, Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: With RTC enabled, large notebooks to fail on save and whole UI becomes extremely sluggish. ## Description When Lab is started with `--collaborative`, certain large notebooks fail to save or take inordinately long to do so. ## Reproduce [This Gist](https://gist.github.com/06cc8cab195a7ebaa97bf42ff77dd141.git) contains a notebook that illustrates the problem. To reproduce, clone the gist and run side-by-side JupyterLab with and without `--collaborative`. I tested on a Mac Mini with Big Sur, Python 3.9, 64GB of RAM and JupyterLab 3.1.10. The version with `--collaborative` on will either not save at all, or take easily 10 minutes to do a save. In addition, the whole UI gets very sluggish, and the browser occasionally pops warnings about the page consuming too many resources (seen on Firefox, Chrome and Safari). ## Additional context We spent some time tracking down this problem [over at the Jupyter meets the Earth repo](https://github.com/pangeo-data/jupyter-earth/issues/78). Flagging that thread for reference, but all relevant info is here. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: Make classifier backbone dynamic maskformer, mask2former ### Feature request Make classifier backbone dynamic maskformer, mask2former. Currently it only supports swin transformer. link example: https://github.com/huggingface/transformers/blob/main/src/transformers/models/mask2former/modeling_mask2former.py#L1393 ### Motivation If this feature request successful, it will help to benchmark for new clasifier easily. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Promote 'TimestampWithNanos' from 'spanner' to 'api_core' It would be useful for other services as they begin to render nanosecond-resolution timestamps. See: #4807 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the PrefectHQ/prefect codebase. Here's the problem: ResultHandlers should be serializable to JSON From PIN2 (https://github.com/PrefectHQ/prefect/pull/570): In order to attach information to `state.metadata` about how each attribute should be handled, all `ResultHandler`s need to be serializable with an associated versioned schema. Currently there are only three result handlers - Local, Cloud, and GCS but soon there will be more. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: Converting masked array from float to uint8 does not convert dtype of fill_value When a masked array of dtype float32 is converted to uint8, the dtype of the fill_value persists. When setting the fill_value to a uint8 value, the value is converted to float. Filling such an array yields an array of dtype object. This was working in numpy==1.14. ### Reproducing code example: <!-- A short code example that reproduces the problem/missing feature. It should be self-contained, i.e., possible to run as-is via 'python myproblem.py' --> ```python import numpy as np foo = np.ma.masked_equal(np.arange(10, dtype="float32"), 5) foo.fill_value = -1.0 bar = foo.astype("uint8") print(bar.fill_value) bar.fill_value = 255 print(bar.fill_value) print(bar.filled().dtype) ``` ### Error message: with numpy==1.14.2 this yields: ``` 255 255 uint8 ``` with numpy==1.15.2 this yields: ``` -1.0 255.0 object ``` ### Numpy/Python version information: Tested with python 2.7 and 3.5.2 numpy==1.15 <!-- Output from 'import sys, numpy; print(numpy.__version__, sys.version)' --> Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: Option to suppress progress There should be an option that still shows the build steps, but not the actual progress bars which spray a huge amount of output on CI servers since the docker commands expect that your using a terminal that has the control codes to write over previous lines. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the google/jax codebase. Here's the problem: forward-mode eigh derivatives return NaN's First off, thanks for developing JAX, it's awesome! I recently noticed that when one computes higher order derivatives of expressions involving eigenvectors obtained from `jax.numpy.eigh` or `jax.scipy.eigh`, NaNs are returned. However, it only appears to be a problem with forward-mode autodiff. This is unfortunate, since forward-mode tends to be more efficient for higher order derivatives. A minimal example: ```python import jax import jax.numpy as np # Create a random symmetric matrix seed = jax.random.PRNGKey(0) tmp = jax.random.uniform(seed, (2,2)) a = np.dot(tmp, tmp.T) def test(inp): val, vec = np.linalg.eigh(inp) return np.dot(np.dot(vec, inp), vec.T) def test_deriv(func, inp): grad_func = jax.jacfwd(func) hess_func = jax.jacfwd(grad_func) cube_func = jax.jacfwd(hess_func) # This derivative returns NaN, but jax.jacrev works! print(grad_func(inp)) print(hess_func(inp)) print(cube_func(inp)) test_deriv(test, a) ``` Additional notes: - Expressions involving eigenvalues do not appear to have this issue - Any array contractions involving eigenvectors and other arrays (`dot`, `einsum`, etc) also exhibit similar behavior: `jacfwd` fails, while `jacrev` works. - I'm using CPU - JAX version 0.1.46 Thanks for your time! Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: MismatchCAPIWarning during build Something we might want to deal with before 1.16: > numpy\core\setup.py:400: MismatchCAPIWarning: API mismatch detected, the C API version numbers have to be updated. Current C api version is 13, with checksum 5b0e8bbded00b166125974fc71e80a33, but recorded checksum for C API version 13 in codegen_dir/cversions.txt is a1bc756c5782853ec2e3616cf66869d8. If functions were added in the C API, you have to update C_API_VERSION in numpy\core\setup_common.py. The build continues despite this warning, so it's possible we just didn't notice it. This is likely my fault as part of #8955 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: improve deprecation warnings in IPython/terminal/interactiveshell.py warn on import, and add version since deprecation. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: IPython 5 shell does not react to SIGQUIT (CTRL + \) In previous IPython versions it was possible to terminate an IPython session quickly by sending an `SIGQUIT`, i.e., by pressing CTRL+ \ . This is useful when having an `embed()` in a loop with a large number of iterations. Pressing CTRL+d followed by y is not practical. Since `%kill_embedded` is currently also broken there is no convenient way to terminate the process. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: Invalid formatting of libdir/includedir in pkg_config generator [bug] Version: conan-1.2.3 OS: Linux https://github.com/conan-io/conan/blob/ac25c8994878706bac23efd009ae74bc294c1add/conans/client/generators/pkg_config.py#L28-L36 `pkg_config` generator doesn't check if libdir or includedir are absolute paths which leads to the following (invalid) `.pc` being generated for `boost_math/1.66.0@bincrafters/stable`: ``` prefix=/home/user/.conan/data/boost_math/1.66.0/bincrafters/stable/package/937af278b1c9d86290dddaf1322dc99fb7d75862 libdir=${prefix}//home/user/.conan/data/boost_level8group/1.66.0/bincrafters/stable/package/afc149253b16bde7d64e527e0dbfa27b203330c8/math/lib includedir=${prefix}//home/user/.conan/data/boost_level8group/1.66.0/bincrafters/stable/package/afc149253b16bde7d64e527e0dbfa27b203330c8/math/include ... ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: [feature request] a tool to clone existing models to make new models with small changes # 🚀 Feature request So we have great templates for creating a new model. ### Can you think of a way to create full clones of existing models? Practically for BigScience needs we will have to create something like GPTMeg which is 99.9% identical to GPT2 with 2-3 tiny changes. And then we will need another GPT2 variant that replaces Positional Embeddings with ALiBi. And there will be more variants. Using templates would be quite expensive, when always everything is really identical. So ideally a user will do: ``` transformers-clone-model GPT2 GPTMeg ``` and voila it'd replicate model's files, tests and docs. If all source files could be easily identified this perhaps could be done in a few perl one liners. Here is a very rough outline: 1. find the pertinent source files grep -Irl GPT2 . 2. rename files/dirs while copying s/gpt2/gpt_meg/ 3. rename internals to s/GPT2/GPTMeg/g The hard to automate part is the index files as they is only one of each I think I can work it out, but I'm afraid that the end result would be a set of Perl one-liners only Stas will know what to do with. So perhaps long term this is not a good solution. Here is the Issue where we need to implement this: https://github.com/bigscience-workshop/Megatron-DeepSpeed/issues/138 and 2 more will be coming soon. @LysandreJik, @patrickvonplaten, @sgugger Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: [bug] Error when using default_options as tuple with equals sign (=) in value To help us debug your issue please explain: - [x] I've read the [CONTRIBUTING guide](https://raw.githubusercontent.com/conan-io/conan/develop/.github/CONTRIBUTING.md). - [x] I've specified the Conan version, operating system version and any tool that can be relevant. - [x] I've explained the steps to reproduce the error or the motivation/use case of the question/suggestion. Similar to #1296, specifying a tuple for `default_options` with an option that has an `=` in the value part fails. ```python from conans import ConanFile class ReproConan(ConanFile): name = "repro" version = "0.1.0" options = {"opt": "ANY"} default_options = ("opt=a=b",) ``` ``` $ conan install . ERROR: /path/to/conanfile.py: Error while initializing options. too many values to unpack (expected 2) ``` This happens on latest on the `develop` branch. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Validate input when setting the QuantumCircuit data attribute directly Similar to #2170 , but for `QuantumCircuit`. The possibility of arbitrary data in `circuit.data` makes modifying or replacing the internals of `QuantumCircuit` difficult and error prone (see discussion around `circuit._append` in #2824 ). We should: - Move `circuit.data` to `circuit._data` - Add a getter for `circuit.data` and document it as a ordered list of `(Instruction, qargs, cargs)`. It's an open question whether or not we should commit to preserving user-insertion order. - Add a setter for `circuit.data` with a `DeprecationWarning`, directing users to e.g. `QuantumCircuit.append` and the rest of the circuit API. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the dagster-io/dagster codebase. Here's the problem: Accept list of AssetsDefinitions directly on repo I've seen a few users get tripped up on this (including myself) Right now you can do this: ``` assets: Sequence[AssetsDefinition, SourceAsset] = ... @repository def repo(): return [*assets] ``` but this fails: ``` assets: Sequence[AssetsDefinition, SourceAsset] = ... @repository def repo(): return [assets] ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: PubSub: Got unbound method error when calling the class method from_service_account_json I am using the service account for authentication. By following the example in [here](https://googlecloudplatform.github.io/google-cloud-python/latest/core/auth.html#service-accounts) , I create the following code: ``` from google.cloud import pubsub logging.debug(dir(pubsub.PublisherClient)) pubsub_publisher_client = pubsub.PublisherClient.from_service_account_json(key_path) ``` The output: ``` DEBUG:root:['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'batch', 'create_topic', 'delete_topic', 'from_service_account_file', 'from_service_account_json', 'get_iam_policy', 'get_topic', 'list_topic_subscriptions', 'list_topics', 'project_path', 'publish', 'set_iam_policy', 'target', 'test_iam_permissions', 'topic_path', 'update_topic'] Traceback (most recent call last): File "main.py", line 81, in <module> pubsub_publisher_client = pubsub.PublisherClient.from_service_account_json(key_path) TypeError: unbound method from_service_account_file() must be called with PublisherClient instance as first argument (got str instance instead) ``` However, when I use similar code to create datastore client with the service account JSON file, it works. E.g. ``` from google.cloud import datastore datastore_client = datastore.Client.from_service_account_json(key_path) ``` After checking the source code, perhaps the decorator in `pubsub/google/cloud pubsub_v1/_gapic.py` failed to create the class method properly. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: Add FLAN-UL2 ### Model description UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. FLAN-UL2 has the same configuration as the original UL2 20B model, except that it has been instruction tuned with Flan. ### Open source status - [X] The model implementation is available - [X] The model weights are available ### Provide useful links for the implementation The model architecture (UL2) is already in Huggingface Transformers. The 20B model weights are here: https://github.com/google-research/google-research/tree/master/ul2#checkpoints Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: distutils/misc_util.py Configuration settings lead to ImportError DLL load failed When building NumPy from the source on Windows 10, the resulting `.libs` folder is not added to the path. ### Reproducing code example: The regular ```bash python setup.py build python setup.py install ``` machinery succeeds however afterwards numpy import fails with the recently reported `ImportError`s elsewhere, say, #14896 and others. This happens also on SciPy builds which were successfully working until I switched to Python 3.8. This is also reported on SciPy https://github.com/scipy/scipy/issues/11062 It seems like the generated `__config__.py` has the lines https://github.com/numpy/numpy/blob/6e75b9ab7888e2d807893a8237811f97f605547e/numpy/distutils/misc_util.py#L2324-L2335 Somehow, .libs folder is not present on the `src.win-amd64-3.8` but it is created in `lib.win-amd64-3.8` and for reasons I have not yet understood, this if branch is not entered. On the linked SciPy issue, the file is modified manually to add the libs to the path. Hence I am under the impression that it is not necessarily an Anaconda issue but something has changed along the way, that is maybe, Python3.8 related. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: Two link objects with name conflicts Right now we have two things called `link`: - One in `traitlets` that links attributes of any `HasTraits` subclass. - One in `widgets` that does a JS based link of any `Widget` subclass. Their API is nearly identical and it is very common to import both at the same time. I spent about 2 hours tracking down a super subtle bug because I had both imported and didn't realize which one I was using. I think we should consider naming the JS one to `jslink`. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: 720x runtime performance regression in `QuantumCircuit.depth()` <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Information - **Qiskit Terra version**: main (after 7c30a6e7) - **Python version**: 3.8 - **Operating system**: Linux ### What is the current behavior? The nightly benchmark runs flagged a regression in the QUEKO transpiler benchmarks caused by https://github.com/Qiskit/qiskit-terra/pull/6998: https://qiskit.github.io/qiskit/#queko.QUEKOTranspilerBench.time_transpile_bigd?cpu=Intel(R)%20Xeon(R)%20E-2174G%20CPU%20%40%203.80GHz&machine=qiskit-benchmarking01&num_cpu=8&os=Ubuntu%2020.04&ram=64GB&python=3.8&p-optimization%20level=0&p-routing%2Flayout%20method=None&commits=7c30a6e7 It is showing a 720x runtime performance slowdown (from 35.824ms to 25.810s). At first I was surprised because a transpiler benchmark shouldn't be using `QuantumCircuit.depth()` but looking at the benchmark code calling `.depth()` on the output circuit is part of the timed function: https://github.com/Qiskit/qiskit/blob/master/test/benchmarks/queko.py#L235 This is arguably a benchmark bug, but it did catch the regression in this case so it's useful for that. The regression wasn't quite as bad on other variants so my guess is the regression scales with number of instructions in the circuit (probably because the filter function runs over circuit.data). ### Steps to reproduce the problem Run `QuantumCircuit.depth()` ### What is the expected behavior? depth() doesn't scale so poorly Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: Plan for Extension Handling After several attempts, we have finally come to the conclusion that it is not feasible to handle extensions without using node to build the assets on the server. Our latest attempt, #1973, was to use a WebPack extension to build individual bundles, and then assemble the bundles on the server, using semver to deduplicate packages. What we soon realized is it is impossible to know for sure which modules from an upstream npm package will be consumed, so we cannot effectively deduplicate packages at the package level without having the entire package. The proposed solution is to bundle the entire `node_modules` tree with an extension, and then merge the trees and build the final WebPack bundle on the server. We rely on `npm` to deduplicate the packages in their entirety, and WebPack to bundle the assets. Note that the above are only required to add new extensions to the system. Bundles can be distributed that contain a preconfigured set of extensions, and the user can be given the opportunity to enable or disable individual extensions in the bundle (cf #1523). The requirement to have `node` installed on the user's machine can be alleviated by using the `node` package from `conda`, or we could use a standalone node executable builder such as [nexe](https://github.com/nexe/nexe) that could be distributed as a Python wheel. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: Regression in Ninja link flags generation in 0.52.0 Meson version: **0.52.0** OS: **Linux** Compiler toolchain: **LLVM/Clang** I believe the change introduced in 0.52.0 wrt link flag generation caused a regression. Not sure what the minimal working example would look like but in my case the error appears when using Boost with `BOOST_ROOT` defined. After successfully running `meson` followed by `ninja` the linker errors out with: ``` [2/2] Linking target test_app. FAILED: test_app clang++ -o test_app ... /opt/boost/lib -L ... ld.lld: error: cannot open /opt/boost/lib: Is a directory ``` Ninja generator puts `-L` after the path instead of the other way around. I've found a probable cause: In 0.51.2: https://github.com/mesonbuild/meson/blob/6857936c592d6f9608add5a74a51ee405aaddc0d/mesonbuild/compilers/clike.py#L270-L271 In 0.52.0: https://github.com/mesonbuild/meson/blob/2779603c689c6347809255a5aa3bd59b46b18b81/mesonbuild/linkers.py#L414-L415 Here: https://github.com/mesonbuild/meson/blob/ba1367b588388ebe9e37057cee9639e2ea2046fb/mesonbuild/backend/ninjabackend.py#L2517 `extend_preserving_lflags` receives an argument that looks like this: ```python # In 0.52.0 linker.get_dependency_link_args(dep) # ['-L', '/boost/root/lib'] # In 0.51.2 dep.get_link_args() # ['-L/boost/root/lib'] ``` And `extend_preserving_lflags` reorders the args here: https://github.com/mesonbuild/meson/blob/ba1367b588388ebe9e37057cee9639e2ea2046fb/mesonbuild/compilers/compilers.py#L589-L598 `-L` goes into `lflags` and `/opt/boost/lib` goes into `normal_flags` then `normal_flags` gets appended first resulting in `[ ..., '/opt/boost/lib', '-L', ...]`. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Add missing generation arguments for bucket and blob methods. Allow setting generation for getting and deleting blobs, and respect generation for rewriting. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: #RREV in conanfile.txt Hello! I try to pin revision for some recipes in conanfile.txt. Conan 1.16.1 What i do: I've added hashtag and number of revision like https://docs.conan.io/en/latest/mastering/revisions.html#package-revisions. [requires] gtest/1.8.1@bincrafters/stable#0 But # - is a comment symbol and "#0" is ignored by Conan. Help please and tell me what i do wrong. PS: on command line it is works normal. conan install gtest/1.8.1@bincrafters/stable#0 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: GPT2TokenizerFast raises pad_token error even if not used # 🐛 Bug ## Information Model: GPT2 Language: English Encoding with the `GPT2TokenizerFast` causes a `pad_token` error to be sent to stderr, despite not attempting to access that property. ## To reproduce ```import transformers from transformers import GPT2TokenizerFast tokenizer = GPT2TokenizerFast.from_pretrained('gpt2') res = tokenizer.encode("This is a sentence") print(transformers.__version__) ``` Output: ``` Using pad_token, but it is not set yet. 2.11.0 ``` ## Expected behavior I'm aware that GPT-2 doesn't include a pad token (#2630) - I haven't tried to use it. I would expect no error to be displayed until I try to access that property. ## Environment info - `transformers` version: 2.11.0 - Platform: Ubuntu - Python version: 3.6.9 - PyTorch version (GPU?): 1.5.0 with CUDA - Tensorflow version (GPU?): n/a - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: Avoid the Windows file lock An idea to avoid the file lock is to move files (which I believe Windows allows) that can't be deleted into some trash directory, which is deleted on future runs of conda. We could do this automatically on a file-by-file basis. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: Labextensions not working on starting notebook Hi, So when I start jupyter lab with `jupyter lab` , labextensions seem to work perfectly fine, but they don't work when I start notebook : `jupyter notebook` and redirect to /lab. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: conda should exec to non-conda subcommands, not subprocess Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Using the rzz gate yields an error on circuit drawing <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Informations - **Qiskit Terra version**: qiskit version 0.7.0 - **Python version**: python 3.6.6 - **Operating system**: Red Hat Entreprise Server 7.4 ### Current behavior At circuit draw I get the following error ```bash Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.6/site-packages/qiskit/circuit/quantumcircuit.py", line 83, in __str__ return str(self.draw(output='text')) File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 413, in __str__ return self.single_string() File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 438, in single_string return "\n".join(self.lines()) File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 472, in lines layers = self.build_layers() File "/usr/local/lib/python3.6/site-packages/qiskit/tools/visualization/_text.py", line 758, in build_layers elif len(instruction['qubits']) >= 2 and not instruction['cargs']: KeyError: 'qubits' ``` ### Steps to reproduce the problem ```python from qiskit import * q = QuantumRegister(2) qc = QuantumCircuit(q); qc.rzz(0, q[0], q[1]) print(qc) ``` ### What is the expected behavior? It should draw the circuit with no problem, but here it gives a KeyError on "qubits" ### Suggested solutions Maybe it is expecting "qargs" instead of "qubits"? Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Datastore query offsets are not working as it supposed to This is a weird behaviour when setting the offset parameter so I have 9825 entities and ```python len(list(client.query(kind="Foo").fetch())) ``` Will yield 9825, but ```python len(list(client.query(kind="Foo").fetch(offset=1))) ``` will yield 9808 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: try falling back to pip-vendored requests if no requests available Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the google/jax codebase. Here's the problem: Open source tests We'd like contributors to be able to make sure their code isn't breaking anything, and that they add tests that cover their own contributions. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: barrier allows qubit reordering <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Information - **Qiskit Terra version**: 0.7.0 - **Python version**: 3.7.1 - **Operating system**: Windows 10 ### What is the current behavior? An inserted barrier allows system to reorder, or act the circuit on different qubits than those declared. ### Steps to reproduce the problem Even without cnots to avoid the possibility of mapping issues, place single qubit rotations on n qubits, then a barrier, then a final single qubit rotation on an (n+1)th qubit. ### What is the expected behavior? Barrier is expected to keep gates on either side of the barrier, not allowing a compiler to modify ordering across this point of the circuit. Barrier is expected to play no role in determining which qubits the circuit is acted upon. ### Suggested solutions Clarify definition of barrier in optimization software for hardware implementation Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Deepcopy of control flow with standalone clbit results in invalid circuit ### Environment - **Qiskit Terra version**: 0.24.1 (but also on main) - **Python version**: 3.11 - **Operating system**: Linux ### What is happening? When running a deepcopy of a circuit object using a clbit as a standalone bit as the condition for a control flow operation results in the deepcopied circuit being invalid so that qasm3 or qpy serialization, and visualization will fail because the internal state of the circuit is corrupted and not valid in the copy. ### How can we reproduce the issue? ```python import copy from qiskit.circuit import QuantumCircuit, Qubit, Clbit, ClassicalRegister, QuantumRegister, Gate, Parameter bits = [Qubit(), Qubit(), Clbit()] qc = QuantumCircuit(bits) with qc.if_test((qc.clbits[0], 1)): qc.x(0) copy_qc = copy.deepcopy(qc) print(copy_qc) ``` ### What should happen? It should print the visualization of the circuit. This will stack trace though during the circuit to dag conversion. You can also replace `print(copy_qc)` with something like: ``` import io from qiskit.qpy import dump with io.BytesIO() as fd: dump(copy_qc, fd) ``` this will also fail on a lookup of the clbit. ### Any suggestions? I think the special `__deepcopy__` handling in the `qiskit.circuit.Instruction` class is not valid for the `ControlFlowOp` when the `.condition` attribute is a clbit. It might just be simplest to revert to the default deepcopy behavior for `ControlFlowOp`. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Lightning-AI/lightning codebase. Here's the problem: [test] Add hydra tests Use hydra in one of the plain lightning tests. omri yadan can review. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: conda run is not preserving exit code ## Current Behavior `conda run` used to return the correct exit status of the target process, but it doesn't anymore. This was apparently working correctly in conda 4.8.0 but it seems broken since version 4.8.1. `conda run` now erroneously always return the exit status of 0. This is a serious problem as jobs fail silently now. ### Steps to Reproduce ``` $ conda -V conda 4.8.1 $ conda run -n myenv /usr/bin/true; echo $? 0 $ conda run -n myenv /usr/bin/false; echo $? ERROR conda.cli.main_run:execute(30): Subprocess for 'conda run ['/usr/bin/false']' command failed. (See above for error) 0 ``` ## Expected Behavior `conda run` should preserve the exit status of the target process, whether the target is `python` or otherwise. It worked correctly with the previous version: ``` $ conda -V conda 4.8.0 $ conda run -n base /usr/bin/true; echo $? 0 $ conda run -n base /usr/bin/false; echo $? ERROR conda.cli.main_run:execute(39): Subprocess for 'conda run ['/usr/bin/false']' command failed. Stderr was: 1 ``` ## Environment Information This issue really is independent of the environment as it happens both in various dockerfiles and on MacOS too. --- @msarahan Please fix this bug considering I see a commit from 25 days that may have introduced it! Adding unit tests would also be wise. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Alternate example for plot_bloch_multivector ### Environment - **Qiskit Terra version**: 0.19.1 ### What is happening? The example for the plot_bloch_multivector doc looks broken - https://qiskit.org/documentation/stubs/qiskit.visualization.plot_bloch_multivector.html#qiskit.visualization.plot_bloch_multivector The example is trying to visualize a bell state and the entanglement isn't clearly visualizable with bloch sphere and hence there are 2 blank bloch spheres that may not depict the idea of the function clearly! ![Screenshot from 2021-12-16 15-38-27](https://user-images.githubusercontent.com/61380731/146354927-ada94920-20d9-46da-91fc-98e4f4d8336e.png) ### How can we reproduce the issue? The issue can be checked out here! https://qiskit.org/documentation/stubs/qiskit.visualization.plot_bloch_multivector.html#qiskit.visualization.plot_bloch_multivector LIne in docs - https://github.com/Qiskit/qiskit-terra/blob/4301fc47c73507e489dbafc7bae490ab29736daa/qiskit/visualization/state_visualization.py#L263-L277 ### What should happen? An example that clearly depicts a use case for the `plot_bloch_multivector` for people checking out the documentation ### Any suggestions? Since the examples are uniform across all the sections of https://qiskit.org/documentation/apidoc/visualization.html#counts-and-state-visualizations, contributor may have to take into consideration that if at all this example is to be modified, the subsection docs `plot_histogram` , `plot_state_city`, `plot_state_hinton`, `plot_state_paulivec` and `plot_state_qsphere `may have to be similarly accommodated since currently all seem to be using the same example across to depict the visualisation ideas uniformly in their respective sections. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Move nested 'storage.acl.ACL.Entity' to module scope Nested classes don't feel right, here. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: Channel pins in "environment.yaml" files are not saved to package specs Channel pins specified in environment files are not respected. For example,run ```conda env create``` with this environment file: ```yaml name: channel-not-written-to-user-specs dependencies: - defaults::six ``` If we look at conda-meta/six*.json we can see that the channel pin has not been added to specs. Where we should read ```"requested_spec": "defaults::six"```, we only find ```"requested_spec": "six"```. This is with conda 4.4.0rc2. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: Icons loading on demand slows their rendering I confirmed what @ian-r-rose observed this morning that our SVG icons are being loaded from the server on demand. When working on remote JupyterLab instances, this can take a long enough that various icons being to be glitchy. Is there a way to ship these inline? Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: Llama2 models not loading (Using main branch) ### System Info Error when loading the model "meta-llama/Llama-2-7b-chat-hf" using the following code: ```python from transformers import AutoModelForCausalLM chk = 'meta-llama/Llama-2-7b-chat-hf' if __name__ == '__main__': model = AutoModelForCausalLM.from_pretrained(chk, load_in_4bit=True, device_map='auto') print("Loaded Ok") ``` The error message was: ```shell `do_sample` is set to `False`. However, temperature is set to 0.9 -- this flag is only used in sample-based generation modes. Set `do_sample=True` or unset temperature to continue. ``` This is because the method GenerationConfig.validate() raises a ValueError and that Error is not controlled in modeling_utils.py file. One possible solution is to add the the ValueError to the except clause in that file: ![image](https://github.com/huggingface/transformers/assets/68216/d359e135-12e0-4730-ae02-8be24361667d) ### Who can help? @gante ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction Using the main branch (install from source code) ```python from transformers import AutoModelForCausalLM chk = 'meta-llama/Llama-2-7b-chat-hf' if __name__ == '__main__': model = AutoModelForCausalLM.from_pretrained(chk, load_in_4bit=True, device_map='auto') print("Loaded Ok") ``` ### Expected behavior To be able to load the model Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: container_name syntax breaks docker-compose run When using the container_name syntax added in #1711 "docker-compose run" fails with a message like the following: ``` Conflict. The name "myservice" is already in use by container bc5601faea86. You have to delete (or rename) that container to be able to reuse that name. ``` Example docker-compose.yml: ``` myservice: container_name: myservice_name image: ubuntu:latest command: tail -f /dev/null ``` Commands run: ``` docker-compose up -d docker-compose run --rm myservice /bin/sleep 3 ``` "docker-compose run" is using the same directive to name its container, as can be verified by changing the container_name in docker-compose.yml after starting the service, and running: ``` docker-compose run -d myservice tail -f /dev/null ``` I changed the service name to "myservice_name_another", which became the instantiated container's name. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: fix multiple deprecation warnings in 4.4.x Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: The "ldap" extra misses libldap dependency The 'ldap' provider misses 'ldap' extra dep (which adds ldap3 pip dependency). Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the PrefectHQ/prefect codebase. Here's the problem: Building ContainerEnvironments should be more verbose Right now no output is piped to the user when building a container environment, which can be a little stressful when it takes a long time to build. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: AIP-44 Migrate DagModel.get_paused_dag_ids to Internal API Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: Ensure all tests that create TaskInstances in DB also create DagRuns As part of the work on AIP-39 we want to make it so that _every_ TaskInstance in the DB has DagRun. Now at "run time" this is already the case (baring the case where someone might delete a DagRun. Rare, now only possible by direct DB? I can't remember if Browse -> DagRuns allows deleting anymore or not) but in our tests this is far from the case There are countless examples of where the tests just directly create TaskInstances, without a matching DagRun. We need to fix that so that the tests reflect reality. - [x] https://github.com/apache/airflow/pull/16889 is part of this -- creating a pytest fixture to reduce amount of duplication in tests - [x] #17556 - [ ] We probably also want to enforce foreign key constraints between DagRun and TaskInstance once we've done this (see also https://github.com/apache/airflow/pull/17030) Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: BigQuery: 'Table.location' should be read-only From https://github.com/GoogleCloudPlatform/google-cloud-python/pull/5678#issuecomment-407830285 Currently, the `Table.location` property is [settable](https://github.com/GoogleCloudPlatform/google-cloud-python/blob/814e75bdff73c8c298eb90f828cc4f8a36f5cc3e/bigquery/google/cloud/bigquery/table.py#L603), but the [API reference](https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#location) marks it as `[Output-only]`. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: Allow configuration_file() without config.h.meson (or input file) It would be great if we could make it so that we can write config.h files without manually maintaining a config.h.meson input file, which is error prone. Also, autoconf doesn't make us do that ;) Case in point: defines that determined symbol visibility weren't written into config.h because the config.h.meson file wasn't fully up-to-date, and meson users then wondered what went wrong during linking of some tests that symbols didn't show up etc. But usually such things fail in much more subtle ways. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: Conan running on ARM device: detected arch issues When running conan in an ARM device the `detected_architecture` is not detecting the arm, eventually, the main problem was that it was considering cross-building and adjusting a wrong triplet in Autotools causing the build to fail. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: [feature] win_bash new approach The current win_bash and run_in_windows_bash needs some improvements. This ticket would be the first iteration for improving that, to better serve the new build system integrations and for Conan 2.0. Important points: - It should have strictly 0 auto-detection. All information that it would need should be extracted from settings or new [conf] - It should have a simple ``win_bash=True`` syntax, not necessary to do ugly checks in recipes about the platform. Internally, it should do nothing if not in Windows. - It should use strictly the 2 profiles approach. - Consider skipping using the WSL as a win_bash, as that would be a Linux environment, not a Windows one? - ``tools.run_in_windows_bash()`` can probably be dropped if nicely supported in ``self.run(..., win_bash=True)`` - No need to integrate it with the existing build integrations, or to be backwards compatible, it can be treated as a completely new feature to replace the previous one. Start simple, probably just the ``bash`` wrapper itself, but not necessarily handling all the possible environment and path (cygwin, and the like) variants. Related issue: https://github.com/conan-io/conan/issues/8883, difficult to solve without breaking... Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Add support for classical expressions to `SwitchCaseOp`, `WhileLoopOp` and `IfElseOp` ### What should we add? This tracks the work labelled "PR 2" in [the initial classical-expression design document](https://github.com/Qiskit/RFCs/blob/master/0010-simple-classical-representations.md). Depends on #10223. Brief overview: - relax the type checking to permit `WhileLoopOp.condition` and `IfElseOp.condition` to be `Expr` values with type `Bool`, and `SwitchCaseOp.target` to be an `Expr` value with type `Bool` or `Uint[n]`. Closing this issue _does not_ require adding support for the builder interface. That's tracked in a follow-up: #10228. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: AIP-56 - FAB AM - Move profile view into auth manager The profile view (`/users/userinfo/`) needs to be moved to FAB auth manager. The profile URL needs to be returned as part of `get_url_account()` as specified in the AIP Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Make Bucket.delete() in storage more restricted / opinionated UPDATED 1/26/14 by @dhermes After discussion with @jgeewax the approach will be - Cap at 512 object deletes (or similar number) and cowardly refuse if more objects exist - Don't try to deal with retries or 409s, just raise the error and give info - RE: eventual consistency, just ignore (maybe log?) 404s for object deletes --- See @thobrla [comment](https://github.com/GoogleCloudPlatform/gcloud-python/pull/535#issuecomment-71094336): > ...If you provide this functionality, it must be made extremely clear that this is a > non-atomic operation that is not expected to succeed if there are concurrent writers to > the bucket. The danger here is providing the illusion of atomicity to the client, especially > because that illusion is likely to work at small scale and then fail cryptically at large scale. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: sync_imports() fails in python 3.3 builtins.**import** no longer seems to use level=-1, view.py, line 481: if level == -1 and key not in modules: This if never get executed and nothing is imported on engines because level defaults to 0 for python 3.3 I changed to: if level == 0 and key not in modules: Which works for me, but is probably not a good fix. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: Aliases not documented We have a page documenting magic functions now, but nothing describing the available aliases. From discussion on #8762 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Add support for logging in gcloud.storage Described here: https://developers.google.com/storage/docs/accesslogs Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: Allow custom signature while saving TF models ### Feature request Currently, when we use the [save_pretrained](https://github.com/huggingface/transformers/blob/ca485e562b675341409e3e27724072fb11e10af7/src/transformers/modeling_tf_utils.py#L2085) function from this library the model signature used to save the model is the default one that only calls the model on the inputs, I would like to be able to provide a custom signature while using the `save_pretrained` function. ### Motivation Persisting models with custom signatures is quite important for models that target production setups, especially if they are going to be served with TF Serving. I might be wrong but it seems that currently, the only way to save a `Transformer` model with a custom signature is by saving it using functions from the TF library, it would be very nice if the HF ecosystem could also support this feature. ### Your contribution I think this might be simple to implement and I would be happy to draft a PR if you think this could be a helpful feature. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: np.histogram erroneously emit warning when density=False As seen in https://travis-ci.org/ejeschke/ginga/jobs/395720074 . The actual code in Ginga calling histogram is: ```python # get image histogram hist, bins = numpy.histogram(some_array, hashsize, density=False) ``` Note that `normed` was not used. However, it caused this warning in Numpy ~~dev~~ prerelease: ```python else: if normed is not None: # 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6) warnings.warn( "Passing normed=False is deprecated, and has no effect. " "Consider passing the density argument instead.", > DeprecationWarning, stacklevel=2) E DeprecationWarning: Passing normed=False is deprecated, and has no effect. Consider passing the density argument instead. ``` I think this is caused by #11352 . When `density=False`, it goes into the logic block that forces `normed=False`, which resulted in it going into this logic block in `np.histogram()`: ```python else: if normed is not None: ... ``` cc @ejeschke Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: BigQuery: Document constants for things like NEWLINE_DELIMITED_JSON and move to bigquery module See: https://github.com/GoogleCloudPlatform/google-cloud-python/pull/4790#discussion_r164172094 I think we used to have constants / enum-like classes for some of these things, but maybe they got lost in the rewrite. I can't find NEWLINE_DELIMITED_JSON in the [reference docs for the BigQuery client](https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/reference.html ). **Feature request:** Add constants for commonly-used strings such as the source formats. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Entities loaded in gcloud.datastore don't have a dataset ``` python >>> dataset = demo.get_dataset() >>> query = dataset.query() >>> entity = query.fetch()[0] >>> entity.delete() ... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "gcloud/datastore/entity.py", line 206, in delete self.dataset().connection().delete_entity( AttributeError: 'NoneType' object has no attribute 'delete_entity' ``` This is because we're creating entities from the protobufs, with the proper `dataset_id` but not a true reference to the Dataset object (which has a pointer to the connection). Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the ipython/ipython codebase. Here's the problem: %aimport autocompletion Currently %aimport autocompletes on variable names, rather than on module names, which is obviously not very useful. I didn't check whether other magics could likewise benefit from a better autocompleter. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: test_qobj_to_circuits_multiple is ocassionally failing <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Informations - **Qiskit Terra version**: latest master - **Python version**: any - **Operating system**: any ### What is the current behavior? When running test_qobj_to_circuits_mutiple we're seeing occasional sporadic failures, like: https://travis-ci.org/Qiskit/qiskit-terra/jobs/451515130#L2711 It looks like something is getting out of order and that's causing the tests to fail because the lists don't match 100%. We're seeing this mostly in CI, although I was able to reproduce after running `tox -epy37 -- --until-failure` (you can replace 'py37' with the version of python you have installed locally and it will work the same) which ran the tests in a loop until it encountered the failure. I've attached the subunit file from the failed run for reference too: [failure.subunit.gz](https://github.com/Qiskit/qiskit-terra/files/2555411/failure.subunit.gz) This leads me to believe there is some non-deterministic behavior in the test or function itself. Or there is a cross interaction with another test in the suite (which by switching to running in parallel in #737 would trigger). ### Steps to reproduce the problem It's not a 100% failure so either run the tests in the loop until it fails, or look at one of the referenced CI failures. ### What is the expected behavior? The test passes 100% of the time ### Suggested solutions Figure out what is causing the failure and address it Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Make sure our 2 years old license headers are in line with Came up because of #2370. Related to #2371. To paraphrase @rimey: The Apache license header given in our internal instructions differs from the one used in this project. /cc @rimey @jgeewax Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: Adding external secrets results in invalid yml produced by config This is the same issue as #4348 and #3429, but for secrets. Running `docker-compose config` with this yml: ``` version: "3.1" services: s1: image: someimage secrets: - secret1 - secret2 secrets: secret1: external: true secret2: external: true ``` produces this: ``` networks: {} secrets: secret1: external: true external_name: secret1 secret2: external: true external_name: secret2 services: s1: image: someimage secrets: - source: secret1 - source: secret2 version: '3.1' volumes: {} ``` Trying to use this with `docker stack deploy` then produces this error: > external_name Additional property external_name is not allowed I tried this with docker 1.12.0 and 1.13.0-rc1 with the same result. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Can not combine the Result object from the same backend (statevector) <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Informations - **Qiskit Terra version**: the master branch - **Python version**: 3.6.5 - **Operating system**: macOS 10.13 ### What is the current behavior? raise error ``` Traceback (most recent call last): File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 125, in __add__ copy_of_self += other File "/Users/rchen/Developer/Quantum/qiskit-terra/qiskit/result/_result.py", line 108, in __iadd__ raise QISKitError('Result objects from different backends cannot be combined.') qiskit._qiskiterror.QISKitError: 'Result objects from different backends cannot be combined.' ``` ### Steps to reproduce the problem Code ```python from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister import qiskit as qk import numpy as np num_qubits = 2 q = QuantumRegister(num_qubits, name='q') c = ClassicalRegister(num_qubits, name='c') circuits = QuantumCircuit(q, c) param_idx = 0 for qubit in range(num_qubits): circuits.u3(0.0, 0.0, 0.0, q[qubit]) circuits.u1(3.0, q[qubit]) # circuits.measure(q, c) my_backend = qk.Aer.get_backend('statevector_simulator') qobj = qk.compile(circuits=circuits, backend=my_backend) job = my_backend.run(qobj) result_a = job.result() qobj = qk.compile(circuits=circuits, backend=my_backend) job = my_backend.run(qobj) result_b = job.result() result = result_a + result_b ``` ### What is the expected behavior? Result objects are combined without error ### Suggested solutions None Note: If I change the backend to `qasm_simulator`, there is no error. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: Flush log buffer when printing logs ``` bash $ docker-compose up -d && docker-compose logs &> composer.log ``` Someone correct me if I am wrong here, but what happens in the above is that nothing gets written to `composer.log` until `docker-compose logs` is killed, at which time stdout/stderr are unbuffered and written to `composer.log`? "name" is not a valid service config field Example: ``` yaml web: image: "alpine:edge" name: "different name" ``` This config would be valid, but shouldn't be. I think `name` is included because we modify the config to move the name in as a field, but I think we should validate it separately as part of the "top level" validation instead. The example config should raise an error about an unsupported key. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: Consider and add common sensitive names **Description** Since sensitive informations in the connection object (specifically the extras field) are now being masked based on sensitive key names, we should consider adding some common sensitive key names. `private_key` from [ssh connection](https://airflow.apache.org/docs/apache-airflow-providers-ssh/stable/connections/ssh.html) is an examples. **Use case / motivation** Extras field used to be blocked out entirely before the sensitive value masking feature (#15599). [Before in 2.0.2](https://github.com/apache/airflow/blob/2.0.2/airflow/hooks/base.py#L78 ) and [after in 2.1.0](https://github.com/apache/airflow/blob/2.1.0/airflow/hooks/base.py#L78 ). Extras field containing sensitive information now shown unless the key contains sensitive names. **Are you willing to submit a PR?** @ashb has expressed interest in adding this. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: Loading aligned dtype error (Trac #1619) _Original ticket http://projects.scipy.org/numpy/ticket/1619 on 2010-09-24 by trac user Ihor.Melnyk, assigned to unknown._ ``` import numpy as np t = np.dtype('i1, i4, i1', align=True) d = np.zeros(1, t) np.save("test.npy", d) data = np.load("test.npy") Traceback (most recent call last): File "D:\Projects\Cuda\Cuda_Git\pathwise\liinc\model\feeds\numpy_bug.py", line 8, in <module> data = np.load("test.npy") File "D:\Projects\Cuda\Cuda_Git\pathwise\pathwise\vendors\lib64\x64\python\numpy\lib\npyio.py", line 314, in load return format.read_array(fid) File "D:\Projects\Cuda\Cuda_Git\pathwise\pathwise\vendors\lib64\x64\python\numpy\lib\format.py", line 440, in read_array shape, fortran_order, dtype = read_array_header_1_0(fp) File "D:\Projects\Cuda\Cuda_Git\pathwise\pathwise\vendors\lib64\x64\python\numpy\lib\format.py", line 358, in read_array_header_1_0 dtype = numpy.dtype(d['descr']) ValueError: two fields with the same name ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: CMake helper requires compiler during package To help us debug your issue please explain: Hi everyone! I'm trying give some support to taocpp/PEGTL and I would like to use CMake helper to install the project. The project is header-only, thus it installs the license, headers and pegtl-config.cmake. However, when the package method runs over Windows, I receive the follow message: conans.errors.ConanExceptionInUserConanfileMethod: pegtl/2.5.2@taocpp/testing: Error in package() method, line 22 cmake = CMake(self) ConanException: You must specify compiler, compiler.version and arch in your settings to use a CMake generator. You can also declare the env variable CONAN_CMAKE_GENERATOR. When I run the same recipe on Linux (Travis CI), I have no problems. Appveyor error log: https://ci.appveyor.com/project/uilianries/pegtl/build/feature/conan-9/job/tnwvkd69gvkgsb6x#L250 The current PR uses just self.copy, but my intention is revert to follow state, using CMake: https://github.com/uilianries/PEGTL/blob/dc4011991624faedf67fd390e8f53737f562a897/conanfile.py Current PR for PEGTL: https://github.com/taocpp/PEGTL/pull/112 I'm curious if CONAN_CMAKE_GENERATOR is required by Conan or is just a CMake limitation. Conan Version: 1.4.3 - [X] I've read the [CONTRIBUTING guide](https://raw.githubusercontent.com/conan-io/conan/develop/.github/CONTRIBUTING.md). - [X] I've specified the Conan version, operating system version and any tool that can be relevant. - [X] I've explained the steps to reproduce the error or the motivation/use case of the question/suggestion. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: Drop `experimental_solver` context key and replace with a new one named `solver`, aliasing the former to the latter for a couple releases Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: AttributeError: 'CustomTargetIndex' object has no attribute 'is_internal' **Describe the bug** meson crashes with the error in the title **To Reproduce** ```meson project('bug', 'c') t = custom_target('dummy', output: [ 'libdummy.a' ], command: [ 'echo', 'dummy' ]) dep = declare_dependency(link_with: t[0]) l = static_library('l', 'src/empty.c', dependencies: dep, install: true) ``` **system parameters** * ArchLinux * Python: 3.8.0 * meson: 0.52.1 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: ValueError: could not broadcast input array from shape (2816,384) into shape (2698,384) the command to reproduce: cd huggingface-transformers/examples/pytorch/question-answering python -m torch.distributed.launch --nproc_per_node=8 ./run_qa.py \ --model_name_or_path roberta-large \ --dataset_name squad \ --do_train --do_eval \ --per_device_train_batch_size 16 \ --per_device_eval_batch_size 256 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir test_result2/$trials --overwrite_output_dir \ --logging_dir test_result2/$trials/tensorboard --logging_first_step --logging_steps 50 \ --fp16 i tried add "--max_eval_samples 10240", this will fix the error, while the AUC result is quite low(exact_match = 4.9414, f1 = 8.9784). and when i ran with 1gpu, the above command can succeed(exact_match = 88.5336, f1 = 94.3266) the full error is "File "./transformers/src/transformers/trainer_pt_utils.py", line 410, in _nested_set_tensors i * slice_len : (i + 1) * slice_len i * slice_len : (i + 1) * slice_len ValueError: could not broadcast input array from shape (2816,384) into shape (2698,384)" Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the celery/celery codebase. Here's the problem: chord_unlock isn't using the same queue as chord I've created a tasks_queue and priority_task_queue. in addition to that i've set the tasks_queue to be used as default queue. Now every time i would like a task to be perfrom right away i use priority_queue which is generally empty most of the time. now, the problem araise whenever i'm using chord. i noticed that chords are unable to complete their task because their chord_unlock counterpart always run on the default tasks queue(tasks_queue) ## Steps to reproduce - Ubnutu 16 server - celery==4.1.0 - kombu==4.1.0 - boto==2.48.0 - boto3==1.4.7 - botocore==1.7.31 - Using SQS for as broker. ## Expected behavior I expected the chord_unlock to be performed in the same queue the chord was executed. This causes a major problem for me. Since SQS does not support priority tasks. I'm forced to use two queues to enable the effect of tasks priority. but since chord_unlock will always use the same default queue. priority_tasks using chords, will never be able to finish whenever the default queue is packed. ## Actual behavior the chord was consumed from priority_task_queue and the chord_unlock got stuck in line on the regular tasks_queue Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Running on statevector_simulator alters the qobj <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues --> ### Informations - **Qiskit Terra version**: - **Python version**: - **Operating system**: ### What is the current behavior? When a qobj is run on a statevector_simulator, it implicitly inserts a snapshot at the end, sets `shots=1`, then runs it on the qasm_simulator. The added instruction makes the qobj not pass validation if run again. ```python statevector_simulator = Aer.get_backend('statevector_simulator') qobj = compile(circ, statevector_simulator, shots=1) statevector_simulator.run(qobj).result().get_statevector() # this works fine statevector_simulator.run(qobj).result().get_statevector() # the second time, SchemaValidationError is raised ``` ### Suggested solution: Move this logic to the transpiler, and make Qobj immutable. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: New-line handling in if statements is silently dangerous. Consider the following `if` statement: ``` if get_option('enable_alsa') == 'true' or (get_option('enable_alsa') == 'auto' and meson.get_compiler('cpp').has_header('alsa/asoundlib.h')) ``` The entire `or` clause is silently ignored. No errors or warnings, it just ignores it. If I put it all one one line is fine, but that's kind of ugly. Does meson not support multi-line statements? Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: Cross compiler ignored when build and host architectures are identical I'm trying to compile for Android x86_64 on Linux x86_64. Unfortunately, Meson selects the native compiler instead of the one provided by the cross-file. The setup is the same as described in #5097. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conda/conda codebase. Here's the problem: conda config --describe reports elements types as str for everything Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: Make sure list_extensions and should_build can run without sudo As reported by @Mou5e on gitter: ``` INFO:root:JupyterLab v0.25.1 INFO:root:Known labextensions: INFO:root: app dir: /opt/test/services/.396762/var/public/conda/share/jupyter/lab INFO:root: jupyterlab-extension ^[[32m enabled ^[[0m ^[[32mOK^[[0m [W 2017-07-16 19:15:42.912 admin notebookapp:1054] Error loading server extension jupyterlab Traceback (most recent call last): File "/opt/test/services/.396762/var/public/conda/lib/python3.5/site-packages/notebook/notebookapp.py", line 1049, in init_server_extensions func(self) File "/opt/test/services/.396762/var/public/conda/lib/python3.5/site-packages/jupyterlab/extension.py", line 72, in load_jupyter_server_extension build_needed, msg = should_build(app_dir) File "/opt/test/services/.396762/var/public/conda/lib/python3.5/site-packages/jupyterlab/commands.py", line 263, in should_build _ensure_package(app_dir, logger=logger) File "/opt/test/services/.396762/var/public/conda/lib/python3.5/site-packages/jupyterlab/commands.py", line 673, in _ensure_package shutil.copy(pjoin(here, fname), dest) File "/opt/test/services/.396762/var/public/conda/lib/python3.5/shutil.py", line 241, in copy copyfile(src, dst, follow_symlinks=follow_symlinks) File "/opt/test/services/.396762/var/public/conda/lib/python3.5/shutil.py", line 121, in copyfile with open(dst, 'wb') as fdst: PermissionError: [Errno 13] Permission denied: '/opt/test/services/.396762/var/public/conda/share/jupyter/lab/staging/index.js' ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the conan-io/conan codebase. Here's the problem: Know who is requiring a missing dependency When the command ``conan info`` or ``conan install`` fails, because there is a missing dependency, it is not possible to know who required it, and that can be specially difficult for complex dependency graphs, using version ranges, etc. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: [AIRFLOW-4438] Add Gzip compression to S3_hook - Added bool parameter gzip to load_file to s3_hook - Tested the load_file with load_file_gzip unittest - Updated the load_file docstring to reflect the extra parameter --- Issue link: [AIRFLOW-4438](https://issues.apache.org/jira/browse/AIRFLOW-4438) Make sure to mark the boxes below before creating PR: [x] - [x] Description above provides context of the change - [x] Commit message/PR title starts with `[AIRFLOW-NNNN]`. AIRFLOW-NNNN = JIRA ID* - [x] Unit tests coverage for changes (not needed for documentation changes) - [x] Commits follow "[How to write a good git commit message](http://chris.beams.io/posts/git-commit/)" - [x] Relevant documentation is updated including usage instructions. - [x] I will engage committers as explained in [Contribution Workflow Example](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst#contribution-workflow-example). * For document-only changes commit message can start with `[AIRFLOW-XXXX]`. --- In case of fundamental code change, Airflow Improvement Proposal ([AIP](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvements+Proposals)) is needed. In case of a new dependency, check compliance with the [ASF 3rd Party License Policy](https://www.apache.org/legal/resolved.html#category-x). In case of backwards incompatible changes please leave a note in [UPDATING.md](https://github.com/apache/airflow/blob/master/UPDATING.md). Read the [Pull Request Guidelines](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst#pull-request-guidelines) for more information. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: Regression: Error in GStreamer configuration I get this error during gst-build configuration, I believe it's ObjectHolder refactoring: `subprojects/gstreamer/gst/meson.build:275:2: ERROR: Gir target must be an executable or library`. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: Small inconsistency in the mpl drawer There is a small inconsistency how the parameters of a gate are showed in the matplotlib, with and without '(...)'. ```python qr = QuantumRegister(2, 'q') circuit_all = QuantumCircuit(qr) circuit_all.u3(pi, pi, pi, qr[0]) circuit_all.cu3(pi, pi, pi, qr[0], qr[1]) circuit_all.draw(output='mpl') ``` ![image](https://user-images.githubusercontent.com/766693/61549782-eb9a2780-aa1e-11e9-81ac-f638cce9c23b.png) ### What is the expected enhancement? If I have to choose, I would go with removing the parenthesis everywhere. But, for sure, it should be everywhere the same... Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: Numpy failing to install with python3.5 The new release of numpy fails to install when using python3.5. It seems clear the problem is the use of f string formatting in the setup.py file that is only supported in python 3.6 and higher. ### Reproducing code example: ``` docker run python:3.5.2-alpine pip install numpy==1.19.0rc1 ``` <!-- Remove these sections for a feature request --> ### Error message: File "/tmp/pip-build-gg798dty/numpy/setup.py", line 182 raise ValueError(f'Submodule {p} missing') ^ SyntaxError: invalid syntax ### Numpy/Python version information: Fails installing `numpy==1.19.0rc1` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: docker-compose super slow on macOS Catalina when i do docker-compose up -d, it takes a while to start the process, same when i do docker-compose restart. in other words, there's a delay of few seconds before it begins to restart containers or stopping containers. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: Error fetching results from a BigQuery asynchronous call in 0.19.0 Referring to the [doc](https://googlecloudplatform.github.io/google-cloud-python/stable/bigquery-usage.html#querying-data-asynchronous), calling `QueryResults.fetch_data` raises an exception since `self.name` is `None`: ``` python if self.name is None: raise ValueError("Query not yet executed: call 'run()'") ``` However, if `QueryResults.run` is called, an exception is also raised since `self._job` already exists: ``` python if self._job is not None: raise ValueError("Query job is already running.") ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the googleapis/google-cloud-python codebase. Here's the problem: BigQuery: expose numDmlAffectedRows in query results See: https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs/query#numDmlAffectedRows It is marked as experimental. This is because DML statements are currently in beta for BigQuery. This will be needed to correctly implement [cursor.rowcount](https://www.python.org/dev/peps/pep-0249/#rowcount) for DB-API support https://github.com/GoogleCloudPlatform/google-cloud-python/issues/2434 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: pkgconfig module should allow nullification of some default variables. Not all projects require the prefix, libdir, and includedir variables in their pkgconfig files. In addition, it should be possbile to remove the `Cflags: -I${includedir}` line either entirely or in order to have your own variables there, minus the `-I${includedir}` addition. For example's sake, this is the entireity of the xorg-libinput.pc file: ``` sdkdir=/usr/include/xorg Name: xorg-libinput Description: X.Org libinput input driver. Version: 0.28.0 Cflags: -I${sdkdir} ``` Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the docker/compose codebase. Here's the problem: Regression in 1.5.2 re: hitting Control-C when running `up` It looks like there is a regression in docker-compose 1.5.2 when compared with 1.5.1. Previously, after running `docker-compose up` in a terminal window, pressing `Control-C` (i.e. sending SIGINT from the same shell window) caused services to enter the "Stopping" phase. However, when I upgraded to 1.5.2, services now immediately go to "Killing." Note that if I go to a _different_ terminal window and send a SIGINT or SIGTERM to that process using `kill`, then the services _do_ go to the "Stopping" phase. So the issue looks like it is restricted to hitting `Control-C` from the same shell window. I'm observing this on Mac OS X 10.11 using Python 3.5.1. One more thing. I did notice one improvement with 1.5.2. Previously, sending a SIGTERM to `docker-compose up` from a different window didn't seem to work and stop services like SIGINT. With 1.5.2, it now does, and behaves the same as sending SIGINT from a different window. So the issue now is with hitting Control-C from the same window. My guess is that this might have to do with the fact that hitting Control-C in a terminal window sends a SIGINT not just to the parent process, but also to all child processes (and there is at least one child with docker-compose). So maybe docker-compose is treating that as receiving two distinct SIGINT signals, causing it to go immediately into the "Killing" phase (as if the user hit Control-C twice). Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the apache/airflow codebase. Here's the problem: Allow for Markup in UI page title ### Description A custom page title can be set on the UI with the `instance_name` variable in `airflow.cfg`. It would be nice to have the option to include Markup text in that variable for further customization of the title, similar to how dashboard alerts introduced in #18284 allow for `html=True`. ### Use case/motivation It would be useful to be able to use formatting like underline, bold, color, etc, in the UI title. For example, color-coded environments to minimize chances of a developer triggering a DAG in the wrong environment: ![title](https://user-images.githubusercontent.com/50751110/149528236-403473ed-eba5-4102-a804-0fa78fca8856.JPG) ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md) Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the mesonbuild/meson codebase. Here's the problem: compiler's rsp syntax should be a Compiler method This is mostly just to help me remember to do this. See: #8500 Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the huggingface/transformers codebase. Here's the problem: Improve the `run_xlni` example to use the Datasets library The [`run_xlni`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py) example should be improved (following the model of [`run_glue`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py)) to use the Datasets library to download and preprocess the datasets. Ideally, copying the `run_glue` example and adapting the relevant parts should be the way to go. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the numpy/numpy codebase. Here's the problem: DOC: misleading description in https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.linalg.lstsq.html This starts by saying "Solves the equation a x = b", which (except in special cases) it does not do. Perhaps better is to start with "Computes a vector x that minimizes the Euclidean 2-norm || b - a x ||^2. If the minimal 2-norm is zero, then we have ax=b." Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the Qiskit/qiskit codebase. Here's the problem: plot_coupling_map() and plot_gate_map() needs snapshot testing Now we have snapshot testing for other graphs besides circuits (thanks to #6672), it should be extended with other graphing functions, like `plot_coupling_map` and `plot_gate_map` (introduced in #6751). Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the jupyterlab/jupyterlab codebase. Here's the problem: Make the extension metadata in package.json take a module for the plugin Up to now, the extension metadata in the package.json only allows a plugin to export the plugin data as the default export of the package: ```json "jupyterlab": { "extension": true }, ``` This means that any libraries the plugin imports must be available to npm. This is onerous if we are wrapping a small private dependency, or a dependency version we don't want to push to npm (a dev version, for example). In these cases, it would be much simpler to include the lab extension in the package itself - but that is impossible if the default export of the package *must* be the plugin data. ## Proposal The `extension` attribute in package.json is either a boolean or a string. If it is `true`, look in the default exports of the package for the plugins as we do now. If it is a string, look in that file of the package for the plugins: ```json "jupyterlab": { "extension": "some/entry/point/that/exports/plugins" }, ``` This makes it possible for a package to export some api *in addition* to providing a lab extension. Another possibility is to make the string the *export* of the package to use (i.e., the export key name), but I think having a separate entry point may make more sense, as you may not want to export the lab plugin data in the public interface of the package. Can you help me debug this systematically and explain your reasoning?


[System] You are an expert software debugging assistant. Help debug issues by analyzing problems systematically and providing clear, step-by-step reasoning.

[User] I'm having an issue with the gitpython-developers/GitPython codebase. Here's the problem: repo.index.add() first argument type accepts os.PathLike but code raises TypeError In the docs and in the code types, the first argument for `repo.index.add()` is `Sequence[Union[str, os.PathLike, git.objects.blob.Blob, git.index.typ.BaseIndexEntry, Submodule]]`. However, actually trying to submit a list of Paths to this argument results in a TypeError, as it seems the code inside `_preprocess_add_items` does not handle Paths. I think this could be fixed by changing line 611 inside of `_preprocess_add_items` in `base.py` to: ``` if isinstance(item, str) or isinstance(item, os.PathLike): ``` https://github.com/gitpython-developers/GitPython/blob/fe54118ec07a68d5dc6f6108510cffc55dfca643/git/index/base.py#L611 Since `_to_relative_path()` already accepts pathlike arguments. Can you help me debug this systematically and explain your reasoning?