[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: config` includes python objects instead of string `PS C:\Users\Administrator\test> docker-compose version docker-compose version 1.6.0dev, build 304a44a docker-py version: 1.6.0 CPython version: 2.7.11 OpenSSL version: OpenSSL 1.0.2d 9 Jul 2015` Given the docker-compose.yml: ``` my_nginx: build: . ports: - 8080:80 volumes: - ".\testdir:\foo" ``` docker-compose config ``` PS C:\Users\Administrator\test> docker-compose config my_nginx: build: !!python/unicode 'C:\Users\Administrator\test' ports: - 8080:80 volumes: - !!python/object/new:compose.config.types.VolumeSpec - !!python/unicode '/c/Users/Administrator/test/testdir' - !!python/unicode '/foo' - !!python/unicode 'rw' ``` 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 failed if is in path what contains dir starting on `_` ## Description of the issue docker-compose failed if is in path what contains dir starting on `_` ## Context information (for bug reports) can run docker-compose build if `docker-compose.yml` is in `_docker` dir as in before version getting ERROR: invalid reference format when I rename dir `_docker` to `docker` everything run good ``` docker-compose version 1.21.1, build 5a3f1a3 docker-py version: 3.3.0 CPython version: 3.6.4 OpenSSL version: OpenSSL 1.0.2o 27 Mar 2018 ``` ``` Client: Version: 18.03.1-ce API version: 1.37 Go version: go1.9.5 Git commit: 9ee9f40 Built: Thu Apr 26 07:13:02 2018 OS/Arch: darwin/amd64 Experimental: false Orchestrator: swarm Server: Engine: Version: 18.03.1-ce API version: 1.37 (minimum version 1.12) Go version: go1.9.5 Git commit: 9ee9f40 Built: Thu Apr 26 07:22:38 2018 OS/Arch: linux/amd64 Experimental: true ``` ## Steps to reproduce the issue 1. move docker-compose.yml (contains service: build: .... ) to _docker dir 2. run docker-compose build ## Additional information Docker for Mac: version: 18.03.1-ce-mac65 (dd2831d4b7421cf559a0881cc7a5fdebeb8c2b98) macOS: version 10.13.4 (build: 17E202) 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: Disabling validation with val_percent_check=0.0 not working <!-- ### Common bugs: 1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79). 2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq) --> ## 🐛 Bug I am trying to train on a small subset of data by settting `train_percent_check` and `val_percent_check` flags. I am not able to disable validation by setting `ver_percent_check=0.0`. Despite it being set to 0, `validation_epoch_end` is executed. <!-- A clear and concise description of what the bug is. --> ### To Reproduce Steps to reproduce the behavior: Running the MNIST model on the Google Colab link <!-- If you have a code sample, error messages, stack traces, please provide it here as well --> <img width="1068" alt="Screen Shot 2020-03-26 at 11 06 16 AM" src="https://user-images.githubusercontent.com/4838833/77662280-0c256480-6f52-11ea-873c-7a9d41bf347c.png"> #### Code sample <!-- Ideally attach a minimal code sample to reproduce the decried issue. Minimal means having the shortest code but still preserving the bug. --> ``` mnist_model = MNISTModel() # most basic trainer, uses good defaults (1 gpu) trainer = pl.Trainer(gpus=1, val_percent_check=0.0, train_percent_check=0.1) trainer.fit(mnist_model) ``` ### Expected behavior <!-- A clear and concise description of what you expected to happen. --> I expected the `validation_epoch_end` to not be invoked but it is. ### Environment Running on Google Colab with the sample MNIST tutorial 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: Fix broken references to old auth doc See #3517 and https://github.com/GoogleCloudPlatform/google-cloud-python/commit/7cb86b47acea2339c6c18cb70189d2b0c347ec5e#commitcomment-22758235 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: error when adding nvbench as subproject Hello, I'm just getting started with meson and would like to add [nvbench](https://github.com/NVIDIA/nvbench) as a subproject in my meson project using the `cmake` module. Following the examples in the documentation, I cloned the `nvbench` repo under `subprojects` and added the following lines to my top-level meson.build file. ```python cmake = import('cmake') opt_var = cmake.subproject_options() opt_var.add_cmake_defines({'CMAKE_CUDA_ARCHITECTURE': 70}) nvbench_proj = cmake.subproject('nvbench', options: opt_var) ``` However, when running `meson setup`, we seem to get through the cmake configure process just fine and fail with the following error: ```txt subprojects/nvbench/meson.build:0:0: ERROR: File nvbench does not exist ``` I can see the generated meson.build file under `builddir/subprojects/nvbench`, but I'm not sure what the next steps are from there. Any help would be appreciated! OS: Ubuntu 20.04 Meson version: 0.62.0 cmake version: 3.22.3 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: Statevector.evolve works incorrectly for subsystem evolution on mixed-dimension subsystems ### Environment - **Qiskit Terra version**: 0.21 - **Python version**: All - **Operating system**: All ### What is happening? `Statevector.evolve` returns the incorrect answer when evolving certain subsystems if the statevector has mixed dimension subsystems. This is due to it incorrectly reshaping the temporary transposed tensors during the evolution contraction when flattening back to a returned statevector. ### How can we reproduce the issue? ```python import qiskit.quantum_info as qi import numpy as np # Mixed dimension statevector dims = (3, 4, 5) state = qi.random_statevector(dims, seed=123) # Evolve subsystem zero by an operator op = qi.random_unitary(dims[:1], seed=123) evo = state.evolve(op, [0]) # Compare to full system evolution mat = op.expand(np.eye(np.prod(dims[1:]))).data target = qi.Statevector(np.dot(mat, state.data), dims) # Fidelity should be 1 qi.state_fidelity(evo, target) 0.011873940293697585 ``` ### What should happen? If things were working correctly the above code snippet should return a fidelity of 1 ### Any suggestions? The error is in the `Statevector._evolve_operator` method where one of the `np.reshape` instructions is given an incorrect dimension specification in certain cases where there are >= 2 non-evolved subsystems with different dimensions. 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: FeaturesManager assumes only one of Torch or TensorFlow is installed ## Environment info - `transformers` version: 4.12.5 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.9.10 - PyTorch version (GPU?): 1.10.0 (False) - Tensorflow version (GPU?): 2.8.0 (False) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: No - Using distributed or parallel set-up in script?: No ### Who can help @michaelbenayoun @Albertobegue ## Information When both Torch and TensorFlow are installed, `FeaturesManager` defaults to using `AutoModel`, so the model returned by `get_model_from_feature` is always Torch. ## To reproduce Steps to reproduce the behavior: 1. Install Torch and TF 2. Call `FeatureManager.get_model_from_feature` with arbitrary but supported `features` and `model_name` arguments 3. The resulting model is always a Torch model ```python features = "default" # randomly chosen, supported feature model_name = "bert" # randomly chosen, supported model model = FeaturesManager.get_model_from_feature(features, model_name) ``` ## Expected behavior Some test environments have both Torch and TensorFlow installed, because the immediate task is to ensure functionality is the same regardless of the framework. I would expect `FeaturesManager.get_model_from_feature` to allow TensorFlow to be used even when Torch is installed. This could be implemented by e.g. a keyword argument to `get_model_from_feature` with a default value of `None`. When the keyword argument is `None`, and both Torch and TensorFlow are installed, `FeatureManager` would default to Torch, as it does now. Otherwise, it would use the specified framework. 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: Local simulators return n_qubits that is supported by local memory <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues to confirm this idea does not exist. --> ### What is the expected enhancement? Currently the simulator backends do not have a `n_qubits` key in their configuration (#1047), whereas the spec says that `n_qubits=-1` for a simulator. Would it not make sense to have local simulators return `n_qubits=N_max`, where `N_max` is the max number of qubits supported by the local memory? 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: [pipeline] Explicitly set framework is ignored ### System Info - `transformers` version: 4.30.2 Omitting the rest because they aren't really relevant. Can submit later if needed. ### Who can help? @Narsil ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction ``` from transformers import pipeline from transformers import WhisperProcessor, WhisperForConditionalGeneration processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") # Using this just to make sure an error if thrown if pipeline tries to check using module instead of specified framework. class FakeWhisper: def __getattr__(self, item): return model.__getattr__(item) pipe = pipeline( "automatic-speech-recognition", model=FakeWhisper(), tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, device=model.device, framework="pt", ) ``` The above code raises this error: ``` TypeError: Could not infer framework from class <class '__main__.FakeWhisper'>. ``` ### Expected behavior When specifying the framework explicitely, there is no need to infer it from the module of the model class, as mentioned here: https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/__init__.py#L784-L796 But then, inside `infer_framework_load_model`, `infer_framework` is called regardless of the value of the `framework` parameter: https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/base.py#L280 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: ReadOptions support for 'Connection.lookup', 'Connection.run_query' The protobuf specs for the [lookup RPC](https://github.com/GoogleCloudPlatform/google-cloud-datastore/blob/v1beta2-rev1-2.1.0/proto/datastore_v1.proto#L439) and the [runQuery RPC](https://github.com/GoogleCloudPlatform/google-cloud-datastore/blob/v1beta2-rev1-2.1.0/proto/datastore_v1.proto#L465) show an optional 'read_options' field, which denominate the consistency level and optional transaction to be used. Do we need to support this field? 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_find_package generator do not include system libraries If the library is not found (with find_library()) is not adding it to the target (nor the global lib list) so it fails linking if the package is declaring system libs (as ws2_32 or pthread) 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: tf.matrix_band_part have been deprecated https://github.com/huggingface/transformers/blob/fae0b9faefc371977d139b60202a63dcedbd8ea8/src/transformers/models/xlnet/modeling_tf_xlnet.py#L500 This should be replaced by `tf.linalg.band_part` 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: Documentation related to token_type_ids for RoBERTa needs to be updated ### System Info This is about online documentation at https://huggingface.co/docs/transformers/v4.23.1/en/model_doc/roberta#transformers.RobertaModel.forward.token_type_ids ### Who can help? _No response_ ### 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 RoBERTa has no token type embeddings by default, while the documentation, indicates: ``` token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]: * 0 corresponds to a sentence A token, * 1 corresponds to a sentence B token. ``` ### Expected behavior The documentation should reflect that the default RoBERTa model has no token type embeddings. 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: add support for dead letter topics Add fields necessary to create/update subscriptions with a [dead letter policy](https://github.com/googleapis/googleapis/blob/master/google/pubsub/v1/pubsub.proto#L627-L658). This allows messages to be redelivered to a separate dead letter topic after a configured number of delivery attempts have failed (nack/ack_deadline expired). In addition to adding `DeadLetterPolicy` to a Subscription, we need to properly expose the [`delivery_attempt` field](https://github.com/googleapis/googleapis/blob/0735b4b096872960568d1f366bfa75b7b0e1f1a3/google/pubsub/v1/pubsub.proto#L757) in the client. This is done one of two ways, so for detailed implementation details, please consult the detailed information guide that Prad wrote. Feel free to ping me if you need a link to 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 PrefectHQ/prefect codebase. Here's the problem: Add executor kwargs to `KubernetesJobEnvironment` I recently realized that to configure a `KubernetesJobEnvironment` to use a non-default executor requires knowledge of the appropriate environment variables. We should expose a better interface for this (similar to the `RemoteEnvironment`) 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: Online help utility broken in QtConsole Whatever I type in online help utility (started in QtConsole), I get help on unicode object, ``` Python 2.7.2 (default, Nov 21 2011, 17:25:27) Type "copyright", "credits" or "license" for more information. IPython 0.12.beta -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details. %guiref -> A brief reference about the graphical user interface. In [1]: help() Welcome to Python 2.7! This is the online help utility. If this is your first time using Python, you should definitely check out the tutorial on the Internet at http://docs.python.org/tutorial/. Enter the name of any module, keyword, or topic to get help on writing Python programs and using Python modules. To quit this help utility and return to the interpreter, just type "quit". To get a list of available modules, keywords, or topics, type "modules", "keywords", or "topics". Each module also comes with a one-line summary of what it does; to list the modules whose summaries contain a given word such as "spam", type "modules spam". help> topics Help on unicode object: class unicode(basestring) | unicode(string [, encoding[, errors]]) -> object | | Create a new Unicode object from the given encoded string. | encoding defaults to the current default string encoding. | errors can be 'strict', 'replace' or 'ignore' and defaults to 'strict'. | | Method resolution order: | unicode | basestring | object | | Methods defined here: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x 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 docker/compose codebase. Here's the problem: Support build --rm=false (reopen ticket) Hi I use circleci, and currently circleci v1 has trouble deleting old containers, as such I would like to use --rm=false Same as [4325](https://github.com/docker/compose/issues/4325) 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: Inconsistency in how compose handles empty environment variables If you want to use the value of an environment variable that is available in the host you can: ``` bash $ HOST_ENV=value docker run -e HOST_ENV ubuntu:14.04 env PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin HOSTNAME=acb280b6edd9 HOST_ENV=value HOME=/root ``` But, if the variable is not defined in the host, then it also doesn't get created in the container: ``` bash $ docker run -e HOST_ENV ubuntu:14.04 env PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin HOSTNAME=29a464f43ecb HOME=/root ``` But, given the following `docker-compose.yml`: ``` yaml banana: image: ubuntu:14.04 command: env environment: HOST_ENV: ``` We get an empty variable: ``` bash $ docker-compose run banana PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin HOSTNAME=9841adaf229b TERM=xterm HOST_ENV= HOME=/root ``` Should this be considered a bug? I would expect the same behavior from both tools. 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: [0.60.2] import('cmake') cannot handle cmake not being on path. Meson just says it cannot find cmake although it has been passed via a native/cross file as: ``` [binaries] ... cmake = 'E:/vcpkg_cache/downloads/tools/cmake-3.21.1-windows/cmake-3.21.1-windows-i386/bin/cmake.exe' .... ``` CI run (https://dev.azure.com/vcpkg/public/_build/results?buildId=64620&view=logs&jobId=7922e5c4-0103-5f8f-ad17-45ce9bb98e80&j=878666d5-db33-5b27-9e7d-b0c7ee352005) The fact that meson assumes stuff to be on path although full paths are passed seems to be a consistent error...... (same goes for ninja) 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: LHS circuit calibration lost when combining with another circuit <!-- ⚠ 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**:0.17.0 - **Python version**:3.9.1 - **Operating system**:macOS Catalina ### What is the current behavior? Calibration information is lost when using "+"(extend) for a lhs calibrated circuit. ### Steps to reproduce the problem from qiskit import QuantumCircuit from qiskit.circuit.library import RZXGate from qiskit.pulse import Schedule qc_cal = QuantumCircuit(2) qc_cal.rzx(0.5, 0, 1) qc_cal.add_calibration(RZXGate, (0, 1), params=[0.5], schedule=Schedule()) # qc_cal = transpile(qc_cal,backend) print(qc_cal.calibrations) qc = QuantumCircuit(2) qc.h(0) new_circ_cal_on_lhs = qc_cal + qc print(new_circ_cal_on_lhs.calibrations) #calibration information is lost new_circ_cal_on_rhs = qc +qc_cal print(new_circ_cal_on_rhs.calibrations) #calibration information is kept ### What is the expected behavior? For the calibrations information to be maintained when making new circuit from "calibrated_circuit+uncalibrated_circuit" ### Suggested solutions Modify extend function to check and add calibrations from lhs circuit. 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: Some magics seems broken Following the merge of cell magic it seems like some of the magics are broken. At 6cb2026f76d00422b9daa55ed590c9ab16f71ec6 the %qtconsole magic from the notebook and the list of all magics in the qtconsole works. In current master 89c23370cacc3bdee12af9a199656e83a387df5b they are broken. Could this be an issue with the merge of the merge kernel branch and the cell magic branch? The list of all magics is now: ``` %ERROR %AttributeError %ZMQInteractiveShell %object %has %no %attribute %lsmagic ``` where lsmagic is the only genuine one. It seems like an error message leaks into the list. 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: [1.27.0+] docker-compose config truncates version ('3' instead of '3.x') <!-- Welcome to the docker-compose issue tracker! Before creating an issue, please heed the following: 1. This tracker should only be used to report bugs and request features / enhancements to docker-compose - For questions and general support, use https://forums.docker.com - For documentation issues, use https://github.com/docker/docker.github.io - For issues with the `docker stack` commands and the version 3 of the Compose file, use https://github.com/docker/cli 2. Use the search function before creating a new issue. Duplicates will be closed and directed to the original discussion. 3. When making a bug report, make sure you provide all required information. The easier it is for maintainers to reproduce, the faster it'll be fixed. --> ## Description of the issue Not sure if it's a bug or a new feature, but ... Since `1.27.0+`, `docker-compose config` returns only the major digit from version field. ## Context information (for bug reports) **Output of `docker-compose version`** ``` docker-compose version 1.27.0, build unknown docker-py version: 4.3.1 CPython version: 3.7.9 OpenSSL version: OpenSSL 1.1.1 11 Sep 2018 ``` **Output of `docker version`** ``` Docker version 19.03.12, build 48a66213fe ``` ## Steps to reproduce the issue 1. Create a docker-compose.yml file with the following content. ```yaml version: '3.7' ``` 2. Run `docker-compose config`. 3. Check the output. ### Observed result ```yaml services: {} version: '3' ``` ### Expected result ```yaml services: {} version: '3.7' ``` ### Stacktrace / full error message *No error* ## Additional information Ubuntu 18.04, docker-compose 1.27.0 installed from pip on Python 3.7.9. 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: TableauRefreshWorkbookOperator fails when using personal access token (Tableau authentication method) **Apache Airflow version**: 2.0.1 **What happened**: The operator fails at the last step, after successfully refreshing the workbook with this error: ``` tableauserverclient.server.endpoint.exceptions.ServerResponseError: 401002: Unauthorized Access Invalid authentication credentials were provided. ``` **What you expected to happen**: It should not fail, like when we use the username/password authentication method (instead of personal_access_token) <!-- What do you think went wrong? --> Tableau server does not allow concurrent connections when using personal_access_token https://github.com/tableau/server-client-python/issues/717 The solution would be redesigning completely the operator to only call the hook once. My quick fix was to edit this in TableauHook: ``` def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: pass ``` **How to reproduce it**: Run this operator TableauRefreshWorkbookOperator using Tableau personal_access_token authentication (token_name, personal_access_token). 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: 'properties' parameter in google.cloud.bigquery.client.Client.update_table is not documented 'properties' parameter in google.cloud.bigquery.client.Client.update_table is not documented. The body of the description is also inaccurate as it always does a PATCH request. See: https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/reference.html#google.cloud.bigquery.client.Client.update_table 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: Coverage regression on macOS Catalina On macOS Mojave, enabling `b_coverage=true` resulted in successfully instrumented test executables generating .gcno files. On Catalina, I instead get ``` ERROR: Linker APPLE ld doesn't implement coverage data generation. ``` The following patch seems to fix coverage generation on Catalina for me: ```diff diff --git a/mesonbuild/linkers.py b/mesonbuild/linkers.py index 84bfceb4..371220bf 100644 --- a/mesonbuild/linkers.py +++ b/mesonbuild/linkers.py @@ -580,6 +580,9 @@ class AppleDynamicLinker(PosixDynamicLinkerMixin, DynamicLinker): result.append(a) return result + def get_coverage_args(self) -> typing.List[str]: + return ['--coverage'] + def sanitizer_args(self, value: str) -> typing.List[str]: if value == 'none': return [] ``` #6160 may be relevant here, as it suggests that clang coverage shouldn't be working at all with meson. I didn't investigate further to figure out why the above patch wasn't necessary on Mojave but now is. If desired, I can open a PR with the above patch, but at this point I'm not confident I know what the root issue is. 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: Make ctypes completely optional on Windows There are a couple of places where `ctypes` is presumed to be available, which means `numpy` cannot be imported on systems/distributions that do not include it. These are [`_dtype_ctypes.py`](https://github.com/numpy/numpy/blob/master/numpy/core/_dtype_ctypes.py), and the [`_distributor_init.py`](https://github.com/numpy/numpy/blob/bfb156741a92f77c13c50d82fc5a1f4d6177a9f5/tools/openblas_support.py#L197) generated for `libopenblas`. The `_dtype_ctypes` module seems easy enough to update to match the other ctypes-using modules (or simply delay the [eager import](https://github.com/numpy/numpy/blob/master/numpy/core/__init__.py#L100) or handle it failing). I can't find any uses in numpy itself though, so I have no idea what its compatibility constraints are. The `_distributor_init.py` file is somewhat redundant with the [generated `__config__.py`](https://github.com/numpy/numpy/blob/8087e1de04d011f7ce66bf80dbef4a0599b14f7a/numpy/distutils/misc_util.py#L2289), but could also just do nothing when ctypes fails to import. I'm happy to send a PR, but it's not obvious which fixes would be preferred. If I were going to keep it simple, I'd defer the `import ctypes` in `_dtype_ctypes` into the function(s) that use it, and add an early exit from the `_distributor_init.py` when ctypes is unavailable. 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: Think of adding a project_URL for 7.0 see for example : https://github.com/jupyter/nbconvert/pull/869/commits/c66d65262567fa0e9d9834f3524a0056d2a38f9e 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: Allow formatting in names of custom gates ### What is the expected enhancement? Allow formatting of custom gate names in circuit diagrams. The names of custom gates are shown as plain text. It would be nice to format these as latex or html. For example the following code generates a circuit diagram with a custom gate ``` from qiskit import QuantumCircuit from qiskit.circuit import Gate qc = QuantumCircuit(1, 1) qc.append(Gate(name=r'X_t', num_qubits=1, params=[]), [0]) qc.measure([0, ], [0,]) qc.draw(output='latex', interactive=False, filename='test.png') ``` The custom gate is labelled as `X_t`, but I want the name to be formatted as an `X` with subscript `t`. The custom gate name is transformed by `utf8tolatex`, but it is escaping the `_`. Also variations as `qc.append(Gate(name=r'$X_t$', num_qubits=1, params=[]), [0])` do not work. Note: here is also a discussion on stack exchange on this: https://quantumcomputing.stackexchange.com/questions/8329/how-to-plot-custom-gate-labels-in-qiskit 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: add custom_jvp / vjp, delete custom_transforms fixes #116, #1097, #1249, #1275, #1366, #1723, #1670, #1875, #1938, #2345, #2346 See the [design doc](https://github.com/google/jax/blob/custom-transforms3/design_notes/custom_derivatives.md) and [user tutorial](https://github.com/google/jax/blob/custom-transforms3/docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb) for details. TODO: - [x] improve error messages - [x] write a PR message / design doc - [x] add docstrings and reference docs - [ ] fix user code (Google code, NumPyro, jaxnet, anything else we think of...) 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] Output of `conan install` displays `Install finished succesfully` instead of `Install finished successfully` ### Environment details * Operating System+version: MacOS Ventura 13.2.1 * Compiler+version: NA * Conan version: 2.0.2 * Python version: 3.9.6 After a successful call of `conan install .`, the final message which is displayed is `Install finished succesfully`. I suggest to use `successfully` instead of `succesfully`. I can create a PR if necessary. ### Steps to reproduce _No response_ ### Logs _No response_ 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: CustomTarget no longer allowed for qt.preprocess in 0.59 Hi! A project I am working on currently fails to build with Meson 0.59 because output of the Qt DBus generator / Qt Remote Object compiler is passed to `qt.preprocess`, resulting in this new error: ``` ERROR: qt.preprocess keyword argument "moc_headers" contained a value of type "CustomTarget" but should have been one of: "File", "str" ``` This has worked fine before (Meson <= 0.58), and is probably a sideeffect of the Qt refactoring that has happened - maybe it's just a matter of allowing `CustomTarget` as a parameter, I will have a look at the code later :-) 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: Documentation for the Pulse module: `Command` linking <!-- ⚠ 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**: - **Python version**: - **Operating system**: ### What is the current behavior? Locally, `Command` API cross references are not working. With the Azure docs artifact, neither Channels nor Commands works, but Channels works for me locally! :( ### Steps to reproduce the problem Build this locally https://github.com/Qiskit/qiskit-terra/pull/3849 ### What is the expected behavior? Clicking `Command` and `commands` should lead to another API page ### Suggested solutions Just need to update `:mod:~qiskit.pulse.commands` probably. It should work though, so unclear! 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: Wav2Vec2 Conformer Encoder # 🚀 Feature request <!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. --> Implementing Conformer version of context encoder for [Wav2Vec2](https://github.com/pytorch/fairseq/blob/main/fairseq/models/wav2vec/wav2vec2.py#L390). ## Motivation Fairseq has recently updated their Wav2Vec2 codebase, incorporating [Conformer-based](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/config/pretraining/wav2vec2_conformer_large_librivox.yaml) encoder as an option with already existing Transformer-based version. As long as a decent number of the new SOTA-matching papers are utilizing Conformers for either of ctc or transducer models, it would be a great addition to Transformers library to have an option of using transformer or conformer based encoder for [Wav2Vec2ForCTC](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/modeling_wav2vec2.py#L1647) Some of the papers: [JUST](https://arxiv.org/pdf/2111.08137v1.pdf), [Pushing the Limits of Semi-Supervised Learning for ASR](https://arxiv.org/pdf/2010.10504.pdf) 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: Big query TIMESTAMP field off by factor of 1000 Using master branch. In BigQuery TIMESTAMP fields are off by a factor of 1000 when using fetch_data. See my in-line comment - https://github.com/GoogleCloudPlatform/gcloud-python/commit/41e64395661a31af8352fb2358e5c95ff5437830#diff-c56e349a3e511bfabb4e8dfcfe686bc8R739 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: BUG: bizarre repr of empty dtype I doubt anyone is using this, but multiple parts of this repr are bizarre: ``` >>> np.zeros(10, np.dtype([])) array([), ), ), ), ), ), ), ), ), )], dtype='[]') ``` Thankfully not a 1.14 regression - was there in 1.13 too 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: Make External Data Source formats into constants As suggested in PR #5491 Make constants for external data source formats to avoid directly using strings like 'CSV' and 'GOOGLE_SHEETS' 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: Learning rate is given as tensor, cannot serialize TrainerState in order to save checkpoint. ### System Info - `transformers` version: 4.21.1 - Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.35 - Python version: 3.10.5 - Huggingface_hub version: 0.8.1 - PyTorch version (GPU?): 1.12.1.post200 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no ### Who can help? @sgugger ### Information - [ ] The official example scripts - [X] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [X] My own task or dataset (give details below) ### Reproduction 1. use Adafactor optimizer and schedule ``` optimizer = Adafactor( model.parameters(), relative_step=True, warmup_init=True, ) lr_scheduler = AdafactorSchedule(optimizer) ``` 2. save a checkpoint ### Expected behavior When using an `AdafactorSchedule` I can't use the `Trainer` class to save a checkpoint. It breaks [here](https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L97) since the learning rate attached to the `TrainerState` is given by a tensor and tensors are not JSON serializable. I dropped a breakpoint at this line and took a look at my `TrainerState`: ``` In [4]: self.log_history[0]['learning_rate'] Out[4]: tensor(0.0001, device='cuda:0') ``` Expected behavior is that the learning rate attached to the log history would be given by a float and would therefore be JSON serializable. 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: File not found with rules calling meson --internal Commit d882b6fbd466940d452cfaa890bd9270e10a93b4 introduced a regression caught by the GNOME Continuous integration builder. While building a project, in this case libinput, after that commit, the process would stop with this error: ``` FAILED: input@sha/libinput.so.10.13.0.symbols /usr/bin/python3 /usr/lib/python3.5/site-packages/mesonbuild/../meson.py --internal symbolextractor libinput.so.10.13.0 'input@sha/libinput.so.10.13.0.symbols' /usr/bin/python3: can't open file '/usr/lib/python3.5/site-packages/mesonbuild/../meson.py': [Errno 2] No such file or directory ninja: build stopped: subcommand failed. ``` Full build log available [here](http://build.gnome.org/continuous/buildmaster/builds/2017/11/21/60/build/log-libinput.txt). 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: RedshiftDataOperator: Add support for Redshift serverless clusters ### Description This feature adds support for Redshift Serverless clusters for the given operator. ### Use case/motivation RedshiftDataOperator currently only supports provisioned clusters since it has the capability of adding `ClusterIdentifier` as a parameter but not `WorkgroupName`. The addition of this feature would help users to use this operator for their serverless cluster as well. ### Related issues None ### Are you willing to submit a PR? - [ ] 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 ipython/ipython codebase. Here's the problem: description keyword for interactive widget When I use the keyword description with interact ``` python from IPython.html import widgets def simpleFunction(a): print a i = interact(simpleFunction, a=widgets.FloatSliderWidget(values=(0,10), description='A label for my widget')) ``` I get this error ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) TypeError: simpleFunction() got an unexpected keyword argument 'A label for my widget' ``` 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: Typo in documentation. Under parameters for `GenerationMixin.generate()` there is no new bullet point for `prefix_allowed_tokens_fn`, and the content for it is under [`diversity_penalty`](https://huggingface.co/docs/transformers/v4.13.0/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate.diversity_penalty). This typo only exists for versions after the document UI update(>= 4.13.0). @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 Qiskit/qiskit codebase. Here's the problem: Expose parameter_prefix to ZZFeatureMap ### What is the expected enhancement? Currently, the `ZZFeatureMap` class inherits from `PauliFeatureMap` but the `parameter_prefix` cannot be set in the constructor. https://github.com/Qiskit/qiskit-terra/blob/fe5acd4d15355a566a32ee63fb4a50771e5994d4/qiskit/circuit/library/data_preparation/zz_feature_map.py#L60-L68 Thus, when one wants to include more than one `ZZFeatureMap`s per quantum circuit, one needs to rebind the parameters by doing: ``` params_a = ParamVector(...) params_b = ParamVector(...) qc_a = fm.bind_parameters(params_a) qc_b = fm.bind_parameters(params_b) ``` Circuits with multiple feature maps are often used when employing data reuploading. If `parameter_prefix` would be exposed to `ZZFeatureMap`, the user could just initialise the different feature maps with distinct parameter prefixes and append them to the larger circuit without having to define/rebind additional parameter vectors. Furthermore, `parameter_prefix` is already exposed to other forms such as `TwoLocal`, so by adding this feature, the code would become more consistent. 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: Firestore: links in docs, static code analysis and autocompletion in PyCharm are broken Due to docstrings formatted like this, static code analysis and autocomplete in PyCharm and the links in the [online Sphinx docs](https://googleapis.github.io/google-cloud-python/latest/firestore/client.html#google.cloud.firestore_v1beta1.client.Client.document) are broken: https://github.com/googleapis/google-cloud-python/blob/de0103cedaf9fb9f03ddca9be801ac64635d7b74/firestore/google/cloud/firestore_v1/client.py#L250-L252 This should result in the same short name with a working link in the Sphinx docs and would not kill autocomplete etc: ``` Returns: :class:`~google.cloud.firestore_v1.document.DocumentReference`: A reference to a document in a collection. ``` 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: Static library may contain stale object files If a meson build script removes files from a static library, they may still reside in the resulting library after rebuilding. For example, if the following is built: ``` project('test', 'c') lib = static_library('foo', 'file1.c') executable('a.out', 'main.c', link_with: lib) ``` libfoo.a will include file1.c.o. However, if meson.build is changed so file2.c is used instead, libfoo.a will include both file1.c.o and file2.c.o: ``` $ nm libfoo.a file1.c.o: ... file2.c.o: ... ``` At least on gcc, linking the final executable will link in file1.o (most likely because it precedes file2.o), which would result in a broken executable if at all. 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: Script to generate contributor list for release note should pick up co-authors Example commit: https://github.com/numpy/numpy/pull/18956/commits/eb231979b90cfe7f434bd598aefe4196df74b32e It looks like the script does not pick up co-authors. The PRs at today's sprint mostly came from pairs of contributors, so we should ensure we fix this before the next release. 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: MSBuild helper useEnv option causes missing include paths in some casess In using MSBuild helper for the first time, I received a number of errors about headers that could not be found. After spending significant time validating that the project works normally when running msbuild manually, I determined that the breaking flag was `/useEnv:True`. I briefly looked at the implementation and it was not immediately obvious to me where the problem might be. I have worked around it by using the `get_command` method from the MSBuild helper, which returns a command where the default of `useEnv` is `False`. It's mysterious why it causes this problem, but if everything is correct, then the requested change would be to simply expose `useEnv` as a parameter in the `build` method of the `MSBuild` helper rather than forcing it to `True`. I will try to provide a way to reproduce the error, but it's a private project and I don't know if I have time to isolate the problem any time soon. I spent to much time just identifying it. - [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. - [] 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 numpy/numpy codebase. Here's the problem: Use of `ravel` in `intersect1d` breaks compatibility with xarray See https://github.com/pydata/xarray/issues/2371 ### Numpy/Python version information: ``` 1.15.0 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 17:14:51) [GCC 7.2.0] ``` 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: BertGenerationTokenizer provides an unexpected value for BertGenerationModel - `transformers` version: 4.2.2 - PyTorch version (GPU?): 1.7.0+cu101 - tokenizers: @n1t0, @LysandreJik ## Information in both models BertGenerationEncoder, BertGenerationDecoder, there's no need for `token_type_ids` however the BertGenerationTokenizer provides it, this issue will be raised if you want to input the tokenizer results directly with `**`, and if it meant to be like this, and the user should be aware of this behaviour, I think a change should be in the documentation. Note: Another issue with BertGenerationTokenizer is the necessity of sentencepiece module, do you prefer that it should for the user to install it separately or it should be included in transformers dependencies. 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 causes conanbuildinfo.cmake to be re-generated even if it hasn't changed I've written my own buildscript that attempts to build my entire project including all dependencies in a single step. On every build, my script calls `conan install` to install the dependencies and then runs `cmake` and `ninja` to build my own source files. I'm calling `conan install` before every build, because otherwise the build process won't pick up any changes in the consumer `conanfile.py` (for example a version update of a dependency). Unfortunately, every time I call `conan install`, conan will re-generate the `conanbuildinfo.cmake`, even when its contents haven't changed. Now the cmake/ninja step will cause a full rebuild of my own sources, because the timestamp on `conanbuildinfo.cmake` was updated. This rebuild is unnecessary (and takes a long time), since nothing has actually changed. Wouldn't it be better if conan only generated that file if it doesn't yet exist or if any changes were made? Or am I missing something? I'm running conan 1.3.3 on macOS and my build system is CMake+ninja. 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: Transactions are commited in CommitRequest.NON_TRANSACTIONAL mode We have just been bitten by this using the latest `0.5.0`. We had the following piece of code, run in two different threads: ``` python with dataset.transaction as t: entity = dataset.get(entity_key) archived_entity = _move_to_archive_key(entity) t.delete(entity.key) t.put(archived_entity) return archived_entity ``` We've seen two copies of the `archived_entity` for the same `entity`. We've managed to reproduce it with two threads performing this concurrently (running separate Connections each, because httplib2 is not thread safe). Having used Datastore's underlying technology before, I expected to get a `ConcurrentModificationException` on delete, and one of the threads throwing it, similar to what the Java API for AppEngine describes: https://cloud.google.com/appengine/docs/java/datastore/transactions#Java_Uses_for_transactions However, both of them succeed creating two copies of `archived_entity`, and there's no `ConcurrentModificationException` anywhere in `gcloud-python` code. We've tracked down to the following: - `Transaction#commit` calls the `Batch#commit` https://github.com/GoogleCloudPlatform/gcloud-python/blob/master/gcloud/datastore/transaction.py#L177 - `Batch#commit` is unaware of the `Transaction#id`, calls `Connection#commit` without the `transaction_id` parameter https://github.com/GoogleCloudPlatform/gcloud-python/blob/master/gcloud/datastore/batch.py#L214 - `Connection#commit`gets `transaction_id`, and sets the REST request's mode to `datastore_pb.CommitRequest.NON_TRANSACTIONAL` in https://github.com/GoogleCloudPlatform/gcloud-python/blob/master/gcloud/datastore/connection.py#L321 _Impact_: This means that **all** transactions made through `python-gcloud` are **non-transactional**. Please treat it as a P0 bug. 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: Meson crashes when calling dependency with no specified languages if a C toolchain is not installed **Describe the bug** If your system doesn't have a C toolchain installed and you have a project with no specified languages, and try to call the dependency function, meson will encounter an uncaught exception and crash. I encountered this while installing meson as an overlay package on fedora silverblue. **To Reproduce** - have no C toolchain installed - place the following in `meson.build`: ```meson project('foo', version: '0.1.0', meson_version: '>= 0.59.0', default_options: [ 'warning_level=2', 'werror=false' ]) dependency('openssl', version: '>= 3.0', required: false) ``` - `meson build` **Expected behavior** No crash, possibly an error telling the user that no C toolchain is installed. **System Parameters** * Native build * Fedora Silverblue 36 * Python 3.10.5 * Meson 0.62.2 * Ninja 1.10.2 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 5.1 sporadically freezes when matplotlib plots are active I've using ipython 5.1 with matplotlib 1.5.1 with the Qt4Agg backend on Mac OS X 10.9.5. There have now been 3 occasions when the ipython terminal froze entirely - it would not accept control-c, control-z, or any command I tried. Quitting the Qt plot windows re-activated the python terminal. This so far has *not* affected me the first or second time I've made a plot window, but eventually it seems to affect every ipython session I've started with a plot window. I upgraded to matplotlib 2.0, and the same thing happened, but this time (now with Qt5Agg as a backend), I get a message: ``` QEventDispatcherMac::unregisterSocketNotifier: Tried to unregister a not registered notifier ``` After that notification, the entire terminal containing ipython became unresponsive. I think the same message was issued under ipython + matplotlib 1.5.1, but the message only appeared after closing ipython entirely, i.e.: ``` In [11]: Do you really want to exit ([y]/n)? QEventDispatcherMac::unregisterSocketNotifier: Tried to unregister a not registered notifier ``` 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: DO NOT MERGE: Error Reporting: fix client_info bug, update docstrings. This PR was generated using Autosynth. :rainbow: Here's the log from Synthtool: ``` synthtool > Cloning googleapis. synthtool > Running generator for google/devtools/clouderrorreporting/artman_errorreporting.yaml. synthtool > Ensuring dependencies. synthtool > Pulling artman image. synthtool > Generated code into /home/kbuilder/.cache/synthtool/googleapis/artman-genfiles/python/error-reporting-v1beta1. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/error_group_service_pb2.py. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/report_errors_service_pb2.py. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/error_group_service_pb2_grpc.py. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/error_stats_service_pb2_grpc.py. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/report_errors_service_pb2_grpc.py. synthtool > Replaced 'from google.devtools.clouderrorreporting_v1beta1.proto import ' in google/cloud/errorreporting_v1beta1/proto/error_stats_service_pb2.py. synthtool > Cleaned up 0 temporary directories. ``` 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: shift_tokens_right function missing for mt5 models ## Environment info - `transformers` version: 4.17.0.dev0 - Platform: Linux-5.11.0-1028-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyTorch version (GPU?): 1.11.0a0+git06838ce (False) - Tensorflow version (GPU?): 2.9.0-dev20220221 (False) - Flax version (CPU?/GPU?/TPU?): 0.4.0 (cpu) - Jax version: 0.3.1 - JaxLib version: 0.3.0 ### Who can help @patil-suraj ## Information Hello I am trying to finetune with Flax on TPU a mt5-small model on a summarization task, using the examples/flax/summarization/run_summarization_flax.py script. When I run the script, I get an error about the fact that shift_tokens_right is not defined for mt5 models: `` File "/home/prod/transformers/examples/flax/summarization/run_summarization_flax.py", line 521, in main # shift_tokens_right_fn = getattr(model_module, "shift_tokens_right") AttributeError: module 'transformers.models.mt5.modeling_flax_mt5' has no attribute 'shift_tokens_right' `` Moreover, the current flax summarization script has a typo line 516 : See https://github.com/huggingface/transformers/blob/master/examples/flax/summarization/run_summarization_flax.py#L516 : `` model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"]) `` (tight should be right !) I have been able to fix the issue by copying the function shift_tokens_right defined in `src/transformers/models/t5/modeling_flax_mt5.py` into the file `src/transformers/models/mt5/modeling_flax_mt5.py` . Now the Flax summarization script works fine. Hope you can fix the mt5 code accordingly ! 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: Allow for setting qobj header description in execute and propagate to job <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues to confirm this idea does not exist. --> ### What is the expected enhancement? The Qobj accepts a description in the header that allows the user to label the qobj with whatever message they want, e.g. `'cool_exp_v1'`. However, this can not be set unless one first does `compile` and then manually sets the description. Because the user never sees the qobj (and perhaps never should since it is just a fancy box) when using `execute` it is impossible to save this info. In addition, if I try to grab a job by the description (see #1710), or some substring in description, to the best of my knowledge I need to load all the jobs, and then make another API call for each job to load the qobj and then search the header description. It would be nice if the qobj header description could be set in execute, and then this description gets added to the resulting job, e.g. `job.description` so that I can search jobs by this value without needing to load qobjs. 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: Cloud spanner client does not seem to be retrying broken streams [Documentation](http://google-cloud-python.readthedocs.io/en/latest/spanner/snapshot-usage.html) for snapshot transactions mentions: ``` If streaming a chunk fails due to a “resumable” error, Session.read() retries the StreamingRead API reqeust, passing the resume_token from the last partial result streamed. ``` This is the correct thing to do but the client actually does not seem to be doing this. I looked around a little bit in the code and I could find anyplace where it is retrying the read with the resume token. This is the place where the stream would throw an error if it broke: https://github.com/GoogleCloudPlatform/google-cloud-python/blob/9d9b6c0708ae6553458ad6107e5ad8efb23762e8/spanner/google/cloud/spanner/streamed.py#L132 I dont see any exception handling happening here and it looks like that the error would just be propagated back to the user. 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: scatter_add requires dimension numbers, default value invalid ```python3 jax.lax.scatter_add(np.zeros(10), np.asarray([3, 1]), np.asarray([5., 7.])) ``` fails with `AssertionError` because of `assert type(dimension_numbers) is ScatterDimensionNumbers` The following works, but there is no documentation on these parameters, so I inferred them from the tests. ```python3 dnums = jax.lax.ScatterDimensionNumbers(update_window_dims=(), inserted_window_dims=(0,), scatter_dims_to_operand_dims=(0,), index_vector_dim=1) jax.lax.scatter_add(np.zeros(10), np.asarray([3, 1]), np.asarray([5., 7.]), dnums) ``` Also, it might be worth adding `jax.lax.scatter` with the first argument set to zeros. 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: Speed up gradient norm clipping. ## 🚀 Feature Improve the speed of gradient clipping. ### Motivation I've noticed that clipping the gradients is quite slow, e.g., for a ResNet50 model (with some minor extensions) gradient clipping takes ~207 ms. The major bottlenecks are the `torch.where` computation within a loop and the manual norm computation. ### Pitch With slight modifications that don't hurt readability and work well on TPU, grad_norm can be computed in ~75ms, i.e., 2.7x as fast. For implementation details see the PR I'm going to link in a second. Reproduce: Train any model you've got lying around with gradient_clip_val=0.1 and take a look at the trace timeline ;) As a reference, here's a before and after comparison for my model (trace after 40 warmup steps): Current: ![image](https://user-images.githubusercontent.com/16101605/88942052-09cd7100-d28a-11ea-93db-73c72e350ac4.png) With speed improvements: ![image](https://user-images.githubusercontent.com/16101605/88941994-f4584700-d289-11ea-9c72-668a8b8d227e.png) 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: marginal_distribution raises for NumPy array indices ### Environment - **Qiskit Terra version**: 0.21 - **Python version**: 3.10 - **Operating system**: osx ### What is happening? `marginal_distribution` raises error when passes a NumPy array of indices: ```python marginal_distribution(counts, np.asarray([0, 2])) ``` --> 225 if indices is not None and (not indices or not set(indices).issubset(range(num_clbits))): 226 raise QiskitError(f"indices must be in range [0, {num_clbits - 1}].") 228 if isinstance(counts, Counts): ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ### How can we reproduce the issue? Try above ### What should happen? It should work for both lists and arrays. ### Any suggestions? _No response_ 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: Using gelf log driver and lose the ability to `fig run <container> bash` Im using `gelf` logging in my docker-compose file and I lose the ability to use `fig run <container> bash` because it gives the error "Error attaching: configured logging reader does not support reading". An example config is: ``` my_container: image: link-to-image log_driver: "gelf" log_opt: gelf-address: udp://0.0.0.0:12201 gelf-tag: my_container ``` I understand that I lose the ability to use `docker-compose logs` or `docker-compose logs my_container` but I would expect `docker-compose run my_container bash` to let me into the container as I am overriding the CMD line manually. ``` docker-compose version: 1.5.1 Docker version 1.9.0, build 76d6bc9 ``` Using gelf log driver and lose the ability to `fig run <container> bash` Im using `gelf` logging in my docker-compose file and I lose the ability to use `fig run <container> bash` because it gives the error "Error attaching: configured logging reader does not support reading". An example config is: ``` my_container: image: link-to-image log_driver: "gelf" log_opt: gelf-address: udp://0.0.0.0:12201 gelf-tag: my_container ``` I understand that I lose the ability to use `docker-compose logs` or `docker-compose logs my_container` but I would expect `docker-compose run my_container bash` to let me into the container as I am overriding the CMD line manually. ``` docker-compose version: 1.5.1 Docker version 1.9.0, build 76d6bc9 ``` 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: cached-property dep unnecessary for py3.8+ ## Description of the issue The dependency on `cached-property` package is unnecessary for Python 3.8 and newer. These versions of Python provide it via `functools.cached_property`. ## Context information (for bug reports) **Output of `docker-compose version`** n/a **Output of `docker version`** n/a **Output of `docker-compose config`** n/a ## Steps to reproduce the issue 1. Install the package on py3.8+ ### Observed result `cached-property` is installed. ### Expected result `cached-property` not installed. ### Stacktrace / full error message n/a ## Additional information n/a 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: custom lookup dependencies have broken .type_name() As discussed on IRC, I was trying to create a new custom lookup dependency and in the process (message() debugging in meson.build) discovered this is a giant pile of fail. Ordinarily, we set e.g. pkg-config lookups as pkg-config types etc. but mesonbuild.dependencies.* is handling this... bizarrely. https://github.com/mesonbuild/meson/blob/15fb6a407f06eae35b9d1ab72953bfdf7d9aa5ed/mesonbuild/dependencies/base.py#L309-L313 But Dependency treats type_name as type_name. And everywhere we create an ExternalDependency, we give it the .name() of the dependency in the type_name position. 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: Pulse simulator QobjConfig <!-- ⚠ If you do not respect this template, your issue will be closed --> <!-- ⚠ Make sure to browse the opened and closed issues to confirm this idea does not exist. --> ### What is the expected enhancement? Pulse simulator configuration needs additional fields: `hamiltonian`, `dt`, `noise`, `ode_options`. These values should be validated and added to `PulseQobjConfig`. 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: completer latex_matches no respecting API It return matched text even if it return no completions. 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: module 'torch.nn' has no attribute 'Identity' Traceback (most recent call last): File "trainer.py", line 17, in <module> model = XLMForSequenceClassification(config) File "/home/ankit/anaconda3/lib/python3.6/site-packages/pytorch_transformers/modeling_xlm.py", line 823, in __init__ self.sequence_summary = SequenceSummary(config) File "/home/ankit/anaconda3/lib/python3.6/site-packages/pytorch_transformers/modeling_utils.py", line 734, in __init__ self.summary = nn.Identity() AttributeError: module 'torch.nn' has no attribute 'Identity' https://github.com/huggingface/pytorch-transformers/blob/2f869dc6651f9cf9253f4c5a43279027a0eccfc5/pytorch_transformers/modeling_utils.py#L734 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: `tokenizer.special_tokens_map` has stringified list for "additional_special_tokens" value. ## Environment info - `transformers` version: 4.8.2 - Platform: macOS-10.15.7-x86_64-i386-64bit - Python version: 3.8.10 - PyTorch version (GPU?): 1.9.0 (False) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: No - Using distributed or parallel set-up in script?: No ### Who can help @LysandreJik maybe? ## Information Model I am using (Bert, XLNet ...): XLMRoberta The problem arises when using: * [ ] the official example scripts: (give details below) * [X] my own modified scripts: (give details below) The tasks I am working on is: * [ ] an official GLUE/SQUaD task: (give the name) * [ ] my own task or dataset: (give details below) ## To reproduce Steps to reproduce the behavior: 1. `>>> from transformers import XLMRobertaTokenizer` 2. `>>> m = XLMRobertaTokenizer.from_pretrained('xlm-roberta-base')` 3. `>>> m.add_special_tokens({"additional_special_tokens": ["<space>"]})` 4. `>>> m.special_tokens_map['additional_special_tokens'] == "['<space>']" # True` ## Expected behavior The value should be a list containing the special characters. :-) The work-around is to use the `additional_special_tokens` attribute directly. 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: Add deprecation warnings for conda 4.6 deprecated modules: * `conda.cli.activate` * `conda.compat` * `conda.install` 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: latex_to_mpl_png relies on deprectated functionality in matplotlib 3.4 Short repro: ``` In [1]: from IPython.lib import latextools In [2]: png = latextools.latex_to_png_mpl('f(x) = y', True) /lib/python3.8/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: The to_png function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead. mt.to_png(f, s, fontsize=12, dpi=dpi, color=color) /lib/python3.8/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: The to_rgba function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead. mt.to_png(f, s, fontsize=12, dpi=dpi, color=color) /lib/python3.8/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: The to_mask function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead. mt.to_png(f, s, fontsize=12, dpi=dpi, color=color) /lib/python3.8/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: The MathtextBackendBitmap class was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead. mt.to_png(f, s, fontsize=12, dpi=dpi, color=color) ``` Deprecation info: https://matplotlib.org/stable/api/api_changes.html#mathtextparser-bitmap-is-deprecated `math_to_image` has been around since the beginning, so it hopefully has no version compatibility issues: https://github.com/matplotlib/matplotlib/blame/e904fa3b1e5455cf741017268dae7c94b6caa5d6/lib/matplotlib/mathtext.py#L587 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: LayoutXLMProcessor applies the english Tesseract model # 🚀 Feature request LayoutXLMProcessor.\_\_call\_\_ should support a language argument for Tesseract OCR ## Motivation LayoutXLM is a multilingual version of the successful LayoutLMv2 model. The main reason to use it over LayoutLMV2 is to handle different languages, yet the current API does not allow specifying the language to be used in apply_tesseract. ## Your contribution I could submit a PR but I am not that familiar with the Transformers library to suggest the best place to add the lang argument. 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: Require for Geometric Distribution It seems that `jax` does not have geometric distribution in its random module like `scipy.stats`. Will it be added? I really need 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 docker/compose codebase. Here's the problem: Don't set hostname to service name when using --x-networking Using the new networking features, when you start a container. The name is correctly `<folder>_<service>_1` but the hostname is `<service>` It's impossible the scale the containers in some case because all the containers get the same hostname. 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: Missing "if __name__ == '__main__':" check in /usr/bin/ipython When executed, the top level ipython script always launches a new instance, even if the module is not being executed as `__main__`. This is currently being worked around in the standard library (by special casing IPython), but the correct fix is for IPython to not launch a new instance when the module is being run as anything other than `__main__`. 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 prefix arg to storage.bucket.get_all_keys add prefix arg to storage.bucket.get_all_keys 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: XLNetLMHeadModel has no attribute 'from_config' ## Environment info <!-- You can run the command `transformers-cli env` and copy-and-paste its output below. Don't forget to fill out the missing fields in that output! --> - `transformers` version: 4.8.0 - Platform: Linux-4.15.0-20-generic-x86_64-with-LinuxMint-19-tara - Python version: 3.6.9 - PyTorch version (GPU?): 1.8.0+cu111 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes ### Who can help @patrickvonplaten ## Information Model I am trying to train a XLNet model from scratch to portuguese but when i run this script: https://github.com/huggingface/transformers/blob/v4.8.2-release/examples/pytorch/language-modeling/run_plm.py i get this error: Traceback (most recent call last): File "run_plm.py", line 498, in <module> main() File "run_plm.py", line 330, in main model = XLNetLMHeadModel.from_config(config) AttributeError: type object 'XLNetLMHeadModel' has no attribute 'from_config' The problem arises when using: * [x] the official example scripts: (give details below) The tasks I am working on is: * [x] my own task or dataset: (give details below) portuguese wikipedia dataset 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_find_package generator not usable with CMake multi-config generators like VS * conan 1.12.3 * CMake 3.13.4 * Windows 10 When using the cmake_find_package generator conan generates FindXXX.cmake modules for consumption. However these find modules only contain one build_type. When using these find modules with a CMake multi configuration build system, like Visual Studio, this does not work, because we need information for all configurations in the find module. Example: Running `conan install -g cmake_find_package zlib/1.2.11@conan/stable` produces a `Findzlib.cmake` file that will contain all relevant information about using zlib in Release configuration. Specifically only the link library name `zlib` is provided. When running `conan install -g cmake_find_package -s build_type=Debug zlib/1.2.11@conan/stable` the find module `Findzlib.cmake` now contains all relevant information for using the debug build of zlib, specifically the link library name now correctly is `zlibd`. Because the find modules only contain one build_type at a time they cannot correctly be used with a multi configuration build system like Visual Studio. However find modules can contain multiple configuration they "only" need to be written accordingly by conan. I stumbled upon this while using the conan cmake wrapper. This script installs all build types when used by a multi configuration generator and then only the find module of the last installed build_type survives. Also I think it is not feasible to have both find modules available (e.g. by using different names or directories) as only one can be used at a time because targets are generated. 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: PTK completion: Close completion drop-down box Completion drop-down box can not be closed with Escape. User will need to type something to close it, or move cursor back. Suggestion: Escape should close completion drop-down box 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: Add back `environment.yml` file I realized why we might want to maintain an `environment.yml` file in parallel with our `requirements.txt` file: `requirements.txt` will be installed via `pip`, whereas if you create an environment via `conda`, the packages will be installed / maintained via `conda`. This can be useful for those who try to `conda install` everything (since it has different package version logic + handles non-python dependencies). 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: ParamsDict represents the class object itself, not keys and values on Task Instance Details ### Apache Airflow version 2.3.3 (latest released) ### What happened ParamsDict's printable presentation shows the class object itself like `<airflow.models.param.ParamsDict object at 0x7fd0eba9bb80>` on the page of Task Instance Detail because it does not have `__repr__` method in its class. <img width="791" alt="image" src="https://user-images.githubusercontent.com/16971553/180902761-88b9dd9f-7102-4e49-b8b8-0282b31dda56.png"> It used to be `dict` object and what keys and values Params include are shown on UI before replacing Params with the advanced Params by #17100. ### What you think should happen instead It was originally shown below when it was `dict` object. ![image](https://user-images.githubusercontent.com/16971553/180904396-7b527877-5bc6-48d2-938f-7d338dfd79a7.png) I think it can be fixed by adding `__repr__` method to the class like below. ```python class ParamsDict(dict): ... def __repr__(self): return f"{self.dump()}" ``` ### How to reproduce I guess it all happens on Airflow using 2.2.0+ ### Operating System Linux, but it's not depending on OS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit 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 google/jax codebase. Here's the problem: Error compiling while_of_pmap The test is lax_control_flow_tests.py:test_while_loop_of_pmap. It fails on CPU, GPU, and TPU. For GPU the failure seems to be ``` F0331 01:03:30.304149 7266 ir_emitter_unnested.cc:1766] Non-OK-status: body->Accept(&ir_emitter_body) status: Unimplemented: Requested AllReduce not implemented on GPU; replica_count: 8; operand_count: 1; IsCrossReplicaAllReduce: 1; NCCL support: 0; first operand array element-type: F64 ``` For TPU the failure is perhaps related but shows up elsewhere: ``` File "/build/work/c62b4eb2372c3477aa8032626244c507067f/google3/runfiles/google3/third_party/py/jax/interpreters/xla.py", line 169, in apply_primitive compiled_fun = xla_primitive_callable(prim, *map(arg_spec, args), **params) File "/build/work/c62b4eb2372c3477aa8032626244c507067f/google3/runfiles/google3/third_party/py/jax/interpreters/xla.py", line 194, in xla_primitive_callable compiled = built_c.Compile(compile_options=options, backend=backend) File "/build/work/c62b4eb2372c3477aa8032626244c507067f/google3/runfiles/google3/third_party/tensorflow/compiler/xla/python/xla_client.py", line 571, in Compile return backend.compile(self.computation, compile_options) File "/build/work/c62b4eb2372c3477aa8032626244c507067f/google3/runfiles/google3/third_party/tensorflow/compiler/xla/python/xla_client.py", line 151, in compile compile_options.tuple_arguments) RuntimeError: Invalid argument: Invalid (replica_count,computation_count) pair: (8,1) ``` 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 googleapis/google-cloud-python codebase. Here's the problem: Support both seconds from epoch and timestamp string If someone already has a timestamp string, we shouldn't force them to convert it to `datetime.datetime` so that we can in turn [convert that](https://github.com/GoogleCloudPlatform/gcloud-python/blob/0c74a599b6aa0a16e21575998e1770182c05e12c/gcloud/bigquery/table.py#L665-L669) into seconds. (Inspired by #1381.) From [the docs](https://cloud.google.com/bigquery/preparing-data-for-bigquery#datatype) > TIMESTAMP data types can be described in two ways: UNIX timestamps or calendar datetimes. BigQuery stores TIMESTAMP data internally as a UNIX timestamp with microsecond precision. so we could allow the user to just pass a string and we "take their word for it" that it's a valid datetime. Since `Table.insert_data` returns a list of errors anyhow, we wouldn't have to do much/anything to enforce 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 googleapis/google-cloud-python codebase. Here's the problem: BigQuery: get table schema if not supplied (and have pyarrow) in `load_table_from_dataframe` Follow-up to https://github.com/googleapis/google-cloud-python/pull/8105#discussion_r286998303 When a table schema isn't supplied in `load_table_from_dataframe`, try to get the existing table schema. This will prevent errors due to ambiguous pandas types (#7370) without having to explicitly provide a schema. Note: this behavior is similar to that of pandas-gbq, which always fetches the table schema and then compares to make sure it's compatible with the dataframe schema. https://github.com/pydata/pandas-gbq/blob/59228d9c20cee12b24caa5cc41d3f2e6c0337932/pandas_gbq/gbq.py#L1115-L1121 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: TypeError: startswith first arg must be bytes or a tuple of bytes, not str when installing on python3 As stated in the description, this error occurs when installing on python3. ``` Collecting conan-0.9.0 from http://github.com/conan-io/conan/archive/release/0.9.0.tar.gz#egg=conan-0.9.0 Downloading http://github.com/conan-io/conan/archive/release/0.9.0.tar.gz (350kB) Complete output from command python setup.py egg_info: Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-build-_afor1qa/conan-0.9.0/setup.py", line 27, in <module> project_requirements = get_requires("conans/requirements.txt") File "/tmp/pip-build-_afor1qa/conan-0.9.0/setup.py", line 23, in get_requires if not line.strip().startswith("#"): ``` 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: `transpile` is always serial on windows `qiskit.tools.parallel_map` defaults to running jobs serially whenever the user is running on windows (since the default behavior of `multiprocess.Pool` under windows is considerably different from than that of mac/linux.) However, there are still some cases (like transpiling for RB) where there may be potential benefit to being able to parallelize jobs under windows. 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: Firestore: add IN and ARRAY_CONTAINS_ANY query operators With an upcoming backend up date Firestore will have support for two new query operators `IN` and `ARRAY_CONTAINS_ANY`. In order for these new operators to be used, they need to be wired up to the high level query system. These new operators allow queries like the following: ```js // IN query firestore.collection('cities') .where("country", 'in', ['USA', 'Japan']) // ARRAY_CONTAINS_ANY firestore.collection('cities') .where('regions', 'array-contains-any', ['west_coast', 'east_coast']) ``` Both operators expect an ArrayValue to match against. #### Additional context * New operators added to protos in https://github.com/googleapis/googleapis/commit/10f91fa12f70e8e0209a45fc10807ed1f77c7e4e * NodeJS reference implementation: https://github.com/googleapis/nodejs-firestore/pull/715 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: Check double retrieval of manifest pre-upload, and remove progress bars. Right now, the ``self._check_recipe_date(conan_ref, upload_remote)`` done in the upload retrieves the manifest first, and then the RestApi class do it again. It is also a bit weird to show progress bars on the download of the manifest prior to start uploading. This already happens in 1.7.3, but need to check it once the manifest comparison is done instead of the md5, merged in develop for 1.8 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: Default compile config seed is not random The default seed in `qiskit._compiler.COMPILE_CONFIG_DEFAULT` is set to 1. By default this should be `None` and result in a random seed being generated by simulator backends. 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: Pulse-optimized SU4 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: psum of complex64 not supported for multi GPU. Complex numbers are not supported for AllReduce. I found this bug when trying to do psum across 4 GPUs with complex64 as the dtype. ``` RuntimeError: Unimplemented: Requested AllReduce not implemented on GPU; replica_count: 4; operand_count: 1; IsCrossReplicaAllReduce: 1; NCCL support: 1; first operand array element-type: C64 ``` Can you help me debug this systematically and explain your reasoning?