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https://api.github.com/repos/huggingface/datasets/issues/1733
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MDU6SXNzdWU3ODQ5MDMwMDI=
| 1,733
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connection issue with glue, what is the data url for glue?
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"Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py"
] | 1,610,527,060,000
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Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks
|
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Couldn't reach swda.py
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[
"Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\r\nLet me know if this helps !",
"Thanks @SBrandeis ,\r\nProblem solved by downloading and installing the latest version datasets."
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MDU6SXNzdWU3ODQ1NjU4OTg=
| 1,729
|
Is there support for Deep learning datasets?
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[
"Hi @ZurMaD!\r\nThanks for your interest in 🤗 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated 🚀\r\n"
] | 1,610,482,961,000
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I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https://github.com/DZPeru/fish-datasets
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| 1,728
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Add an entry to an arrow dataset
|
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[
"Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!",
"That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n num_rows: 493\r\n })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?",
"You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.",
"> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.",
"Thanks @thomwolf. Your solution worked!"
] | 1,610,474,507,000
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Is it possible to add an entry to a dataset object?
**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**
For example, say we have the following code:
``` python
from datasets import load_dataset
# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```
Is it possible to add an entry to `squad_dataset`? Something like the following?
``` python
squad_dataset.append({'text': "This is a new sentence"})
```
The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.
If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?
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|
BLEURT score calculation raises UnrecognizedFlagError
|
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[
"Upgrading tensorflow to version 2.4.0 solved the issue.",
"I still have the same error even with TF 2.4.0.",
"And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!",
"I'm seeing the same issue with TF 2.4.1 when running the following in https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb:\r\n```\r\n!pip install git+https://github.com/google-research/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions = [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```",
"@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```",
"This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```",
"I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.",
"Seems to be a known issue in the bleurt codebase: https://github.com/google-research/bleurt/issues/24.",
"Hi, the problem should be solved now.",
"Hi @tsellam! I can verify that the issue is indeed fixed now. Thanks!"
] | 1,610,472,422,000
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NONE
| null | null |
Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`.
My environment:
```
python==3.8.5
datasets==1.2.0
tensorflow==2.3.1
cudatoolkit==11.0.221
```
Test code for reproducing the error:
```
from datasets import load_metric
bleurt = load_metric('bleurt')
gen_text = "I am walking on the promenade today"
ref_text = "I am walking along the promenade on this sunny day"
bleurt.compute(predictions=[test_text], references=[test_text])
```
Error Output:
```
Using default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').
INFO:tensorflow:Reading checkpoint /home/ubuntu/.cache/huggingface/metrics/bleurt/default/downloads/extracted/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4/bleurt-base-128.
INFO:tensorflow:Config file found, reading.
INFO:tensorflow:Will load checkpoint bert_custom
INFO:tensorflow:Performs basic checks...
INFO:tensorflow:... name:bert_custom
INFO:tensorflow:... vocab_file:vocab.txt
INFO:tensorflow:... bert_config_file:bert_config.json
INFO:tensorflow:... do_lower_case:True
INFO:tensorflow:... max_seq_length:128
INFO:tensorflow:Creating BLEURT scorer.
INFO:tensorflow:Loading model...
INFO:tensorflow:BLEURT initialized.
---------------------------------------------------------------------------
UnrecognizedFlagError Traceback (most recent call last)
<ipython-input-12-8b3f4322318a> in <module>
2 gen_text = "I am walking on the promenade today"
3 ref_text = "I am walking along the promenade on this sunny day"
----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])
~/anaconda3/envs/noved/lib/python3.8/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/bleurt/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778/bleurt.py in _compute(self, predictions, references)
103
104 def _compute(self, predictions, references):
--> 105 scores = self.scorer.score(references=references, candidates=predictions)
106 return {"scores": scores}
~/anaconda3/envs/noved/lib/python3.8/site-packages/bleurt/score.py in score(self, references, candidates, batch_size)
164 """
165 if not batch_size:
--> 166 batch_size = FLAGS.bleurt_batch_size
167
168 candidates, references = list(candidates), list(references)
~/anaconda3/envs/noved/lib/python3.8/site-packages/tensorflow/python/platform/flags.py in __getattr__(self, name)
83 # a flag.
84 if not wrapped.is_parsed():
---> 85 wrapped(_sys.argv)
86 return wrapped.__getattr__(name)
87
~/anaconda3/envs/noved/lib/python3.8/site-packages/absl/flags/_flagvalues.py in __call__(self, argv, known_only)
643 for name, value in unknown_flags:
644 suggestions = _helpers.get_flag_suggestions(name, list(self))
--> 645 raise _exceptions.UnrecognizedFlagError(
646 name, value, suggestions=suggestions)
647
UnrecognizedFlagError: Unknown command line flag 'f'
```
Possible Fix:
Modify `_compute` method https://github.com/huggingface/datasets/blob/7e64851a12263dc74d41c668167918484c8000ab/metrics/bleurt/bleurt.py#L104
to receive a `batch_size` argument, for example:
```
def _compute(self, predictions, references, batch_size=1):
scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)
return {"scores": scores}
```
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MDU6SXNzdWU3ODQxODIyNzM=
| 1,725
|
load the local dataset
|
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[
"You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit’s not possible to understand it and help you with only this information.",
"sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n",
"Did you try to follow the instructions in the documentation?\r\nHere: https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files",
"thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?",
"We will solve this soon (cf #1724)",
"thanks a lot",
"Hi! `json` is a packaged dataset now, which means its script comes with the library and doesn't require an internet connection."
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your guidebook's example is like
>>>from datasets import load_dataset
>>> dataset = load_dataset('json', data_files='my_file.json')
but the first arg is path...
so how should i do if i want to load the local dataset for model training?
i will be grateful if you can help me handle this problem!
thanks a lot!
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| 1,724
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could not run models on a offline server successfully
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[
"Transferred to `datasets` based on the stack trace.",
"Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets/datasets/text`: https://github.com/huggingface/datasets/blob/master/datasets/text/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n datasets = load_dataset('/path/to/text.py', data_files=data_files)\r\n```\r\nWhere `/path/to/text.py` is the path on the server where you saved the `text.py` script.",
"We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline",
"The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon",
"> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?",
"Yes it's been available since datasets 1.3.0 !"
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Hi, I really need your help about this.
I am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:
is there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...
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Possible cache miss in datasets
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[
"Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.",
"Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n group_texts,\r\n batched=True,\r\n num_proc=60,\r\n load_from_cache_file=True,\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 119, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 157, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1491, in _map_single\r\n tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 690, in NamedTemporaryFile\r\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n File \"/usr/lib/python3.6/tempfile.py\", line 401, in _mkstemp_inner\r\n fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"test.py\", line 48, in <module>\r\n cache_file_names={k: f'.cache/{str(k)}' for k in tokenized_datasets}\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in map\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 303, in <dictcomp>\r\n for k, dataset in self.items()\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1317, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/venv/lib/python3.6/site-packages/multiprocess/pool.py\", line 644, in get\r\n raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache/tmpsvszxtop'\r\n```\r\n",
"The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n results of the computation instead of the automatically generated cache file name.\r\n You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```",
"Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```",
"Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in `datasets==1.1.3`",
"Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path",
"I fixed the docstring. Hopefully this is less confusing now: https://github.com/huggingface/datasets/commit/42ccc0012ba8864e6db1392430100f350236183a",
"I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button ",
"This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda functions.",
"I opened a PR to change this, let me know what you think.",
"Sounds great, thank you for your quick responses and help! Looking forward for the next release.",
"I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n # Datasets\r\n train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n if args.dev_split >= len(train):\r\n raise ValueError(\"Not enough dev files\")\r\n dev = []\r\n state = random.Random(1001)\r\n for _ in range(args.dev_split):\r\n dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n def group_texts(examples):\r\n # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n # Truncate (not implementing padding)\r\n total_length = (total_length // max_seq_length) * max_seq_length\r\n # Split by chunks of max_seq_length\r\n result = {\r\n k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n for k, t in concatenated_examples.items()\r\n }\r\n return result\r\n\r\n datasets = load_dataset(\r\n 'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n cache_dir=args.data_cache_dir)\r\n datasets = datasets.map(tokenize_function, \r\n batched=True, remove_columns=['text'], \r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n datasets = datasets.map(group_texts, \r\n batched=True,\r\n cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04/26/2021 10:26:59 - WARNING - datasets.builder - Reusing dataset text (/home/manjavacasema/data/.cache/text/DBNL-f8d988ad33ccf2c1/0.0.0/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 21.07ba/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 24.28ba/s]\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/train-grouped\r\n04/26/2021 10:27:01 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/manjavacasema/data/.cache/dev-grouped\r\n```\r\n",
"Hi ! What tokenizer are you using ?",
"It's the ByteLevelBPETokenizer",
"This error happened to me too, when I tried to supply my own fingerprint to `map()` via the `new_fingerprint` arg.\r\n\r\nEdit: realized it was because my path was weird and had colons and brackets and slashes in it, since one of the variable values I included in the fingerprint was a dataset split like \"train[:10%]\". I fixed it with [this solution](https://stackoverflow.com/a/13593932/2287177) from StackOverflow to just remove those invalid characters from the fingerprint.",
"Good catch @jxmorris12, maybe we should do additional checks on the valid characters for fingerprints ! Would you like to contribute this ?\r\n\r\nI think this can be added here, when we set the fingerprint(s) that are passed `map`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/25bb7c9cbf519fbbf9abf3898083b529e7762705/src/datasets/fingerprint.py#L449-L454\r\n\r\nmaybe something like\r\n```python\r\nif kwargs.get(fingerprint_name) is None:\r\n ...\r\nelse:\r\n # In this case, it's the user who specified the fingerprint manually:\r\n # we need to make sure it's a valid hash\r\n validate_fingerprint(kwargs[fingerprint_name])\r\n```\r\n\r\nOtherwise I can open a PR later",
"I opened a PR here to add the fingerprint validation: https://github.com/huggingface/datasets/pull/4587\r\n\r\nEDIT: merged :)",
"thank you!"
] | 1,610,379,451,000
| 1,656,514,482,000
| 1,611,629,279,000
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NONE
| null | null |
Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
```
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MDU6SXNzdWU3ODMwNzQyNTU=
| 1,717
|
SciFact dataset - minor changes
|
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[
"Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! 🤗\r\nYou will find the relevant information about opening a PR in the [contributing guide](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md) and in the [dataset addition guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.",
"> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n",
"\r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https://sdproc.org/2021/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. \r\n\r\n",
"I opened a PR about this: https://github.com/huggingface/datasets/pull/1780. Closing this issue, will continue there."
] | 1,610,342,800,000
| 1,611,629,537,000
| 1,611,629,537,000
|
CONTRIBUTOR
| null | null |
Hi,
SciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!
I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?
It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https://github.com/huggingface/datasets/blob/master/datasets/scifact/scifact.py). Can you help me update the version on gcloud?
Thanks,
Dave
|
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| 1,713
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Installation using conda
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[
"Yes indeed the idea is to have the next release on conda cc @LysandreJik ",
"Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.",
"I think we can have `datasets` on conda by next week. Will see what I can do!",
"Thank you. Looking forward to it.",
"`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```"
] | 1,610,133,135,000
| 1,631,882,860,000
| 1,631,882,860,000
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NONE
| null | null |
Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.
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MDU6SXNzdWU3ODE5MTQ5NTE=
| 1,710
|
IsADirectoryError when trying to download C4
|
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[
"I haven't tested C4 on my side so there so there may be a few bugs in the code/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.",
"Fixed once processed data is used instead:\r\n- #2575"
] | 1,610,091,090,000
| 1,659,614,170,000
| 1,659,614,104,000
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NONE
| null | null |
**TLDR**:
I fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.
How can the problem be fixed?
**VERBOSE**:
I use Python version 3.7 and have the following dependencies listed in my project:
```
datasets==1.2.0
apache-beam==2.26.0
```
When running the following code, where `/data/huggingface/unpacked/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:
```
from datasets import load_dataset
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
```
I get the following stacktrace:
```
/Users/fredriko/venv/misc/bin/python /Users/fredriko/source/misc/main.py
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/fredriko/.cache/huggingface/datasets/c4/en/2.3.0/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...
Traceback (most recent call last):
File "/Users/fredriko/source/misc/main.py", line 3, in <module>
load_dataset("c4", "en", data_dir="/data/huggingface/unpacked", beam_runner='DirectRunner')
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/load.py", line 612, in load_dataset
ignore_verifications=ignore_verifications,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 527, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 1066, in _download_and_prepare
pipeline=pipeline,
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/builder.py", line 582, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/fredriko/.cache/huggingface/modules/datasets_modules/datasets/c4/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283/c4.py", line 190, in _split_generators
file_paths = dl_manager.download_and_extract(files_to_download)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 258, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 189, in download
self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 117, in _record_sizes_checksums
self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)
File "/Users/fredriko/venv/misc/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 80, in get_size_checksum_dict
with open(path, "rb") as f:
IsADirectoryError: [Errno 21] Is a directory: '/'
Process finished with exit code 1
```
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| 1,709
|
Databases
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[] | 1,610,086,443,000
| 1,610,096,408,000
| 1,610,096,408,000
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NONE
| null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
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| 1,708
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<html dir="ltr" lang="en" class="focus-outline-visible"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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[] | 1,610,055,924,000
| 1,610,096,401,000
| 1,610,096,401,000
|
NONE
| null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
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| 781,494,476
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MDU6SXNzdWU3ODE0OTQ0NzY=
| 1,706
|
Error when downloading a large dataset on slow connection.
|
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[
"Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?\r\n\r\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\r\nCould you try loading it again with \r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```"
] | 1,610,041,695,000
| 1,610,534,102,000
| null |
CONTRIBUTOR
| null | null |
I receive the following error after about an hour trying to download the `openwebtext` dataset.
The code used is:
```python
import datasets
datasets.load_dataset("openwebtext")
```
> Traceback (most recent call last): [4/28]
> File "<stdin>", line 1, in <module>
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset
> ignore_verifications=ignore_verifications,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare
> dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/builder.py", line 570, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> File "/home/lucadiliello/.cache/huggingface/modules/datasets_modules/datasets/openwebtext/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02/openwebtext.py", line 62, in _split_generators
> dl_dir = dl_manager.download_and_extract(_URL)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
> return self.extract(self.download(url_or_urls))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 235, in extract
> num_proc=num_proc,
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
> return function(data_struct)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
> tar_file.extractall(output_path_extracted)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2000, in extractall
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2042, in extract
> numeric_owner=numeric_owner)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2112, in _extract_member
> self.makefile(tarinfo, targetpath)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 2161, in makefile
> copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/tarfile.py", line 253, in copyfileobj
> buf = src.read(remainder)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/lzma.py", line 200, in read
> return self._buffer.read(size)
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 68, in readinto
> data = self.read(len(byte_view))
> File "/home/lucadiliello/anaconda3/envs/nlp/lib/python3.7/_compression.py", line 99, in read
> raise EOFError("Compressed file ended before the "
> EOFError: Compressed file ended before the end-of-stream marker was reached
|
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Some datasets miss dataset_infos.json or dummy_data.zip
|
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[
"Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n",
"Closing since the dummy data generation is deprecated now (and the issue with missing metadata seems to be addressed)."
] | 1,610,029,033,000
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| 1,667,574,360,000
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CONTRIBUTOR
| null | null |
While working on dataset REAME generation script at https://github.com/madlag/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json :
```
c4
lm1b
reclor
wikihow
```
And some does not have a dummy_data.zip :
```
kor_nli
math_dataset
mlqa
ms_marco
newsgroup
qa4mre
qangaroo
reddit_tifu
super_glue
trivia_qa
web_of_science
wmt14
wmt15
wmt16
wmt17
wmt18
wmt19
xtreme
```
But it seems that some of those last do have a "dummy" directory .
|
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Unable to install datasets
|
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[
"Maybe try to create a virtual env with python 3.8 or 3.7",
"Thanks, @thomwolf! I fixed the issue by downgrading python to 3.7. ",
"Damn sorry",
"Damn sorry"
] | 1,610,004,277,000
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NONE
| null | null |
** Edit **
I believe there's a bug with the package when you're installing it with Python 3.9. I recommend sticking with previous versions. Thanks, @thomwolf for the insight!
**Short description**
I followed the instructions for installing datasets (https://huggingface.co/docs/datasets/installation.html). However, while I tried to download datasets using `pip install datasets` I got a massive error message after getting stuck at "Installing build dependencies..."
I was wondering if this problem can be fixed by creating a virtual environment, but it didn't help. Can anyone offer some advice on how to fix this issue?
Here's an error message:
`(env) Gas-MacBook-Pro:Downloads destiny$ pip install datasets
Collecting datasets
Using cached datasets-1.2.0-py3-none-any.whl (159 kB)
Collecting numpy>=1.17
Using cached numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl (15.6 MB)
Collecting pyarrow>=0.17.1
Using cached pyarrow-2.0.0.tar.gz (58.9 MB)
....
_configtest.c:9:5: warning: incompatible redeclaration of library function 'ceilf' [-Wincompatible-library-redeclaration]
int ceilf (void);
^
_configtest.c:9:5: note: 'ceilf' is a builtin with type 'float (float)'
_configtest.c:10:5: warning: incompatible redeclaration of library function 'rintf' [-Wincompatible-library-redeclaration]
int rintf (void);
^
_configtest.c:10:5: note: 'rintf' is a builtin with type 'float (float)'
_configtest.c:11:5: warning: incompatible redeclaration of library function 'truncf' [-Wincompatible-library-redeclaration]
int truncf (void);
^
_configtest.c:11:5: note: 'truncf' is a builtin with type 'float (float)'
_configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtf' [-Wincompatible-library-redeclaration]
int sqrtf (void);
^
_configtest.c:12:5: note: 'sqrtf' is a builtin with type 'float (float)'
_configtest.c:13:5: warning: incompatible redeclaration of library function 'log10f' [-Wincompatible-library-redeclaration]
int log10f (void);
^
_configtest.c:13:5: note: 'log10f' is a builtin with type 'float (float)'
_configtest.c:14:5: warning: incompatible redeclaration of library function 'logf' [-Wincompatible-library-redeclaration]
int logf (void);
^
_configtest.c:14:5: note: 'logf' is a builtin with type 'float (float)'
_configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pf' [-Wincompatible-library-redeclaration]
int log1pf (void);
^
_configtest.c:15:5: note: 'log1pf' is a builtin with type 'float (float)'
_configtest.c:16:5: warning: incompatible redeclaration of library function 'expf' [-Wincompatible-library-redeclaration]
int expf (void);
^
_configtest.c:16:5: note: 'expf' is a builtin with type 'float (float)'
_configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1f' [-Wincompatible-library-redeclaration]
int expm1f (void);
^
_configtest.c:17:5: note: 'expm1f' is a builtin with type 'float (float)'
_configtest.c:18:5: warning: incompatible redeclaration of library function 'asinf' [-Wincompatible-library-redeclaration]
int asinf (void);
^
_configtest.c:18:5: note: 'asinf' is a builtin with type 'float (float)'
_configtest.c:19:5: warning: incompatible redeclaration of library function 'acosf' [-Wincompatible-library-redeclaration]
int acosf (void);
^
_configtest.c:19:5: note: 'acosf' is a builtin with type 'float (float)'
_configtest.c:20:5: warning: incompatible redeclaration of library function 'atanf' [-Wincompatible-library-redeclaration]
int atanf (void);
^
_configtest.c:20:5: note: 'atanf' is a builtin with type 'float (float)'
_configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhf' [-Wincompatible-library-redeclaration]
int asinhf (void);
^
_configtest.c:21:5: note: 'asinhf' is a builtin with type 'float (float)'
_configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshf' [-Wincompatible-library-redeclaration]
int acoshf (void);
^
_configtest.c:22:5: note: 'acoshf' is a builtin with type 'float (float)'
_configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhf' [-Wincompatible-library-redeclaration]
int atanhf (void);
^
_configtest.c:23:5: note: 'atanhf' is a builtin with type 'float (float)'
_configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotf' [-Wincompatible-library-redeclaration]
int hypotf (void);
^
_configtest.c:24:5: note: 'hypotf' is a builtin with type 'float (float, float)'
_configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2f' [-Wincompatible-library-redeclaration]
int atan2f (void);
^
_configtest.c:25:5: note: 'atan2f' is a builtin with type 'float (float, float)'
_configtest.c:26:5: warning: incompatible redeclaration of library function 'powf' [-Wincompatible-library-redeclaration]
int powf (void);
^
_configtest.c:26:5: note: 'powf' is a builtin with type 'float (float, float)'
_configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodf' [-Wincompatible-library-redeclaration]
int fmodf (void);
^
_configtest.c:27:5: note: 'fmodf' is a builtin with type 'float (float, float)'
_configtest.c:28:5: warning: incompatible redeclaration of library function 'modff' [-Wincompatible-library-redeclaration]
int modff (void);
^
_configtest.c:28:5: note: 'modff' is a builtin with type 'float (float, float *)'
_configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpf' [-Wincompatible-library-redeclaration]
int frexpf (void);
^
_configtest.c:29:5: note: 'frexpf' is a builtin with type 'float (float, int *)'
_configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpf' [-Wincompatible-library-redeclaration]
int ldexpf (void);
^
_configtest.c:30:5: note: 'ldexpf' is a builtin with type 'float (float, int)'
_configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2f' [-Wincompatible-library-redeclaration]
int exp2f (void);
^
_configtest.c:31:5: note: 'exp2f' is a builtin with type 'float (float)'
_configtest.c:32:5: warning: incompatible redeclaration of library function 'log2f' [-Wincompatible-library-redeclaration]
int log2f (void);
^
_configtest.c:32:5: note: 'log2f' is a builtin with type 'float (float)'
_configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignf' [-Wincompatible-library-redeclaration]
int copysignf (void);
^
_configtest.c:33:5: note: 'copysignf' is a builtin with type 'float (float, float)'
_configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterf' [-Wincompatible-library-redeclaration]
int nextafterf (void);
^
_configtest.c:34:5: note: 'nextafterf' is a builtin with type 'float (float, float)'
_configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtf' [-Wincompatible-library-redeclaration]
int cbrtf (void);
^
_configtest.c:35:5: note: 'cbrtf' is a builtin with type 'float (float)'
35 warnings generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:1:5: warning: incompatible redeclaration of library function 'sinl' [-Wincompatible-library-redeclaration]
int sinl (void);
^
_configtest.c:1:5: note: 'sinl' is a builtin with type 'long double (long double)'
_configtest.c:2:5: warning: incompatible redeclaration of library function 'cosl' [-Wincompatible-library-redeclaration]
int cosl (void);
^
_configtest.c:2:5: note: 'cosl' is a builtin with type 'long double (long double)'
_configtest.c:3:5: warning: incompatible redeclaration of library function 'tanl' [-Wincompatible-library-redeclaration]
int tanl (void);
^
_configtest.c:3:5: note: 'tanl' is a builtin with type 'long double (long double)'
_configtest.c:4:5: warning: incompatible redeclaration of library function 'sinhl' [-Wincompatible-library-redeclaration]
int sinhl (void);
^
_configtest.c:4:5: note: 'sinhl' is a builtin with type 'long double (long double)'
_configtest.c:5:5: warning: incompatible redeclaration of library function 'coshl' [-Wincompatible-library-redeclaration]
int coshl (void);
^
_configtest.c:5:5: note: 'coshl' is a builtin with type 'long double (long double)'
_configtest.c:6:5: warning: incompatible redeclaration of library function 'tanhl' [-Wincompatible-library-redeclaration]
int tanhl (void);
^
_configtest.c:6:5: note: 'tanhl' is a builtin with type 'long double (long double)'
_configtest.c:7:5: warning: incompatible redeclaration of library function 'fabsl' [-Wincompatible-library-redeclaration]
int fabsl (void);
^
_configtest.c:7:5: note: 'fabsl' is a builtin with type 'long double (long double)'
_configtest.c:8:5: warning: incompatible redeclaration of library function 'floorl' [-Wincompatible-library-redeclaration]
int floorl (void);
^
_configtest.c:8:5: note: 'floorl' is a builtin with type 'long double (long double)'
_configtest.c:9:5: warning: incompatible redeclaration of library function 'ceill' [-Wincompatible-library-redeclaration]
int ceill (void);
^
_configtest.c:9:5: note: 'ceill' is a builtin with type 'long double (long double)'
_configtest.c:10:5: warning: incompatible redeclaration of library function 'rintl' [-Wincompatible-library-redeclaration]
int rintl (void);
^
_configtest.c:10:5: note: 'rintl' is a builtin with type 'long double (long double)'
_configtest.c:11:5: warning: incompatible redeclaration of library function 'truncl' [-Wincompatible-library-redeclaration]
int truncl (void);
^
_configtest.c:11:5: note: 'truncl' is a builtin with type 'long double (long double)'
_configtest.c:12:5: warning: incompatible redeclaration of library function 'sqrtl' [-Wincompatible-library-redeclaration]
int sqrtl (void);
^
_configtest.c:12:5: note: 'sqrtl' is a builtin with type 'long double (long double)'
_configtest.c:13:5: warning: incompatible redeclaration of library function 'log10l' [-Wincompatible-library-redeclaration]
int log10l (void);
^
_configtest.c:13:5: note: 'log10l' is a builtin with type 'long double (long double)'
_configtest.c:14:5: warning: incompatible redeclaration of library function 'logl' [-Wincompatible-library-redeclaration]
int logl (void);
^
_configtest.c:14:5: note: 'logl' is a builtin with type 'long double (long double)'
_configtest.c:15:5: warning: incompatible redeclaration of library function 'log1pl' [-Wincompatible-library-redeclaration]
int log1pl (void);
^
_configtest.c:15:5: note: 'log1pl' is a builtin with type 'long double (long double)'
_configtest.c:16:5: warning: incompatible redeclaration of library function 'expl' [-Wincompatible-library-redeclaration]
int expl (void);
^
_configtest.c:16:5: note: 'expl' is a builtin with type 'long double (long double)'
_configtest.c:17:5: warning: incompatible redeclaration of library function 'expm1l' [-Wincompatible-library-redeclaration]
int expm1l (void);
^
_configtest.c:17:5: note: 'expm1l' is a builtin with type 'long double (long double)'
_configtest.c:18:5: warning: incompatible redeclaration of library function 'asinl' [-Wincompatible-library-redeclaration]
int asinl (void);
^
_configtest.c:18:5: note: 'asinl' is a builtin with type 'long double (long double)'
_configtest.c:19:5: warning: incompatible redeclaration of library function 'acosl' [-Wincompatible-library-redeclaration]
int acosl (void);
^
_configtest.c:19:5: note: 'acosl' is a builtin with type 'long double (long double)'
_configtest.c:20:5: warning: incompatible redeclaration of library function 'atanl' [-Wincompatible-library-redeclaration]
int atanl (void);
^
_configtest.c:20:5: note: 'atanl' is a builtin with type 'long double (long double)'
_configtest.c:21:5: warning: incompatible redeclaration of library function 'asinhl' [-Wincompatible-library-redeclaration]
int asinhl (void);
^
_configtest.c:21:5: note: 'asinhl' is a builtin with type 'long double (long double)'
_configtest.c:22:5: warning: incompatible redeclaration of library function 'acoshl' [-Wincompatible-library-redeclaration]
int acoshl (void);
^
_configtest.c:22:5: note: 'acoshl' is a builtin with type 'long double (long double)'
_configtest.c:23:5: warning: incompatible redeclaration of library function 'atanhl' [-Wincompatible-library-redeclaration]
int atanhl (void);
^
_configtest.c:23:5: note: 'atanhl' is a builtin with type 'long double (long double)'
_configtest.c:24:5: warning: incompatible redeclaration of library function 'hypotl' [-Wincompatible-library-redeclaration]
int hypotl (void);
^
_configtest.c:24:5: note: 'hypotl' is a builtin with type 'long double (long double, long double)'
_configtest.c:25:5: warning: incompatible redeclaration of library function 'atan2l' [-Wincompatible-library-redeclaration]
int atan2l (void);
^
_configtest.c:25:5: note: 'atan2l' is a builtin with type 'long double (long double, long double)'
_configtest.c:26:5: warning: incompatible redeclaration of library function 'powl' [-Wincompatible-library-redeclaration]
int powl (void);
^
_configtest.c:26:5: note: 'powl' is a builtin with type 'long double (long double, long double)'
_configtest.c:27:5: warning: incompatible redeclaration of library function 'fmodl' [-Wincompatible-library-redeclaration]
int fmodl (void);
^
_configtest.c:27:5: note: 'fmodl' is a builtin with type 'long double (long double, long double)'
_configtest.c:28:5: warning: incompatible redeclaration of library function 'modfl' [-Wincompatible-library-redeclaration]
int modfl (void);
^
_configtest.c:28:5: note: 'modfl' is a builtin with type 'long double (long double, long double *)'
_configtest.c:29:5: warning: incompatible redeclaration of library function 'frexpl' [-Wincompatible-library-redeclaration]
int frexpl (void);
^
_configtest.c:29:5: note: 'frexpl' is a builtin with type 'long double (long double, int *)'
_configtest.c:30:5: warning: incompatible redeclaration of library function 'ldexpl' [-Wincompatible-library-redeclaration]
int ldexpl (void);
^
_configtest.c:30:5: note: 'ldexpl' is a builtin with type 'long double (long double, int)'
_configtest.c:31:5: warning: incompatible redeclaration of library function 'exp2l' [-Wincompatible-library-redeclaration]
int exp2l (void);
^
_configtest.c:31:5: note: 'exp2l' is a builtin with type 'long double (long double)'
_configtest.c:32:5: warning: incompatible redeclaration of library function 'log2l' [-Wincompatible-library-redeclaration]
int log2l (void);
^
_configtest.c:32:5: note: 'log2l' is a builtin with type 'long double (long double)'
_configtest.c:33:5: warning: incompatible redeclaration of library function 'copysignl' [-Wincompatible-library-redeclaration]
int copysignl (void);
^
_configtest.c:33:5: note: 'copysignl' is a builtin with type 'long double (long double, long double)'
_configtest.c:34:5: warning: incompatible redeclaration of library function 'nextafterl' [-Wincompatible-library-redeclaration]
int nextafterl (void);
^
_configtest.c:34:5: note: 'nextafterl' is a builtin with type 'long double (long double, long double)'
_configtest.c:35:5: warning: incompatible redeclaration of library function 'cbrtl' [-Wincompatible-library-redeclaration]
int cbrtl (void);
^
_configtest.c:35:5: note: 'cbrtl' is a builtin with type 'long double (long double)'
35 warnings generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:8:12: error: use of undeclared identifier 'HAVE_DECL_SIGNBIT'
(void) HAVE_DECL_SIGNBIT;
^
1 error generated.
failure.
removing: _configtest.c _configtest.o
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:1:5: warning: incompatible redeclaration of library function 'cabs' [-Wincompatible-library-redeclaration]
int cabs (void);
^
_configtest.c:1:5: note: 'cabs' is a builtin with type 'double (_Complex double)'
_configtest.c:2:5: warning: incompatible redeclaration of library function 'cacos' [-Wincompatible-library-redeclaration]
int cacos (void);
^
_configtest.c:2:5: note: 'cacos' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:3:5: warning: incompatible redeclaration of library function 'cacosh' [-Wincompatible-library-redeclaration]
int cacosh (void);
^
_configtest.c:3:5: note: 'cacosh' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:4:5: warning: incompatible redeclaration of library function 'carg' [-Wincompatible-library-redeclaration]
int carg (void);
^
_configtest.c:4:5: note: 'carg' is a builtin with type 'double (_Complex double)'
_configtest.c:5:5: warning: incompatible redeclaration of library function 'casin' [-Wincompatible-library-redeclaration]
int casin (void);
^
_configtest.c:5:5: note: 'casin' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:6:5: warning: incompatible redeclaration of library function 'casinh' [-Wincompatible-library-redeclaration]
int casinh (void);
^
_configtest.c:6:5: note: 'casinh' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:7:5: warning: incompatible redeclaration of library function 'catan' [-Wincompatible-library-redeclaration]
int catan (void);
^
_configtest.c:7:5: note: 'catan' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:8:5: warning: incompatible redeclaration of library function 'catanh' [-Wincompatible-library-redeclaration]
int catanh (void);
^
_configtest.c:8:5: note: 'catanh' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:9:5: warning: incompatible redeclaration of library function 'ccos' [-Wincompatible-library-redeclaration]
int ccos (void);
^
_configtest.c:9:5: note: 'ccos' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:10:5: warning: incompatible redeclaration of library function 'ccosh' [-Wincompatible-library-redeclaration]
int ccosh (void);
^
_configtest.c:10:5: note: 'ccosh' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:11:5: warning: incompatible redeclaration of library function 'cexp' [-Wincompatible-library-redeclaration]
int cexp (void);
^
_configtest.c:11:5: note: 'cexp' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:12:5: warning: incompatible redeclaration of library function 'cimag' [-Wincompatible-library-redeclaration]
int cimag (void);
^
_configtest.c:12:5: note: 'cimag' is a builtin with type 'double (_Complex double)'
_configtest.c:13:5: warning: incompatible redeclaration of library function 'clog' [-Wincompatible-library-redeclaration]
int clog (void);
^
_configtest.c:13:5: note: 'clog' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:14:5: warning: incompatible redeclaration of library function 'conj' [-Wincompatible-library-redeclaration]
int conj (void);
^
_configtest.c:14:5: note: 'conj' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:15:5: warning: incompatible redeclaration of library function 'cpow' [-Wincompatible-library-redeclaration]
int cpow (void);
^
_configtest.c:15:5: note: 'cpow' is a builtin with type '_Complex double (_Complex double, _Complex double)'
_configtest.c:16:5: warning: incompatible redeclaration of library function 'cproj' [-Wincompatible-library-redeclaration]
int cproj (void);
^
_configtest.c:16:5: note: 'cproj' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:17:5: warning: incompatible redeclaration of library function 'creal' [-Wincompatible-library-redeclaration]
int creal (void);
^
_configtest.c:17:5: note: 'creal' is a builtin with type 'double (_Complex double)'
_configtest.c:18:5: warning: incompatible redeclaration of library function 'csin' [-Wincompatible-library-redeclaration]
int csin (void);
^
_configtest.c:18:5: note: 'csin' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:19:5: warning: incompatible redeclaration of library function 'csinh' [-Wincompatible-library-redeclaration]
int csinh (void);
^
_configtest.c:19:5: note: 'csinh' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrt' [-Wincompatible-library-redeclaration]
int csqrt (void);
^
_configtest.c:20:5: note: 'csqrt' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:21:5: warning: incompatible redeclaration of library function 'ctan' [-Wincompatible-library-redeclaration]
int ctan (void);
^
_configtest.c:21:5: note: 'ctan' is a builtin with type '_Complex double (_Complex double)'
_configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanh' [-Wincompatible-library-redeclaration]
int ctanh (void);
^
_configtest.c:22:5: note: 'ctanh' is a builtin with type '_Complex double (_Complex double)'
22 warnings generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsf' [-Wincompatible-library-redeclaration]
int cabsf (void);
^
_configtest.c:1:5: note: 'cabsf' is a builtin with type 'float (_Complex float)'
_configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosf' [-Wincompatible-library-redeclaration]
int cacosf (void);
^
_configtest.c:2:5: note: 'cacosf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshf' [-Wincompatible-library-redeclaration]
int cacoshf (void);
^
_configtest.c:3:5: note: 'cacoshf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:4:5: warning: incompatible redeclaration of library function 'cargf' [-Wincompatible-library-redeclaration]
int cargf (void);
^
_configtest.c:4:5: note: 'cargf' is a builtin with type 'float (_Complex float)'
_configtest.c:5:5: warning: incompatible redeclaration of library function 'casinf' [-Wincompatible-library-redeclaration]
int casinf (void);
^
_configtest.c:5:5: note: 'casinf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhf' [-Wincompatible-library-redeclaration]
int casinhf (void);
^
_configtest.c:6:5: note: 'casinhf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:7:5: warning: incompatible redeclaration of library function 'catanf' [-Wincompatible-library-redeclaration]
int catanf (void);
^
_configtest.c:7:5: note: 'catanf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhf' [-Wincompatible-library-redeclaration]
int catanhf (void);
^
_configtest.c:8:5: note: 'catanhf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosf' [-Wincompatible-library-redeclaration]
int ccosf (void);
^
_configtest.c:9:5: note: 'ccosf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshf' [-Wincompatible-library-redeclaration]
int ccoshf (void);
^
_configtest.c:10:5: note: 'ccoshf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpf' [-Wincompatible-library-redeclaration]
int cexpf (void);
^
_configtest.c:11:5: note: 'cexpf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagf' [-Wincompatible-library-redeclaration]
int cimagf (void);
^
_configtest.c:12:5: note: 'cimagf' is a builtin with type 'float (_Complex float)'
_configtest.c:13:5: warning: incompatible redeclaration of library function 'clogf' [-Wincompatible-library-redeclaration]
int clogf (void);
^
_configtest.c:13:5: note: 'clogf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:14:5: warning: incompatible redeclaration of library function 'conjf' [-Wincompatible-library-redeclaration]
int conjf (void);
^
_configtest.c:14:5: note: 'conjf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowf' [-Wincompatible-library-redeclaration]
int cpowf (void);
^
_configtest.c:15:5: note: 'cpowf' is a builtin with type '_Complex float (_Complex float, _Complex float)'
_configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojf' [-Wincompatible-library-redeclaration]
int cprojf (void);
^
_configtest.c:16:5: note: 'cprojf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:17:5: warning: incompatible redeclaration of library function 'crealf' [-Wincompatible-library-redeclaration]
int crealf (void);
^
_configtest.c:17:5: note: 'crealf' is a builtin with type 'float (_Complex float)'
_configtest.c:18:5: warning: incompatible redeclaration of library function 'csinf' [-Wincompatible-library-redeclaration]
int csinf (void);
^
_configtest.c:18:5: note: 'csinf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhf' [-Wincompatible-library-redeclaration]
int csinhf (void);
^
_configtest.c:19:5: note: 'csinhf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtf' [-Wincompatible-library-redeclaration]
int csqrtf (void);
^
_configtest.c:20:5: note: 'csqrtf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanf' [-Wincompatible-library-redeclaration]
int ctanf (void);
^
_configtest.c:21:5: note: 'ctanf' is a builtin with type '_Complex float (_Complex float)'
_configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhf' [-Wincompatible-library-redeclaration]
int ctanhf (void);
^
_configtest.c:22:5: note: 'ctanhf' is a builtin with type '_Complex float (_Complex float)'
22 warnings generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:1:5: warning: incompatible redeclaration of library function 'cabsl' [-Wincompatible-library-redeclaration]
int cabsl (void);
^
_configtest.c:1:5: note: 'cabsl' is a builtin with type 'long double (_Complex long double)'
_configtest.c:2:5: warning: incompatible redeclaration of library function 'cacosl' [-Wincompatible-library-redeclaration]
int cacosl (void);
^
_configtest.c:2:5: note: 'cacosl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:3:5: warning: incompatible redeclaration of library function 'cacoshl' [-Wincompatible-library-redeclaration]
int cacoshl (void);
^
_configtest.c:3:5: note: 'cacoshl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:4:5: warning: incompatible redeclaration of library function 'cargl' [-Wincompatible-library-redeclaration]
int cargl (void);
^
_configtest.c:4:5: note: 'cargl' is a builtin with type 'long double (_Complex long double)'
_configtest.c:5:5: warning: incompatible redeclaration of library function 'casinl' [-Wincompatible-library-redeclaration]
int casinl (void);
^
_configtest.c:5:5: note: 'casinl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:6:5: warning: incompatible redeclaration of library function 'casinhl' [-Wincompatible-library-redeclaration]
int casinhl (void);
^
_configtest.c:6:5: note: 'casinhl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:7:5: warning: incompatible redeclaration of library function 'catanl' [-Wincompatible-library-redeclaration]
int catanl (void);
^
_configtest.c:7:5: note: 'catanl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:8:5: warning: incompatible redeclaration of library function 'catanhl' [-Wincompatible-library-redeclaration]
int catanhl (void);
^
_configtest.c:8:5: note: 'catanhl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:9:5: warning: incompatible redeclaration of library function 'ccosl' [-Wincompatible-library-redeclaration]
int ccosl (void);
^
_configtest.c:9:5: note: 'ccosl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:10:5: warning: incompatible redeclaration of library function 'ccoshl' [-Wincompatible-library-redeclaration]
int ccoshl (void);
^
_configtest.c:10:5: note: 'ccoshl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:11:5: warning: incompatible redeclaration of library function 'cexpl' [-Wincompatible-library-redeclaration]
int cexpl (void);
^
_configtest.c:11:5: note: 'cexpl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:12:5: warning: incompatible redeclaration of library function 'cimagl' [-Wincompatible-library-redeclaration]
int cimagl (void);
^
_configtest.c:12:5: note: 'cimagl' is a builtin with type 'long double (_Complex long double)'
_configtest.c:13:5: warning: incompatible redeclaration of library function 'clogl' [-Wincompatible-library-redeclaration]
int clogl (void);
^
_configtest.c:13:5: note: 'clogl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:14:5: warning: incompatible redeclaration of library function 'conjl' [-Wincompatible-library-redeclaration]
int conjl (void);
^
_configtest.c:14:5: note: 'conjl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:15:5: warning: incompatible redeclaration of library function 'cpowl' [-Wincompatible-library-redeclaration]
int cpowl (void);
^
_configtest.c:15:5: note: 'cpowl' is a builtin with type '_Complex long double (_Complex long double, _Complex long double)'
_configtest.c:16:5: warning: incompatible redeclaration of library function 'cprojl' [-Wincompatible-library-redeclaration]
int cprojl (void);
^
_configtest.c:16:5: note: 'cprojl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:17:5: warning: incompatible redeclaration of library function 'creall' [-Wincompatible-library-redeclaration]
int creall (void);
^
_configtest.c:17:5: note: 'creall' is a builtin with type 'long double (_Complex long double)'
_configtest.c:18:5: warning: incompatible redeclaration of library function 'csinl' [-Wincompatible-library-redeclaration]
int csinl (void);
^
_configtest.c:18:5: note: 'csinl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:19:5: warning: incompatible redeclaration of library function 'csinhl' [-Wincompatible-library-redeclaration]
int csinhl (void);
^
_configtest.c:19:5: note: 'csinhl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:20:5: warning: incompatible redeclaration of library function 'csqrtl' [-Wincompatible-library-redeclaration]
int csqrtl (void);
^
_configtest.c:20:5: note: 'csqrtl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:21:5: warning: incompatible redeclaration of library function 'ctanl' [-Wincompatible-library-redeclaration]
int ctanl (void);
^
_configtest.c:21:5: note: 'ctanl' is a builtin with type '_Complex long double (_Complex long double)'
_configtest.c:22:5: warning: incompatible redeclaration of library function 'ctanhl' [-Wincompatible-library-redeclaration]
int ctanhl (void);
^
_configtest.c:22:5: note: 'ctanhl' is a builtin with type '_Complex long double (_Complex long double)'
22 warnings generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:2:12: warning: unused function 'static_func' [-Wunused-function]
static int static_func (char * restrict a)
^
1 warning generated.
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:3:19: warning: unused function 'static_func' [-Wunused-function]
static inline int static_func (void)
^
1 warning generated.
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
removing: _configtest.c _configtest.o _configtest.o.d
File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h
#define SIZEOF_PY_INTPTR_T 8
#define SIZEOF_OFF_T 8
#define SIZEOF_PY_LONG_LONG 8
#define MATHLIB
#define HAVE_SIN 1
#define HAVE_COS 1
#define HAVE_TAN 1
#define HAVE_SINH 1
#define HAVE_COSH 1
#define HAVE_TANH 1
#define HAVE_FABS 1
#define HAVE_FLOOR 1
#define HAVE_CEIL 1
#define HAVE_SQRT 1
#define HAVE_LOG10 1
#define HAVE_LOG 1
#define HAVE_EXP 1
#define HAVE_ASIN 1
#define HAVE_ACOS 1
#define HAVE_ATAN 1
#define HAVE_FMOD 1
#define HAVE_MODF 1
#define HAVE_FREXP 1
#define HAVE_LDEXP 1
#define HAVE_RINT 1
#define HAVE_TRUNC 1
#define HAVE_EXP2 1
#define HAVE_LOG2 1
#define HAVE_ATAN2 1
#define HAVE_POW 1
#define HAVE_NEXTAFTER 1
#define HAVE_STRTOLL 1
#define HAVE_STRTOULL 1
#define HAVE_CBRT 1
#define HAVE_STRTOLD_L 1
#define HAVE_BACKTRACE 1
#define HAVE_MADVISE 1
#define HAVE_XMMINTRIN_H 1
#define HAVE_EMMINTRIN_H 1
#define HAVE_XLOCALE_H 1
#define HAVE_DLFCN_H 1
#define HAVE_SYS_MMAN_H 1
#define HAVE___BUILTIN_ISNAN 1
#define HAVE___BUILTIN_ISINF 1
#define HAVE___BUILTIN_ISFINITE 1
#define HAVE___BUILTIN_BSWAP32 1
#define HAVE___BUILTIN_BSWAP64 1
#define HAVE___BUILTIN_EXPECT 1
#define HAVE___BUILTIN_MUL_OVERFLOW 1
#define HAVE___BUILTIN_CPU_SUPPORTS 1
#define HAVE__M_FROM_INT64 1
#define HAVE__MM_LOAD_PS 1
#define HAVE__MM_PREFETCH 1
#define HAVE__MM_LOAD_PD 1
#define HAVE___BUILTIN_PREFETCH 1
#define HAVE_LINK_AVX 1
#define HAVE_LINK_AVX2 1
#define HAVE_XGETBV 1
#define HAVE_ATTRIBUTE_NONNULL 1
#define HAVE_ATTRIBUTE_TARGET_AVX 1
#define HAVE_ATTRIBUTE_TARGET_AVX2 1
#define HAVE___THREAD 1
#define HAVE_SINF 1
#define HAVE_COSF 1
#define HAVE_TANF 1
#define HAVE_SINHF 1
#define HAVE_COSHF 1
#define HAVE_TANHF 1
#define HAVE_FABSF 1
#define HAVE_FLOORF 1
#define HAVE_CEILF 1
#define HAVE_RINTF 1
#define HAVE_TRUNCF 1
#define HAVE_SQRTF 1
#define HAVE_LOG10F 1
#define HAVE_LOGF 1
#define HAVE_LOG1PF 1
#define HAVE_EXPF 1
#define HAVE_EXPM1F 1
#define HAVE_ASINF 1
#define HAVE_ACOSF 1
#define HAVE_ATANF 1
#define HAVE_ASINHF 1
#define HAVE_ACOSHF 1
#define HAVE_ATANHF 1
#define HAVE_HYPOTF 1
#define HAVE_ATAN2F 1
#define HAVE_POWF 1
#define HAVE_FMODF 1
#define HAVE_MODFF 1
#define HAVE_FREXPF 1
#define HAVE_LDEXPF 1
#define HAVE_EXP2F 1
#define HAVE_LOG2F 1
#define HAVE_COPYSIGNF 1
#define HAVE_NEXTAFTERF 1
#define HAVE_CBRTF 1
#define HAVE_SINL 1
#define HAVE_COSL 1
#define HAVE_TANL 1
#define HAVE_SINHL 1
#define HAVE_COSHL 1
#define HAVE_TANHL 1
#define HAVE_FABSL 1
#define HAVE_FLOORL 1
#define HAVE_CEILL 1
#define HAVE_RINTL 1
#define HAVE_TRUNCL 1
#define HAVE_SQRTL 1
#define HAVE_LOG10L 1
#define HAVE_LOGL 1
#define HAVE_LOG1PL 1
#define HAVE_EXPL 1
#define HAVE_EXPM1L 1
#define HAVE_ASINL 1
#define HAVE_ACOSL 1
#define HAVE_ATANL 1
#define HAVE_ASINHL 1
#define HAVE_ACOSHL 1
#define HAVE_ATANHL 1
#define HAVE_HYPOTL 1
#define HAVE_ATAN2L 1
#define HAVE_POWL 1
#define HAVE_FMODL 1
#define HAVE_MODFL 1
#define HAVE_FREXPL 1
#define HAVE_LDEXPL 1
#define HAVE_EXP2L 1
#define HAVE_LOG2L 1
#define HAVE_COPYSIGNL 1
#define HAVE_NEXTAFTERL 1
#define HAVE_CBRTL 1
#define HAVE_DECL_SIGNBIT
#define HAVE_COMPLEX_H 1
#define HAVE_CABS 1
#define HAVE_CACOS 1
#define HAVE_CACOSH 1
#define HAVE_CARG 1
#define HAVE_CASIN 1
#define HAVE_CASINH 1
#define HAVE_CATAN 1
#define HAVE_CATANH 1
#define HAVE_CCOS 1
#define HAVE_CCOSH 1
#define HAVE_CEXP 1
#define HAVE_CIMAG 1
#define HAVE_CLOG 1
#define HAVE_CONJ 1
#define HAVE_CPOW 1
#define HAVE_CPROJ 1
#define HAVE_CREAL 1
#define HAVE_CSIN 1
#define HAVE_CSINH 1
#define HAVE_CSQRT 1
#define HAVE_CTAN 1
#define HAVE_CTANH 1
#define HAVE_CABSF 1
#define HAVE_CACOSF 1
#define HAVE_CACOSHF 1
#define HAVE_CARGF 1
#define HAVE_CASINF 1
#define HAVE_CASINHF 1
#define HAVE_CATANF 1
#define HAVE_CATANHF 1
#define HAVE_CCOSF 1
#define HAVE_CCOSHF 1
#define HAVE_CEXPF 1
#define HAVE_CIMAGF 1
#define HAVE_CLOGF 1
#define HAVE_CONJF 1
#define HAVE_CPOWF 1
#define HAVE_CPROJF 1
#define HAVE_CREALF 1
#define HAVE_CSINF 1
#define HAVE_CSINHF 1
#define HAVE_CSQRTF 1
#define HAVE_CTANF 1
#define HAVE_CTANHF 1
#define HAVE_CABSL 1
#define HAVE_CACOSL 1
#define HAVE_CACOSHL 1
#define HAVE_CARGL 1
#define HAVE_CASINL 1
#define HAVE_CASINHL 1
#define HAVE_CATANL 1
#define HAVE_CATANHL 1
#define HAVE_CCOSL 1
#define HAVE_CCOSHL 1
#define HAVE_CEXPL 1
#define HAVE_CIMAGL 1
#define HAVE_CLOGL 1
#define HAVE_CONJL 1
#define HAVE_CPOWL 1
#define HAVE_CPROJL 1
#define HAVE_CREALL 1
#define HAVE_CSINL 1
#define HAVE_CSINHL 1
#define HAVE_CSQRTL 1
#define HAVE_CTANL 1
#define HAVE_CTANHL 1
#define NPY_RESTRICT restrict
#define NPY_RELAXED_STRIDES_CHECKING 1
#define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE 1
#define NPY_PY3K 1
#ifndef __cplusplus
/* #undef inline */
#endif
#ifndef _NPY_NPY_CONFIG_H_
#error config.h should never be included directly, include npy_config.h instead
#endif
EOF
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources.
Generating build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
_configtest.c:1:5: warning: incompatible redeclaration of library function 'exp' [-Wincompatible-library-redeclaration]
int exp (void);
^
_configtest.c:1:5: note: 'exp' is a builtin with type 'double (double)'
1 warning generated.
clang _configtest.o -o _configtest
success!
removing: _configtest.c _configtest.o _configtest.o.d _configtest
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -c'
clang: _configtest.c
success!
removing: _configtest.c _configtest.o _configtest.o.d
File: build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h
#define NPY_SIZEOF_SHORT SIZEOF_SHORT
#define NPY_SIZEOF_INT SIZEOF_INT
#define NPY_SIZEOF_LONG SIZEOF_LONG
#define NPY_SIZEOF_FLOAT 4
#define NPY_SIZEOF_COMPLEX_FLOAT 8
#define NPY_SIZEOF_DOUBLE 8
#define NPY_SIZEOF_COMPLEX_DOUBLE 16
#define NPY_SIZEOF_LONGDOUBLE 16
#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 32
#define NPY_SIZEOF_PY_INTPTR_T 8
#define NPY_SIZEOF_OFF_T 8
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_ISFINITE
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_USE_C99_COMPLEX 1
#define NPY_HAVE_COMPLEX_DOUBLE 1
#define NPY_HAVE_COMPLEX_FLOAT 1
#define NPY_HAVE_COMPLEX_LONG_DOUBLE 1
#define NPY_RELAXED_STRIDES_CHECKING 1
#define NPY_USE_C99_FORMATS 1
#define NPY_VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
#define NPY_ABI_VERSION 0x01000009
#define NPY_API_VERSION 0x0000000D
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
EOF
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources.
numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core._multiarray_tests" sources
creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c
building extension "numpy.core._multiarray_umath" sources
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h' to sources.
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h' to sources.
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c
creating build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath' to include_dirs.
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath' to include_dirs.
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h
adding 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/funcs.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/simd.inc', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_internal.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/src/common/templ_common.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/config.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/_numpyconfig.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h', 'build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._umath_tests" sources
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c
building extension "numpy.core._rational_tests" sources
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c
building extension "numpy.core._struct_ufunc_tests" sources
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c
building extension "numpy.core._operand_flag_tests" sources
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c
building extension "numpy.fft.fftpack_lite" sources
building extension "numpy.linalg.lapack_lite" sources
creating build/src.macosx-10.15-x86_64-3.9/numpy/linalg
adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources.
building extension "numpy.linalg._umath_linalg" sources
adding 'numpy/linalg/lapack_lite/python_xerbla.c' to sources.
conv_template:> build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c
building extension "numpy.random.mtrand" sources
creating build/src.macosx-10.15-x86_64-3.9/numpy/random
building data_files sources
build_src: building npy-pkg config files
running build_py
creating build/lib.macosx-10.15-x86_64-3.9
creating build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/conftest.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/_globals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/dual.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/_distributor_init.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/ctypeslib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/matlib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying numpy/_pytesttester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
copying build/src.macosx-10.15-x86_64-3.9/numpy/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy
creating build/lib.macosx-10.15-x86_64-3.9/numpy/compat
copying numpy/compat/py3k.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat
copying numpy/compat/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat
copying numpy/compat/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat
copying numpy/compat/_inspect.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/compat
creating build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/umath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/fromnumeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_dtype.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_add_newdocs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_methods.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_internal.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_string_helpers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/multiarray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/records.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/setup_common.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_aliased_types.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/memmap.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/overrides.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/getlimits.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_dtype_ctypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/defchararray.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/machar.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/numeric.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/einsumfunc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/umath_tests.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/numerictypes.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/_type_aliases.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/cversions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/arrayprint.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
copying numpy/core/code_generators/generate_numpy_api.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/core
creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/unixccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/numpy_distribution.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/conv_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/cpuinfo.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/msvc9compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/npy_pkg_config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/compat.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/misc_util.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/log.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/line_endings.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/lib2def.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/pathccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/system_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/exec_command.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/from_template.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/mingw32ccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/extension.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/msvccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/intelccompiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying numpy/distutils/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
copying build/src.macosx-10.15-x86_64-3.9/numpy/distutils/__config__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils
creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/config_compiler.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build_ext.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/config.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/install_headers.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build_py.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build_src.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/sdist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build_scripts.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/bdist_rpm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/install_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/build_clib.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/autodist.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/egg_info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/install.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/develop.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
copying numpy/distutils/command/install_data.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/command
creating build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/gnu.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/compaq.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/intel.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/none.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/nag.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/pg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/ibm.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/sun.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/lahey.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/g95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/mips.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/hpux.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/environment.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/pathf95.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/absoft.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/vast.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/distutils/fcompiler
creating build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/misc.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/internals.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/creation.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/constants.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/ufuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/broadcasting.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/basics.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/subclassing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/indexing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/byteswapping.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/structured_arrays.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
copying numpy/doc/glossary.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/doc
creating build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/cfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/common_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/crackfortran.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/cb_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/f2py2e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/func2subr.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/__version__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/diagnose.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/capi_maps.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/f90mod_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/f2py_testing.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/use_rules.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/auxfuncs.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
copying numpy/f2py/__main__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/f2py
creating build/lib.macosx-10.15-x86_64-3.9/numpy/fft
copying numpy/fft/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft
copying numpy/fft/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft
copying numpy/fft/helper.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft
copying numpy/fft/fftpack.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft
copying numpy/fft/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/fft
creating build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/_iotools.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/mixins.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/nanfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/recfunctions.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/histograms.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/scimath.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/_version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/user_array.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/format.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/twodim_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/financial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/index_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/npyio.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/shape_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/stride_tricks.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/arrayterator.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/function_base.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/arraysetops.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/arraypad.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/type_check.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/_datasource.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
copying numpy/lib/ufunclike.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/lib
creating build/lib.macosx-10.15-x86_64-3.9/numpy/linalg
copying numpy/linalg/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg
copying numpy/linalg/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg
copying numpy/linalg/linalg.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg
copying numpy/linalg/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/linalg
creating build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/extras.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/version.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/testutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/core.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/bench.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/timer_comparison.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
copying numpy/ma/mrecords.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/ma
creating build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib
copying numpy/matrixlib/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib
copying numpy/matrixlib/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib
copying numpy/matrixlib/defmatrix.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/matrixlib
creating build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/laguerre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/_polybase.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/polyutils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/hermite_e.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/chebyshev.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/polynomial.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/legendre.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
copying numpy/polynomial/hermite.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/polynomial
creating build/lib.macosx-10.15-x86_64-3.9/numpy/random
copying numpy/random/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random
copying numpy/random/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random
copying numpy/random/info.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/random
creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/setup.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/print_coercion_tables.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
copying numpy/testing/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing
creating build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/nosetester.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/__init__.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/noseclasses.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/utils.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/parameterized.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
copying numpy/testing/_private/decorators.py -> build/lib.macosx-10.15-x86_64-3.9/numpy/testing/_private
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
building 'npymath' library
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9
creating build/temp.macosx-10.15-x86_64-3.9/numpy
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath
creating build/temp.macosx-10.15-x86_64-3.9/build
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath
compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: numpy/core/src/npymath/npy_math.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c
clang: numpy/core/src/npymath/halffloat.c
numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable]
static const volatile npy_float tiny = 3.9443045e-31f;
^
numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable]
static const npy_cfloat c_halff = {0.5F, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable]
static const npy_cfloat c_if = {0.0, 1.0F};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable]
static const npy_cfloat c_ihalff = {0.0, 0.5F};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function]
caddf(npy_cfloat a, npy_cfloat b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function]
csubf(npy_cfloat a, npy_cfloat b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function]
cnegf(npy_cfloat a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function]
cmulif(npy_cfloat a)
^
numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable]
static const npy_cdouble c_half = {0.5, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable]
static const npy_cdouble c_i = {0.0, 1.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable]
static const npy_cdouble c_ihalf = {0.0, 0.5};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function]
cadd(npy_cdouble a, npy_cdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function]
csub(npy_cdouble a, npy_cdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function]
cneg(npy_cdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function]
cmuli(npy_cdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable]
static const npy_clongdouble c_halfl = {0.5L, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable]
static const npy_clongdouble c_il = {0.0, 1.0L};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable]
static const npy_clongdouble c_ihalfl = {0.0, 0.5L};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function]
caddl(npy_clongdouble a, npy_clongdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function]
csubl(npy_clongdouble a, npy_clongdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function]
cnegl(npy_clongdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function]
cmulil(npy_clongdouble a)
^
22 warnings generated.
ar: adding 4 object files to build/temp.macosx-10.15-x86_64-3.9/libnpymath.a
ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpymath.a
building 'npysort' library
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort
compile options: '-Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/quicksort.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/mergesort.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/heapsort.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/selection.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npysort/binsearch.c
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
numpy/core/src/npysort/selection.c.src:328:9: warning: code will never be executed [-Wunreachable-code]
npy_intp k;
^~~~~~~~~~~
numpy/core/src/npysort/selection.c.src:326:14: note: silence by adding parentheses to mark code as explicitly dead
else if (0 && kth == num - 1) {
^
/* DISABLES CODE */ ( )
22 warnings generated.
ar: adding 5 object files to build/temp.macosx-10.15-x86_64-3.9/libnpysort.a
ranlib:@ build/temp.macosx-10.15-x86_64-3.9/libnpysort.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
building 'numpy.core._dummy' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: numpy/core/src/dummymodule.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/dummymodule.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_dummy.cpython-39-darwin.so
building 'numpy.core._multiarray_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.c
clang: numpy/core/src/common/mem_overlap.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/_multiarray_tests.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/mem_overlap.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_multiarray_tests.cpython-39-darwin.so
building 'numpy.core._multiarray_umath' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray
creating build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath
creating build/temp.macosx-10.15-x86_64-3.9/private
creating build/temp.macosx-10.15-x86_64-3.9/private/var
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils
creating build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'
clang: numpy/core/src/multiarray/alloc.c
clang: numpy/core/src/multiarray/calculation.cclang: numpy/core/src/multiarray/array_assign_scalar.c
clang: numpy/core/src/multiarray/convert.c
clang: numpy/core/src/multiarray/ctors.c
clang: numpy/core/src/multiarray/datetime_busday.c
clang: numpy/core/src/multiarray/dragon4.cclang: numpy/core/src/multiarray/flagsobject.c
numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/ctors.c:2261:36: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/ctors.c:2261:36: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
if (!(PyUString_Check(name) && PyUString_GET_SIZE(name) == 0)) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
clang: numpy/core/src/multiarray/arrayobject.c
clang: numpy/core/src/multiarray/array_assign_array.c
clang: numpy/core/src/multiarray/convert_datatype.c
clang: numpy/core/src/multiarray/getset.c
clang: numpy/core/src/multiarray/datetime_busdaycal.c
clang: numpy/core/src/multiarray/buffer.c
clang: numpy/core/src/multiarray/compiled_base.c
clang: numpy/core/src/multiarray/hashdescr.c
clang: numpy/core/src/multiarray/descriptor.c
numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
if (PyUString_GET_SIZE(name) == 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/descriptor.c:453:13: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
if (PyUString_GET_SIZE(name) == 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/descriptor.c:453:13: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
if (PyUString_GET_SIZE(name) == 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/descriptor.c:460:48: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/descriptor.c:460:48: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
else if (PyUString_Check(title) && PyUString_GET_SIZE(title) > 0) {
^
numpy/core/include/numpy/npy_3kcompat.h:110:28: note: expanded from macro 'PyUString_GET_SIZE'
#define PyUString_GET_SIZE PyUnicode_GET_SIZE
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
clang: numpy/core/src/multiarray/conversion_utils.c
clang: numpy/core/src/multiarray/item_selection.c
clang: numpy/core/src/multiarray/dtype_transfer.c
clang: numpy/core/src/multiarray/mapping.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.c
3 warnings generated.
clang: numpy/core/src/multiarray/datetime.c
numpy/core/src/multiarray/arraytypes.c.src:477:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
ptr = PyUnicode_AS_UNICODE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
datalen = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
datalen = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/arraytypes.c.src:482:15: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
datalen = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
clang: numpy/core/src/multiarray/common.c
numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:187:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:187:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:239:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:239:28: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
itemsize = PyUnicode_GET_DATA_SIZE(temp);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
int itemsize = PyUnicode_GET_DATA_SIZE(obj);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:282:24: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
int itemsize = PyUnicode_GET_DATA_SIZE(obj);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/common.c:282:24: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
int itemsize = PyUnicode_GET_DATA_SIZE(obj);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
6 warnings generated.
clang: numpy/core/src/multiarray/nditer_pywrap.c
9 warnings generated.
clang: numpy/core/src/multiarray/sequence.c
clang: numpy/core/src/multiarray/shape.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.c
clang: numpy/core/src/multiarray/methods.c
clang: numpy/core/src/multiarray/iterators.c
clang: numpy/core/src/multiarray/datetime_strings.c
clang: numpy/core/src/multiarray/number.c
clang: numpy/core/src/multiarray/scalarapi.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.c
numpy/core/src/multiarray/scalarapi.c:74:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
return (void *)PyUnicode_AS_DATA(scalar);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'
((const char *)(PyUnicode_AS_UNICODE(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalarapi.c:135:28: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
return (void *)PyUnicode_AS_DATA(scalar);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'
((const char *)(PyUnicode_AS_UNICODE(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
descr->elsize = PyUnicode_GET_DATA_SIZE(sc);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalarapi.c:568:29: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
descr->elsize = PyUnicode_GET_DATA_SIZE(sc);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalarapi.c:568:29: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
descr->elsize = PyUnicode_GET_DATA_SIZE(sc);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
ip = dptr = PyUnicode_AS_UNICODE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]
new = PyUnicode_FromUnicode(ip, len);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:475:17: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
ip = dptr = PyUnicode_AS_UNICODE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:476:11: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
len = PyUnicode_GET_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:481:11: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]
new = PyUnicode_FromUnicode(ip, len);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:1849:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
buffer = PyUnicode_AS_DATA(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:283:21: note: expanded from macro 'PyUnicode_AS_DATA'
((const char *)(PyUnicode_AS_UNICODE(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:279:7: note: expanded from macro 'PyUnicode_AS_UNICODE'
PyUnicode_AsUnicode(_PyObject_CAST(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
buflen = PyUnicode_GET_DATA_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
buflen = PyUnicode_GET_DATA_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/scalartypes.c.src:1850:18: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
buflen = PyUnicode_GET_DATA_SIZE(self);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:268:6: note: expanded from macro 'PyUnicode_GET_DATA_SIZE'
(PyUnicode_GET_SIZE(op) * Py_UNICODE_SIZE)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
5 warnings generated.
clang: numpy/core/src/multiarray/typeinfo.c
clang: numpy/core/src/multiarray/refcount.c
clang: numpy/core/src/multiarray/usertypes.c
clang: numpy/core/src/multiarray/multiarraymodule.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.c
clang: numpy/core/src/multiarray/vdot.c
clang: numpy/core/src/umath/umathmodule.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.c
clang: numpy/core/src/umath/reduction.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.c
clang: numpy/core/src/multiarray/nditer_api.c
14 warnings generated.
clang: numpy/core/src/multiarray/strfuncs.c
numpy/core/src/umath/loops.c.src:655:18: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]
result = PyEval_CallObject(tocall, arglist);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'
PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here
Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/strfuncs.c:178:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]
s = PyEval_CallObject(PyArray_ReprFunction, arglist);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'
PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here
Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/core/src/multiarray/strfuncs.c:195:13: warning: 'PyEval_CallObjectWithKeywords' is deprecated [-Wdeprecated-declarations]
s = PyEval_CallObject(PyArray_StrFunction, arglist);
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:24:5: note: expanded from macro 'PyEval_CallObject'
PyEval_CallObjectWithKeywords(callable, arg, (PyObject *)NULL)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/ceval.h:17:1: note: 'PyEval_CallObjectWithKeywords' has been explicitly marked deprecated here
Py_DEPRECATED(3.9) PyAPI_FUNC(PyObject *) PyEval_CallObjectWithKeywords(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
2 warnings generated.
clang: numpy/core/src/multiarray/temp_elide.c
clang: numpy/core/src/umath/cpuid.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.c
clang: numpy/core/src/umath/ufunc_object.c
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'byte_long' [-Wunused-function]
byte_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ubyte_long' [-Wunused-function]
ubyte_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'short_long' [-Wunused-function]
short_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ushort_long' [-Wunused-function]
ushort_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'int_long' [-Wunused-function]
int_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'uint_long' [-Wunused-function]
uint_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'long_long' [-Wunused-function]
long_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulong_long' [-Wunused-function]
ulong_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longlong_long' [-Wunused-function]
longlong_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'ulonglong_long' [-Wunused-function]
ulonglong_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'half_long' [-Wunused-function]
half_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'float_long' [-Wunused-function]
float_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'double_long' [-Wunused-function]
double_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'longdouble_long' [-Wunused-function]
longdouble_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cfloat_long' [-Wunused-function]
cfloat_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'cdouble_long' [-Wunused-function]
cdouble_long(PyObject *obj)
^
numpy/core/src/umath/scalarmath.c.src:1449:1: warning: unused function 'clongdouble_long' [-Wunused-function]
clongdouble_long(PyObject *obj)
^
clang: numpy/core/src/multiarray/nditer_constr.c
numpy/core/src/umath/ufunc_object.c:657:19: warning: comparison of integers of different signs: 'int' and 'size_t' (aka 'unsigned long') [-Wsign-compare]
for (i = 0; i < len; i++) {
~ ^ ~~~
clang: numpy/core/src/umath/override.c
clang: numpy/core/src/npymath/npy_math.c
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.c
numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]
npy_intp n = dimensions[0];
^~~~~~~~~~
numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead
if (IS_BINARY_REDUCE && 0) {
^
/* DISABLES CODE */ ( )
numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]
npy_intp n = dimensions[0];
^~~~~~~~~~
numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead
if (IS_BINARY_REDUCE && 0) {
^
/* DISABLES CODE */ ( )
numpy/core/src/umath/loops.c.src:2527:22: warning: code will never be executed [-Wunreachable-code]
npy_intp n = dimensions[0];
^~~~~~~~~~
numpy/core/src/umath/loops.c.src:2526:29: note: silence by adding parentheses to mark code as explicitly dead
if (IS_BINARY_REDUCE && 0) {
^
/* DISABLES CODE */ ( )
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.c
numpy/core/src/npymath/npy_math_complex.c.src:48:33: warning: unused variable 'tiny' [-Wunused-const-variable]
static const volatile npy_float tiny = 3.9443045e-31f;
^
numpy/core/src/npymath/npy_math_complex.c.src:67:25: warning: unused variable 'c_halff' [-Wunused-const-variable]
static const npy_cfloat c_halff = {0.5F, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:25: warning: unused variable 'c_if' [-Wunused-const-variable]
static const npy_cfloat c_if = {0.0, 1.0F};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:25: warning: unused variable 'c_ihalff' [-Wunused-const-variable]
static const npy_cfloat c_ihalff = {0.0, 0.5F};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddf' [-Wunused-function]
caddf(npy_cfloat a, npy_cfloat b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubf' [-Wunused-function]
csubf(npy_cfloat a, npy_cfloat b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegf' [-Wunused-function]
cnegf(npy_cfloat a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulif' [-Wunused-function]
cmulif(npy_cfloat a)
^
numpy/core/src/npymath/npy_math_complex.c.src:67:26: warning: unused variable 'c_half' [-Wunused-const-variable]
static const npy_cdouble c_half = {0.5, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:26: warning: unused variable 'c_i' [-Wunused-const-variable]
static const npy_cdouble c_i = {0.0, 1.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:26: warning: unused variable 'c_ihalf' [-Wunused-const-variable]
static const npy_cdouble c_ihalf = {0.0, 0.5};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'cadd' [-Wunused-function]
cadd(npy_cdouble a, npy_cdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csub' [-Wunused-function]
csub(npy_cdouble a, npy_cdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cneg' [-Wunused-function]
cneg(npy_cdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmuli' [-Wunused-function]
cmuli(npy_cdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:67:30: warning: unused variable 'c_halfl' [-Wunused-const-variable]
static const npy_clongdouble c_halfl = {0.5L, 0.0};
^
numpy/core/src/npymath/npy_math_complex.c.src:68:30: warning: unused variable 'c_il' [-Wunused-const-variable]
static const npy_clongdouble c_il = {0.0, 1.0L};
^
numpy/core/src/npymath/npy_math_complex.c.src:69:30: warning: unused variable 'c_ihalfl' [-Wunused-const-variable]
static const npy_clongdouble c_ihalfl = {0.0, 0.5L};
^
numpy/core/src/npymath/npy_math_complex.c.src:79:1: warning: unused function 'caddl' [-Wunused-function]
caddl(npy_clongdouble a, npy_clongdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:87:1: warning: unused function 'csubl' [-Wunused-function]
csubl(npy_clongdouble a, npy_clongdouble b)
^
numpy/core/src/npymath/npy_math_complex.c.src:137:1: warning: unused function 'cnegl' [-Wunused-function]
cnegl(npy_clongdouble a)
^
numpy/core/src/npymath/npy_math_complex.c.src:144:1: warning: unused function 'cmulil' [-Wunused-function]
cmulil(npy_clongdouble a)
^
22 warnings generated.
clang: numpy/core/src/common/mem_overlap.c
clang: numpy/core/src/npymath/halffloat.c
clang: numpy/core/src/common/array_assign.c
clang: numpy/core/src/common/ufunc_override.c
clang: numpy/core/src/common/npy_longdouble.c
clang: numpy/core/src/common/numpyos.c
clang: numpy/core/src/common/ucsnarrow.c
1 warning generated.
clang: numpy/core/src/umath/extobj.c
numpy/core/src/common/ucsnarrow.c:139:34: warning: 'PyUnicode_FromUnicode' is deprecated [-Wdeprecated-declarations]
ret = (PyUnicodeObject *)PyUnicode_FromUnicode((Py_UNICODE*)buf,
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:551:1: note: 'PyUnicode_FromUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(PyObject*) PyUnicode_FromUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
1 warning generated.
clang: numpy/core/src/common/python_xerbla.c
clang: numpy/core/src/common/cblasfuncs.c
clang: /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c
In file included from /private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.c:26:
In file included from numpy/core/include/numpy/arrayobject.h:4:
In file included from numpy/core/include/numpy/ndarrayobject.h:21:
build/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: unused function '_import_array' [-Wunused-function]
_import_array(void)
^
1 warning generated.
17 warnings generated.
clang: numpy/core/src/umath/ufunc_type_resolution.c
4 warnings generated.
4 warnings generated.
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/alloc.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arrayobject.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/arraytypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_scalar.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/array_assign_array.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/buffer.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/calculation.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/compiled_base.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/common.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/convert_datatype.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/conversion_utils.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/ctors.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_strings.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busday.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/datetime_busdaycal.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/descriptor.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dragon4.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/dtype_transfer.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/einsum.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/flagsobject.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/getset.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/hashdescr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/item_selection.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/iterators.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/lowlevel_strided_loops.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/mapping.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/methods.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/multiarraymodule.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_templ.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_api.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_constr.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/nditer_pywrap.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/number.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/refcount.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/sequence.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/shape.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalarapi.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/scalartypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/strfuncs.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/temp_elide.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/typeinfo.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/usertypes.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/multiarray/vdot.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/umathmodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/reduction.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/loops.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/matmul.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_object.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/extobj.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/cpuid.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/scalarmath.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/ufunc_type_resolution.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/umath/override.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/ieee754.o build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/npy_math_complex.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/npymath/halffloat.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/array_assign.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/mem_overlap.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/npy_longdouble.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/ucsnarrow.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/ufunc_override.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/numpyos.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/cblasfuncs.o build/temp.macosx-10.15-x86_64-3.9/numpy/core/src/common/python_xerbla.o build/temp.macosx-10.15-x86_64-3.9/private/var/folders/fz/0j719tys48x7jlnjnwc69smr0000gn/T/pip-install-ufzck51l/numpy_b0e8a3953a1d4b46801f12bcea55536e/numpy/_build_utils/src/apple_sgemv_fix.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -lnpysort -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_multiarray_umath.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate
building 'numpy.core._umath_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_umath_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_umath_tests.cpython-39-darwin.so
building 'numpy.core._rational_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_rational_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_rational_tests.cpython-39-darwin.so
building 'numpy.core._struct_ufunc_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_struct_ufunc_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_struct_ufunc_tests.cpython-39-darwin.so
building 'numpy.core._operand_flag_tests' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
compile options: '-DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/core/src/umath/_operand_flag_tests.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/core/_operand_flag_tests.cpython-39-darwin.so
building 'numpy.fft.fftpack_lite' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/numpy/fft
compile options: '-Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: numpy/fft/fftpack_litemodule.c
clang: numpy/fft/fftpack.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/fft/fftpack.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/fft/fftpack_lite.cpython-39-darwin.so
building 'numpy.linalg.lapack_lite' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg
creating build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite
compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'
clang: numpy/linalg/lapack_litemodule.c
clang: numpy/linalg/lapack_lite/python_xerbla.c
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_litemodule.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate
building 'numpy.linalg._umath_linalg' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg
compile options: '-DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
extra options: '-msse3 -I/System/Library/Frameworks/vecLib.framework/Headers'
clang: build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.c
numpy/linalg/umath_linalg.c.src:735:32: warning: unknown warning group '-Wmaybe-uninitialized', ignored [-Wunknown-warning-option]
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
^
numpy/linalg/umath_linalg.c.src:541:1: warning: unused function 'dump_ufunc_object' [-Wunused-function]
dump_ufunc_object(PyUFuncObject* ufunc)
^
numpy/linalg/umath_linalg.c.src:566:1: warning: unused function 'dump_linearize_data' [-Wunused-function]
dump_linearize_data(const char* name, const LINEARIZE_DATA_t* params)
^
numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_FLOAT_matrix' [-Wunused-function]
dump_FLOAT_matrix(const char* name,
^
numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_DOUBLE_matrix' [-Wunused-function]
dump_DOUBLE_matrix(const char* name,
^
numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CFLOAT_matrix' [-Wunused-function]
dump_CFLOAT_matrix(const char* name,
^
numpy/linalg/umath_linalg.c.src:602:1: warning: unused function 'dump_CDOUBLE_matrix' [-Wunused-function]
dump_CDOUBLE_matrix(const char* name,
^
numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_FLOAT_matrix' [-Wunused-function]
zero_FLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data)
^
numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_DOUBLE_matrix' [-Wunused-function]
zero_DOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data)
^
numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CFLOAT_matrix' [-Wunused-function]
zero_CFLOAT_matrix(void *dst_in, const LINEARIZE_DATA_t* data)
^
numpy/linalg/umath_linalg.c.src:865:1: warning: unused function 'zero_CDOUBLE_matrix' [-Wunused-function]
zero_CDOUBLE_matrix(void *dst_in, const LINEARIZE_DATA_t* data)
^
numpy/linalg/umath_linalg.c.src:1862:1: warning: unused function 'dump_geev_params' [-Wunused-function]
dump_geev_params(const char *name, GEEV_PARAMS_t* params)
^
numpy/linalg/umath_linalg.c.src:2132:1: warning: unused function 'init_cgeev' [-Wunused-function]
init_cgeev(GEEV_PARAMS_t* params,
^
numpy/linalg/umath_linalg.c.src:2213:1: warning: unused function 'process_cgeev_results' [-Wunused-function]
process_cgeev_results(GEEV_PARAMS_t *NPY_UNUSED(params))
^
numpy/linalg/umath_linalg.c.src:2376:1: warning: unused function 'dump_gesdd_params' [-Wunused-function]
dump_gesdd_params(const char *name,
^
numpy/linalg/umath_linalg.c.src:2864:1: warning: unused function 'dump_gelsd_params' [-Wunused-function]
dump_gelsd_params(const char *name,
^
16 warnings generated.
clang -bundle -undefined dynamic_lookup -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk build/temp.macosx-10.15-x86_64-3.9/build/src.macosx-10.15-x86_64-3.9/numpy/linalg/umath_linalg.o build/temp.macosx-10.15-x86_64-3.9/numpy/linalg/lapack_lite/python_xerbla.o -L/usr/local/lib -L/usr/local/opt/openssl@1.1/lib -L/usr/local/opt/sqlite/lib -Lbuild/temp.macosx-10.15-x86_64-3.9 -lnpymath -o build/lib.macosx-10.15-x86_64-3.9/numpy/linalg/_umath_linalg.cpython-39-darwin.so -Wl,-framework -Wl,Accelerate
building 'numpy.random.mtrand' extension
compiling C sources
C compiler: clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers
creating build/temp.macosx-10.15-x86_64-3.9/numpy/random
creating build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand
compile options: '-D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c'
clang: numpy/random/mtrand/mtrand.c
clang: numpy/random/mtrand/initarray.cclang: numpy/random/mtrand/randomkit.c
clang: numpy/random/mtrand/distributions.c
numpy/random/mtrand/mtrand.c:40400:34: error: no member named 'tp_print' in 'struct _typeobject'
__pyx_type_6mtrand_RandomState.tp_print = 0;
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ^
numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42673:22: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42673:22: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42673:52: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42673:52: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:26: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:26: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:261:7: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op) : \
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:59: warning: 'PyUnicode_AsUnicode' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:262:14: note: expanded from macro 'PyUnicode_GET_SIZE'
((void)PyUnicode_AsUnicode(_PyObject_CAST(op)),\
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:580:1: note: 'PyUnicode_AsUnicode' has been explicitly marked deprecated here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
numpy/random/mtrand/mtrand.c:42689:59: warning: '_PyUnicode_get_wstr_length' is deprecated [-Wdeprecated-declarations]
(PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:264:8: note: expanded from macro 'PyUnicode_GET_SIZE'
PyUnicode_WSTR_LENGTH(op)))
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:451:35: note: expanded from macro 'PyUnicode_WSTR_LENGTH'
#define PyUnicode_WSTR_LENGTH(op) _PyUnicode_get_wstr_length((PyObject*)op)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/cpython/unicodeobject.h:445:1: note: '_PyUnicode_get_wstr_length' has been explicitly marked deprecated here
Py_DEPRECATED(3.3)
^
/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9/pyport.h:508:54: note: expanded from macro 'Py_DEPRECATED'
#define Py_DEPRECATED(VERSION_UNUSED) __attribute__((__deprecated__))
^
12 warnings and 1 error generated.
error: Command "clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/usr/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX10.15.sdk/System/Library/Frameworks/Tk.framework/Versions/8.5/Headers -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/local/include -I/usr/local/opt/openssl@1.1/include -I/usr/local/opt/sqlite/include -I/Users/destiny/Downloads/env/include -I/usr/local/Cellar/python@3.9/3.9.0_1/Frameworks/Python.framework/Versions/3.9/include/python3.9 -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/common -Ibuild/src.macosx-10.15-x86_64-3.9/numpy/core/src/npymath -c numpy/random/mtrand/mtrand.c -o build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o -MMD -MF build/temp.macosx-10.15-x86_64-3.9/numpy/random/mtrand/mtrand.o.d" failed with exit status 1
|
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MDU6SXNzdWU3NzkwMDQ4OTQ=
| 1,687
|
Question: Shouldn't .info be a part of DatasetDict?
|
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[
"We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.",
"Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). "
] | 1,609,852,121,000
| 1,610,014,686,000
| null |
CONTRIBUTOR
| null | null |
Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets.
For instance:
```
>>> ds = datasets.load_dataset("conll2002", "es")
>>> ds.info
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'DatasetDict' object has no attribute 'info'
```
I could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets.
Intuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.
It is entirely possible that I am missing another perspective
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Dataset Error: DaNE contains empty samples at the end
|
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[
"Thanks for reporting, I opened a PR to fix that",
"One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```",
"If you have other questions feel free to reopen :) "
] | 1,609,847,666,000
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| 1,609,855,213,000
|
CONTRIBUTOR
| null | null |
The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.
```python
>>> import datasets
[...]
>>> dataset = datasets.load_dataset("dane")
[...]
>>> dataset["test"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
>>> dataset["train"][-1]
{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}
```
Best,
Kenneth
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`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
|
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[
"Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.",
"It makes sense :D\r\n\r\nIt seems to work! Thanks a lot :))\r\n\r\nClosing the issue"
] | 1,609,786,073,000
| 1,609,787,085,000
| 1,609,787,085,000
|
CONTRIBUTOR
| null | null |
It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
```
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MDU6SXNzdWU3Nzc2NDQxNjM=
| 1,681
|
Dataset "dane" missing
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[
"Hi @KennethEnevoldsen ,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\r\npip install git+https://github.com/huggingface/datasets.git@master",
"The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dane` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dane\")\r\n```",
"Thanks. Solved the problem."
] | 1,609,682,583,000
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CONTRIBUTOR
| null | null |
the `dane` dataset appear to be missing in the latest version (1.1.3).
```python
>>> import datasets
>>> datasets.__version__
'1.1.3'
>>> "dane" in datasets.list_datasets()
True
```
As we can see it should be present, but doesn't seem to be findable when using `load_dataset`.
```python
>>> datasets.load_dataset("dane")
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 300, in cached_path
output_path = get_from_cache(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 588, in load_dataset
module_path, hash = prepare_module(
File "/home/kenneth/.Envs/EDP/lib/python3.8/site-packages/datasets/load.py", line 280, in prepare_module
raise FileNotFoundError(
FileNotFoundError: Couldn't find file locally at dane/dane.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dane/dane.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dane/dane.py
```
This issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.
|
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| 1,679
|
Can't import cc100 dataset
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[
"cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", lang=lang, split=\"train\")\r\n```"
] | 1,609,657,976,000
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NONE
| null | null |
There is some issue to import cc100 dataset.
```
from datasets import load_dataset
dataset = load_dataset("cc100")
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at cc100/cc100.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/cc100/cc100.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/cc100/cc100.py
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Add the 800GB Pile dataset?
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[
"The pile dataset would be very nice.\r\nBenchmarks show that pile trained models achieve better results than most of actually trained models",
"The pile can very easily be added and adapted using this [tfds implementation](https://github.com/EleutherAI/The-Pile/blob/master/the_pile/tfds_pile.py) from the repo. \r\n\r\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.",
"With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\r\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards",
"Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!",
"Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\r\n\r\n```python\r\n# Install master branch of `datasets`\r\npip install git+https://github.com/huggingface/datasets.git#egg=datasets[streaming]\r\npip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"json\", data_files=\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\", streaming=True, split=\"train\")\r\nnext(iter(dset))\r\n# {'meta': {'pile_set_name': 'Pile-CC'},\r\n# 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web ... '}\r\n```\r\n\r\nNext step is to add the Pile as a \"canonical\" dataset that can be streamed without specifying the file names explicitly :)",
"> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\r\n\r\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https://the-eye.eu/public/AI/pile_preliminary_components/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\r\n\r\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train / dev / test splits that EleutherAI released [here](https://the-eye.eu/public/AI/pile/), e.g.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# Load a single component\r\nyoutube_subtitles = load_dataset(\"the_pile\", \"youtube_subtitles\")\r\n# Load the train / dev / test splits of the whole corpus\r\ndset = load_dataset(\"the_pile\", \"all\")\r\n```\r\n\r\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\r\n\r\ncc @lhoestq ",
"Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https://github.com/huggingface/datasets/pull/2817#issuecomment-901874049) :)"
] | 1,609,541,892,000
| 1,638,372,547,000
| 1,638,372,547,000
|
MEMBER
| null | null |
## Adding a Dataset
- **Name:** The Pile
- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https://twitter.com/nabla_theta/status/1345130408170541056?s=20) for the Twitter announcement
- **Paper:** https://pile.eleuther.ai/paper.pdf
- **Data:** https://pile.eleuther.ai/
- **Motivation:** Enables hardcore (GPT-3 scale!) language modelling
## Remarks
Given the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` 🤯 . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.
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dutch_social can't be loaded
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[
"exactly the same issue in some other datasets.\r\nDid you find any solution??\r\n",
"Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\r\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \r\nLet me know if this helps!",
"Maybe we should do a small release on Monday in the meantime @lhoestq ?",
"Yes sure !",
"I just did the release :)\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dutch_social` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dutch_social\")\r\n```",
"@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?",
"The issue is different for this one, let me give more details in the issue",
"Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread."
] | 1,609,522,628,000
| 1,664,975,006,000
| 1,664,975,006,000
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Hi all,
I'm trying to import the `dutch_social` dataset described [here](https://huggingface.co/datasets/dutch_social).
However, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.
```
(base) Koens-MacBook-Pro:~ koenvandenberge$ python
Python 3.7.4 (default, Aug 13 2019, 15:17:50)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datasets import load_dataset
dataset = load_dataset(
'dutch_social')
>>> dataset = load_dataset(
... 'dutch_social')
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/koenvandenberge/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at dutch_social/dutch_social.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/dutch_social/dutch_social.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/dutch_social/dutch_social.py
```
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MDU6SXNzdWU3NzcyNjM2NTE=
| 1,673
|
Unable to Download Hindi Wikipedia Dataset
|
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[
"Currently this dataset is only available when the library is installed from source since it was added after the last release.\r\n\r\nWe pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.\r\n\r\nWe'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours.",
"So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then?",
"Yes",
"Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.\r\n\r\nThe Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error\r\n\r\n\r\n",
"Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.\r\nYou can try to load a more recent version of wikipedia\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wikipedia\", language=\"hi\", date=\"20210101\", split=\"train\", beam_runner=\"DirectRunner\")\r\n```",
"Okay, thank you so much"
] | 1,609,498,373,000
| 1,609,842,132,000
| 1,609,842,132,000
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NONE
| null | null |
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
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| 1,672
|
load_dataset hang on file_lock
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[
"Can you try to upgrade to a more recent version of datasets?",
"Thank, upgrading to 1.1.3 resolved the issue.",
"Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04\r\n\r\n```py\r\nIn [1]: from datasets import load_dataset \r\n\r\nIn [2]: wikipedia = load_dataset('kilt_wikipedia')['full'] \r\nDownloading: 7.37kB [00:00, 2.74MB/s] \r\nDownloading: 3.33kB [00:00, 1.44MB/s] \r\n^C---------------------------------------------------------------------------\r\nOSError Traceback (most recent call last)\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)\r\n 380 try:\r\n--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n 382 except (IOError, OSError):\r\n\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nKeyboardInterrupt Traceback (most recent call last)\r\n<ipython-input-2-f412d3d46ec9> in <module>\r\n----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav\r\ne_infos, script_version, **config_kwargs)\r\n 601 hash=hash,\r\n 602 features=features,\r\n--> 603 **config_kwargs,\r\n 604 )\r\n 605 \r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)\r\n 841 def __init__(self, *args, **kwargs):\r\n 842 self._writer_batch_size = kwargs.pop(\"writer_batch_size\", self._writer_batch_size)\r\n--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n 844 \r\n 845 @abc.abstractmethod\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n 174 os.makedirs(self._cache_dir_root, exist_ok=True)\r\n 175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, \"_\") + \".lock\")\r\n--> 176 with FileLock(lock_path):\r\n 177 if os.path.exists(self._cache_dir): # check if data exist\r\n 178 if len(os.listdir(self._cache_dir)) > 0:\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)\r\n 312 \r\n 313 def __enter__(self):\r\n--> 314 self.acquire()\r\n 315 return self\r\n 316 \r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)\r\n 261 if not self.is_locked:\r\n 262 logger().debug(\"Attempting to acquire lock %s on %s\", lock_id, lock_filename)\r\n--> 263 self._acquire()\r\n 264 \r\n 265 if self.is_locked:\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)\r\n 379 \r\n 380 try:\r\n--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n 382 except (IOError, OSError):\r\n 383 os.close(fd)\r\n\r\nKeyboardInterrupt: \r\n\r\n```"
] | 1,609,496,707,000
| 1,617,207,853,000
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NONE
| null | null |
I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
```
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MDU6SXNzdWU3NzY2NTIxOTM=
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connection issue
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[
"Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do \r\n\r\nd = datasets.load_from_disk(\"imdb\")\r\nd = d[\"train\"][:10] => the format of this is no more in datasets format\r\nthis is different from you call load_datasets(\"train[10]\")\r\n\r\ncould you tell me how I can make the two datastes the same format @lhoestq \r\n\r\n",
"> `\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`\r\n\r\nDo you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?\r\n\r\n> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\nHF_HOME is used to specify the directory for the cache files of this library.\r\nYou can use save_to_disk and load_from_disk without changing the HF_HOME:\r\n```python\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"/idiap/temp/rkarimi/hf_datasets/imdb\")\r\nimdb = datasets.load_from_disk(\"/idiap/temp/rkarimi/hf_datasets/imdb\")\r\n```\r\n\r\n> could you tell me how I can make the two datastes the same format\r\n\r\nIndeed they returns different things:\r\n- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets(\"imdb\", split=\"train[10]\")` returns a `Dataset` object containing 10 elements.\r\n- doing `d[\"train\"][:10]` on a DatasetDict \"d\" gets the train split `d[\"train\"]` as a `Dataset` object and then gets the first 10 elements as a dictionary"
] | 1,609,365,380,000
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NONE
| null | null |
Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
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MDU6SXNzdWU3NzY2MDg1Nzk=
| 1,670
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wiki_dpr pre-processing performance
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[
"Hi ! And thanks for the tips :) \r\n\r\nIndeed currently `wiki_dpr` takes some time to be processed.\r\nMultiprocessing for dataset generation is definitely going to speed up things.\r\n\r\nRegarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.\r\n\r\nAny contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.\r\n\r\n",
"I'd be happy to contribute something when I get the time, probably adding multiprocessing and / or cython support to wiki_dpr. I've written cythonized apache beam code before as well.\r\n\r\nFor sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow.",
"Nice thanks ! That would be awesome to make its construction faster :) "
] | 1,609,357,303,000
| 1,611,826,896,000
| null |
NONE
| null | null |
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
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| 1,669
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wiki_dpr dataset pre-processesing performance
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[
"Sorry, double posted."
] | 1,609,357,269,000
| 1,609,357,345,000
| 1,609,357,345,000
|
NONE
| null | null |
I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).
I won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.
The other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.
It looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.
References:
[1] https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py
[2] https://github.com/huggingface/datasets/blob/master/src/datasets/search.py
[3] https://github.com/facebookresearch/faiss/blob/master/benchs/bench_hnsw.py
[4] https://github.com/facebookresearch/faiss/issues/422
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MDU6SXNzdWU3NzU4OTAxNTQ=
| 1,662
|
Arrow file is too large when saving vector data
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[
"Hi !\r\nThe arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is\r\n\r\n20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB\r\n\r\nIf you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.\r\n",
"Thanks for your reply @lhoestq.\r\nI want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file?",
"Arrow doesn't have compression since it is designed to have no serialization overhead",
"I see. Thank you."
] | 1,609,248,192,000
| 1,611,238,359,000
| 1,611,238,359,000
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NONE
| null | null |
I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?
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MDU6SXNzdWU3NzU1MjU3OTk=
| 1,647
|
NarrativeQA fails to load with `load_dataset`
|
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[
"Hi @eric-mitchell,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`",
"@bhavitvyamalik Great, thanks for this! Confirmed that the problem is resolved on master at [cbbda53](https://github.com/huggingface/datasets/commit/cbbda53ac1520b01f0f67ed6017003936c41ec59).",
"Update: HuggingFace did an intermediate release yesterday just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n\r\n`pip install --upgrade datasets`"
] | 1,609,179,369,000
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| 1,609,696,685,000
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NONE
| null | null |
When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https://huggingface.co/datasets/narrativeqa), I receive a cascade of exceptions, ending with
FileNotFoundError: Couldn't find file locally at narrativeqa/narrativeqa.py, or remotely at
https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/narrativeqa/narrativeqa.py or
https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/narrativeqa/narrativeqa.py
Workaround: manually copy the `narrativeqa.py` builder into my local directory with
curl https://raw.githubusercontent.com/huggingface/datasets/master/datasets/narrativeqa/narrativeqa.py -o narrativeqa.py
and load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.
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MDU6SXNzdWU3NzUzNzU4ODA=
| 1,644
|
HoVeR dataset fails to load
|
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[
"Hover was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `hover` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"hover\")\r\n```"
] | 1,609,158,427,000
| 1,664,973,634,000
| 1,664,973,634,000
|
NONE
| null | null |
Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.
Steps to reproduce the error:
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("hover")
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/urikz/anaconda/envs/mentionmemory/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at hover/hover.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/hover/hover.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/hover/hover.py
```
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MDU6SXNzdWU3NzUyODAwNDY=
| 1,643
|
Dataset social_bias_frames 404
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[
"I see, master is already fixed in https://github.com/huggingface/datasets/commit/9e058f098a0919efd03a136b9b9c3dec5076f626"
] | 1,609,144,534,000
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| 1,609,144,687,000
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NONE
| null | null |
```
>>> from datasets import load_dataset
>>> dataset = load_dataset("social_bias_frames")
...
Downloading and preparing dataset social_bias_frames/default
...
~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)
484 )
485 elif response is not None and response.status_code == 404:
--> 486 raise FileNotFoundError("Couldn't find file at {}".format(url))
487 raise ConnectionError("Couldn't reach {}".format(url))
488
FileNotFoundError: Couldn't find file at https://homes.cs.washington.edu/~msap/social-bias-frames/SocialBiasFrames_v2.tgz
```
[Here](https://homes.cs.washington.edu/~msap/social-bias-frames/) we find button `Download data` with the correct URL for the data: https://homes.cs.washington.edu/~msap/social-bias-frames/SBIC.v2.tgz
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muchocine dataset cannot be dowloaded
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[
"I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"squad\") # Works\r\ndataset = load_dataset(\"code_search_net\", \"python\") # Error\r\ndataset = load_dataset(\"covid_qa_deepset\") # Error\r\n\r\npath = \"/huggingface/datasets/datasets/{}/\"\r\ndataset = load_dataset(path.format(\"code_search_net\"), \"python\") # Works\r\ndataset = load_dataset(path.format(\"covid_qa_deepset\")) # Works\r\n```\r\n\r\n",
"Hi @mrm8488 and @amoux!\r\n The datasets you are trying to load have been added to the library during the community sprint for v2 last month. They will be available with the v2 release!\r\nFor now, there are still a couple of solutions to load the datasets:\r\n1. As suggested by @amoux, you can clone the git repo and pass the local path to the script\r\n2. You can also install the latest (master) version of `datasets` using pip: `pip install git+https://github.com/huggingface/datasets.git@master`",
"If you don't want to clone entire `datasets` repo, just download the `muchocine` directory and pass the local path to the directory. Cheers!",
"Muchocine was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `muchocine` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"muchocine\", split=\"train\")\r\n```",
"Thanks @lhoestq "
] | 1,609,104,388,000
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| 1,627,967,249,000
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CONTRIBUTOR
| null | null |
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
7 frames
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
281 raise FileNotFoundError(
282 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 283 combined_path, github_file_path, file_path
284 )
285 )
FileNotFoundError: Couldn't find file locally at muchocine/muchocine.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/muchocine/muchocine.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/muchocine/muchocine.py
```
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MDU6SXNzdWU3NzQ5MDM0NzI=
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bug with sst2 in glue
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[
"Maybe you can use nltk's treebank detokenizer ?\r\n```python\r\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\r\n\r\nTreebankWordDetokenizer().detokenize(\"it 's a charming and often affecting journey . \".split())\r\n# \"it's a charming and often affecting journey.\"\r\n```",
"I am looking for alternative file URL here instead of adding extra processing code: https://github.com/huggingface/datasets/blob/171f2bba9dd8b92006b13cf076a5bf31d67d3e69/datasets/glue/glue.py#L174",
"I don't know if there exists a detokenized version somewhere. Even the version on kaggle is tokenized"
] | 1,609,001,843,000
| 1,664,973,616,000
| 1,664,973,616,000
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NONE
| null | null |
Hi
I am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.
Is there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq
```
>>> a = datasets.load_dataset('glue', 'sst2', split="validation", script_version="master")
Reusing dataset glue (/julia/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)
>>> a[:10]
{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': ["it 's a charming and often affecting journey . ", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", "it 's slow -- very , very slow . ", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', "or doing last year 's taxes with your ex-wife . ", "you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . ", "in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . "]}
```
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MDU6SXNzdWU3NzQ1NzQzNzg=
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winogrande cannot be dowloaded
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[
"I have same issue for other datasets (`myanmar_news` in my case).\r\n\r\nA version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at \r\n```\r\nhttps://raw.githubusercontent.com/huggingface/datasets/master/datasets/myanmar_news/myanmar_news.py\r\n```\r\n\r\nMeanwhile, other version runs on Colab (**with GPU**) failed to download the dataset. It try to find the dataset at `1.1.3` instead of `master` . If I disable GPU on my Colab, the code can load the dataset without any problem.\r\n\r\nMaybe there is some version missmatch with the GPU and CPU version of code for these datasets?",
"It looks like they're two different issues\r\n\r\n----------\r\n\r\nFirst for `myanmar_news`: \r\n\r\nIt must come from the way you installed `datasets`.\r\nIf you install `datasets` from source, then the `myanmar_news` script will be loaded from `master`.\r\nHowever if you install from `pip` it will get it using the version of the lib (here `1.1.3`) and `myanmar_news` is not available in `1.1.3`.\r\n\r\nThe difference between your GPU and CPU executions must be the environment, one seems to have installed `datasets` from source and not the other.\r\n\r\n----------\r\n\r\nThen for `winogrande`:\r\n\r\nThe errors says that the url https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py is not reachable.\r\nHowever it works fine on my side.\r\n\r\nDoes your machine have an internet connection ? Are connections to github blocked by some sort of proxy ?\r\nCan you also try again in case github had issues when you tried the first time ?\r\n"
] | 1,608,848,902,000
| 1,664,973,344,000
| 1,664,973,344,000
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NONE
| null | null |
Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
```
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Persian Abstractive/Extractive Text Summarization
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CONTRIBUTOR
| null | null |
Assembling datasets tailored to different tasks and languages is a precious target. This would be great to have this dataset included.
## Adding a Dataset
- **Name:** *pn-summary*
- **Description:** *A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.*
- **Paper:** *https://arxiv.org/abs/2012.11204*
- **Data:** *https://github.com/hooshvare/pn-summary/#download*
- **Motivation:** *It is the first Persian abstractive/extractive Text summarization dataset (like cnn_dailymail for English)!*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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MDU6SXNzdWU3NzQ0ODc5MzQ=
| 1,634
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Inspecting datasets per category
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[
"That's interesting, can you tell me what you think would be useful to access to inspect a dataset?\r\n\r\nYou can filter them in the hub with the search by the way: https://huggingface.co/datasets have you seen it?",
"Hi @thomwolf \r\nthank you, I was not aware of this, I was looking into the data viewer linked into readme page. \r\n\r\nThis is exactly what I was looking for, but this does not work currently, please see the attached \r\nI am selecting to see all nli datasets in english and it retrieves none. thanks\r\n\r\n\r\n\r\n\r\n\r\n",
"I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https://huggingface.co/datasets?filter=task_ids:natural-language-inference,languages:en",
"Hi! You can use `huggingface_hub`'s `list_datasets` for that now:\r\n```python\r\nimport huggingface_hub # pip install huggingface_hub\r\nhuggingface_hub.list_datasets(filter=\"task_categories:question-answering\")\r\n# or\r\nhuggingface_hub.list_datasets(filter=(\"task_categories:natural-language-inference\", \"languages:\"en\"))\r\n```"
] | 1,608,823,594,000
| 1,664,895,453,000
| 1,664,895,453,000
|
NONE
| null | null |
Hi
Is there a way I could get all NLI datasets/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq
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social_i_qa wrong format of labels
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"@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file",
"Sure feel free to open a PR thanks !"
] | 1,608,815,514,000
| 1,609,348,729,000
| 1,609,348,729,000
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NONE
| null | null |
Hi,
there is extra "\n" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.
so label is 'label': '1\n', not '1'
thanks
```
>>> import datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset(
... 'social_i_qa')
cahce dir /julia/cache/datasets
Downloading: 4.72kB [00:00, 3.52MB/s]
cahce dir /julia/cache/datasets
Downloading: 2.19kB [00:00, 1.81MB/s]
Using custom data configuration default
Reusing dataset social_i_qa (/julia/datasets/social_i_qa/default/0.1.0/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)
>>> dataset['train'][0]
{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\n', 'question': 'How would Others feel as a result?'}
```
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SICK dataset
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CONTRIBUTOR
| null | null |
Hi, this would be great to have this dataset included. I might be missing something, but I could not find it in the list of already included datasets. Thank you.
## Adding a Dataset
- **Name:** SICK
- **Description:** SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic, and semantic phenomena.
- **Paper:** https://www.aclweb.org/anthology/L14-1314/
- **Data:** http://marcobaroni.org/composes/sick.html
- **Motivation:** This dataset is well-known in the NLP community used for recognizing entailment between sentences.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Adding UKP Argument Aspect Similarity Corpus
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[
"Adding a link to the guide on adding a dataset if someone want to give it a try: https://github.com/huggingface/datasets#add-a-new-dataset-to-the-hub\r\n\r\nwe should add this guide to the issue template @lhoestq ",
"thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. ",
"Available here: https://huggingface.co/datasets/UKPLab/UKP_ASPECT"
] | 1,608,807,691,000
| 1,664,973,372,000
| 1,664,973,372,000
|
CONTRIBUTOR
| null | null |
Hi, this would be great to have this dataset included.
## Adding a Dataset
- **Name:** UKP Argument Aspect Similarity Corpus
- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either “high similarity”, “some similarity”, “no similarity” or “not related” with respect to the topic.
- **Paper:** https://www.aclweb.org/anthology/P19-1054/
- **Data:** https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/1998
- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https://arxiv.org/pdf/2005.00247.pdf
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Thank you
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| 1,627
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`Dataset.map` disable progress bar
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[
"Progress bar can be disabled like this:\r\n```python\r\nfrom datasets.utils.logging import set_verbosity_error\r\nset_verbosity_error()\r\n```\r\n\r\nThere is this line in `Dataset.map`:\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nSo any logging level higher than `WARNING` turns off the progress bar.",
"From the linked issues above, an up-to-date solution is:\r\n\r\n```python\r\nfrom datasets.utils.logging import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nhttps://github.com/huggingface/datasets/blob/c6e08fcfc3a04e53430c26fa7c07da4cb18d977d/src/datasets/utils/logging.py#L233-L236"
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|
NONE
| null | null |
I can't find anything to turn off the `tqdm` progress bars while running a preprocessing function using `Dataset.map`. I want to do akin to `disable_tqdm=True` in the case of `transformers`. Is there something like that?
|
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Cannot download ade_corpus_v2
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[
"Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`",
"`ade_corpus_v2` was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `ade_corpus_v2` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"ade_corpus_v2\", \"Ade_corpos_v2_drug_ade_relation\")\r\n```\r\n\r\n(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon)"
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| 1,627,967,334,000
|
NONE
| null | null |
I tried this to get the dataset following this url : https://huggingface.co/datasets/ade_corpus_v2
but received this error :
`Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 278, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 486, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/opt/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 282, in prepare_module
combined_path, github_file_path, file_path
FileNotFoundError: Couldn't find file locally at ade_corpus_v2/ade_corpus_v2.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/ade_corpus_v2/ade_corpus_v2.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/ade_corpus_v2/ade_corpus_v2.py`
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Can't call shape on the output of select()
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[
"Indeed that's a typo, do you want to open a PR to fix it?",
"Yes, created a PR"
] | 1,608,643,120,000
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CONTRIBUTOR
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I get the error `TypeError: tuple expected at most 1 argument, got 2` when calling `shape` on the output of `select()`.
It's line 531 in shape in arrow_dataset.py that causes the problem:
``return tuple(self._indices.num_rows, self._data.num_columns)``
This makes sense, since `tuple(num1, num2)` is not a valid call.
Full code to reproduce:
```python
dataset = load_dataset("cnn_dailymail", "3.0.0")
train_set = dataset["train"]
t = train_set.select(range(10))
print(t.shape)
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Can't filter language:EN on https://huggingface.co/datasets
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[
"cc'ing @mapmeld ",
"Full language list is now deployed to https://huggingface.co/datasets ! Recommend close",
"Cool @mapmeld ! My 2 cents (for a next iteration), it would be cool to have a small search widget in the filter dropdown as you have a ton of languages now here! Closing this in the meantime."
] | 1,608,564,203,000
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NONE
| null | null |
When visiting https://huggingface.co/datasets, I don't see an obvious way to filter only English datasets. This is unexpected for me, am I missing something? I'd expect English to be selectable in the language widget. This problem reproduced on Mozilla Firefox and MS Edge:
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Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`
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[
"Hi @SapirWeissbuch,\r\nWhen you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!\r\n\r\n",
"Hi @bhavitvyamalik \r\nThanks for the reply!\r\nActually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.\r\n\r\nWhat version of Datasets are you using?\r\n",
"I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only\r\n`dataset = datasets.load_dataset(\"trivia_qa\", \"rc\")`\r\n\r\nTried that on colab and it's working there too\r\n\r\n",
"Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now!",
"Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.\r\nI have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue?",
"I tried running this again - More details of the problem:\r\nCode:\r\n```\r\ndatasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir=\"/path/to/cache\")\r\n```\r\n\r\nThe output:\r\n```\r\nDownloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path/to/cache/trivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d... \r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.67G/2.67G [03:38<00:00, 12.2MB/s]\r\n\r\n```\r\nThe process continues (no progress bar is visible).\r\nI tried `du -sh .` in `path/to/cache`, and the size keeps increasing, reached 35G before I killed the process.\r\n\r\nGoogle Colab with custom `cache_dir` has same issue.\r\nhttps://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan",
"1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.\r\n\r\n2) I tried on colab `dataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \"./datasets\")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows \r\n\r\nwhich means it's loaded now.",
"Facing the same issue.\r\nI am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download. \r\nTried for `data = load_dataset(\"cnn_dailymail\", \"3.0.0\")`",
"Hi @ashutoshml,\r\nI tried this and it worked for me:\r\n`data = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"./dummy\")`\r\n\r\nI'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples.",
"Ok. I waited for 20-30 mins, and it still is stuck.\r\nI am using datasets==1.8.0.\r\n\r\nIs there anyway to check what is happening? like a` --verbose` flag?\r\n\r\n\r\n"
] | 1,608,485,258,000
| 1,624,626,693,000
| null |
NONE
| null | null |
Hello,
I'm having issue downloading TriviaQA dataset with `load_dataset`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
dataset = datasets.load_dataset("trivia_qa", "rc", cache_dir = "./datasets")
```
## The output:
1. Download begins:
```
Downloading and preparing dataset trivia_qa/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to /cs/labs/gabis/sapirweissbuch/tr
ivia_qa/rc/1.1.0/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...
Downloading: 17%|███████████████████▉ | 446M/2.67G [00:37<04:45, 7.77MB/s]
```
2. 100% is reached
3. It got stuck here for about an hour, and added additional 30G of data to "./datasets" directory. I killed the process eventually.
A similar issue can be observed in Google Colab:
https://colab.research.google.com/drive/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing
## Expected behaviour:
The dataset "TriviaQA" should be successfully downloaded.
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shuffle with torch generator
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[
"Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks ",
"@lhoestq let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large datasets using huggingface library, then I need to define a distributed sampler on top of it, for this I need to shard the datasets and give each shard to each core, but before sharding I need to shuffle the dataset, if you are familiar with distributed sampler in pytorch, this needs to be done based on seed+epoch generator to make it consistent across the cores they do it through defining a torch generator, I was wondering if you could tell me how I can shuffle the data for now, I am unfortunately blocked by this and have a limited time left, and I greatly appreciate your help on this. thanks ",
"@lhoestq Is there a way I could shuffle the datasets from this library with a custom defined shuffle function? thanks for your help on this. ",
"Right now the shuffle method only accepts the `seed` (optional int) or `generator` (optional `np.random.Generator`) parameters.\r\n\r\nHere is a suggestion to shuffle the data using your own shuffle method using `select`.\r\n`select` can be used to re-order the dataset samples or simply pick a few ones if you want.\r\nIt's what is used under the hood when you call `dataset.shuffle`.\r\n\r\nTo use `select` you must have the list of re-ordered indices of your samples.\r\n\r\nLet's say you have a `shuffle` methods that you want to use. Then you can first build your shuffled list of indices:\r\n```python\r\nshuffled_indices = shuffle(range(len(dataset)))\r\n```\r\n\r\nThen you can shuffle your dataset using the shuffled indices with \r\n```python\r\nshuffled_dataset = dataset.select(shuffled_indices)\r\n```\r\n\r\nHope that helps",
"thank you @lhoestq thank you very much for responding to my question, this greatly helped me and remove the blocking for continuing my work, thanks. ",
"@lhoestq could you confirm the method proposed does not bring the whole data into memory? thanks ",
"Yes the dataset is not loaded into memory",
"great. thanks a lot."
] | 1,608,425,834,000
| 1,654,097,413,000
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NONE
| null | null |
Hi
I need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help
@lhoestq
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shuffle does not accept seed
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[
"Hi, did you check the doc on `shuffle`?\r\nhttps://huggingface.co/docs/datasets/package_reference/main_classes.html?datasets.Dataset.shuffle#datasets.Dataset.shuffle",
"Hi Thomas\r\nthanks for reponse, yes, I did checked it, but this does not work for me please see \r\n\r\n```\r\n(internship) rkarimi@italix17:/idiap/user/rkarimi/dev$ python \r\nPython 3.7.9 (default, Aug 31 2020, 12:42:55) \r\n[GCC 7.3.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets \r\n2020-12-20 01:48:50.766004: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-20 01:48:50.766029: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n>>> data = datasets.load_dataset(\"scitail\", \"snli_format\")\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nReusing dataset scitail (/idiap/temp/rkarimi/cache_home_1/datasets/scitail/snli_format/1.1.0/fd8ccdfc3134ce86eb4ef10ba7f21ee2a125c946e26bb1dd3625fe74f48d3b90)\r\n>>> data.shuffle(seed=2)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nTypeError: shuffle() got an unexpected keyword argument 'seed'\r\n\r\n```\r\n\r\ndatasets version\r\n`datasets 1.1.2 <pip>\r\n`\r\n",
"Thanks for reporting ! \r\n\r\nIndeed it looks like an issue with `suffle` on `DatasetDict`. We're going to fix that.\r\nIn the meantime you can shuffle each split (train, validation, test) separately:\r\n```python\r\nshuffled_train_dataset = data[\"train\"].shuffle(seed=42)\r\n```\r\n"
] | 1,608,411,579,000
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CONTRIBUTOR
| null | null |
Hi
I need to shuffle the dataset, but this needs to be based on epoch+seed to be consistent across the cores, when I pass seed to shuffle, this does not accept seed, could you assist me with this? thanks @lhoestq
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MDU6SXNzdWU3NzE0MjE4ODE=
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Not able to use 'jigsaw_toxicity_pred' dataset
|
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[
"Hi @jassimran,\r\nThe `jigsaw_toxicity_pred` dataset has not been released yet, it will be available with version 2 of `datasets`, coming soon.\r\nYou can still access it by installing the master (unreleased) version of datasets directly :\r\n`pip install git+https://github.com/huggingface/datasets.git@master`\r\nPlease let me know if this helps",
"Thanks.That works for now."
] | 1,608,399,348,000
| 1,608,655,344,000
| 1,608,655,343,000
|
NONE
| null | null |
When trying to use jigsaw_toxicity_pred dataset, like this in a [colab](https://colab.research.google.com/drive/1LwO2A5M2X5dvhkAFYE4D2CUT3WUdWnkn?usp=sharing):
```
from datasets import list_datasets, list_metrics, load_dataset, load_metric
ds = load_dataset("jigsaw_toxicity_pred")
```
I see below error:
> FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/jigsaw_toxicity_pred/jigsaw_toxicity_pred.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/jigsaw_toxicity_pred/jigsaw_toxicity_pred.py
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
280 raise FileNotFoundError(
281 "Couldn't find file locally at {}, or remotely at {} or {}".format(
--> 282 combined_path, github_file_path, file_path
283 )
284 )
FileNotFoundError: Couldn't find file locally at jigsaw_toxicity_pred/jigsaw_toxicity_pred.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/jigsaw_toxicity_pred/jigsaw_toxicity_pred.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/jigsaw_toxicity_pred/jigsaw_toxicity_pred.py
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MDU6SXNzdWU3NzA5Nzk2MjA=
| 1,605
|
Navigation version breaking
|
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"Not relevant for our current docs :)."
] | 1,608,305,784,000
| 1,664,973,311,000
| 1,664,973,311,000
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NONE
| null | null |
Hi,
when navigating docs (Chrome, Ubuntu) (e.g. on this page: https://huggingface.co/docs/datasets/loading_metrics.html#using-a-custom-metric-script) the version control dropdown has the wrong string displayed as the current version:
**Edit:** this actually happens _only_ if you open a link to a concrete subsection.
IMO, the best way to fix this without getting too deep into the intricacies of retrieving version numbers from the URL would be to change [this](https://github.com/huggingface/datasets/blob/master/docs/source/_static/js/custom.js#L112) line to:
```
let label = (version in versionMapping) ? version : stableVersion
```
which delegates the check to the (already maintained) keys of the version mapping dictionary & should be more robust. There's a similar ternary expression [here](https://github.com/huggingface/datasets/blob/master/docs/source/_static/js/custom.js#L97) which should also fail in this case.
I'd also suggest swapping this [block](https://github.com/huggingface/datasets/blob/master/docs/source/_static/js/custom.js#L80-L90) to `string.contains(version) for version in versionMapping` which might be more robust. I'd add a PR myself but I'm by no means competent in JS :)
I also have a side question wrt. docs versioning: I'm trying to make docs for a project which are versioned alike to your dropdown versioning. I was wondering how do you handle storage of multiple doc versions on your server? Do you update what `https://huggingface.co/docs/datasets` points to for every stable release & manually create new folders for each released version?
So far I'm building & publishing (scping) the docs to the server with a github action which works well for a single version, but would ideally need to reorder the public files triggered on a new release.
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| 1,604
|
Add tests for the download functions ?
|
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"We have some tests now for it under `tests/test_download_manager.py`."
] | 1,608,295,765,000
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CONTRIBUTOR
| null | null |
AFAIK the download functions in `DownloadManager` are not tested yet. It could be good to add some to ensure behavior is as expected.
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|
AttributeError: 'DatasetDict' object has no attribute 'train_test_split'
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[
"Hi @david-waterworth!\r\n\r\nAs indicated in the error message, `load_dataset(\"csv\")` returns a `DatasetDict` object, which is mapping of `str` to `Dataset` objects. I believe in this case the behavior is to return a `train` split with all the data.\r\n`train_test_split` is a method of the `Dataset` object, so you will need to do something like this:\r\n```python\r\ndataset_dict = load_dataset(`'csv', data_files='data.txt')\r\ndataset = dataset_dict['split name, eg train']\r\ndataset.train_test_split(test_size=0.1)\r\n```\r\n\r\nPlease let me know if this helps. 🙂 ",
"Thanks, that's working - the same issue also tripped me up with training. \r\n\r\nI also agree https://github.com/huggingface/datasets/issues/767 would be a useful addition. ",
"Closing this now",
"> ```python\r\n> dataset_dict = load_dataset(`'csv', data_files='data.txt')\r\n> dataset = dataset_dict['split name, eg train']\r\n> dataset.train_test_split(test_size=0.1)\r\n> ```\r\n\r\nI am getting error like\r\nKeyError: 'split name, eg train'\r\nCould you please tell me how to solve this?",
"dataset = load_dataset('csv', data_files=['files/datasets/dataset.csv'])\r\ndataset = dataset['train']\r\ndataset = dataset.train_test_split(test_size=0.1)"
] | 1,608,269,830,000
| 1,623,756,346,000
| 1,608,536,338,000
|
NONE
| null | null |
The following code fails with "'DatasetDict' object has no attribute 'train_test_split'" - am I doing something wrong?
```
from datasets import load_dataset
dataset = load_dataset('csv', data_files='data.txt')
dataset = dataset.train_test_split(test_size=0.1)
```
> AttributeError: 'DatasetDict' object has no attribute 'train_test_split'
|
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MDU6SXNzdWU3Njk3NDc3Njc=
| 1,594
|
connection error
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[
"This happen quite often when they are too many concurrent requests to github.\r\n\r\ni can understand it’s a bit cumbersome to handle on the user side. Maybe we should try a few times in the lib (eg with timeout) before failing, what do you think @lhoestq ?",
"Yes currently there's no retry afaik. We should add retries",
"Retries were added in #1603 :) \r\nIt will be available in the next release",
"Hi @lhoestq thank you for the modification, I will use`script_version=\"master\"` for now :), to my experience, also setting timeout to a larger number like 3*60 which I normally use helps a lot on this.\r\n"
] | 1,608,196,714,000
| 1,654,097,622,000
| 1,654,097,621,000
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NONE
| null | null |
Hi
I am hitting to this error, thanks
```
> Traceback (most recent call last):
File "finetune_t5_trainer.py", line 379, in <module>
main()
File "finetune_t5_trainer.py", line 208, in main
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
File "finetune_t5_trainer.py", line 207, in <dictcomp>
for task in data_args.eval_tasks}
File "/workdir/seq2seq/data/tasks.py", line 70, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 66, in load_dataset
return datasets.load_dataset(self.task.name, split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/boolq/boolq.py
el/0 I1217 01:11:33.898849 354161 main shadow.py:210 Current job status: FINISHED
```
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MDU6SXNzdWU3Njk2MTEzODY=
| 1,593
|
Access to key in DatasetDict map
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[
"Indeed that would be cool\r\n\r\nAlso FYI right now the easiest way to do this is\r\n```python\r\ndataset_dict[\"train\"] = dataset_dict[\"train\"].map(my_transform_for_the_train_set)\r\ndataset_dict[\"test\"] = dataset_dict[\"test\"].map(my_transform_for_the_test_set)\r\n```",
"I don't feel like adding an extra param for this simple usage makes sense, considering how many args `map` already has. \r\n\r\n(Feel free to re-open this issue if you don't agree with me)",
"I still think this is useful, since it's common that the data processing is different for training/dev/testing. And I don't know if the fact that `map` currently takes many arguments is a good reason not to support a useful feature."
] | 1,608,188,540,000
| 1,664,977,648,000
| 1,664,973,186,000
|
NONE
| null | null |
It is possible that we want to do different things in the `map` function (and possibly other functions too) of a `DatasetDict`, depending on the key. I understand that `DatasetDict.map` is a really thin wrapper of `Dataset.map`, so it is easy to directly implement this functionality in the client code. Still, it'd be nice if there can be a flag, similar to `with_indices`, that allows the callable to know the key inside `DatasetDict`.
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IWSLT-17 Link Broken
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[
"Sorry, this is a duplicate of #1287. Not sure why it didn't come up when I searched `iwslt` in the issues list.",
"Closing this since its a duplicate"
] | 1,608,166,002,000
| 1,608,278,796,000
| 1,608,278,728,000
|
NONE
| null | null |
```
FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz
```
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MDU6SXNzdWU3NjkyNDI4NTg=
| 1,590
|
Add helper to resolve namespace collision
|
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[
"Do you have an example?",
"I was thinking about using something like [importlib](https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly) to over-ride the collision. \r\n\r\n**Reason requested**: I use the [following template](https://github.com/jramapuram/ml_base/) repo where I house all my datasets as a submodule.",
"Alternatively huggingface could consider some submodule type structure like:\r\n\r\n`import huggingface.datasets`\r\n`import huggingface.transformers`\r\n\r\n`datasets` is a very common module in ML and should be an end-user decision and not scope all of python ¯\\_(ツ)_/¯ \r\n",
"That's a interesting option indeed. We'll think about it.",
"It also wasn't initially obvious to me that the samples which contain `import datasets` were in fact importing a huggingface library (in fact all the huggingface imports are very generic - transformers, tokenizers, datasets...)"
] | 1,608,149,844,000
| 1,654,097,524,000
| 1,654,097,524,000
|
NONE
| null | null |
Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict.
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| 768,831,171
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MDU6SXNzdWU3Njg4MzExNzE=
| 1,585
|
FileNotFoundError for `amazon_polarity`
|
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[
"Hi @phtephanx , the `amazon_polarity` dataset has not been released yet. It will be available in the coming soon v2of `datasets` :) \r\n\r\nYou can still access it now if you want, but you will need to install datasets via the master branch:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`"
] | 1,608,123,065,000
| 1,608,134,576,000
| 1,608,134,576,000
|
NONE
| null | null |
Version: `datasets==v1.1.3`
### Reproduction
```python
from datasets import load_dataset
data = load_dataset("amazon_polarity")
```
crashes with
```bash
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/amazon_polarity/amazon_polarity.py
```
and
```bash
FileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/amazon_polarity/amazon_polarity.py
```
and
```bash
FileNotFoundError: Couldn't find file locally at amazon_polarity/amazon_polarity.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/amazon_polarity/amazon_polarity.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/amazon_polarity/amazon_polarity.py
```
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MDU6SXNzdWU3NjgzMjA1OTQ=
| 1,581
|
Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers'
|
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[
"Thanks for reporting !\r\nYou can override the directory in which cache file are stored using for example\r\n```\r\nENV HF_HOME=\"/root/cache/hf_cache_home\"\r\n```\r\n\r\nThis way both `transformers` and `datasets` will use this directory instead of the default `.cache`",
"Great, thanks. I didn't see documentation about than ENV variable, looks like an obvious solution. ",
"> Thanks for reporting !\r\n> You can override the directory in which cache file are stored using for example\r\n> \r\n> ```\r\n> ENV HF_HOME=\"/root/cache/hf_cache_home\"\r\n> ```\r\n> \r\n> This way both `transformers` and `datasets` will use this directory instead of the default `.cache`\r\n\r\ncan we disable caching directly?",
"Hi ! Unfortunately no since we need this directory to load datasets.\r\nWhen you load a dataset, it downloads the raw data files in the cache directory inside <cache_dir>/downloads. Then it builds the dataset and saves it as arrow data inside <cache_dir>/<dataset_name>.\r\n\r\nHowever you can specify the directory of your choice, and it can be a temporary directory if you want to clean everything up at one point.",
"I'm closing this to keep issues a bit cleaner"
] | 1,608,076,941,000
| 1,623,944,445,000
| 1,623,944,445,000
|
NONE
| null | null |
I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `/.cache`:
```
$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)/data:/root/data -v $(pwd):/root -v $(pwd)/models/:/root/models -v $(pwd)/saved_models/:/root/saved_models -e "HOST_HOSTNAME=$(hostname)" hf-error:latest /bin/bash
________ _______________
___ __/__________________________________ ____/__ /________ __
__ / _ _ \_ __ \_ ___/ __ \_ ___/_ /_ __ /_ __ \_ | /| / /
_ / / __/ / / /(__ )/ /_/ / / _ __/ _ / / /_/ /_ |/ |/ /
/_/ \___//_/ /_//____/ \____//_/ /_/ /_/ \____/____/|__/
You are running this container as user with ID 1000 and group 1000,
which should map to the ID and group for your user on the Docker host. Great!
tf-docker /root > python
Python 3.6.9 (default, Oct 8 2020, 12:12:24)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import transformers
2020-12-15 23:53:21.165827: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/transformers/__init__.py", line 22, in <module>
from .integrations import ( # isort:skip
File "/usr/local/lib/python3.6/dist-packages/transformers/integrations.py", line 5, in <module>
from .trainer_utils import EvaluationStrategy
File "/usr/local/lib/python3.6/dist-packages/transformers/trainer_utils.py", line 25, in <module>
from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available
File "/usr/local/lib/python3.6/dist-packages/transformers/file_utils.py", line 88, in <module>
import datasets # noqa: F401
File "/usr/local/lib/python3.6/dist-packages/datasets/__init__.py", line 26, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py", line 40, in <module>
from .arrow_reader import ArrowReader
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 31, in <module>
from .utils import cached_path, logging
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/__init__.py", line 20, in <module>
from .download_manager import DownloadManager, GenerateMode
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/download_manager.py", line 25, in <module>
from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 118, in <module>
os.makedirs(HF_MODULES_CACHE, exist_ok=True)
File "/usr/lib/python3.6/os.py", line 210, in makedirs
makedirs(head, mode, exist_ok)
File "/usr/lib/python3.6/os.py", line 210, in makedirs
makedirs(head, mode, exist_ok)
File "/usr/lib/python3.6/os.py", line 220, in makedirs
mkdir(name, mode)
PermissionError: [Errno 13] Permission denied: '/.cache'
```
I've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.
```
FROM tensorflow/tensorflow:latest-gpu-jupyter
WORKDIR /root
EXPOSE 80
EXPOSE 8888
EXPOSE 6006
ENV SHELL /bin/bash
ENV PATH="/root/.local/bin:${PATH}"
ENV CUDA_CACHE_PATH="/root/cache/cuda"
ENV CUDA_CACHE_MAXSIZE="4294967296"
ENV TFHUB_CACHE_DIR="/root/cache/tfhub"
RUN pip install --upgrade pip
RUN apt update -y && apt upgrade -y
RUN pip install transformers
#Installing datasets will throw the error, try commenting and rebuilding
RUN pip install datasets
#Another workaround is creating the directory and give permissions explicitly
#RUN mkdir /.cache
#RUN chmod 777 /.cache
```
|
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MDU6SXNzdWU3NjU0MzA1ODY=
| 1,541
|
connection issue while downloading data
|
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[] |
[
"could you tell me how I can avoid download, by pre-downloading the data first, put them in a folder so the code does not try to redownload? could you tell me the path to put the downloaded data, and how to do it? thanks\r\n@lhoestq ",
"Does your instance have an internet connection ?\r\n\r\nIf you don't have an internet connection you'll need to have the dataset on the instance disk.\r\nTo do so first download the dataset on another machine using `load_dataset` and then you can save it in a folder using `my_dataset.save_to_disk(\"path/to/folder\")`. Once the folder is copied on your instance you can reload the dataset with `datasets.load_from_disk(\"path/to/folder\")`"
] | 1,607,869,620,000
| 1,664,973,209,000
| 1,664,973,209,000
|
NONE
| null | null |
Hi
I am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks
```
Traceback (most recent call last):
File "finetune_t5_trainer.py", line 361, in <module>
main()
File "finetune_t5_trainer.py", line 269, in main
add_prefix=False if training_args.train_adapters else True)
File "/workdir/seq2seq/data/tasks.py", line 70, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 306, in load_dataset
return datasets.load_dataset('glue', 'cola', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
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MDU6SXNzdWU3NjQwMTcxNDg=
| 1,514
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how to get all the options of a property in datasets
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"In a dataset, labels correspond to the `ClassLabel` feature that has the `names` property that returns string represenation of the integer classes (or `num_classes` to get the number of different classes).",
"I think the `features` attribute of the dataset object is what you are looking for:\r\n```\r\n>>> dataset.features\r\n{'sentence1': Value(dtype='string', id=None),\r\n 'sentence2': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\r\n 'idx': Value(dtype='int32', id=None)\r\n}\r\n>>> dataset.features[\"label\"].names\r\n['not_equivalent', 'equivalent']\r\n```\r\n\r\nFor reference: https://huggingface.co/docs/datasets/exploring.html"
] | 1,607,790,248,000
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| 1,653,496,049,000
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CONTRIBUTOR
| null | null |
Hi
could you tell me how I can get all unique options of a property of dataset?
for instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks
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| 1,478
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Inconsistent argument names.
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[
"Also for the `Accuracy` metric the `accuracy_score` method should have its args in the opposite order so `accuracy_score(predictions, references,,,)`.",
"Thanks for pointing this out ! 🕵🏻 \r\nPredictions and references should indeed be swapped in the docstring.\r\nHowever, the call to `accuracy_score` should not be changed, it [signature](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score) being:\r\n```\r\nsklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)\r\n```\r\n\r\nFeel free to open a PR if you want to fix this :)"
] | 1,607,689,178,000
| 1,608,390,219,000
| 1,608,390,219,000
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CONTRIBUTOR
| null | null |
Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:
https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_utils.py#L51-L61
While in many datasets metrics they are the ground truth labels:
https://github.com/huggingface/datasets/blob/c3f53792a744ede18d748a1133b6597fdd2d8d18/metrics/accuracy/accuracy.py#L31-L40
Do you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree.
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| 1,452
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SNLI dataset contains labels with value -1
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[
"I believe the `-1` label is used for missing/NULL data as per HuggingFace Dataset conventions. If I recall correctly SNLI has some entries with no (gold) labels in the dataset.",
"Ah, you're right. The dataset has some pairs with missing labels. Thanks for reminding me."
] | 1,607,595,415,000
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| 1,607,622,595,000
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NONE
| null | null |
```
import datasets
nli_data = datasets.load_dataset("snli")
train_data = nli_data['train']
train_labels = train_data['label']
label_set = set(train_labels)
print(label_set)
```
**Output:**
`{0, 1, 2, -1}`
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| 1,444
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FileNotFound remotly, can't load a dataset
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[
"This dataset will be available in version-2 of the library. If you want to use this dataset now, install datasets from `master` branch rather.\r\n\r\nCommand to install datasets from `master` branch:\r\n`!pip install git+https://github.com/huggingface/datasets.git@master`",
"Closing this, thanks @VasudevGupta7 "
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| 1,608,054,074,000
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NONE
| null | null |
```py
!pip install datasets
import datasets as ds
corpus = ds.load_dataset('large_spanish_corpus')
```
gives the error
> FileNotFoundError: Couldn't find file locally at large_spanish_corpus/large_spanish_corpus.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/large_spanish_corpus/large_spanish_corpus.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/large_spanish_corpus/large_spanish_corpus.py
not just `large_spanish_corpus`, `zest` too, but `squad` is available.
this was using colab and localy
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MDU6SXNzdWU3NjA3MDcxMTM=
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Can't map dataset (loaded from csv)
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[
"Please could you post the whole script? I can't reproduce your issue. After updating the feature names/labels to match with the data, everything works fine for me. Try to update datasets/transformers to the newest version.",
"Actually, the problem was how `tokenize` function was defined. This was completely my side mistake, so there are really no needs in this issue anymore"
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NONE
| null | null |
Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.
Below steps are similar with [this notebook](https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.
Here is how I load it:
```python
data_path = 'data.csv'
data = pd.read_csv(data_path)
# process class name to indices
classes = ['neg', 'pos']
class_to_idx = { cl: i for i, cl in enumerate(classes) }
# now data is like {'label': int, 'text' str}
data['label'] = data['label'].apply(lambda x: class_to_idx[x])
# load dataset and map it with defined `tokenize` function
features = Features({
target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),
feature: Value(dtype='string', id=None),
})
dataset = Dataset.from_pandas(data, features=features)
dataset.map(tokenize, batched=True, batch_size=len(dataset))
```
It ruins on the last line with following error:
```
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-112-32b6275ce418> in <module>()
9 })
10 dataset = Dataset.from_pandas(data, features=features)
---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1237 test_inputs = self[:2] if batched else self[0]
1238 test_indices = [0, 1] if batched else 0
-> 1239 update_data = does_function_return_dict(test_inputs, test_indices)
1240 logger.info("Testing finished, running the mapping function on the dataset")
1241
/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices)
1208 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
1209 processed_inputs = (
-> 1210 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1211 )
1212 does_return_dict = isinstance(processed_inputs, Mapping)
/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)
2281 )
2282 ), (
-> 2283 "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
2284 "or `List[List[str]]` (batch of pretokenized examples)."
2285 )
AssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).
```
which I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.
For reproducing this, I used [this dataset from kaggle](https://www.kaggle.com/team-ai/spam-text-message-classification)
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MDU6SXNzdWU3NTk1ODc4NjQ=
| 1,324
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❓ Sharing ElasticSearch indexed dataset
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[
"Hello @pietrolesci , I am not sure to understand what you are trying to do here.\r\n\r\nIf you're looking for ways to save a dataset on disk, you can you the `save_to_disk` method:\r\n```python\r\n>>> import datasets\r\n>>> loaded_dataset = datasets.load(\"dataset_name\")\r\n>>> loaded_dataset.save_to_disk(\"/path/on/your/disk\")\r\n```\r\n\r\nThe saved dataset can later be retrieved using:\r\n```python\r\n>>> loaded_dataset = datasets.Dataset.load_from_disk(\"/path/on/your/disk\")\r\n```\r\n\r\nAlso, I'd recommend posting your question directly in the issue section of the [elasticsearch repo](https://github.com/elastic/elasticsearch)",
"Hi @SBrandeis,\n\nThanks a lot for picking up my request. \n\nMaybe I can clarify my use-case with a bit of context. Say I have the IMDb dataset. I create an ES index on it. Now I can save and reload the dataset from disk normally. Once I reload the dataset, it is easy to retrieve the ES index on my machine. I was wondering: is there a way I can share the (now) indexed version of the IMDb dataset with my colleagues without requiring them to re-index it?\n\nThanks a lot in advance for your consideration.\n\nBest,\n\nPietro",
"Thanks for the clarification.\r\n\r\nI am not familiar with ElasticSearch, but if I understand well you're trying to migrate your data along with the ES index.\r\nMy advice would be to check out ES documentation, for instance, this might help you: https://www.elastic.co/guide/en/cloud/current/ec-migrate-data.html\r\n\r\nLet me know if it helps"
] | 1,607,444,758,000
| 1,608,623,456,000
| null |
NONE
| null | null |
Hi there,
First of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.
**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering
- how can I know where it has been saved?
- how can I share the indexed dataset with others?
I tried to dig into the docs, but could not find anything about that.
Thank you very much for your help.
Best,
Pietro
Edit: apologies for the wrong label
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MDU6SXNzdWU3NTk0MTQ1NjY=
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can't load "german_legal_entity_recognition" dataset
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"Please if you could tell me more about the error? \r\n\r\n1. Please check the directory you've been working on\r\n2. Check for any typos",
"> Please if you could tell me more about the error?\r\n> \r\n> 1. Please check the directory you've been working on\r\n> 2. Check for any typos\r\n\r\nError happens during the execution of this line:\r\ndataset = load_dataset(\"german_legal_entity_recognition\")\r\n\r\nAlso, when I try to open mentioned links via Opera I have errors \"404: Not Found\" and \"This XML file does not appear to have any style information associated with it. The document tree is shown below.\" respectively.",
"Hello @nataly-obr, the `german_legal_entity_recognition` dataset has not yet been released (it is part of the coming soon v2 release).\r\n\r\nYou can still access it now if you want, but you will need to install `datasets` via the master branch:\r\n`pip install git+https://github.com/huggingface/datasets.git@master`\r\n\r\nPlease let me know if it solves the issue :) "
] | 1,607,431,321,000
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NONE
| null | null |
FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition/german_legal_entity_recognition.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/german_legal_entity_recognition/german_legal_entity_recognition.py
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imdb dataset cannot be downloaded
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"Hi @rabeehk , I am unable to reproduce your problem locally.\r\nCan you try emptying the cache (removing the content of `/idiap/temp/rkarimi/cache_home_1/datasets`) and retry ?",
"Hi,\r\nthanks, I did remove the cache and still the same error here\r\n\r\n```\r\n>>> a = datasets.load_dataset(\"imdb\", split=\"train\")\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nDownloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads\r\nTraceback (most recent call last): \r\n File \"<stdin>\", line 1, in <module>\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 476, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 558, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 73, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=4902716, num_examples=3680, dataset_name='imdb')}]\r\n```\r\n\r\ndatasets version\r\n```\r\ndatasets 1.1.2 <pip>\r\ntensorflow-datasets 4.1.0 <pip>\r\n\r\n```",
"resolved with moving to version 1.1.3"
] | 1,607,424,456,000
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| 1,608,831,489,000
|
CONTRIBUTOR
| null | null |
hi
please find error below getting imdb train spli:
thanks
`
datasets.load_dataset>>> datasets.load_dataset("imdb", split="train")`
errors
```
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]
```
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MDU6SXNzdWU3NTkzMDA5OTI=
| 1,287
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'iwslt2017-ro-nl', cannot be downloaded
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[
"the same issue with datasets.load_dataset(\"iwslt2017\", 'iwslt2017-en-nl', split=split), ..... ",
"even with setting master like the following command, still remains \r\n\r\ndatasets.load_dataset(\"iwslt2017\", 'iwslt2017-en-nl', split=\"train\", script_version=\"master\")\r\n",
"Looks like the data has been moved from its original location to google drive\r\n\r\nNew url: https://drive.google.com/u/0/uc?id=12ycYSzLIG253AFN35Y6qoyf9wtkOjakp&export=download",
"Fixed by #4481 "
] | 1,607,421,415,000
| 1,655,116,893,000
| 1,655,116,893,000
|
CONTRIBUTOR
| null | null |
Hi
I am trying
`>>> datasets.load_dataset("iwslt2017", 'iwslt2017-ro-nl', split="train")`
getting this error thank you for your help
```
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset iwsl_t217/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/iwsl_t217/iwslt2017-ro-nl/1.0.0/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/iwslt2017/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd/iwslt2017.py", line 118, in _split_generators
dl_dir = dl_manager.download_and_extract(MULTI_URL)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz
```
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[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): terminate called after throwing an instance of 'google::protobuf::FatalException' what(): CHECK failed: (index) >= (0): Aborted
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"I remember also getting the same issue for several other translation datasets like all the iwslt2017 group, this is blokcing me and I really need to fix it and I was wondering if you have an idea on this. @lhoestq thanks,. ",
"maybe there is an empty line or something inside these datasets? could you tell me why this is happening? thanks ",
"I just checked and the wmt16 en-ro doesn't have empty lines\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nlen(d) # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"en\"].strip()) > 0)) # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"ro\"].strip()) > 0)) # 610320\r\n# also tested for split=\"validation\" and \"test\"\r\n```\r\n\r\nCan you open an issue on the `transformers` repo ? also cc @sgugger ",
"Hi @lhoestq \r\nI am not really sure which part is causing this, to me this is more related to dataset library as this is happening for some of the datassets below please find the information to reprodcue the bug, this is really blocking me and I appreciate your help\r\n\r\n\r\n## Environment info\r\n- `transformers` version: 3.5.1\r\n- Platform: GPU\r\n- Python version: 3.7 \r\n- PyTorch version (GPU?): 1.0.4\r\n- Tensorflow version (GPU?): - \r\n- Using GPU in script?: - \r\n- Using distributed or parallel set-up in script?: - \r\n\r\n### Who can help\r\n tokenizers: @mfuntowicz\r\n Trainer: @sgugger\r\n TextGeneration: @TevenLeScao \r\n nlp datasets: [different repo](https://github.com/huggingface/nlp)\r\n rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)\r\n examples/seq2seq: @patil-suraj\r\n\r\n## Information\r\nHi\r\nI am testing seq2seq model with T5 on different datasets and this is always getting the following bug, this is really blocking me as this fails for many datasets. could you have a look please? thanks \r\n\r\n```\r\n[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n what(): CHECK failed: (index) >= (0): \r\nAborted\r\n\r\n```\r\n\r\nTo reproduce the error please run on 1 GPU:\r\n```\r\ngit clone git@github.com:rabeehk/debug-seq2seq.git\r\npython setup.py develop \r\ncd seq2seq \r\npython finetune_t5_trainer.py temp.json\r\n\r\n```\r\n\r\nFull output of the program:\r\n\r\n```\r\n(internship) rkarimi@vgnh008:/idiap/user/rkarimi/dev/debug-seq2seq/seq2seq$ python finetune_t5_trainer.py temp.json \r\n2020-12-12 15:38:16.234542: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-12 15:38:16.234598: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n12/12/2020 15:38:32 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\r\n12/12/2020 15:38:32 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(output_dir='outputs/test', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=False, evaluation_strategy=<EvaluationStrategy.NO: 'no'>, prediction_loss_only=False, per_device_train_batch_size=64, per_device_eval_batch_size=64, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=0.01, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=2, max_steps=-1, warmup_steps=500, logging_dir='runs/Dec12_15-38-32_vgnh008', logging_first_step=True, logging_steps=200, save_steps=200, save_total_limit=1, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=200, dataloader_num_workers=0, past_index=-1, run_name='outputs/test', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.1, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear', fixed_length_emb=None, encoder_projection=None, encoder_pooling=None, projection_length=None, only_projection_bottleneck=False, concat_projection_token=False, gcs_bucket='ruse-xcloud-bucket', temperature=10, train_adapters=True, do_finetune=True, parametric_task_embedding=False, 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'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.weight', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.bias']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140079090376272 acquired on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140079090376272 released on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140082549312272 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140082549312272 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140082549365648 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (/idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12/12/2020 15:38:44 - INFO - filelock - Lock 140082549365648 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at /idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534/cache-6810ece2a440c3be.arrow\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549560848 acquired on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549560848 released on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549560848 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549560848 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549365200 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (/idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12/12/2020 15:38:45 - INFO - filelock - Lock 140082549365200 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at /idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534/cache-9a2822394a3a4e34.arrow\r\n12/12/2020 15:38:45 - INFO - seq2seq.metrics.metrics - selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b464cc20> for task boolq\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - ***** Running training *****\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Num examples = 10\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Num Epochs = 2\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Instantaneous batch size per device = 64\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Gradient Accumulation steps = 1\r\n12/12/2020 15:38:45 - INFO - seq2seq.trainers.trainer - Total optimization steps = 2\r\n{'loss': 529.79443359375, 'learning_rate': 2e-05, 'epoch': 1.0} \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.37it/s]12/12/2020 15:38:46 - INFO - seq2seq.trainers.trainer - \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co/models =)\r\n\r\n\r\n{'epoch': 2.0} \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.43it/s]\r\n12/12/2020 15:38:46 - INFO - seq2seq.trainers.trainer - Saving model checkpoint to outputs/test\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079084929680 acquired on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079084929680 released on /idiap/home/rkarimi/.cache/huggingface/datasets/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079084929360 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079084929360 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079085355216 acquired on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (/idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12/12/2020 15:38:59 - INFO - filelock - Lock 140079085355216 released on /idiap/temp/rkarimi/cache_home_1/datasets/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at /idiap/temp/rkarimi/cache_home_1/datasets/boolq/default/0.1.0/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534/cache-164dd1d57e9fa69a.arrow\r\n12/12/2020 15:38:59 - INFO - seq2seq.metrics.metrics - selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b40c67a0> for task boolq\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - ***** Running training *****\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Num examples = 1\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Num Epochs = 2\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Instantaneous batch size per device = 64\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Gradient Accumulation steps = 1\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Total optimization steps = 2\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Continuing training from checkpoint, will skip to saved global_step\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Continuing training from epoch 2\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Continuing training from global step 2\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Will skip the first 0 steps in the first epoch\r\n 0%| | 0/2 [00:00<?, ?it/s]12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co/models =)\r\n\r\n\r\n{'epoch': 2.0} \r\n 0%| | 0/2 [00:00<?, ?it/s]\r\n12/12/2020 15:38:59 - INFO - seq2seq.trainers.trainer - Saving model checkpoint to outputs/finetune-adapter/test-n-1-lr-1e-02-e-20/boolq\r\n12/12/2020 15:39:07 - INFO - seq2seq.utils.utils - using task specific params for boolq: {'max_length': 3}\r\n12/12/2020 15:39:07 - INFO - seq2seq.trainers.trainer - ***** Running Evaluation *****\r\n12/12/2020 15:39:07 - INFO - seq2seq.trainers.trainer - Num examples = 3269\r\n12/12/2020 15:39:07 - INFO - seq2seq.trainers.trainer - Batch size = 64\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 52/52 [00:12<00:00, 4.86it/s][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n what(): CHECK failed: (index) >= (0): \r\nAborted\r\n```\r\n\r\n\r\n\r\n",
"solved see https://github.com/huggingface/transformers/issues/9079?_pjax=%23js-repo-pjax-container ",
"Hii please follow me"
] | 1,607,420,655,000
| 1,607,801,782,000
| 1,607,790,156,000
|
CONTRIBUTOR
| null | null |
Hi
I am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help
{'epoch': 20.0}
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:16<00:00, 1.22it/s]
12/08/2020 10:41:19 - INFO - seq2seq.trainers.trainer - Saving model checkpoint to outputs/experiment/joint/finetune/lr-2e-5
12/08/2020 10:41:24 - INFO - __main__ - {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}
12/08/2020 10:41:24 - INFO - __main__ - *** Evaluate ***
12/08/2020 10:41:24 - INFO - seq2seq.utils.utils - using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - ***** Running Evaluation *****
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Num examples = 1998
12/08/2020 10:41:24 - INFO - seq2seq.trainers.trainer - Batch size = 64
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:37<00:00, 1.19s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
Aborted
|
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| 1,285
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boolq does not work
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[
"here is the minimal code to reproduce\r\n\r\n`datasets>>> datasets.load_dataset(\"boolq\", \"train\")\r\n\r\nthe errors\r\n\r\n```\r\n`cahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nUsing custom data configuration train\r\nDownloading and preparing dataset boolq/train (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /idiap/temp/rkarimi/cache_home_1/datasets/boolq/train/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 476, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 531, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \" /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py\", line 74, in _split_generators\r\n downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py\", line 149, in download_custom\r\n custom_download(url, path)\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py\", line 516, in copy_v2\r\n compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\n\r\n\r\n\r\n```",
"This has been fixed by #881 \r\nthis fix will be available in the next release soon.\r\n\r\nIf you don't want to wait for the release you can actually load the latest version of boolq by specifying `script_version=\"master\"` in `load_dataset`",
"thank you this solved this issue, for now seems to work, thanks "
] | 1,607,419,727,000
| 1,607,420,830,000
| 1,607,420,830,000
|
CONTRIBUTOR
| null | null |
Hi
I am getting this error when trying to load boolq, thanks for your help
ts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock
Traceback (most recent call last):
File "finetune_t5_trainer.py", line 274, in <module>
main()
File "finetune_t5_trainer.py", line 147, in main
for task in data_args.tasks]
File "finetune_t5_trainer.py", line 147, in <listcomp>
for task in data_args.tasks]
File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 58, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/ruse/seq2seq/tasks/tasks.py", line 54, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
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❓ On-the-fly tokenization with datasets, tokenizers, and torch Datasets and Dataloaders
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"We're working on adding on-the-fly transforms in datasets.\r\nCurrently the only on-the-fly functions that can be applied are in `set_format` in which we transform the data in either numpy/torch/tf tensors or pandas.\r\nFor example\r\n```python\r\ndataset.set_format(\"torch\")\r\n```\r\napplies `torch.Tensor` to the dataset entries on-the-fly.\r\n\r\nWe plan to extend this to user-defined formatting transforms.\r\nFor example\r\n```python\r\ndataset.set_format(transform=tokenize)\r\n```\r\n\r\nWhat do you think ?"
] | 1,607,187,776,000
| 1,610,534,888,000
| null |
NONE
| null | null |
Hi there,
I have a question regarding "on-the-fly" tokenization. This question was elicited by reading the "How to train a new language model from scratch using Transformers and Tokenizers" [here](https://huggingface.co/blog/how-to-train). Towards the end there is this sentence: "If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step". I've tried coming up with a solution that would combine both `datasets` and `tokenizers`, but did not manage to find a good pattern.
I guess the solution would entail wrapping a dataset into a Pytorch dataset.
As a concrete example from the [docs](https://huggingface.co/transformers/custom_datasets.html)
```python
import torch
class SquadDataset(torch.utils.data.Dataset):
def __init__(self, encodings):
# instead of doing this beforehand, I'd like to do tokenization on the fly
self.encodings = encodings
def __getitem__(self, idx):
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
def __len__(self):
return len(self.encodings.input_ids)
train_dataset = SquadDataset(train_encodings)
```
How would one implement this with "on-the-fly" tokenization exploiting the vectorized capabilities of tokenizers?
----
Edit: I have come up with this solution. It does what I want, but I feel it's not very elegant
```python
class CustomPytorchDataset(Dataset):
def __init__(self):
self.dataset = some_hf_dataset(...)
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
def __getitem__(self, batch_idx):
instance = self.dataset[text_col][batch_idx]
tokenized_text = self.tokenizer(instance, truncation=True, padding=True)
return tokenized_text
def __len__(self):
return len(self.dataset)
@staticmethod
def collate_fn(batch):
# batch is a list, however it will always contain 1 item because we should not use the
# batch_size argument as batch_size is controlled by the sampler
return {k: torch.tensor(v) for k, v in batch[0].items()}
torch_ds = CustomPytorchDataset()
# NOTE: batch_sampler returns list of integers and since here we have SequentialSampler
# it returns: [1, 2, 3], [4, 5, 6], etc. - check calling `list(batch_sampler)`
batch_sampler = BatchSampler(SequentialSampler(torch_ds), batch_size=3, drop_last=True)
# NOTE: no `batch_size` as now the it is controlled by the sampler!
dl = DataLoader(dataset=torch_ds, sampler=batch_sampler, collate_fn=torch_ds.collate_fn)
```
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MDU6SXNzdWU3NTcxMjc1Mjc=
| 1,115
|
Incorrect URL for MRQA SQuAD train subset
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[
"good catch !"
] | 1,607,090,724,000
| 1,607,274,862,000
| 1,607,274,862,000
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CONTRIBUTOR
| null | null |
https://github.com/huggingface/datasets/blob/4ef4c8f8b7a60e35c6fa21115fca9faae91c9f74/datasets/mrqa/mrqa.py#L53
The URL for `train+SQuAD` subset of MRQA points to the dev set instead of train set. It should be `https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz`.
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MDU6SXNzdWU3NTcwODI2Nzc=
| 1,110
|
Using a feature named "_type" fails with certain operations
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[
"Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode/decode features to a python dictionary that we can save/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward compatible though.\r\n\r\nFeel free to try a fix and open a PR, and to ping me if I can help :) "
] | 1,607,086,593,000
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| 1,642,183,620,000
|
CONTRIBUTOR
| null | null |
A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:
```python
from datasets import Dataset, concatenate_datasets
ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.
Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.
Best wishes and keep up the awesome work!
|
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MDU6SXNzdWU3NTcwMTY4MjA=
| 1,103
|
Add support to download kaggle datasets
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"Hey, I think this is great idea. Any plan to integrate kaggle private datasets loading to `datasets`?"
] | 1,607,080,117,000
| 1,626,889,093,000
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MEMBER
| null | null |
We can use API key
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| 1,102
|
Add retries to download manager
|
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| 1,064
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Not support links with 302 redirect
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[
"Hi !\r\nThis kind of links is now supported by the library since #1316",
"> Hi !\r\n> This kind of links is now supported by the library since #1316\r\n\r\nI updated links in TLC datasets to be the github links in this pull request \r\n https://github.com/huggingface/datasets/pull/1737\r\n\r\nEverything works now. Thank you."
] | 1,607,015,083,000
| 1,610,592,685,000
| 1,610,592,685,000
|
CONTRIBUTOR
| null | null |
I have an issue adding this download link https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz
it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests).
```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)
# <Response [403]>
```
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MDU6SXNzdWU3NTYxMjI3MDk=
| 1,046
|
Dataset.map() turns tensors into lists?
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[
"A solution is to have the tokenizer return a list instead of a tensor, and then use `dataset_tok.set_format(type = 'torch')` to convert that list into a tensor. Still not sure if bug.",
"It is expected behavior, you should set the format to `\"torch\"` as you mentioned to get pytorch tensors back.\r\nBy default datasets returns pure python objects."
] | 1,606,995,826,000
| 1,664,971,961,000
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NONE
| null | null |
I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!
```import datasets
import torch
from datasets import load_dataset
print("version datasets", datasets.__version__)
dataset = load_dataset("snli", split='train[0:50]')
def tokenizer_fn(example):
# actually uses a tokenizer which does something like:
return {'input_ids': torch.tensor([[0, 1, 2]])}
print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```
The output is:
```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
{'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
{'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
{'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```
Or am I doing something wrong?
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Hi
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[] | 1,606,952,834,000
| 1,607,013,761,000
| 1,607,013,761,000
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NONE
| null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Lío o
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NONE
| null | null |
````l`````````
```
O
```
`````
Ño
```
````
```
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|
how large datasets are handled under the hood
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[
"This library uses Apache Arrow under the hood to store datasets on disk.\r\nThe advantage of Apache Arrow is that it allows to memory map the dataset. This allows to load datasets bigger than memory and with almost no RAM usage. It also offers excellent I/O speed.\r\n\r\nFor example when you access one element or one batch\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nsquad = load_dataset(\"squad\", split=\"train\")\r\nfirst_element = squad[0]\r\none_batch = squad[:8]\r\n```\r\n\r\nthen only this element/batch is loaded in memory, while the rest of the dataset is memory mapped.",
"How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nEDIT:\r\nMy fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.",
"> How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nLoading arrow data from disk is done with memory-mapping. This allows to load huge datasets without filling your RAM.\r\nMemory mapping is almost instantaneous and is done within one process.\r\n\r\nThen, the speed of querying examples from the dataset is I/O bounded depending on your disk. If it's an SSD then fetching examples from the dataset will be very fast.\r\nBut since the I/O speed of an SSD is lower than the one of RAM it's expected to be slower to fetch data from disk than from memory.\r\nStill, if you load the dataset in different processes then it can be faster but there will still be the I/O bottleneck of the disk.\r\n\r\n> EDIT:\r\n> My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.\r\n\r\nOk let me know if that helps !\r\n"
] | 1,606,919,560,000
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NONE
| null | null |
Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks
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MDU6SXNzdWU3NTUxNzYwODQ=
| 996
|
NotADirectoryError while loading the CNN/Dailymail dataset
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[
"Looks like the google drive download failed.\r\nI'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.\r\n\r\nWe should consider finding a better host than google drive for this dataset imo\r\nrelated : #873 #864 ",
"It is working now, thank you. \r\n\r\nShould I leave this issue open to address the Quota-exceeded error?",
"Yes please. It's been happening several times, we definitely need to address it",
"Any updates on this one? I'm facing a similar issue trying to add CelebA.",
"I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..\r\nPlease try to use other hosts when possible",
"The original links are google drive links. Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.",
"It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.\r\nOtherwise you can use the google drive link, but it it's not that convenient because of this quota issue.",
"Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.",
"I am getting this error as well. Is there a fix?",
"Not as long as the data is stored on GG drive unfortunately.\r\nMaybe we can ask if there's a mirror ?\r\n\r\nHi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?\r\n\r\nTo give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.",
"The following copy of CNN/DM dataset, fixed the problem for me:\r\nhttps://huggingface.co/datasets/ccdv/cnn_dailymail",
"Thanks for the link @mrazizi !\r\n\r\nApparently the original authors don't host the dataset themselves (\"for legal reasons\", source [here](https://github.com/abisee/cnn-dailymail/issues/9))."
] | 1,606,907,276,000
| 1,645,107,219,000
| 1,645,107,219,000
|
NONE
| null | null |
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-9-cd4bf8bea840> in <module>()
22
23
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')
5 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
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MDU6SXNzdWU3NTUxMzU3Njg=
| 993
|
Problem downloading amazon_reviews_multi
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[
"Hi @hfawaz ! This is working fine for me. Is it a repeated occurence? Have you tried from the latest verion?",
"Hi, it seems a connection problem. \r\nNow it says: \r\n`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_ja_train.json`"
] | 1,606,904,157,000
| 1,664,972,494,000
| 1,664,972,494,000
|
CONTRIBUTOR
| null | null |
Thanks for adding the dataset.
After trying to load the dataset, I am getting the following error:
`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json
`
I used the following code to load the dataset:
`load_dataset(
dataset_name,
"all_languages",
cache_dir=".data"
)`
I am using version 1.1.3 of `datasets`
Note that I can perform a successfull `wget https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json`
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MDU6SXNzdWU3NTUwNjkxNTk=
| 988
|
making sure datasets are not loaded in memory and distributed training of them
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[
"my implementation of sharding per TPU core: https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/trainers/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/tasks/tasks.py#L496 ",
"Hi! You can use the `assert not bool(dataset.cache_files)` assertion to ensure your data is in memory. And I suggest using `accelerate` for distributed training."
] | 1,606,898,715,000
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| 1,664,974,842,000
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CONTRIBUTOR
| null | null |
Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks
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| 961
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sample multiple datasets
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"here I share my dataloader currently for multiple tasks: https://gist.github.com/rabeehkarimimahabadi/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to use iterative datasets, could you have a look in this dataloader and tell me if this is indeed the case? not sure how to make datasets being iterative to not load them in memory, then I remove the sampler for dataloader, and shard the data per core, could you tell me please how I should implement this case in datasets library? and how do you find my implementation in terms of correctness? thanks \r\n"
] | 1,606,832,402,000
| 1,606,872,764,000
| null |
CONTRIBUTOR
| null | null |
Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is:
- I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it
sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help.
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D
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NONE
| null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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| 937
|
Local machine/cluster Beam Datasets example/tutorial
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[
"I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to make your processing work ?"
] | 1,606,785,103,000
| 1,608,731,696,000
| null |
NONE
| null | null |
Hi,
I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.
Thanks!
Shang
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Hello
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NONE
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|
wrong length with datasets
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[
"Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ",
"sorry I misunderstood length of dataset with dataloader, closed. thanks "
] | 1,606,739,019,000
| 1,606,739,847,000
| 1,606,739,846,000
|
CONTRIBUTOR
| null | null |
Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:
`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`
I feed it to a dataloader:
```
dataloader = DataLoader(
train_dataset,
batch_size=self.args.train_batch_size,
sampler=train_sampler,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
)
```
now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks
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| 915
|
Shall we change the hashing to encoding to reduce potential replicated cache files?
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[
"This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization ?",
"@lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equivalent to the transformation we need now.\r\n- or, calculate all the possible hash value of the current chain for comparison so that we could continue to use hashing.\r\nIf we find one, we can adjust the list in `self._fingerprint` to it.\r\n\r\nAs for the transformation reordering rules, we can just start with some manual rules, like two sort on the same column should merge to one, filter and select can change orders.\r\n\r\nAnd for encoding and decoding, we can just manually specify `sort` is 0, `shuffling` is 2 and create a base-n number or use some general algorithm like `base64.urlsafe_b64encode`.\r\n\r\nBecause we are not doing lazy evaluation now, we may not be able to normalize the transformation to its minimal form. If we want to support that, we can provde a `Sequential` api and let user input a list or transformation, so that user would not use the intermediate datasets. This would look like tf.data.Dataset."
] | 1,606,708,246,000
| 1,608,786,709,000
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NONE
| null | null |
Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.
If you have interest in this, I'd love to help :).
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datasets module not found
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[
"nvm, I'd made an assumption that the library gets installed with transformers. "
] | 1,606,613,055,000
| 1,606,660,389,000
| 1,606,660,389,000
|
NONE
| null | null |
Currently, running `from datasets import load_dataset` will throw a `ModuleNotFoundError: No module named 'datasets'` error.
|
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| 910
|
Grindr meeting app web.Grindr
|
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[] | 1,606,599,383,000
| 1,606,644,711,000
| 1,606,644,711,000
|
NONE
| null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
|
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| 900
|
datasets.load_dataset() custom chaching directory bug
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[
"Thanks for reporting ! I'm looking into it."
] | 1,606,479,533,000
| 1,606,690,133,000
| 1,606,690,133,000
|
NONE
| null | null |
Hello,
I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to
`~/.cache`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
from pathlib import Path
validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data"))
```
## The output:
* The dataset is downloaded to my home directory's `.cache`
* A new empty directory named "`natural_questions` is created in the specified directory `.data`
* `tree data` in the shell outputs:
```
data
└── natural_questions
└── default
└── 0.0.2
3 directories, 0 files
```
The output:
```
Downloading: 8.61kB [00:00, 5.11MB/s]
Downloading: 13.6kB [00:00, 7.89MB/s]
Using custom data configuration default
Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8
3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531...
Downloading: 100%|██████████████████████████████████████████████████| 13.6k/13.6k [00:00<00:00, 1.51MB/s]
Downloading: 7%|███▎ | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s]
```
## Expected behaviour:
The dataset "Natural Questions" should be downloaded to the directory "./data"
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| 897
|
Dataset viewer issues
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[] |
[
"Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?",
"Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewer`, let me know because I'll need to change our nginx config at the same time",
"9",
"⠀⠀⠀ ⠀ ",
"⠀⠀⠀ ⠀ "
] | 1,606,468,474,000
| 1,635,671,521,000
| 1,635,671,521,000
|
CONTRIBUTOR
| null | null |
I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:
- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user
```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
_marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
* (len(clabels[0]) - len(hidden_columns))
```
- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
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MDU6SXNzdWU3NTA5NDQ0MjI=
| 888
|
Nested lists are zipped unexpectedly
|
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[] |
[
"Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https://huggingface.co/docs/datasets/features.html?highlight=features) for more details",
"Thanks.\r\nThis is a bit (very) confusing, but I guess if its intended, I'll just work with it as if its how my data was originally structured :) \r\n"
] | 1,606,320,466,000
| 1,606,325,439,000
| 1,606,325,439,000
|
CONTRIBUTOR
| null | null |
I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
"middle": datasets.features.Sequence({
"bottom": datasets.Value("int32")
})
})
```
And I then create an example:
```python
yield 1, {
"top": [{
"middle": [
{"bottom": 1},
{"bottom": 2}
]
}]
}
```
I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```
and expect to be able to access `data[0]["top"][0]["middle"][0]`.
That is not the case. Here is `data[0]` as JSON:
```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```
Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
```
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| 887
|
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>
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[] |
[
"Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.\r\nWith the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype=\"float32\")` for example since the [underlying arrow type](https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L236) allows dynamic sizes.\r\n\r\nFor now I'd suggest the use of nested `Sequence` types. Once we have the dynamic sizes you can update the dataset.\r\nWhat do you think ?",
"> Yes right now ArrayXD can only be used as a column feature type, not a subtype. \r\n\r\nMeaning it can't be nested under `Sequence`?\r\nIf so, for now I'll just make it a python list and make it with the nested `Sequence` type you suggested.",
"Yea unfortunately..\r\nThat's a current limitation with Arrow ExtensionTypes that can't be used in the default Arrow Array objects.\r\nWe already have an ExtensionArray that allows us to use them as column types but not for subtypes.\r\nMaybe we can extend it, I haven't experimented with that yet",
"Cool\r\nSo please consider this issue as a feature request for:\r\n```\r\nArray3D(shape=(None, 137, 2), dtype=\"float32\")\r\n```\r\n\r\nits a way to represent videos, poses, and other cool sequences",
"@lhoestq well, so sequence of sequences doesn't work either...\r\n\r\n```\r\npyarrow.lib.ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\n\r\n\r\n",
"Working with Arrow can be quite fun sometimes.\r\nYou can fix this issue by trying to reduce the writer batch size (same trick than the one used to reduce the RAM usage in https://github.com/huggingface/datasets/issues/741).\r\n\r\nLet me know if it works.\r\nI haven't investigated yet on https://github.com/huggingface/datasets/issues/741 since I was preparing this week's sprint to add datasets but this is in my priority list for early next week.",
"The batch size fix doesn't work... not for #741 and not for this dataset I'm trying (DGS corpus)\r\nLoading the DGS corpus takes 400GB of RAM, which is fine with me as my machine is large enough\r\n",
"Sorry it doesn't work. Will let you know once I fixed it",
"Hi @lhoestq , any update on dynamic sized arrays?\r\n(`Array3D(shape=(None, 137, 2), dtype=\"float32\")`)",
"Not yet, I've been pretty busy with the dataset sprint lately but this is something that's been asked several times already. So I'll definitely work on this as soon as I'm done with the sprint and with the RAM issue you reported.",
"Hi @lhoestq,\r\nAny chance you have some updates on the supporting `ArrayXD` as a subtype or support of dynamic sized arrays?\r\n\r\ne.g.:\r\n`datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))`\r\n`Array3D(shape=(None, 137, 2), dtype=\"float32\")`",
"Hi ! We haven't worked in this lately and it's not in our very short-term roadmap since it requires a bit a work to make it work with arrow. Though this will definitely be added at one point.",
"@lhoestq, thanks for the update.\r\n\r\nI actually tried to modify some piece of code to make it work. Can you please tell if I missing anything here?\r\nI think that for vast majority of cases it's enough to make first dimension of the array dynamic i.e. `shape=(None, 100, 100)`. For that, it's enough to modify class [ArrayExtensionArray](https://github.com/huggingface/datasets/blob/9ca24250ea44e7611c4dabd01ecf9415a7f0be6c/src/datasets/features.py#L397) to output list of arrays of different sizes instead of list of arrays of same sizes (current version)\r\nBelow are my modifications of this class.\r\n\r\n```\r\nclass ArrayExtensionArray(pa.ExtensionArray):\r\n def __array__(self):\r\n zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n return self.to_numpy(zero_copy_only=zero_copy_only)\r\n\r\n def __getitem__(self, i):\r\n return self.storage[i]\r\n\r\n def to_numpy(self, zero_copy_only=True):\r\n storage: pa.ListArray = self.storage\r\n size = 1\r\n for i in range(self.type.ndims):\r\n size *= self.type.shape[i]\r\n storage = storage.flatten()\r\n numpy_arr = storage.to_numpy(zero_copy_only=zero_copy_only)\r\n numpy_arr = numpy_arr.reshape(len(self), *self.type.shape)\r\n return numpy_arr\r\n\r\n def to_list_of_numpy(self, zero_copy_only=True):\r\n storage: pa.ListArray = self.storage\r\n shape = self.type.shape\r\n arrays = []\r\n for dim in range(1, self.type.ndims):\r\n assert shape[dim] is not None, f\"Support only dynamic size on first dimension. Got: {shape}\"\r\n\r\n first_dim_offsets = np.array([off.as_py() for off in storage.offsets])\r\n for i in range(len(storage)):\r\n storage_el = storage[i:i+1]\r\n first_dim = first_dim_offsets[i+1] - first_dim_offsets[i]\r\n # flatten storage\r\n for dim in range(self.type.ndims):\r\n storage_el = storage_el.flatten()\r\n\r\n numpy_arr = storage_el.to_numpy(zero_copy_only=zero_copy_only)\r\n arrays.append(numpy_arr.reshape(first_dim, *shape[1:]))\r\n\r\n return arrays\r\n\r\n def to_pylist(self):\r\n zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n if self.type.shape[0] is None:\r\n return self.to_list_of_numpy(zero_copy_only=zero_copy_only)\r\n else:\r\n return self.to_numpy(zero_copy_only=zero_copy_only).tolist()\r\n```\r\n\r\nI ran few tests and it works as expected. Let me know what you think.",
"Thanks for diving into this !\r\n\r\nIndeed focusing on making the first dimensions dynamic make total sense (and users could still re-order their dimensions to match this constraint).\r\nYour code looks great :) I think it can even be extended to support several dynamic dimensions if we want to.\r\n\r\nFeel free to open a PR to include these changes, then we can update our test suite to make sure it works in all use cases.\r\nIn particular I think we might need a few tweaks to allow it to be converted to pandas (though I haven't tested yet):\r\n\r\n```python\r\nfrom datasets import Dataset, Features, Array3D\r\n\r\n# this works\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(1, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix]]})\r\nprint(d.to_pandas())\r\n\r\n# this should work as well\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(None, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix] * 2]})\r\nprint(d.to_pandas())\r\n```\r\n\r\nI'll be happy to help you on this :)"
] | 1,606,314,741,000
| 1,631,207,020,000
| null |
CONTRIBUTOR
| null | null |
I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic)
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _generate_examples(self):
""" Yields examples. """
yield 1, {
"pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
}
```
But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>
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MDU6SXNzdWU3NTA3ODkwNTI=
| 885
|
Very slow cold-start
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[
"Good point!",
"Yes indeed. We can probably improve that by using lazy imports",
"#1690 added fast start-up of the library "
] | 1,606,308,478,000
| 1,610,537,485,000
| 1,610,537,485,000
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CONTRIBUTOR
| null | null |
Hi,
I expect when importing `datasets` that nothing major happens in the background, and so the import should be insignificant.
When I load a metric, or a dataset, its fine that it takes time.
The following ranges from 3 to 9 seconds:
```
python -m timeit -n 1 -r 1 'from datasets import load_dataset'
```
edit:
sorry for the mis-tag, not sure how I added it.
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MDU6SXNzdWU3NDk3NTA4MDE=
| 883
|
Downloading/caching only a part of a datasets' dataset.
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[
"Not at the moment but we could likely support this feature.",
"?",
"I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources."
] | 1,606,227,918,000
| 1,606,485,115,000
| null |
NONE
| null | null |
Hi,
I want to use the validation data *only* (of natural question).
I don't want to have the whole dataset cached in my machine, just the dev set.
Is this possible? I can't find a way to do it in the docs.
Thank you,
Sapir
|
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MDU6SXNzdWU3NDg5NDk2MDY=
| 880
|
Add SQA
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[
"I’ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq ",
"@thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https://github.com/google-research/tapas/blob/master/tapas/utils/interaction_utils.py) that is used to turn the `answer_coordinates` and `answer_texts` columns into true Python lists of tuples/strings:\r\n\r\n```\r\nimport pandas as pd\r\nimport ast\r\n\r\ndata = pd.read_csv(\"/content/sqa_data/random-split-1-dev.tsv\", sep='\\t')\r\n\r\ndef _parse_answer_coordinates(answer_coordinate_str):\r\n \"\"\"Parses the answer_coordinates of a question.\r\n Args:\r\n answer_coordinate_str: A string representation of a Python list of tuple\r\n strings.\r\n For example: \"['(1, 4)','(1, 3)', ...]\"\r\n \"\"\"\r\n\r\n try:\r\n answer_coordinates = []\r\n # make a list of strings\r\n coords = ast.literal_eval(answer_coordinate_str)\r\n # parse each string as a tuple\r\n for row_index, column_index in sorted(\r\n ast.literal_eval(coord) for coord in coords):\r\n answer_coordinates.append((row_index, column_index))\r\n except SyntaxError:\r\n raise ValueError('Unable to evaluate %s' % answer_coordinate_str)\r\n \r\n return answer_coordinates\r\n\r\n\r\ndef _parse_answer_text(answer_text):\r\n \"\"\"Populates the answer_texts field of `answer` by parsing `answer_text`.\r\n Args:\r\n answer_text: A string representation of a Python list of strings.\r\n For example: \"[u'test', u'hello', ...]\"\r\n \"\"\"\r\n try:\r\n answer = []\r\n for value in ast.literal_eval(answer_text):\r\n answer.append(value)\r\n except SyntaxError:\r\n raise ValueError('Unable to evaluate %s' % answer_text)\r\n\r\n return answer\r\n\r\ndata['answer_coordinates'] = data['answer_coordinates'].apply(lambda coords_str: _parse_answer_coordinates(coords_str))\r\ndata['answer_text'] = data['answer_text'].apply(lambda txt: _parse_answer_text(txt))\r\n```\r\n\r\nHere I'm using Pandas to read in one of the TSV files (the dev set). \r\n\r\n",
"Closing since SQA was added in #1566 "
] | 1,606,149,115,000
| 1,608,731,904,000
| 1,608,731,903,000
|
CONTRIBUTOR
| null | null |
## Adding a Dataset
- **Name:** SQA (Sequential Question Answering) by Microsoft.
- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.
- **Paper:** https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/
- **Data:** https://www.microsoft.com/en-us/download/details.aspx?id=54253
- **Motivation:** currently, the [Tapas](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https://github.com/huggingface/transformers/pull/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).
Note 1: this dataset actually consists of 2 types of files:
1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)
2) a folder of csv files, which contain the actual tabular data
Note 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.
Adding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https://github.com/ppasupat/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https://github.com/wenhuchen/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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MDU6SXNzdWU3NDg4NDg4NDc=
| 879
|
boolq does not load
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[
"Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.",
"hey\ni do the exact same commands. for me it fails i guess might be issues with\ncaching maybe?\nthanks\nbest\nrabeeh\n\nOn Tue, Nov 24, 2020, 10:24 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi ! It runs on my side without issues. I tried\n>\n> from datasets import load_datasetload_dataset(\"boolq\")\n>\n> What version of datasets and tensorflow are your runnning ?\n> Also if you manage to get a minimal reproducible script (on google colab\n> for example) that would be useful.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/879#issuecomment-732769114>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABP4ZCGGDR2FUMRKZTIY5CTSRN3VXANCNFSM4T7R3U6A>\n> .\n>\n",
"Could you check if it works on the master branch ?\r\nYou can use `load_dataset(\"boolq\", script_version=\"master\")` to do so.\r\nWe did some changes recently in boolq to remove the TF dependency and we changed the way the data files are downloaded in https://github.com/huggingface/datasets/pull/881"
] | 1,606,141,708,000
| 1,664,972,612,000
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CONTRIBUTOR
| null | null |
Hi
I am getting these errors trying to load boolq thanks
Traceback (most recent call last):
File "test.py", line 5, in <module>
data = AutoTask().get("boolq").get_dataset("train", n_obs=10)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 42, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 38, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 150, in download_custom
get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 472, in get_from_cache
f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
FileNotFoundError: Cannot find the requested files in the cached path at /idiap/home/rkarimi/.cache/huggingface/datasets/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.
|
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MDU6SXNzdWU3NDg2MjE5ODE=
| 878
|
Loading Data From S3 Path in Sagemaker
|
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[] |
[
"This would be a neat feature",
"> neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these ",
"It could maybe work almost out of the box just by using `cached_path` in the text/csv/json scripts, no?",
"Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you guys said, \r\n\r\nI have solved the problem as follows:\r\n\r\n1. read the csv file using pandas from s3 \r\n2. Convert to dictionary key as column name and values as list column data\r\n3. convert it to Dataset using \r\n`from datasets import Dataset`\r\n`train_dataset = Dataset.from_dict(train_dict)`",
"We were brainstorming around your use-case.\r\n\r\nLet's keep the issue open for now, I think this is an interesting question to think about.",
"> We were brainstorming around your use-case.\r\n> \r\n> Let's keep the issue open for now, I think this is an interesting question to think about.\r\n\r\nSure thomwolf, Thanks for your concern ",
"I agree it would be cool to have that feature. Also that's good to know that pandas supports this.\r\nFor the moment I'd suggest to first download the files locally as thom suggested and then load the dataset by providing paths to the local files",
"Don't get\n",
"Any updates on this issue?\r\nI face a similar issue. I have many parquet files in S3 and I would like to train on them. \r\nTo be honest I even face issues with only getting the last layer embedding out of them.",
"Hi dorlavie, \r\nYou can find one solution that i have mentioned above, that can help you. \r\nAnd there is one more solution also which is downloading files locally\r\n",
"> Hi dorlavie,\r\n> You can find one solution that i have mentioned above, that can help you.\r\n> And there is one more solution also which is downloading files locally\r\n\r\nmahesh1amour, thanks for the fast reply\r\n\r\nUnfortunately, in my case I can not read with pandas. The dataset is too big (50GB). \r\nIn addition, due to security concerns I am not allowed to save the data locally",
"@dorlavie could use `boto3` to download the data to your local machine and then load it with `dataset`\r\n\r\nboto3 example [documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-download-file.html)\r\n```python\r\nimport boto3\r\n\r\ns3 = boto3.client('s3')\r\ns3.download_file('BUCKET_NAME', 'OBJECT_NAME', 'FILE_NAME')\r\n```\r\n\r\ndatasets example [documentation](https://huggingface.co/docs/datasets/loading_datasets.html)\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=['my_file_1.csv', 'my_file_2.csv', 'my_file_3.csv'])\r\n```\r\n",
"Thanks @philschmid for the suggestion.\r\nAs I mentioned in the previous comment, due to security issues I can not save the data locally.\r\nI need to read it from S3 and process it directly.\r\n\r\nI guess that many other people try to train / fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?",
"If I understand correctly you're not allowed to write data on disk that you downloaded from S3 for example ?\r\nOr is it the use of the `boto3` library that is not allowed in your case ?",
"@lhoestq yes you are correct.\r\nI am not allowed to save the \"raw text\" locally - The \"raw text\" must be saved only on S3.\r\nI am allowed to save the output of any model locally. \r\nIt doesn't matter how I do it boto3/pandas/pyarrow, it is forbidden",
"@dorlavie are you using sagemaker for training too? Then you could use S3 URI, for example `s3://my-bucket/my-training-data` and pass it within the `.fit()` function when you start the sagemaker training job. Sagemaker would then download the data from s3 into the training runtime and you could load it from disk\r\n\r\n**sagemaker start training job**\r\n```python\r\npytorch_estimator.fit({'train':'s3://my-bucket/my-training-data','eval':'s3://my-bucket/my-evaluation-data'})\r\n```\r\n\r\n**in the train.py script**\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ntrain_dataset = load_from_disk(os.environ['SM_CHANNEL_TRAIN'])\r\n```\r\n\r\nI have created an example of how to use transformers and datasets with sagemaker. \r\nhttps://github.com/philschmid/huggingface-sagemaker-example/tree/main/03_huggingface_sagemaker_trainer_with_data_from_s3\r\n\r\nThe example contains a jupyter notebook `sagemaker-example.ipynb` and an `src/` folder. The sagemaker-example is a jupyter notebook that is used to create the training job on AWS Sagemaker. The `src/` folder contains the `train.py`, our training script, and `requirements.txt` for additional dependencies.\r\n\r\n"
] | 1,606,123,042,000
| 1,608,717,188,000
| null |
NONE
| null | null |
In Sagemaker Im tring to load the data set from S3 path as follows
`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
data_files = {}
data_files["train"] = train_path
data_files["validation"] = valid_path
data_files["test"] = test_path
extension = train_path.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
print(datasets)`
I getting an error of
`algo-1-7plil_1 | File "main.py", line 21, in <module>
algo-1-7plil_1 | datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1 | m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1 | return os.stat(filename).st_mtime
algo-1-7plil_1 | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`
But when im trying with pandas , it is able to load from S3
Does the datasets library support S3 path to load
|
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MDU6SXNzdWU3NDgyMzQ0Mzg=
| 877
|
DataLoader(datasets) become more and more slowly within iterations
|
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[
"Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or not",
"> Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\n> It would be nice to know whether it comes from the dataloader or not\r\n\r\nI did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`, around 20000it/s."
] | 1,606,048,870,000
| 1,606,664,712,000
| 1,606,664,712,000
|
NONE
| null | null |
Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!
```
dataset = load_from_disk(dataset_path) # around 21,000,000 lines
lineloader = tqdm(DataLoader(dataset, batch_size=1))
for idx, line in enumerate(lineloader):
# do some thing for each line
```
In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s.
And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s.
Could you please help me with this problem?
Thanks a lot!
|
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