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Add SILICONE benchmark
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[ "Thanks for the feedback. All your comments have been addressed!", "Thank you for your constructive feedback! I now know how to best format future datasets that our team plans to publish in the near future :)", "Awesome ! Looking forward to it :) ", "Hi @lhoestq ! One last question. Our research team would like to distribute a link to this dataset amongst the spoken dialogue research community but the dataset does not show in the dropdown menu at huggingface.co. Is there anything else we must do in order to find the dataset there ?\r\n\r\nOnce the dataset does show in the dropdown menu, how can I affiliate it with the Telecom Paris organization that I already created at the website ?", "The files are not located in the right place in the repo. Let me move them", "I created a PR at https://github.com/huggingface/datasets/pull/1794", "I just merged the change @eusip, now the dataset page is available at the url:\r\nhttps://huggingface.co/datasets/silicone", "Thank you for moving the folder for me :)" ]
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My collaborators and I within the Affective Computing team at Telecom Paris would like to re-submit our spoken dialogue dataset for publication. This is a new pull request relative to the [previously closed request](https://github.com/huggingface/datasets/pull/1712) which was reviewed by @lhoestq.
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More tags
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[ "Conll has `multilingual` but is only tagged as `en`", "good catch, that was a bad copy paste x)" ]
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Since the hub v2 is going to be released soon I figured it would be great to add the missing tags at least for some of the datasets of reference listed [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#write-the-loadingprocessing-code)
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wikipedia dataset incomplete
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[ "Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?", "yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. ", "The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https://github.com/earwig/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n", "ok great. Thank you, @lhoestq. " ]
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Hey guys, I am using the https://github.com/huggingface/datasets/tree/master/datasets/wikipedia dataset. Unfortunately, I found out that there is an incompleteness for the German dataset. For reasons unknown to me, the number of inhabitants has been removed from many pages: Thorey-sur-Ouche has 128 inhabitants according to the webpage (https://de.wikipedia.org/wiki/Thorey-sur-Ouche). The pickle file however shows: französische Gemeinde mit Einwohnern (Stand). Is it possible to fix this? Best regards Chris
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dataset.search() (elastic) cannot reliably retrieve search results
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[ "Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?", "Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!" ]
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I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices. The problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer. I am indexing data that looks like the following from the HF SQuAD 2.0 data set: ``` ['57318658e6313a140071d02b', '56f7165e3d8e2e1400e3733a', '570e2f6e0b85d914000d7d21', '5727e58aff5b5019007d97d0', '5a3b5a503ff257001ab8441f', '57262fab271a42140099d725'] ``` To reproduce the issue, try: ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # build base examples, features set of training data shuffled_idx = pd.read_csv('https://raw.githubusercontent.com/afogarty85/temp/main/idx.csv')['idx'].to_list() examples = load_dataset("squad_v2").shuffle(seed=1)['train'] features = load_dataset("squad_v2").shuffle(seed=1)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # reorder features by the training process features = features.select(indices=shuffled_idx) # get the example ids to match with the "example" data; get unique entries id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions in the examples data set; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # retrieve the example index example_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] example_idx_k1 = [item for sublist in example_idx_k1 for item in sublist] example_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list] example_idx_k2 = [item for sublist in example_idx_k2 for item in sublist] len(example_idx_k1) # should be 130319 len(example_idx_k2) # should be 130319 #trial 1 lengths: # k=1: 130314 # k=3: 130319 # trial 2: # just run k=3 first: 130310 # try k=1 after k=3: 130319 ```
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FewRel
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[ "+1", "@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.", "Hi @lhoestq,\r\n\r\nThis issue can be closed, I guess.", "Yes :) closing\r\nThanks again for adding FewRel !", "Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!" ]
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## Adding a Dataset - **Name:** FewRel - **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset - **Paper:** @inproceedings{han2018fewrel, title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation}, author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong}, booktitle={EMNLP}, year={2018}} - **Data:** https://github.com/ProKil/FewRel - **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated. 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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Ccaligned multilingual translation dataset
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## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). - **Paper:** *link to the dataset paper if available* - https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf - **Data:** *link to the Github repository or current dataset location* - http://www.statmt.org/cc-aligned/ - **Motivation:** *what are some good reasons to have this dataset* - The authors says it's an high quality dataset. - it's pretty large and includes many language pairs. It could be interesting training mt5 on this task. 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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Using select/reordering datasets slows operations down immensely
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[ "You can use `Dataset.flatten_indices()` to make it fast after a select or shuffle.", "Thanks for the input! I gave that a try by adding this after my selection / reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!" ]
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I am using portions of HF's helpful work in preparing / scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour. The below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score. The problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes. ``` from datasets import load_dataset, load_metric from transformers import BertTokenizerFast, BertForQuestionAnswering from elasticsearch import Elasticsearch import numpy as np import collections from tqdm.auto import tqdm import torch # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') max_length = 384 # The maximum length of a feature (question and context) doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. pad_on_right = tokenizer.padding_side == "right" squad_v2 = True # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def prepare_validation_features(examples): # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # We keep the example_id that gave us this feature and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (list(o) if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples # from https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=941LPhDWeYv- def postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30): all_start_logits, all_end_logits = starting_logits, ending_logits # Build a map example to its corresponding features. example_id_to_index = {k: i for i, k in enumerate(examples["id"])} features_per_example = collections.defaultdict(list) for i, feature in enumerate(features): features_per_example[example_id_to_index[feature["example_id"]]].append(i) # The dictionaries we have to fill. predictions = collections.OrderedDict() # Logging. print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.") # Let's loop over all the examples! for example_index, example in enumerate(tqdm(examples)): # Those are the indices of the features associated to the current example. feature_indices = features_per_example[example_index] min_null_score = None # Only used if squad_v2 is True. valid_answers = [] context = example["context"] # Looping through all the features associated to the current example. for feature_index in feature_indices: # We grab the predictions of the model for this feature. start_logits = all_start_logits[feature_index] end_logits = all_end_logits[feature_index] # This is what will allow us to map some the positions in our logits to span of texts in the original # context. offset_mapping = features[feature_index]["offset_mapping"] # Update minimum null prediction. cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id) feature_null_score = start_logits[cls_index] + end_logits[cls_index] if min_null_score is None or min_null_score < feature_null_score: min_null_score = feature_null_score # Go through all possibilities for the `n_best_size` greater start and end logits. start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist() end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist() for start_index in start_indexes: for end_index in end_indexes: # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond # to part of the input_ids that are not in the context. if ( start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None ): continue # Don't consider answers with a length that is either < 0 or > max_answer_length. if end_index < start_index or end_index - start_index + 1 > max_answer_length: continue start_char = offset_mapping[start_index][0] end_char = offset_mapping[end_index][1] valid_answers.append( { "score": start_logits[start_index] + end_logits[end_index], "text": context[start_char: end_char] } ) if len(valid_answers) > 0: best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] else: # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid # failure. best_answer = {"text": "", "score": 0.0} # Let's pick our final answer: the best one or the null answer (only for squad_v2) if not squad_v2: predictions[example["id"]] = best_answer["text"] else: answer = best_answer["text"] if best_answer["score"] > min_null_score else "" predictions[example["id"]] = answer return predictions # build base examples, features from training data examples = load_dataset("squad_v2").shuffle(seed=5)['train'] features = load_dataset("squad_v2").shuffle(seed=5)['train'].map( prepare_validation_features, batched=True, remove_columns=['answers', 'context', 'id', 'question', 'title']) # sim some shuffled training indices that we want to use to re-order the data to compare how we did shuffle_idx = np.arange(0, 131754) np.random.shuffle(shuffle_idx) # create a new dataset with rows selected following the training shuffle features = features.select(indices=shuffle_idx) # get unique example ids to match with the "example" data id_list = list(dict.fromkeys(features['example_id'])) # now search for their index positions; load elastic search es = Elasticsearch([{'host': 'localhost'}]).ping() # add an index to the id column for the examples examples.add_elasticsearch_index(column='id') # search the examples for their index position example_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list] # drop the elastic search examples.drop_index(index_name='id') # put examples in the right order examples = examples.select(indices=example_idx) # generate some fake data logits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)} def score_squad(logits, n_best_size, max_answer): # proceed with QA calculation final_predictions = postprocess_qa_predictions(examples=examples, features=features, starting_logits=logits['starting_logits'], ending_logits=logits['ending_logits'], n_best_size=20, max_answer_length=30) metric = load_metric("squad_v2") formatted_predictions = [{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in final_predictions.items()] references = [{"id": ex["id"], "answers": ex["answers"]} for ex in examples] metrics = metric.compute(predictions=formatted_predictions, references=references) return metrics metrics = score_squad(logits, n_best_size=20, max_answer=30) ```
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Use a config id in the cache directory names for custom configs
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As noticed by @JetRunner there was some issues when trying to generate a dataset using a custom config that is based on an existing config. For example in the following code the `mnli_custom` would reuse the cache used to create `mnli` instead of generating a new dataset with the new label classes: ```python from datasets import load_dataset mnli = load_dataset("glue", "mnli") mnli_custom = load_dataset("glue", "mnli", label_classes=["contradiction", "entailment", "neutral"]) ``` I fixed that by extending the cache directory definition of a dataset that is being generated. Instead of using the config name in the cache directory name, I switched to using a `config_id`. By default it is equal to the config name. However the name of a config is not sufficent to have a unique identifier for the dataset being generated since it doesn't take into account: - the config kwargs that can be used to overwrite attributes - the custom features used to write the dataset - the data_files for json/text/csv/pandas datasets Therefore the config id is just the config name with an optional suffix based on these. In particular taking into account the config kwargs fixes the issue with the `label_classes` above. I completed the current test cases by adding the case that was missing: overwriting an already existing config.
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fix comet citations
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I realized COMET citations were not showing in the hugging face metrics page: <img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105164848-8b9da900-5b0d-11eb-9e20-a38f559d2037.png"> This pull request is intended to fix that. Thanks!
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COMET metric citation
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[ "I think its better to create a new branch with this fix. I forgot I was still using the old branch." ]
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In my last pull request to add COMET metric, the citations where not following the usual "format". Because of that they where not correctly displayed on the website: <img width="814" alt="Screenshot 2021-01-20 at 09 48 44" src="https://user-images.githubusercontent.com/17256847/105158000-686efb80-5b05-11eb-8bb0-9c85fdac2938.png"> This pull request is only intended to fix that.
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Updated README for the Social Bias Frames dataset
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See the updated card at https://github.com/mcmillanmajora/datasets/tree/add-SBIC-card/datasets/social_bias_frames. I incorporated information from the [SBIC data statement](https://homes.cs.washington.edu/~msap/social-bias-frames/DATASTATEMENT.html), paper, and the corpus README file included with the dataset download.
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Fix typo in README.md of cnn_dailymail
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[ "Good catch, thanks!", "Thank you for merging!" ]
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When I read the README.md of `CNN/DailyMail Dataset`, there seems to be a typo `CCN`. I am afraid this is a trivial matter, but I would like to make a suggestion for revision.
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Added metadata and correct splits for swda.
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[ "I will push updates tomorrow.", "@lhoestq thank you for your comments! I went ahead and fixed the code 😃. Please let me know if I missed anything." ]
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Switchboard Dialog Act Corpus I made some changes following @bhavitvyamalik recommendation in #1678: * Contains all metadata. * Used official implementation from the [/swda](https://github.com/cgpotts/swda) repo. * Add official train and test splits used in [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97) and validation split used in [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier).
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add Stuctured Argument Extraction for Korean dataset
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datasets slicing with seed
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[ "Hi :) \r\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows", "thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\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/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n" ]
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Hi I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html I could not find a seed option, could you assist me please how I can get a slice for different seeds? thank you. @lhoestq
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Fix release conda worflow
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The current workflow yaml file is not valid according to https://github.com/huggingface/datasets/actions/runs/487638110
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difference between wsc and wsc.fixed for superglue
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[ "From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```" ]
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Hi I see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq
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Add missing "brief" entries to reuters
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[ "@lhoestq I ran `make style` but CI code quality still failing and I don't have access to logs", "It's also likely that due to the previous placement of the field initialization, much of the data about topics etc was simply wrong and carried over from previous entries. Model scores seem to improve significantly with this PR." ]
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This brings the number of examples for ModApte to match the stated `Training set (9,603 docs)...Test Set (3,299 docs)`
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Issue while Creating Custom Metric
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[ "Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence({\r\n \"references_ids\": datasets.Value(\"int32\"),\r\n \"offset_mapping\": datasets.Value(\"int32\"),\r\n 'text': datasets.Value('string'),\r\n \"ground\": datasets.Value(\"int32\")\r\n }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n 'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https://github.com/huggingface/datasets/pull/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?", "Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know." ]
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Hi Team, I am trying to create a custom metric for my training as follows, where f1 is my own metric: ```python def _info(self): # TODO: Specifies the datasets.MetricInfo object return datasets.MetricInfo( # This is the description that will appear on the metrics page. description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, # This defines the format of each prediction and reference features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}), # Homepage of the metric for documentation homepage="http://metric.homepage", # Additional links to the codebase or references codebase_urls=["http://github.com/path/to/codebase/of/new_metric"], reference_urls=["http://path.to.reference.url/new_metric"] ) def _compute(self,predictions,references,text,offset_mapping,spans): pred_spans = [] for i,preds in enumerate(predictions): current_preds = [] for j,token_preds in enumerate(preds): if (preds>0.5): current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1])) pred_spans.append(current_spans) return { "Token Wise F1": f1_score(references,predictions,labels=[0,1]), "Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)]) } ``` I believe this is not correct. But that's not the issue I am facing right now. I get this error : ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-144-ed7349b50821> in <module>() ----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] ) 2 frames /usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch) 802 encoded_batch = {} 803 if set(batch) != set(self): --> 804 print(batch) 805 print(self) 806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self))) ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'} ``` On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references. How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work? Thanks, Gunjan
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Add GLUE Compat (compatible with transformers<3.5.0)
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[ "Maybe it would be simpler to just overwrite the order of the label classes of the `glue` dataset ?\r\n```python\r\nmnli = load_dataset(\"glue\", \"mnli\", label_classes=[\"contradiction\", \"entailment\", \"neutral\"])\r\n```", "Sounds good. Will close the issue if that works." ]
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Link to our discussion on Slack (HF internal) https://huggingface.slack.com/archives/C014N4749J9/p1609668119337400 The next step is to add a compatible option in the new `run_glue.py` I duplicated `glue` and made the following changes: 1. Change the name to `glue_compat`. 2. Change the label assignments for MNLI and AX.
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error when run fine_tuning on text_classification
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dataset:sem_eval_2014_task_1 pretrained_model:bert-base-uncased error description: when i use these resoruce to train fine_tuning a text_classification on sem_eval_2014_task_1,there always be some problem(when i use other dataset ,there exist the error too). And i followed the colab code (url:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb#scrollTo=TlqNaB8jIrJW). the error is like this : `File "train.py", line 69, in <module> trainer.train() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/transformers/trainer.py", line 784, in train for step, inputs in enumerate(epoch_iterator): File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/projects/anaconda3/envs/calibration/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] KeyError: 2` this is my code : ```dataset_name = 'sem_eval_2014_task_1' num_labels_size = 3 batch_size = 4 model_checkpoint = 'bert-base-uncased' number_train_epoch = 5 def tokenize(batch): return tokenizer(batch['premise'], batch['hypothesis'], truncation=True, ) def compute_metrics(pred): labels = pred.label_ids preds = pred.predictions.argmax(-1) precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro') acc = accuracy_score(labels, preds) return { 'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall } model = BertForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels_size) tokenizer = BertTokenizerFast.from_pretrained(model_checkpoint, use_fast=True) train_dataset = load_dataset(dataset_name, split='train') test_dataset = load_dataset(dataset_name, split='test') train_encoded_dataset = train_dataset.map(tokenize, batched=True) test_encoded_dataset = test_dataset.map(tokenize, batched=True) args = TrainingArguments( output_dir='./results', evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=number_train_epoch, weight_decay=0.01, do_predict=True, ) trainer = Trainer( model=model, args=args, compute_metrics=compute_metrics, train_dataset=train_encoded_dataset, eval_dataset=test_encoded_dataset, tokenizer=tokenizer ) trainer.train() trainer.evaluate()
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add id_liputan6 dataset
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id_liputan6 is a large-scale Indonesian summarization dataset. The articles were harvested from an online news portal, and obtain 215,827 document-summary pairs: https://arxiv.org/abs/2011.00679
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fixes and improvements for the WebNLG loader
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[ "The dataset card is fantastic!\r\n\r\nLooks good to me! Did you check that this still passes the slow tests with the existing dummy data?", "Yes, I ran and passed all the tests specified in [this guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata), including the slow ones.", "I just added the `from pathlib import Path` at the top to fix the script", "I ran the tests locally and they all pass, merging", "Thank you for the review!" ]
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- fixes test sets loading in v3.0 - adds additional fields for v3.0_ru - adds info to the WebNLG data card
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Conda support
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[ "Nice thanks :) \r\nNote that in `datasets` the tags are simply the version without the `v`. For example `1.2.1`.", "Do you push tags only for versions?", "Yes I've always used tags only for versions" ]
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Will push a new version on anaconda cloud every time a tag starting with `v` is pushed (like `v1.2.2`). Will appear here: https://anaconda.org/huggingface/datasets Depends on `conda-forge` for now, so the following is required for installation: ``` conda install -c huggingface -c conda-forge datasets ```
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update link in TLC to be github links
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[ "Thanks for updating this!" ]
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Base on this issue https://github.com/huggingface/datasets/issues/1064, I can now use the official links.
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Adjust BrWaC dataset features name
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I added this dataset some days ago, and today I used it to train some models and realized that the names of the features aren't so good. Looking at the current features hierarchy, we have "paragraphs" with a list of "sentences" with a list of "sentences?!". But the actual hierarchy is a "text" with a list of "paragraphs" with a list of "sentences". I confused myself trying to use the dataset with these names. So I think it's better to change it.
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Update add new dataset template
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[ "Add new \"dataset\"? ;)", "Lol, too used to Transformers ;-)" ]
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This PR fixes a few typos in the "Add new dataset template" and clarifies a bit what to do for the dummy data creation when the `auto_generate` flag can't work.
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Fix empty token bug for `thainer` and `lst20`
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add a condition to check if tokens exist before yielding in `thainer` and `lst20`
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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" ]
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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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[GEM Dataset] Added TurkCorpus, an evaluation dataset for sentence simplification.
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[ "Thank you for the feedback! I updated the code. " ]
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We want to use TurkCorpus for validation and testing of the sentence simplification task.
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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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ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/swda/swda.py
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Add MNIST dataset
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This PR adds the MNIST dataset to the library.
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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" ]
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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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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!" ]
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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." ]
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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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Offline loading
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[ "It's maybe a bit annoying to add but could we maybe have as well a version of the local data loading scripts in the package?\r\nThe `text`, `json`, `csv`. Thinking about people like in #1725 who are expecting to be able to work with local data without downloading anything.\r\n\r\nMaybe we can add them to package_data or something?", "Yes I mentioned this in #824 as well. I'm looking into it", "Alright now `csv`, `json`, `text` and `pandas` are \"packaged datasets\", i.e. they're part of the `datasets` package, which makes them available in offline mode without any change in terms of API:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\", data_files=[\"path/to/data.csv\"])\r\n```\r\n\r\nInstead of loading the dataset script from the module cache, it's loaded from inside the `datasets` package.\r\n\r\nI updated the test to still be able to fetch the dummy data files for those datasets from `datasets/{text|csv|pandas|json}/dummy` in the repo.", "Alright now all test pass :)\r\n(I don't thank you windows)", "LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?", "> LGTM! Since you're getting the local script's last modification date anyways do you think it might be a good idea to show it in the warning?\r\n\r\nYep good idea. I added the date in the warning. For example `(last modified on Mon Nov 30 11:01:56 2020)`" ]
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As discussed in #824 it would be cool to make the library work in offline mode. Currently if there's not internet connection then modules (datasets or metrics) that have already been loaded in the past can't be loaded and it raises a ConnectionError. This is because `prepare_module` fetches online for the latest version of the module. To make it work in offline mode one suggestion was to reload the latest local version of the module. I implemented that and I also raise a warning saying that the module that is loaded is the latest local version. ```python logger.warning( f"Using the latest cached version of the module from {cached_module_path} since it " f"couldn't be found locally at {input_path} or remotely ({error_type_that_prevented_reaching_out_remote_stuff})." ) ``` I added tests to make sure it works as expected and I needed to do a few changes in the code to be able to test things properly. In particular I added a parameter `hf_modules_cache` to `init_dynamic_modules` for testing purposes. It makes it possible to have temporary modules caches for testing. I also added a `offline` context utility that allows to test part of the code by making all the requests fail as if there was no internet.
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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" ]
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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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ADD S3 support for downloading and uploading processed datasets
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[ "I created the documentation for `FileSystem Integration for cloud storage` with loading and saving datasets to/from a filesystem with an example of using `datasets.filesystem.S3Filesystem`. I added a note on the `Saving a processed dataset on disk and reload` saying that it is also possible to use other filesystems and cloud storages such as S3 with a link to the newly created documentation page from me. \r\nI Attach a screenshot of it here. \r\n![screencapture-localhost-5500-docs-build-html-filesystems-html-2021-01-19-17_16_10](https://user-images.githubusercontent.com/32632186/105062131-8d6a5c80-5a7a-11eb-90b0-f6128b758605.png)\r\n" ]
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# What does this PR do? This PR adds the functionality to load and save `datasets` from and to s3. You can save `datasets` with either `Dataset.save_to_disk()` or `DatasetDict.save_to_disk`. You can load `datasets` with either `load_from_disk` or `Dataset.load_from_disk()`, `DatasetDict.load_from_disk()`. Loading `csv` or `json` datasets from s3 is not implemented. To save/load datasets to s3 you either need to provide an `aws_profile`, which is set up on your machine, per default it uses the `default` profile or you have to pass an `aws_access_key_id` and `aws_secret_access_key`. The implementation was done with the `fsspec` and `boto3`. ### Example `aws_profile` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") ``` </details> ### Example `aws_access_key_id` and `aws_secret_access_key` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_access_key_id="fake_access_key", aws_secret_access_key="fake_secret_key" ) load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_access_key_id="fake_access_key", aws_secret_access_key="fake_secret_key" ) ``` </details> If you want to load a dataset from a public s3 bucket you can pass `anon=True` ### Example `anon=True` : <details> ```python dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") load_from_disk("s3://moto-mock-s3-bucketdatasets/sdk",anon=True) ``` </details> ### Full Example ```python import datasets dataset = datasets.load_dataset("imdb") print(f"DatasetDict contains {len(dataset)} datasets") print(f"train Dataset has the size of: {len(dataset['train'])}") dataset.save_to_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") remote_dataset = datasets.load_from_disk("s3://moto-mock-s3-bucket/datasets/sdk", aws_profile="hf-sm") print(f"DatasetDict contains {len(remote_dataset)} datasets") print(f"train Dataset has the size of: {len(remote_dataset['train'])}") ``` Related to #878 I would also adjust the documentation after the code would be reviewed, as long as I leave the PR in "draft" status. Something that we can consider is renaming the functions and changing the `_disk` maybe to `_filesystem`
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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: ![image](https://user-images.githubusercontent.com/49967236/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png) 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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Added unfiltered versions of the Wiki-Auto training data for the GEM simplification task.
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[ "The current version of Wiki-Auto dataset contains a filtered version of the aligned dataset. The commit adds unfiltered versions of the data that can be useful the GEM task participants." ]
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[Scientific papers] Mirror datasets zip
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[ "> Nice !\r\n> \r\n> Could you try to reduce the size of the dummy_data.zip files ? they're quite big (300KB)\r\n\r\nYes, I think it might make sense to enhance the tool a tiny bit to prevent this automatically", "That's the lightest I can make it...it's long-range summarization so a single sample has ~11000 tokens. ", "Ok thanks :)", "Awesome good to merge for me :-) " ]
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Datasets were uploading to https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip and https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip respectively to escape google drive quota and enable faster download.
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Adding the NorNE dataset for NER
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[ "Quick question, @lhoestq. In this specific dataset, two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. However, I have not found an easy way to implement that. Using splits or configs does not seem appropriate.\r\n", "About the `GPE_LOC` and `GPE_ORG`. The original NorNE paper in which they published the dataset, does an evaluation on three different NER tag sets, one considering `GPE_LOC` and `GPE_ORG` as they are, another changing them to be just `GPE`, and another one by changing it to become `LOC` and `ORG`. The called these sets, `norne-full`, `norne-7`, and `norne-9`. What I would like is to provide a way for the user of this dataset to get `norne-7` and `norne-9` without having to duplicate the code.", "Ok I see !\r\nI guess you can have three configurations `norne-full`, `norne-7` and `norne-9`.\r\nEach config can have different feature types. You can simply check for the `self.config.name` in the `_info(self)` method and pick the right ClassLabel names accordingly. And then in `_generate_examples` as well you can check for `self.config.name` to know how to process the labels to yield either GPE_LOC/GPE_ORG, GPE or LOC/ORG", "But I'm already using the configurations for the different language\nvarieties. So you propose having something like `bokmaal`, `bokmaal-7`,\netc? Would there be a different way? If not, I'd be fine the corpus as it\nis until we come up with a solution. Thanks in any case.\n\n--\nSent using a cell-phone, so sorry for the typos and wrong auto-corrections.\n\nOn Tue, Jan 19, 2021, 4:56 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Ok I see !\n> I guess you can have three configurations norne-full, norne-7 and norne-9.\n> Each config can have different feature types. You can simply check for the\n> self.config.name in the _info(self) method and pick the right ClassLabel\n> names accordingly. And then in _generate_examples as well you can check\n> for self.config.name to know how to process the labels to yield either\n> GPE_LOC/GPE_ORG, GPE or LOC/ORG\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/pull/1720#issuecomment-762936612>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AABKLYOWNDBD76WZPJHFCWLS2WTTHANCNFSM4V6GSUQA>\n> .\n>\n", "The first option about having configurations like `bokmaal-7`, `bokmaal-9` etc. would definitely work.\r\n\r\nA second option would be to add a parameter `ner_tags_set` to `NorneConfig` and then one could load them with\r\n```python\r\nbokmaal_full = load_dataset(\"norne\", \"bokmaal\", ner_tags_set=\"norne-full\")\r\n```\r\nfor example.\r\n\r\nWhat do you think ?", "Hi @versae have you had a chance to consider one of the two options for the config ?\r\nI think both are ok but I have a small preference for the first one since it's simpler to implement.\r\n\r\nFeel free to ping me if you have questions or if I can help :) ", "Hi @lhoestq. Agree, option 1 seems easier to implement. Just haven't had bandwidth to get to it yet. Hopefully starting next week I'll be able to update the PR.", "Hi @versae ! Did you manage to add the configurations ? Let me know if we can help you on this", "Hi @lhoestq, I do actually have to code ready, just need to generate the dummy data for it. ", "One thing I don't know how to do is to make `_info(self)` return the different NER tags in its `DatasetInfo` object depending on the specific config.", "OK, I think it's ready now.", "Closing this one and opening a new one with a cleaner commit log.", "All set now in #2154." ]
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NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
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Fix column list comparison in transmit format
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As noticed in #1718 the cache might not reload the cache files when new columns were added. This is because of an issue in `transmit_format` where the column list comparison fails because the order was not deterministic. This causes the `transmit_format` to apply an unnecessary `set_format` transform with shuffled column names. I fixed that by sorting the columns for the comparison and added a test. To properly test that I added a third column `col_3` to the dummy_dataset used for tests.
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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" ]
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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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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." ]
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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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Add Hatexplain Dataset
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Adding Hatexplain - the first benchmark hate speech dataset covering multiple aspects of the issue
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add Korean intonation-aided intention identification dataset
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Adding adversarialQA dataset
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[ "Oh that's a really cool one, we'll review/merge it soon!\r\n\r\nIn the meantime, do you have any specific positive/negative feedback on the process of adding a datasets Max?\r\nDid you follow the instruction in the [detailed step-by-step](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)?", "Thanks Thom, been a while, hope all is well!\r\n\r\nYes, I followed the step by step instructions and found them pretty straightforward. The only things I wasn't sure of were what should go into the YAML tags field for the dataset card, and whether there was a list of options somewhere (maybe akin to the metrics?) of the possible supported tasks. I found the rest very intuitive and the automated metadata and dummy data generation very handy. Thanks!", "Good point! pinging @yjernite here so he can improve this part!", "@maxbartolo cool addition!\r\n\r\nFor the YAML tag, you should use the tagging app we provide to choose from a drop-down menu:\r\nhttps://github.com/huggingface/datasets-tagging\r\n\r\nThe process is described toward the end of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card), do you have any suggestions for making it easier to find?\r\n\r\nOtherwise, the dataset card is really cool, thanks for making it so complete!\r\n", "@yjernite\r\n\r\nThanks, YAML tags added. I think my main issue was with the flow of the [step-by-step guide](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). For example, the [card creator](https://huggingface.co/datasets/card-creator/) is introduced in Step 4, right after creating an empty directory for your dataset. The first field it requires are the YAML tags, which (at least for me) was the last step of the process.\r\n\r\nI'd suggest having the guide structured in the same order as the creation process. For me it was something like:\r\n- Step 1: Preparing your env\r\n- Step 2: Write the loading/processing code\r\n- Step 3: Automatically generate dummy data and `dataset_infos.json`\r\n- Step 4: Tag the dataset\r\n- Step 5: Write the dataset card using the [card creator](https://huggingface.co/datasets/card-creator/)\r\n- Step 6: Open a Pull Request on the main HuggingFace repo and share your work!!\r\n\r\nThanks again!" ]
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Adding the adversarialQA dataset (https://adversarialqa.github.io/) from Beat the AI (https://arxiv.org/abs/2002.00293)
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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```" ]
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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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[ "When should we expect to see our dataset appear in the search dropdown at huggingface.co?", "Hi @eusip,\r\n\r\n> When should we expect to see our dataset appear in the search dropdown at huggingface.co?\r\n\r\nwhen this PR is merged.", "Thanks!", "I've implemented all the changes requested by @lhoestq but I made the mistake of trying to change the remote branch name. \r\n\r\nHopefully the changes are seen on your end as both branches `silicone` and `main` should be up-to-date.", "It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)\r\n\r\nMaybe you can try to create another branch and another PR ?", "> It looks like the PR includes changes about many other files than the ones for Silicone (+30,000 line changes)\r\n> \r\n> Maybe you can try to create another branch and another PR ?\r\n\r\nSure. I will make a new pull request." ]
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My collaborators and I within the Affective Computing team at Telecom Paris would like to push our spoken dialogue dataset for publication.
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Fix windows path scheme in cached path
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As noticed in #807 there's currently an issue with `cached_path` not raising `FileNotFoundError` on windows for absolute paths. This is due to the way we check for a path to be local or not. The check on the scheme using urlparse was incomplete. I fixed this and added tests
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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." ]
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**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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## 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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## 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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Added generated READMEs for datasets that were missing one.
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[ "Looks like we need to trim the ones with too many configs, will look into it tomorrow!" ]
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This is it: we worked on a generator with Yacine @yjernite , and we generated dataset cards for all missing ones (161), with all the information we could gather from datasets repository, and using dummy_data to generate examples when possible. Code is available here for the moment: https://github.com/madlag/datasets_readme_generator . We will move it to a Hugging Face repository and to https://huggingface.co/datasets/card-creator/ later.
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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```" ]
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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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Related to #215. Missing improvements from @lhoestq's #1703.
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Update XSUM Factuality DatasetCard
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Improvements regarding caching and fingerprinting
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[ "I few comments here for discussion:\r\n- I'm not convinced yet the end user should really have to understand the difference between \"caching\" and 'fingerprinting\", what do you think? I think fingerprinting should probably stay as an internal thing. Is there a case where we want cahing without fingerprinting or vice-versa?\r\n- while I think the random fingerprint mechanism is smart, I have one question: when we disable caching or fingerprinting we also probably don't want the disk usage to grow so we should then try to keep only one cache file. Is it the case currently?\r\n- the warning should be emitted only once per session if possible (we have a mechanism to do that in transformers, you should ask Lysandre/Sylvain)\r\n\r\n", "About your points:\r\n- Yes I agree, I just wanted to bring the discussion on this point. Until now fingerprinting hasn't been blocking for user experience. I'll probably remove the enable/disable fingerprinting function to keep things simple from the user's perspective.\r\n- Right now every time a not in-place transform (i.e. map, filter) is applied, a new cache file is created. It is the case even if caching is disabled since disabling it only means that the cache file won't be reloaded. Therefore you're right that it might end up filling the disk with files that won't be reused. I like the idea of keeping only one cache file. Currently all the cache files are kept on disk until the user clears the cache. To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example\r\n```python\r\n# case 1 - keep both cache files (dataset1 and dataset2)\r\ndataset2 = dataset1.map(...)\r\n# case 2 - keep only the new cache file\r\ndataset1 = dataset1.map(...)\r\n```\r\nIn python it doesn't seem trivial to detect such changes. One thing that we can actually do on the other hand is store the cache files in a temporary directory that is cleared when the session closes. I think that's a good a simple solution for this problem.\r\n- Yes good idea ! I don't like spam either :) ", "> * To be able to keep only one, we need to know if a dataset that has been transformed is still loaded or not. For example\r\n> \r\n> ```python\r\n> # case 1 - keep both cache files (dataset1 and dataset2)\r\n> dataset2 = dataset1.map(...)\r\n> # case 2 - keep only the new cache file\r\n> dataset1 = dataset1.map(...)\r\n> ```\r\n\r\nI see what you mean. It's a tricky question. One option would be that if caching is deactivated we have a single memory mapped file and have copy act as a copy by reference instead of a copy by value. We will then probably want a `copy()` or `deepcopy()` functionality. Maybe we should think a little bit about it though.", "- I like the idea of using a temporary directory per session!\r\n- If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.\r\n- So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?", "> * If the default behavior when caching is disabled is to re-use the same file, I'm a little worried about people making mistakes and having to re-download and process from scratch.\r\n\r\nWe should distinguish between the caching from load_dataset (base dataset cache files) and the caching after dataset transforms such as map or filter (transformed dataset cache files). When disabling caching only the second type (for map and filter) doesn't reload from cache files.\r\nTherefore nothing is re-downloaded. To re-download the dataset entirely the argument `download_mode=\"force_redownload\"` must be used in `load_dataset`.\r\nDo we have to think more about the naming to make things less confusing in your opinion ?\r\n\r\n> * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?\r\n\r\nThere's no such `in_place` parameter in map, what do you mean exactly ?", "I updated the PR:\r\n- I removed the enable/disable fingerprinting function\r\n- if caching is disabled arrow files are written in a temporary directory that is deleted when session closes\r\n- the warning that is showed when hashing a transform fails is only showed once\r\n- I added the `set_caching_enabled` function to the docs and explained the caching mechanism and its relation with fingerprinting\r\n\r\nI would love to have some feedback :) ", "> > * So we already have a keyword argument for `dataset1 = dataset1.map(..., in_place=True)`?\r\n> \r\n> There's no such `in_place` parameter in map, what do you mean exactly ?\r\n\r\nSorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:\r\n\r\n```\r\n# case 1 - keep both cache files (dataset1 and dataset2)\r\ndataset2 = dataset1.map(...)\r\n# case 2 - keep only the new cache file\r\ndataset1 = dataset1.map(..., in_place=True)\r\n```\r\n\r\nCase 1 returns a new reference using the new cache file, case 2 returns the same reference", "> Sorry, that wasn't clear at all. I was responding to your previous comment about case 1 / case 2. I don't think the behavior should depend on the command, but we could have:\r\n> \r\n> ```\r\n> # case 1 - keep both cache files (dataset1 and dataset2)\r\n> dataset2 = dataset1.map(...)\r\n> # case 2 - keep only the new cache file\r\n> dataset1 = dataset1.map(..., in_place=True)\r\n> ```\r\n> \r\n> Case 1 returns a new reference using the new cache file, case 2 returns the same reference\r\n\r\nOk I see !\r\n`in_place` is a parameter that is used in general to designate a transform so I would name that differently (maybe `overwrite` or something like that).\r\nNot sure if it's possible to update an already existing arrow file that is memory-mapped, let me check real quick.\r\nAlso it's possible to call `dataset2.cleanup_cache_files()` to delete the other cache files if we create a new one after the transform. Or even to get the cache file with `dataset1.cache_files` and let the user remove them by hand.\r\n\r\nEDIT: updating an arrow file in place is not part of the current API of pyarrow, so we would have to make new files.\r\n" ]
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This PR adds these features: - Enable/disable caching If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. It is equivalent to setting `load_from_cache` to `False` in dataset transforms. ```python from datasets import set_caching_enabled set_caching_enabled(False) ``` - Allow unpicklable functions in `map` If an unpicklable function is used, then it's not possible to hash it to update the dataset fingerprint that is used to name cache files. To workaround that, a random fingerprint is generated instead and a warning is raised. ```python logger.warning( f"Transform {transform} couldn't be hashed properly, a random hash was used instead. " "Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. " "If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything." ) ``` and also (open to discussion, EDIT: actually NOT included): - Enable/disable fingerprinting Fingerprinting allows to have one deterministic fingerprint per dataset state. A dataset fingerprint is updated after each transform. Re-running the same transforms on a dataset in a different session results in the same fingerprint. Disabling the fingerprinting mechanism makes all the fingerprints random. Since the caching mechanism uses fingerprints to name the cache files, then cache file names will be different. Therefore disabling fingerprinting will prevent the caching mechanism from reloading datasets files that have already been computed. Disabling fingerprinting may speed up the lib for users that don't care about this feature and don't want to use caching. ```python from datasets import set_fingerprinting_enabled set_fingerprinting_enabled(False) ``` Other details: - I renamed the `fingerprint` decorator to `fingerprint_transform` since the name was clearly not explicit. This decorator is used on dataset transform functions to allow them to update fingerprints. - I added some `ignore_kwargs` when decorating transforms with `fingerprint_transform`, to make the fingerprint update not sensible to kwargs like `load_from_cache` or `cache_file_name`. Todo: tests for set_fingerprinting_enabled + documentation for all the above features
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Fix importlib metdata import in py38
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In Python 3.8 there's no need to install `importlib_metadata` since it already exists as `importlib.metadata` in the standard lib.
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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" ]
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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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Update Curiosity dialogs DatasetCard
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Update Curiosity dialogs DatasetCard There are some entries in the data fields section yet to be filled. There is little information regarding those fields.
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Update DBRD dataset card and download URL
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[ "not sure why the CI was not triggered though" ]
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I've added the Dutch Bood Review Dataset (DBRD) during the recent sprint. This pull request makes two minor changes: 1. I'm changing the download URL from Google Drive to the dataset's GitHub release package. This is now possible because of PR #1316. 2. I've updated the dataset card. Cheers! 😄
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Update Coached Conv Pref DatasetCard
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[ "Really cool!\r\n\r\nCan you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)?" ]
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Update Coached Conversation Preferance DatasetCard
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Update DialogRE DatasetCard
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[ "Same as #1698, can you add a task tag for dialogue-modeling (under sequence-modeling) :) ?" ]
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Update the information in the dataset card for the Dialog RE dataset.
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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" ]
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** 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 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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) ^ 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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 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fix ner_tag bugs in thainer
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[ "> Thanks :)\r\n> \r\n> Apparently the dummy_data.zip got removed. Is this expected ?\r\n> Also can you remove the `data-pos.conll` file that you added ?\r\n\r\nNot expected. I forgot to remove the `dummy_data` folder used to create `dummy_data.zip`. \r\nChanged to only `dummy_data.zip`." ]
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fix bug that results in `ner_tag` always equal to 'O'.
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Add OSCAR
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[ "Hi @lhoestq, on the OSCAR dataset, the document boundaries are defined by an empty line. Are there any chances to keep this empty line or explicitly group the sentences of a document? I'm asking for this 'cause I need to know if some sentences belong to the same document on my current OSCAR dataset usage.", "Indeed currently it yields one example per line and ignore the empty lines.\r\nMaybe the best is to group them by paragraph then, and yield one example when an empty line is found.\r\nWhat do you think ?", "I think to group them is the best choice indeed, I actually did this on [brwac](https://github.com/huggingface/datasets/tree/master/datasets/brwac) dataset too, it's another huge textual dataset.", "Ok I just launched the computation of the dataset_infos.json again by grouping lines in paragraphs.\r\nThe new _generate_examples is\r\n```python\r\n def _generate_examples(self, filepaths):\r\n \"\"\"This function returns the examples in the raw (text) form.\"\"\"\r\n id_ = 0\r\n current_lines = []\r\n for filepath in filepaths:\r\n logging.info(\"generating examples from = %s\", filepath)\r\n with gzip.open(filepath, \"rt\") as f:\r\n for line in f:\r\n if len(line.strip()) > 0:\r\n current_lines.append(line)\r\n else:\r\n feature = id_, {\"id\": id_, \"text\": \"\".join(current_lines)}\r\n yield feature\r\n id_ += 1\r\n current_lines = []\r\n # last paragraph\r\n if current_lines:\r\n feature = id_, {\"id\": id_, \"text\": \"\".join(current_lines)}\r\n yield feature\r\n```", "Is there any chance to also keep the sentences raw (without the `\"\".join()`)?. This is useful if you wanna train models where one of the tasks you perform is document sentence permutation... that's my case :)", "They are raw in the sense that nothing is changed from the raw file for each paragraph.\r\nYou can split sentences on new lines `\\n` for example.\r\n\r\nThe first example for the unshuffled deduplicated english is going to be \r\n> Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visit to Malawi. Chief Napoleon conveyed the desperate need for a program to intervene and care for the orphans and vulnerable children (OVC) in Malawi, and John committed to help.\r\n> Established in honor of John & Lindy’s son, Christopher Blanchard, this particular program is very dear to the Blanchard family. Dana Blanchard, or Mama Dana as she is more commonly referred to at Mtendere, lived on site during the initial development, and she returns each summer to spend the season with her Malawian family. The heart of the program is to be His hands and feet by caring for the children at Mtendere, and meeting their spiritual, physical, academic, and emotional needs.\r\n> [...]\r\n> 100X Development Foundation, Inc. is registered 503 (c)(3) nonprofit organization. Donations are deductable to the full extent allowable under IRS regulations.", "I thought the line reader would omit the `\\n` character. I can easily split the sentences as you suggested. Thanks @lhoestq! 😃 ", "The recomputation of the metadata finished a few days ago, I'll update the PR soon :) ", "Let me know if you have comments @pjox @jonatasgrosman :) \r\n\r\nOtherwise we can merge it", "Everything seems fine to me 😄 " ]
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Continuation of #348 The files have been moved to S3 and only the unshuffled version is available. Both original and deduplicated versions of each language are available. Example of usage: ```python from datasets import load_dataset oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train") oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train") ``` cc @pjox @jonatasgrosman ------------- To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation. ``` datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0 ``` ------------- ToDo: add the dummy_data
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Fix reuters metadata parsing errors
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Was missing the last entry in each metadata category
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Updated HuggingFace Datasets README (fix typos)
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Awesome work on 🤗 Datasets. I found a couple of small typos in the README. Hope this helps. ![](https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/160/google/56/hugging-face_1f917.png)
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Fast start up
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Currently if optional dependencies such as tensorflow, torch, apache_beam, faiss and elasticsearch are installed, then it takes a long time to do `import datasets` since it imports all of these heavy dependencies. To make a fast start up for `datasets` I changed that so that they are not imported when `datasets` is being imported. On my side it changed the import time of `datasets` from 5sec to 0.5sec, which is enjoyable. To be able to check if optional dependencies are available without importing them I'm using `importlib_metadata`, which is part of the standard lib in python>=3.8 and was backported. The difference with `importlib` is that it also enables to get the versions of the libraries without importing them. I added this dependency in `setup.py`.
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Fix ade_corpus_v2 config names
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There are currently some typos in the config names of the `ade_corpus_v2` dataset, I fixed them: - Ade_corpos_v2_classificaion -> Ade_corpus_v2_classification - Ade_corpos_v2_drug_ade_relation -> Ade_corpus_v2_drug_ade_relation - Ade_corpos_v2_drug_dosage_relation -> Ade_corpus_v2_drug_dosage_relation
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Fix DaNE last example
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The last example from the DaNE dataset is empty. Fix #1686
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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). " ]
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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 :) " ]
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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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Update README.md of covid-tweets-japanese
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[ "Thanks for reviewing and merging!" ]
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Update README.md of covid-tweets-japanese added by PR https://github.com/huggingface/datasets/pull/1367 and https://github.com/huggingface/datasets/pull/1402. - Update "Data Splits" to be more precise that no information is provided for now. - old: [More Information Needed] - new: No information about data splits is provided for now. - The automatic generation of links seemed not working properly, so I added a space before and after the URL to make the links work correctly.
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Add CANER Corpus
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What does this PR do? Adds the following dataset: https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus Who can review? @lhoestq
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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" ]
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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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Don't use xlrd for xlsx files
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Since the latest release of `xlrd` (2.0), the support for xlsx files stopped. Therefore we needed to use something else. A good alternative is `openpyxl` which has also an integration with pandas si we can still call `pd.read_excel`. I left the unused import of `openpyxl` in the dataset scripts to show users that this is a required dependency to use the scripts. I tested the different datasets using `datasets-cli test` and the tests are successful (no missing examples).
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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." ]
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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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added TurkishProductReviews dataset
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[ "@lhoestq, can you please review this PR?", "Thanks for the suggestions. Updates were made and dataset_infos.json file was created again." ]
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This PR added **Turkish Product Reviews Dataset contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews**. - **Repository:** [turkish-text-data](https://github.com/fthbrmnby/turkish-text-data) - **Point of Contact:** Fatih Barmanbay - @fthbrmnby
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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```" ]
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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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Switchboard Dialog Act Corpus added under `datasets/swda`
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[ "@lhoestq Thank you for your detailed comments! I fixed everything you suggested.\r\n\r\nPlease let me know if I'm missing anything else.", "It looks like the Transcript and Utterance objects are missing, maybe we can mention it in the README ? Or just add them ? @gmihaila @bhavitvyamalik ", "Hi @lhoestq,\r\nI'm working on this to add the full dataset", "> It looks like the Transcript and Utterance objects are missing, maybe we can mention it in the README ? Or just add them ? @gmihaila @bhavitvyamalik\r\n\r\n@lhoestq Any info on how to add them?", "@gmihaila, instead of using the current repo you should look into [this](https://github.com/cgpotts/swda). You can use the `csv` files uploaded in this repo (`swda.zip`) to access other fields and include them in this dataset. It has one dependency too, `swda.py`, you can download that separately and include it in your dataset's folder to be imported while reading the `csv` files.\r\n\r\nAlmost all the attributes of `Transcript` and `Utterance` objects are of the type str, int, or list. As far as `trees` attribute is concerned in utterance object you can simply parse it as string and user can maybe later convert it to nltk.tree object", "@bhavitvyamalik Thank you for the clarification! \r\n\r\nI didn't use [that](https://github.com/cgpotts/swda) because it doesn't have the splits. I think in combination with [what I used](https://github.com/NathanDuran/Switchboard-Corpus) would help.\r\n\r\nLet me know if I can help! I can make those changes if you don't have the time.", "I'm a bit busy for the next 2 weeks. I'll be able to complete it by end of January only. Maybe you can start with it and I'll help you?\r\nAlso, I looked into the official train/val/test splits and not all the files are there in the repo I used so I think either we'll have to skip them or put all of that into just train", "Yes, I can start working on it and ask you to do a code review.\r\n\r\nYes, not all files are there. I'll try to find papers that have the correct and full splits, if not, I'll do like you suggested.\r\n\r\nThank you again for your help @bhavitvyamalik !" ]
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Switchboard Dialog Act Corpus Intro: The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2, with turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s. Details: [homepage](http://compprag.christopherpotts.net/swda.html) [repo](https://github.com/NathanDuran/Switchboard-Corpus/raw/master/swda_data/) I believe this is an important dataset to have since there is no dataset related to dialogue act added. I didn't find any formatting for pull request. I hope all this information is enough. For any support please contact me.
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Switchboard Dialog Act Corpus added under `datasets/swda`
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Pleased to announced that I added my first dataset **Switchboard Dialog Act Corpus**. I think this is an important datasets to be added since it is the only one related to dialogue act classification. Hope the pull request is ok. Wasn't able to see any special formatting for the pull request form. The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2, with turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s. [webpage](http://compprag.christopherpotts.net/swda.html) [repo](https://github.com/NathanDuran/Switchboard-Corpus/raw/master/swda_data/) Please contact me for any support! All tests passed and followed all steps in the contribution guide!
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new version of Ted Talks IWSLT (WIT3)
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[ "> Nice thank you ! Actually as it is a translation dataset we should probably have one configuration = one language pair no ?\r\n> \r\n> Could you use the same trick for this dataset ?\r\n\r\nI was looking for this input, infact I had written a long post on the Slack channel,...(_but unfortunately due to the holidays didn;t get a respones_). Initially I had tried with language pairs and then with specific language configs. \r\n\r\nI'll have a look at the `opus-gnomes` dataset\r\n", "Oh sorry I must have missed your message then :/\r\nI was off a few days during the holidays\r\n\r\nHopefully this trick can enable the use of any language pair (+ year ?) combination and also simplify a lot the dummy data creation since it will only require a few configs.", "Updated it as per the comments. But couldn't figure out why the dummy tests are failing >> \r\n```\r\n$RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_ted_talks_iwslt\r\n.....\r\n....\r\ntests/test_dataset_common.py:198: in check_load_dataset\r\n self.parent.assertTrue(len(dataset[split]) > 0)\r\nE AssertionError: False is not true\r\n```" ]
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In the previous iteration #1608 I had used language pairs. Which created 21,582 configs (109*108) !!! Now, TED talks in _each language_ is a separate config. So it's more cleaner with _just 109 configs_ (one for each language). Dummy files were created manually. Locally I was able to clear the `python datasets-cli test datasets/......` . Which created the `dataset_info.json` file . The test for the dummy files was also cleared. However couldn't figure out how to specify the local data folder for the real dataset **Note: that this requires manual download of the dataset.** **Note2: The high number of _Files changed (112)_ is because of the large number of dummy files/configs!**
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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) :)" ]
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## 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." ]
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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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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![Error](https://user-images.githubusercontent.com/30871963/103479005-69f3b080-4df0-11eb-83ae-58d7bb56a90e.png)\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" ]
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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. ![Code](https://user-images.githubusercontent.com/30871963/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png) ![Error](https://user-images.githubusercontent.com/30871963/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)
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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```" ]
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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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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" ]
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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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[ "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 :) " ]
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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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wiki_dpr dataset pre-processesing performance
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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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xed_en_fi dataset Cleanup
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Fix ClassLabel feature type and minor mistakes in the dataset card
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Fix NER metric example in Overview notebook
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Fix errors in `NER metric example` section in `Overview.ipynb`. ``` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-37-ee559b166e25> in <module>() ----> 1 ner_metric = load_metric('seqeval') 2 references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 3 predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 4 ner_metric.compute(predictions, references) /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) 340 if needs_to_be_installed: 341 raise ImportError( --> 342 f"To be able to use this {module_type}, you need to install the following dependencies" 343 f"{[lib_name for lib_name, lib_path in needs_to_be_installed]} using 'pip install " 344 f"{' '.join([lib_path for lib_name, lib_path in needs_to_be_installed])}' for instance'" ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' ``` ``` ValueError Traceback (most recent call last) <ipython-input-39-ee559b166e25> in <module>() 2 references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] 3 predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] ----> 4 ner_metric.compute(predictions, references) /usr/local/lib/python3.6/dist-packages/datasets/metric.py in compute(self, *args, **kwargs) 378 """ 379 if args: --> 380 raise ValueError("Please call `compute` using keyword arguments.") 381 382 predictions = kwargs.pop("predictions", None) ValueError: Please call `compute` using keyword arguments. ```
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Add language to dataset card for Makhzan dataset.
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Add language to dataset card.
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Add language to dataset card for Counter dataset.
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Add language.
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removed \n in labels
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updated social_i_qa labels as per #1633
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update saving and loading methods for faiss index so to accept path l…
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[ "Seems ok for me, what do you think @lhoestq ?" ]
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- Update saving and loading methods for faiss index so to accept path like objects from pathlib The current code only supports using a string type to save and load a faiss index. This change makes it possible to use a string type OR a Path from [pathlib](https://docs.python.org/3/library/pathlib.html). The codes becomes a more intuitive this way I think.
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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." ]
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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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updated dataset cards
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added dataset instance in the card.
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