(empty)Fix get_patterns_in_dataset_repository error

README.md

@@ -3,9 +3,9 @@ annotations_creators: []
 language_creators:
 - crowdsourced
 - expert-generated
-language: ["code"]
-license:
-- mit
+languages: ["code"]
+licenses:
+- other-multiple
 multilinguality:
 - multilingual
 pretty_name: code-clippy-github-code
@@ -173,4 +173,4 @@ The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org
 - The query was executed on _February 1, 2022, 12:15:59 AM EST_
 
 ## Acknowledgements
-This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the [codeparrot team at Hugging face](https://huggingface.co/codeparrot) for open sourcing their documentation on [github-code](https://huggingface.co/datasets/codeparrot/github-code) which we used for the readme in this dataset. For another similar dataset to this please check github-code!
+This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs.

code_clippy_github.py

@@ -20,7 +20,6 @@ import os
 
 
 import datasets
-from datasets.download.streaming_download_manager import xopen
 from huggingface_hub import HfApi, HfFolder
 from datasets.data_files import DataFilesDict
 
@@ -157,12 +156,19 @@ class CodeClippyGithub(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        num_shards = 50_000
-        data_files = [
-            f"github-dedup-{_index:012d}.json.gz"
-            for _index in range(num_shards)
-        ]
-        files = dl_manager.download(data_files)
+        
+        hfh_dataset_info = HfApi(datasets.config.HF_ENDPOINT).dataset_info(
+            _REPO_NAME,
+            timeout=100.0,
+        )
+
+        patterns = datasets.data_files.get_patterns_in_dataset_repository(hfh_dataset_info)
+        data_files = datasets.data_files.DataFilesDict.from_hf_repo(
+            patterns,
+            dataset_info=hfh_dataset_info,
+        )
+
+        files = dl_manager.download_and_extract(data_files["train"])
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -175,31 +181,30 @@ class CodeClippyGithub(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, files):
         key = 0
         for file_idx, file in enumerate(files):
-            with xopen(file, "rb") as file:  # download file if in streaming mode
-                with gzip.open(file, "rb") as f:
-
-                    uncompressed_data = f.readlines()
-
-                    for batch_idx, code_base in enumerate(uncompressed_data):
-                        j_dict = json.loads(code_base.decode('utf-8'))
-
-
-
-                        lang = lang_from_name(j_dict['path'])
-                        license = j_dict["license"]
-
-                        if self.config.filter_languages and not lang in self.config.languages:
-                            continue
-                        if self.config.filter_licenses and not license in self.config.licenses:
-                            continue
-                        # TODO: Add more features like header comments, filename, and other features useful in a prompt.
-                        yield key, {"code_text": j_dict['content'],
-                                    "repo_name": j_dict['repo_name'],
-                                    "file_path": j_dict['path'],
-                                    "license": license,
-                                    "language": lang,
-                                    "size": int(j_dict['f0_'])}
-                        key += 1
+            with gzip.open(file, "rb") as f:
+                
+                uncompressed_data = f.readlines()
+                
+                for batch_idx, code_base in enumerate(uncompressed_data):
+                    j_dict = json.loads(code_base.decode('utf-8'))
+                   
+                   
+                        
+                    lang = lang_from_name(j_dict['path'])
+                    license = j_dict["license"]
+                    
+                    if self.config.filter_languages and not lang in self.config.languages:
+                        continue
+                    if self.config.filter_licenses and not license in self.config.licenses:
+                        continue
+                    # TODO: Add more features like header comments, filename, and other features useful in a prompt.
+                    yield key, {"code_text": j_dict['content'],
+                                "repo_name": j_dict['repo_name'],
+                                "file_path": j_dict['path'],
+                                "license": license,
+                                "language": lang,
+                                "size": int(j_dict['f0_'])}    
+                    key += 1
 
                         
 def lang_from_name(name):