Update coco.py

coco.py

@@ -21,10 +21,6 @@ import os
 
 import datasets
 
-_URL = "dataset_coco.json"
-_URLS = {
-    "train": _URL
-}
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -52,9 +48,9 @@ _LICENSE = ""
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "dataset_coco.json"
 _URLS = {
-    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+    "train": _URL
 }
 
 
@@ -76,15 +72,15 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers a first domain"),
         datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
     ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "train"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if self.config.name == "train":  # This is the name of the configuration selected in BUILDER_CONFIGS above
             features = datasets.Features(
                 {
                     "images": datasets.Sequence(
@@ -148,113 +144,30 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
 
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls_to_download = self._URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+    
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.jsonl"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "split": "test"
-                },
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }
-                    
-class SuperGlueConfig(datasets.BuilderConfig):
-    """BuilderConfig for SuperGLUE."""
-
-    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
-        """BuilderConfig for SuperGLUE.
-
-        Args:
-        features: *list[string]*, list of the features that will appear in the
-            feature dict. Should not include "label".
-        data_url: *string*, url to download the zip file from.
-        citation: *string*, citation for the data set.
-        url: *string*, url for information about the data set.
-        label_classes: *list[string]*, the list of classes for the label if the
-            label is present as a string. Non-string labels will be cast to either
-            'False' or 'True'.
-        **kwargs: keyword arguments forwarded to super.
-        """
-        # Version history:
-        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
-        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
-        #        the full release (v2.0).
-        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
-        # 0.0.2: Initial version.
-        super(SuperGlueConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs)
-        self.features = features
-        self.label_classes = label_classes
-        self.data_url = data_url
-        self.citation = citation
-        self.url = url 
-        
-class SuperGlue(datasets.GeneratorBasedBuilder):
-    """The SuperGLUE benchmark."""
-
-    BUILDER_CONFIGS = [
-        SuperGlueConfig(
-            name="images",
-            description=_BOOLQ_DESCRIPTION,
-            features=["filepath", "sentids","filename","imgid","split","sentences","cocoid"],
-            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip",
-            citation=_BOOLQ_CITATION,
-            url="https://github.com/google-research-datasets/boolean-questions",
-        ),
-        ...
-        ...
-        SuperGlueConfig(
-            name="datasets",
-            description=_AXG_DESCRIPTION,
-            features=[],
-            label_classes=["entailment", "not_entailment"],
-            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip",
-            citation=_AXG_CITATION,
-            url="https://github.com/rudinger/winogender-schemas",
-        ),
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath) as f:
+            squad = json.load(f)
+            for image in squad["images"]:
+                filepath = image["filepath"]
+                filename = image["filename"]
+                split = image["split"]
+                for sentence in image["sentences"]:
+                    raw = sentence["raw"]
+                    yield id_, {
+                        "filepath": filepath,
+                        "filename": filename,
+                        "split": split,
+                        "raw": raw
+                    }