Update wit.py

wit.py

@@ -1,8 +1,7 @@
 import json
 
 import datasets
-from datasets import Value, Sequence, Features
-
+from datasets import Features, Sequence, Value
 
 _CITATION = """\\n@article{srinivasan2021wit,
   title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
@@ -11,52 +10,52 @@ _CITATION = """\\n@article{srinivasan2021wit,
   year={2021}
 }
 """
-
 _DESCRIPTION = """\\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
  of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
  size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
 """
-
 _HOMEPAGE = "https://github.com/google-research-datasets/wit"
-
 _URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/"
 _URLS = {
-    # TODO - This should be in range(400). Haven't mirrored all the files yet.
-    'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(10)]
+    'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(400)]
 }
 
+
 class Wit(datasets.GeneratorBasedBuilder):
     """WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning"""
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=Features({
-                'b64_bytes': Value('string'),
-                'embedding': Sequence(Value('float64')),
-                'image_url': Value('string'),
-                'metadata_url': Value('string'),
-                'wit_features': Sequence({
-                    "language": Value('string'),
-                    "page_url": Value('string'),
-                    "image_url": Value('string'),
-                    "attribution_passes_lang_id": Value("string"),
-                    "caption_alt_text_description": Value('string'),
-                    "caption_attribution_description": Value('string'),
-                    "caption_reference_description": Value('string'),
-                    "caption_title_and_reference_description": Value('string'),
-                    "context_page_description": Value('string'),
-                    "context_section_description": Value('string'),
-                    "hierarchical_section_title": Value('string'),
-                    "is_main_image": Value('string'),
-                    "mime_type": Value('string'),
-                    "original_height": Value('string'),
-                    "original_width": Value('string'),
-                    "page_changed_recently": Value('string'),
-                    "page_title": Value('string'),
-                    "section_title": Value('string'),
-                })
-            }),
+            features=Features(
+                {
+                    'b64_bytes': Value('string'),
+                    'embedding': Sequence(Value('float64')),
+                    'image_url': Value('string'),
+                    'metadata_url': Value('string'),
+                    'original_height': Value('int32'),
+                    'original_width': Value('int32'),
+                    'mime_type': Value('string'),
+                    'caption_attribution_description': Value('string'),
+                    'wit_features': Sequence(
+                        {
+                            "language": Value('string'),
+                            "page_url": Value('string'),
+                            "attribution_passes_lang_id": Value("string"),
+                            "caption_alt_text_description": Value('string'),
+                            "caption_reference_description": Value('string'),
+                            "caption_title_and_reference_description": Value('string'),
+                            "context_page_description": Value('string'),
+                            "context_section_description": Value('string'),
+                            "hierarchical_section_title": Value('string'),
+                            "is_main_image": Value('string'),
+                            "page_changed_recently": Value('string'),
+                            "page_title": Value('string'),
+                            "section_title": Value('string'),
+                        }
+                    ),
+                }
+            ),
             homepage=_HOMEPAGE,
             citation=_CITATION,
         )
@@ -78,7 +77,22 @@ class Wit(datasets.GeneratorBasedBuilder):
                     line = line.strip()
                     row_data = json.loads(line, encoding='utf-8')
                     for feature in row_data['wit_features']:
+                        
+                        # If a feature is missing from feature dict, add it as None
                         for fname in wit_feature_names:
                             if fname not in feature:
                                 feature[fname] = None
+
+                        # Here we take redundant values from wit_features and add them to row_data to avoid unnecessary duplication
+                        extra_wit_feature_keys = [k for k in feature.keys() if k not in wit_feature_names]
+                        for k in extra_wit_feature_keys:
+                            data = feature.pop(k)
+                            if isinstance(data, list):
+                                data = data[0]
+                            row_data[k] = data
+
+                    # Check row_data now for missing keys, adding None for most, but -1 for int features to avoid failures.
+                    missing_keys = [x for x in self.info.features.keys() if x not in row_data]
+                    for missing_key in missing_keys:
+                        row_data[missing_key] = None if missing_key not in ['original_height', 'original_width'] else -1
                     yield str(i), row_data