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bird_sql_database.py

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+import datasets
+import pandas as pd
+import json
+
+_DESCRIPTION = """
+BIRD SQL Dataset with complete database content.
+Total rows: 366,787,649
+Total chunks: 3660
+"""
+
+class BirdSQLDatabase(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="database_content", description="Complete database content"),
+        datasets.BuilderConfig(name="table_metadata", description="Table metadata"),
+    ]
+    
+    DEFAULT_CONFIG_NAME = "database_content"
+    
+    def _info(self):
+        if self.config.name == "database_content":
+            features = datasets.Features({
+                "db_id": datasets.Value("string"),
+                "table_name": datasets.Value("string"), 
+                "row_index": datasets.Value("int64"),
+                "row_data": datasets.Value("string"),
+                "split": datasets.Value("string")
+            })
+        else:  # table_metadata
+            features = datasets.Features({
+                "db_id": datasets.Value("string"),
+                "table_name": datasets.Value("string"),
+                "columns": datasets.Value("string"),
+                "column_types": datasets.Value("string"),
+                "primary_keys": datasets.Value("string"),
+                "total_rows": datasets.Value("int64"),
+                "split": datasets.Value("string")
+            })
+        
+        return datasets.DatasetInfo(description=_DESCRIPTION, features=features)
+    
+    def _split_generators(self, dl_manager):
+        if self.config.name == "database_content":
+            manifest_file = dl_manager.download("database_content_manifest.json")
+            with open(manifest_file) as f:
+                manifest = json.load(f)
+            
+            chunk_files = []
+            for chunk_name in manifest["files"]:
+                chunk_file = dl_manager.download(chunk_name)
+                chunk_files.append(chunk_file)
+            
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"chunk_files": chunk_files})]
+        else:
+            metadata_file = dl_manager.download("table_metadata.parquet")
+            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": metadata_file})]
+    
+    def _generate_examples(self, filepath=None, chunk_files=None):
+        if self.config.name == "database_content":
+            idx = 0
+            for chunk_file in chunk_files:
+                df = pd.read_parquet(chunk_file)
+                for _, row in df.iterrows():
+                    yield idx, {
+                        "db_id": row["db_id"],
+                        "table_name": row["table_name"],
+                        "row_index": row["row_index"],
+                        "row_data": row["row_data"],
+                        "split": row["split"]
+                    }
+                    idx += 1
+        else:
+            df = pd.read_parquet(filepath)
+            for idx, row in df.iterrows():
+                yield idx, {
+                    "db_id": row["db_id"],
+                    "table_name": row["table_name"],
+                    "columns": row["columns"],
+                    "column_types": row["column_types"],
+                    "primary_keys": row["primary_keys"],
+                    "total_rows": row["total_rows"],
+                    "split": row["split"]
+                }