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Running on Zero
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7f9dfed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | from __future__ import annotations
import csv
import json
from dataclasses import dataclass
from importlib import import_module
from pathlib import Path
from typing import Any
@dataclass(frozen=True)
class DatasetPreview:
"""Small local dataset preview result."""
source: str
rows: int
columns: list[str]
samples: list[dict[str, Any]]
def as_table(self) -> list[list[str]]:
table = [
["source", self.source],
["rows", str(self.rows)],
["columns", ", ".join(self.columns)],
]
for index, sample in enumerate(self.samples, start=1):
table.append([f"sample_{index}", json.dumps(sample, ensure_ascii=False)])
return table
@dataclass(frozen=True)
class DatasetStats:
"""Basic dataset statistics for local preview and tools."""
source: str
rows: int
columns: int
column_names: list[str]
non_empty_by_column: dict[str, int]
def as_dict(self) -> dict[str, Any]:
return {
"source": self.source,
"rows": self.rows,
"columns": self.columns,
"column_names": self.column_names,
"non_empty_by_column": self.non_empty_by_column,
}
@dataclass(frozen=True)
class HuggingFaceDatasetPreview:
"""Small preview result for optional Hugging Face datasets integration."""
dataset_id: str
split: str
rows: int
columns: list[str]
samples: list[dict[str, Any]]
status: str = "loaded"
def as_table(self) -> list[list[str]]:
table = [
["dataset_id", self.dataset_id],
["split", self.split],
["rows", str(self.rows)],
["columns", ", ".join(self.columns)],
["status", self.status],
]
for index, sample in enumerate(self.samples, start=1):
table.append([f"sample_{index}", json.dumps(sample, ensure_ascii=False)])
return table
def preview_local_dataset(path: str | Path, limit: int = 5) -> DatasetPreview:
dataset_path = Path(path)
if not dataset_path.exists():
raise FileNotFoundError(f"Dataset path not found: {dataset_path}")
if dataset_path.suffix.lower() == ".csv":
return _preview_csv(dataset_path, limit)
if dataset_path.suffix.lower() in {".jsonl", ".ndjson"}:
return _preview_jsonl(dataset_path, limit)
raise ValueError(f"Unsupported dataset format: {dataset_path.suffix}")
def dataset_statistics(path: str | Path) -> DatasetStats:
preview = preview_local_dataset(path, limit=0)
non_empty_by_column = {column: 0 for column in preview.columns}
dataset_path = Path(path)
if dataset_path.suffix.lower() == ".csv":
with dataset_path.open(newline="", encoding="utf-8") as f:
for row in csv.DictReader(f):
for column in preview.columns:
if str(row.get(column, "")).strip():
non_empty_by_column[column] += 1
else:
with dataset_path.open(encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
row = json.loads(line)
for column in preview.columns:
if str(row.get(column, "")).strip():
non_empty_by_column[column] += 1
return DatasetStats(
source=preview.source,
rows=preview.rows,
columns=len(preview.columns),
column_names=preview.columns,
non_empty_by_column=non_empty_by_column,
)
def preview_huggingface_dataset(
dataset_id: str,
split: str = "train",
limit: int = 5,
) -> HuggingFaceDatasetPreview:
datasets_module = import_module("datasets")
load_dataset = getattr(datasets_module, "load_dataset", None)
if load_dataset is None:
return HuggingFaceDatasetPreview(
dataset_id=dataset_id,
split=split,
rows=0,
columns=[],
samples=[],
status="Hugging Face package 'datasets' is not installed.",
)
dataset = load_dataset(dataset_id, split=split)
rows = len(dataset)
columns = list(dataset.column_names)
samples = [dict(dataset[index]) for index in range(min(limit, rows))]
return HuggingFaceDatasetPreview(dataset_id, split, rows, columns, samples)
def _preview_csv(path: Path, limit: int) -> DatasetPreview:
samples: list[dict[str, Any]] = []
rows = 0
columns: list[str] = []
with path.open(newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
columns = list(reader.fieldnames or [])
for row in reader:
rows += 1
if len(samples) < limit:
samples.append(dict(row))
return DatasetPreview(str(path), rows, columns, samples)
def _preview_jsonl(path: Path, limit: int) -> DatasetPreview:
samples: list[dict[str, Any]] = []
rows = 0
columns: set[str] = set()
with path.open(encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
rows += 1
item = json.loads(line)
if not isinstance(item, dict):
raise ValueError("JSONL rows must be objects")
columns.update(item)
if len(samples) < limit:
samples.append(item)
return DatasetPreview(str(path), rows, sorted(columns), samples)
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