import io import itertools from dataclasses import dataclass from typing import Optional import pandas as pd import pyarrow as pa import pyarrow.json as paj import datasets import datasets.config from datasets.table import table_cast from datasets.utils.file_utils import readline logger = datasets.utils.logging.get_logger(__name__) def ujson_dumps(*args, **kwargs): try: return pd.io.json.ujson_dumps(*args, **kwargs) except AttributeError: # Before pandas-2.2.0, ujson_dumps was renamed to dumps: import ujson_dumps as dumps return pd.io.json.dumps(*args, **kwargs) def ujson_loads(*args, **kwargs): try: return pd.io.json.ujson_loads(*args, **kwargs) except AttributeError: # Before pandas-2.2.0, ujson_loads was renamed to loads: import ujson_loads as loads return pd.io.json.loads(*args, **kwargs) def pandas_read_json(path_or_buf, **kwargs): if datasets.config.PANDAS_VERSION.major >= 2: kwargs["dtype_backend"] = "pyarrow" return pd.read_json(path_or_buf, **kwargs) @dataclass class JsonConfig(datasets.BuilderConfig): """BuilderConfig for JSON.""" features: Optional[datasets.Features] = None encoding: str = "utf-8" encoding_errors: Optional[str] = None field: Optional[str] = None use_threads: bool = True # deprecated block_size: Optional[int] = None # deprecated chunksize: int = 10 << 20 # 10MB newlines_in_values: Optional[bool] = None def __post_init__(self): super().__post_init__() class Json(datasets.ArrowBasedBuilder): BUILDER_CONFIG_CLASS = JsonConfig def _info(self): if self.config.block_size is not None: logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead") self.config.chunksize = self.config.block_size if self.config.use_threads is not True: logger.warning( "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." ) if self.config.newlines_in_values is not None: raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported") return datasets.DatasetInfo(features=self.config.features) def _split_generators(self, dl_manager): """We handle string, list and dicts in datafiles""" if not self.config.data_files: raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") dl_manager.download_config.extract_on_the_fly = True data_files = dl_manager.download_and_extract(self.config.data_files) splits = [] for split_name, files in data_files.items(): if isinstance(files, str): files = [files] files = [dl_manager.iter_files(file) for file in files] splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files})) return splits def _cast_table(self, pa_table: pa.Table) -> pa.Table: if self.config.features is not None: # adding missing columns for column_name in set(self.config.features) - set(pa_table.column_names): type = self.config.features.arrow_schema.field(column_name).type pa_table = pa_table.append_column(column_name, pa.array([None] * len(pa_table), type=type)) # more expensive cast to support nested structures with keys in a different order # allows str <-> int/float or str to Audio for example pa_table = table_cast(pa_table, self.config.features.arrow_schema) return pa_table def _generate_tables(self, files): for file_idx, file in enumerate(itertools.chain.from_iterable(files)): # If the file is one json object and if we need to look at the items in one specific field if self.config.field is not None: with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f: dataset = ujson_loads(f.read()) # We keep only the field we are interested in dataset = dataset[self.config.field] df = pandas_read_json(io.StringIO(ujson_dumps(dataset))) if df.columns.tolist() == [0]: df.columns = list(self.config.features) if self.config.features else ["text"] pa_table = pa.Table.from_pandas(df, preserve_index=False) yield file_idx, self._cast_table(pa_table) # If the file has one json object per line else: with open(file, "rb") as f: batch_idx = 0 # Use block_size equal to the chunk size divided by 32 to leverage multithreading # Set a default minimum value of 16kB if the chunk size is really small block_size = max(self.config.chunksize // 32, 16 << 10) encoding_errors = ( self.config.encoding_errors if self.config.encoding_errors is not None else "strict" ) while True: batch = f.read(self.config.chunksize) if not batch: break # Finish current line try: batch += f.readline() except (AttributeError, io.UnsupportedOperation): batch += readline(f) # PyArrow only accepts utf-8 encoded bytes if self.config.encoding != "utf-8": batch = batch.decode(self.config.encoding, errors=encoding_errors).encode("utf-8") try: while True: try: pa_table = paj.read_json( io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) ) break except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e: if ( isinstance(e, pa.ArrowInvalid) and "straddling" not in str(e) or block_size > len(batch) ): raise else: # Increase the block size in case it was too small. # The block size will be reset for the next file. logger.debug( f"Batch of {len(batch)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}." ) block_size *= 2 except pa.ArrowInvalid as e: try: with open( file, encoding=self.config.encoding, errors=self.config.encoding_errors ) as f: df = pandas_read_json(f) except ValueError: logger.error(f"Failed to load JSON from file '{file}' with error {type(e)}: {e}") raise e if df.columns.tolist() == [0]: df.columns = list(self.config.features) if self.config.features else ["text"] try: pa_table = pa.Table.from_pandas(df, preserve_index=False) except pa.ArrowInvalid as e: logger.error( f"Failed to convert pandas DataFrame to Arrow Table from file '{file}' with error {type(e)}: {e}" ) raise ValueError( f"Failed to convert pandas DataFrame to Arrow Table from file {file}." ) from None yield file_idx, self._cast_table(pa_table) break yield (file_idx, batch_idx), self._cast_table(pa_table) batch_idx += 1