text: # coding=utf-8
  import io
  import json
  from dataclasses import dataclass
  from typing import Optional
  
  import pyarrow as pa
  import pyarrow.json as paj
  
  import datasets
  from datasets.utils.file_utils import readline
  
  
  logger = datasets.utils.logging.get_logger(__name__)
  
  
  @dataclass
  class JsonConfig(datasets.BuilderConfig):
      """BuilderConfig for JSON."""
  
      features: Optional[datasets.Features] = 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
  
      @property
      def schema(self):
          return pa.schema(self.features.type) if self.features is not None else None
  
  
  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}")
          data_files = dl_manager.download_and_extract(self.config.data_files)
          if isinstance(data_files, (str, list, tuple)):
              files = data_files
              if isinstance(files, str):
                  files = [files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files})]
          splits = []
          for split_name, files in data_files.items():
              if isinstance(files, str):
                  files = [files]
              splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"files": files}))
          return splits
  
      def _cast_classlabels(self, pa_table: pa.Table) -> pa.Table:
          if self.config.features:
              # Encode column if ClassLabel
              for i, col in enumerate(self.config.features.keys()):
                  if isinstance(self.config.features[col], datasets.ClassLabel):
                      pa_table = pa_table.set_column(
                          i, self.config.schema.field(col), [self.config.features[col].str2int(pa_table[col])]
                      )
              # Cast allows str <-> int/float
              # Before casting, rearrange JSON field names to match passed features schema field names order
              pa_table = pa.Table.from_arrays(
                  [pa_table[name] for name in self.config.features], schema=self.config.schema
              )
          return pa_table
  
      def _generate_tables(self, files):
          for file_idx, file in enumerate(files):
  
              # If the file is one json object and if we need to look at the list of items in one specific field
              if self.config.field is not None:
                  with open(file, encoding="utf-8") as f:
                      dataset = json.load(f)
  
                  # We keep only the field we are interested in
                  dataset = dataset[self.config.field]
  
                  # We accept two format: a list of dicts or a dict of lists
                  if isinstance(dataset, (list, tuple)):
                      mapping = {col: [dataset[i][col] for i in range(len(dataset))] for col in dataset[0].keys()}
                  else:
                      mapping = dataset
                  pa_table = pa.Table.from_pydict(mapping=mapping)
                  yield file_idx, self._cast_classlabels(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)
                      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)
                          try:
                              while True:
                                  try:
                                      pa_table = paj.read_json(
                                          io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                                      )
                                      break
                                  except pa.ArrowInvalid as e:
                                      if "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:
                              logger.error(f"Failed to read file '{file}' with error {type(e)}: {e}")
                              try:
                                  with open(file, encoding="utf-8") as f:
                                      dataset = json.load(f)
                              except json.JSONDecodeError:
                                  raise e
                              raise ValueError(
                                  f"Not able to read records in the JSON file at {file}. "
                                  f"You should probably indicate the field of the JSON file containing your records. "
                                  f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
                                  f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "
                              ) from None
                          # Uncomment for debugging (will print the Arrow table size and elements)
                          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                          yield (file_idx, batch_idx), self._cast_classlabels(pa_table)
                          batch_idx += 1
  