

UpperCamelCase__  :      str			=    [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
				SCREAMING_SNAKE_CASE_									=						0
				SCREAMING_SNAKE_CASE_									=						0
				while place < len(__A       ):
								if (place + 1 < len(__A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						[]
				for arabic, roman in ROMAN:
								(SCREAMING_SNAKE_CASE_)									=						divmod(__A ,   __A       )
								result.append(roman * factor       )
								if number == 0:
												break
				return "".join(__A       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCamelCase__  :      Optional[int]			=    datasets.utils.logging.get_logger(__name__)



@dataclass
class       __snake_case  (    datasets.BuilderConfig       ):
  __lowerCAmelCase     :	Optional[datasets.Features]						 =  None
  __lowerCAmelCase     :	str						 =  "utf-8"
  __lowerCAmelCase     :	Optional[str]						 =  None
  __lowerCAmelCase     :	Optional[str]						 =  None
  __lowerCAmelCase     :	bool						 =  True  # deprecated
  __lowerCAmelCase     :	Optional[int]						 =  None  # deprecated
  __lowerCAmelCase     :	int						 =  10 << 20  # 10MB
  __lowerCAmelCase     :	Optional[bool]						 =  None






class       __snake_case  (    datasets.ArrowBasedBuilder       ):
  __lowerCAmelCase     :	int						 =  JsonConfig

  def 						lowerCAmelCase__     (					self):
      if self.config.block_size is not None:
          logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead')
          SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A):
      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}""")
      SCREAMING_SNAKE_CASE_									=						dl_manager.download_and_extract(self.config.data_files)
      if isinstance(_A						,		(str, list, tuple)):
          SCREAMING_SNAKE_CASE_									=						data_files
          if isinstance(_A						,		_A):
              SCREAMING_SNAKE_CASE_									=						[files]
          SCREAMING_SNAKE_CASE_									=						[dl_manager.iter_files(_A) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,		gen_kwargs={'files': files})]
      SCREAMING_SNAKE_CASE_									=						[]
      for split_name, files in data_files.items():
          if isinstance(_A						,		_A):
              SCREAMING_SNAKE_CASE_									=						[files]
          SCREAMING_SNAKE_CASE_									=						[dl_manager.iter_files(_A) for file in files]
          splits.append(datasets.SplitGenerator(name=_A						,		gen_kwargs={'files': files}))
      return splits

  def 						lowerCAmelCase__     (					self						,		_A):
      if self.config.features is not None:
          # adding missing columns
          for column_name in set(self.config.features) - set(pa_table.column_names):
              SCREAMING_SNAKE_CASE_									=						self.config.features.arrow_schema.field(_A).type
              SCREAMING_SNAKE_CASE_									=						pa_table.append_column(_A						,		pa.array([None] * len(_A)						,		type=_A))
          # more expensive cast to support nested structures with keys in a different order
          # allows str <-> int/float or str to Audio for example
          SCREAMING_SNAKE_CASE_									=						table_cast(_A						,		self.config.features.arrow_schema)
      return pa_table


  def 						lowerCAmelCase__     (					self						,		_A):
      for file_idx, file in enumerate(itertools.chain.from_iterable(_A)):
          # 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(_A						,		encoding=self.config.encoding						,		errors=self.config.encoding_errors) as f:
                  SCREAMING_SNAKE_CASE_									=						json.load(_A)

              # We keep only the field we are interested in
              SCREAMING_SNAKE_CASE_									=						dataset[self.config.field]

              # We accept two format: a list of dicts or a dict of lists
              if isinstance(_A						,		(list, tuple)):
                  SCREAMING_SNAKE_CASE_									=						set().union(*[row.keys() for row in dataset])
                  SCREAMING_SNAKE_CASE_									=						{col: [row.get(_A) for row in dataset] for col in keys}
              else:
                  SCREAMING_SNAKE_CASE_									=						dataset
              SCREAMING_SNAKE_CASE_									=						pa.Table.from_pydict(_A)
              yield file_idx, self._cast_table(_A)

          # If the file has one json object per line
          else:
              with open(_A						,		'rb') as f:
                  SCREAMING_SNAKE_CASE_									=						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
                  SCREAMING_SNAKE_CASE_									=						max(self.config.chunksize // 32						,		16 << 10)
                  SCREAMING_SNAKE_CASE_									=						(
                      self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                  )
                  while True:
                      SCREAMING_SNAKE_CASE_									=						f.read(self.config.chunksize)
                      if not batch:
                          break
                      # Finish current line
                      try:
                          batch += f.readline()
                      except (AttributeError, io.UnsupportedOperation):
                          batch += readline(_A)
                      # PyArrow only accepts utf-8 encoded bytes
                      if self.config.encoding != "utf-8":
                          SCREAMING_SNAKE_CASE_									=						batch.decode(self.config.encoding						,		errors=_A).encode('utf-8')
                      try:
                          while True:
                              try:
                                  SCREAMING_SNAKE_CASE_									=						paj.read_json(
                                      io.BytesIO(_A)						,		read_options=paj.ReadOptions(block_size=_A))
                                  break
                              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                  if (
                                      isinstance(_A						,		pa.ArrowInvalid)
                                      and "straddling" not in str(_A)
                                      or block_size > len(_A)
                                  ):
                                      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(_A)} 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(
                                  _A						,		encoding=self.config.encoding						,		errors=self.config.encoding_errors) as f:
                                  SCREAMING_SNAKE_CASE_									=						json.load(_A)
                          except json.JSONDecodeError:
                              logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {e}""")
                              raise e
                          # If possible, parse the file as a list of json objects and exit the loop
                          if isinstance(_A						,		_A):  # list is the only sequence type supported in JSON
                              try:
                                  SCREAMING_SNAKE_CASE_									=						set().union(*[row.keys() for row in dataset])
                                  SCREAMING_SNAKE_CASE_									=						{col: [row.get(_A) for row in dataset] for col in keys}
                                  SCREAMING_SNAKE_CASE_									=						pa.Table.from_pydict(_A)
                              except (pa.ArrowInvalid, AttributeError) as e:
                                  logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {e}""")
                                  raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                              yield file_idx, self._cast_table(_A)
                              break
                          else:
                              logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {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_table(_A)
                      batch_idx += 1


