

"""simple docstring"""






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


snake_case_ : Optional[Any]   =							datasets.utils.logging.get_logger(__name__)







@dataclass
class    snake_case__	(			datasets.BuilderConfig  ):
			SCREAMING_SNAKE_CASE__  					=    None
			SCREAMING_SNAKE_CASE__  					=    '''utf-8'''
			SCREAMING_SNAKE_CASE__  					=    None
			SCREAMING_SNAKE_CASE__  					=    None
			SCREAMING_SNAKE_CASE__  					=    True  # deprecated
			SCREAMING_SNAKE_CASE__  					=    None  # deprecated
			SCREAMING_SNAKE_CASE__  					=    10 << 20  # 10MB
			SCREAMING_SNAKE_CASE__  					=    None







class    snake_case__	(			datasets.ArrowBasedBuilder  ):
			SCREAMING_SNAKE_CASE__  					=    JsonConfig







			def       __lowerCAmelCase					(      self      :			Dict	):





								'''simple docstring'''



								if self.config.block_size is not None:
													logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"	)
													UpperCAmelCase      : Optional[int]   =					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      :			Optional[int]      ,	lowercase      :			int	):





								'''simple docstring'''



								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}"""	)
								UpperCAmelCase      : Dict   =					dl_manager.download_and_extract(self.config.data_files	)
								if isinstance(__UpperCamelCase      ,	(str, list, tuple)	):
													UpperCAmelCase      : Dict   =					data_files
													if isinstance(__UpperCamelCase      ,	__UpperCamelCase	):
																		UpperCAmelCase      : List[str]   =					[files]
													UpperCAmelCase      : int   =					[dl_manager.iter_files(__UpperCamelCase	) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,	gen_kwargs={"files": files}	)]
								UpperCAmelCase      : Dict   =					[]
								for split_name, files in data_files.items():
													if isinstance(__UpperCamelCase      ,	__UpperCamelCase	):
																		UpperCAmelCase      : Union[str, Any]   =					[files]
													UpperCAmelCase      : Dict   =					[dl_manager.iter_files(__UpperCamelCase	) for file in files]
													splits.append(datasets.SplitGenerator(name=__UpperCamelCase      ,	gen_kwargs={"files": files}	)	)
								return splits







			def       __lowerCAmelCase					(      self      :			Any      ,	lowercase      :			pa.Table	):





								'''simple docstring'''



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







			def       __lowerCAmelCase					(      self      :			Union[str, Any]      ,	lowercase      :			Optional[Any]	):





								'''simple docstring'''



								for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase	)	):
													# 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(__UpperCamelCase      ,	encoding=self.config.encoding      ,	errors=self.config.encoding_errors	) as f:
																							UpperCAmelCase      : str   =					json.load(__UpperCamelCase	)

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

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

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowercase   =	{}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





