




import os
import sys

UpperCAmelCase_						=os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCAmelCase_						=[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]



@add_start_docstrings(AutoConfig.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoConfig.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoTokenizer.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoModel.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoModel.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoModelForCausalLM.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoModelForMaskedLM.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoModelForSequenceClassification.from_pretrained(*_snake_case						, **_snake_case							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def       UpperCAmelCase       (     *_snake_case						, **_snake_case							):
							return AutoModelForQuestionAnswering.from_pretrained(*_snake_case						, **_snake_case							)









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_						=datasets.utils.logging.get_logger(__name__)


@dataclass
class 							__UpperCamelCase			( datasets.BuilderConfig  ):





				'''simple docstring'''



				__a							: Optional[datasets.Features]										=None
				__a							: str										="utf-8"
				__a							: Optional[str]										=None
				__a							: Optional[str]										=None
				__a							: bool										=True  # deprecated
				__a							: Optional[int]										=None  # deprecated
				__a							: int										=1_0 << 2_0  # 10MB
				__a							: Optional[bool]										=None




class 							__UpperCamelCase			( datasets.ArrowBasedBuilder  ):





				'''simple docstring'''



				__a							: str										=JsonConfig



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



				def       __snake_case		( self       , UpperCAmelCase_						):
											if self.config.features is not None:
																		# adding missing columns
																		for column_name in set(self.config.features						) - set(pa_table.column_names						):
																									lowerCAmelCase   =		self.config.features.arrow_schema.field(UpperCAmelCase_						).type
																									lowerCAmelCase   =		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
																		lowerCAmelCase   =		table_cast(UpperCAmelCase_       , self.config.features.arrow_schema						)
											return pa_table






				def       __snake_case		( self       , UpperCAmelCase_						):
											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:
																																lowerCAmelCase   =		json.load(UpperCAmelCase_						)

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

																									# We accept two format: a list of dicts or a dict of lists
																									if isinstance(UpperCAmelCase_       , (list, tuple)						):
																																lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																									else:
																																lowerCAmelCase   =		dataset
																									lowerCAmelCase   =		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:
																																lowerCAmelCase   =		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
																																lowerCAmelCase   =		max(self.config.chunksize // 32       , 16 << 10						)
																																lowerCAmelCase   =		(
																																    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																																)
																																while True:
																																							lowerCAmelCase   =		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":
																																														lowerCAmelCase   =		batch.decode(self.config.encoding       , errors=UpperCAmelCase_						).encode('''utf-8'''						)
																																							try:
																																														while True:
																																																					try:
																																																												lowerCAmelCase   =		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:
																																																												lowerCAmelCase   =		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:
																																																												lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																																												lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																																																												lowerCAmelCase   =		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



