

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 				lowercase       ( datasets.BuilderConfig    ):
				__SCREAMING_SNAKE_CASE      :				Optional[datasets.Features]     =    None
				__SCREAMING_SNAKE_CASE      :				str     =    "utf-8"
				__SCREAMING_SNAKE_CASE      :				Optional[str]     =    None
				__SCREAMING_SNAKE_CASE      :				Optional[str]     =    None
				__SCREAMING_SNAKE_CASE      :				bool     =    True  # deprecated
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    None  # deprecated
				__SCREAMING_SNAKE_CASE      :				int     =    10 << 20  # 10MB
				__SCREAMING_SNAKE_CASE      :				Optional[bool]     =    None







class 				lowercase       ( datasets.ArrowBasedBuilder    ):
				__SCREAMING_SNAKE_CASE      :				Tuple     =    JsonConfig




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




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




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

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

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

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







from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]




				def 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											snake_case_           =					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
											self.assertEqual(dset.info   ,     dset_from_dict.info )




				def 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )




