




'''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


a    :							Union[str, Any]										=    datasets.utils.logging.get_logger(__name__)






@dataclass
class 		a							(  datasets.BuilderConfig	):
				snake_case_           =						None
				snake_case_           =						"utf-8"
				snake_case_           =						None
				snake_case_           =						None
				snake_case_           =						True  # deprecated
				snake_case_           =						None  # deprecated
				snake_case_           =						10 << 20  # 10MB
				snake_case_           =						None







class 		a							(  datasets.ArrowBasedBuilder	):
				snake_case_           =						JsonConfig


				def     A_					(						self       :   List[str]   ):
									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       :   List[Any]			,							lowercase_       :   Dict   ):
									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(lowercase_			,							(str, list, tuple)   ):
														snake_case_		     =							data_files
														if isinstance(lowercase_			,							lowercase_   ):
																			snake_case_		     =							[files]
														snake_case_		     =							[dl_manager.iter_files(lowercase_   ) 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(lowercase_			,							lowercase_   ):
																			snake_case_		     =							[files]
														snake_case_		     =							[dl_manager.iter_files(lowercase_   ) for file in files]
														splits.append(datasets.SplitGenerator(name=lowercase_			,							gen_kwargs={'''files''': files}   )   )
									return splits


				def     A_					(						self       :   Any			,							lowercase_       :   pa.Table   ):
									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(lowercase_   ).type
																			snake_case_		     =							pa_table.append_column(lowercase_			,							pa.array([None] * len(lowercase_   )			,							type=lowercase_   )   )
														# 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(lowercase_			,							self.config.features.arrow_schema   )
									return pa_table


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Dict   ):
									for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_   )   ):
														# 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(lowercase_			,							encoding=self.config.encoding			,							errors=self.config.encoding_errors   ) as f:
																								snake_case_		     =							json.load(lowercase_   )

																			# 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(lowercase_			,							(list, tuple)   ):
																								snake_case_		     =							set().union(*[row.keys() for row in dataset]   )
																								snake_case_		     =							{col: [row.get(lowercase_   ) for row in dataset] for col in keys}
																			else:
																								snake_case_		     =							dataset
																			snake_case_		     =							pa.Table.from_pydict(lowercase_   )
																			yield file_idx, self._cast_table(lowercase_   )

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







'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

