





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






@dataclass
class 						__snake_case   (						datasets.BuilderConfig	):





 """simple docstring"""


 _lowerCamelCase								=	None
 _lowerCamelCase								=	"utf-8"
 _lowerCamelCase								=	None
 _lowerCamelCase								=	None
 _lowerCamelCase								=	True  # deprecated
 _lowerCamelCase								=	None  # deprecated
 _lowerCamelCase								=	10 << 20  # 10MB
 _lowerCamelCase								=	None






class 						__snake_case   (						datasets.ArrowBasedBuilder	):





 """simple docstring"""


 _lowerCamelCase								=	JsonConfig

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''    )
         __A       :		Tuple							=	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    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''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    ):
             __A       :		Optional[int]							=	self.config.features.arrow_schema.field(__lowerCamelCase    ).type
             __A       :		Any							=	pa_table.append_column(__lowerCamelCase   ,		pa.array([None] * len(__lowerCamelCase    )   ,		type=__lowerCamelCase    )    )
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         __A       :		List[Any]							=	table_cast(__lowerCamelCase   ,		self.config.features.arrow_schema    )
     return pa_table







 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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

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

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

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











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int = 2000000		)  ->int:



    '''simple docstring'''







    __A       :		List[Any]							=	[0 for i in range(n + 1		)]
    __A       :		str							=	1
    __A       :		Dict							=	1

    for i in range(2							,int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i							,n + 1							,snake_case_		):
                __A       :		List[Any]							=	1
    __A       :		Union[str, Any]							=	0
    for i in range(snake_case_		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(f'''{solution() = }''')




