


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


lowerCamelCase__							:						int							     =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 		__magic_name__						(datasets.BuilderConfig				):





     '''simple docstring'''



     __lowercase				:							Optional[datasets.Features]	   =       None
     __lowercase				:							str	   =       "utf-8"
     __lowercase				:							Optional[str]	   =       None
     __lowercase				:							Optional[str]	   =       None
     __lowercase				:							bool	   =       True  # deprecated
     __lowercase				:							Optional[int]	   =       None  # deprecated
     __lowercase				:							int	   =       10 << 20  # 10MB
     __lowercase				:							Optional[bool]	   =       None




class 		__magic_name__						(datasets.ArrowBasedBuilder				):





     '''simple docstring'''



     __lowercase				:							int	   =       JsonConfig

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:int   ):
       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(_a     ,		(str, list, tuple)   ):
         snake_case__						= data_files
         if isinstance(_a     ,		_a   ):
           snake_case__						= [files]
         snake_case__						= [dl_manager.iter_files(_a   ) 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(_a     ,		_a   ):
           snake_case__						= [files]
         snake_case__						= [dl_manager.iter_files(_a   ) for file in files]
         splits.append(datasets.SplitGenerator(name=_a     ,		gen_kwargs={'''files''': files}   )   )
       return splits

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a: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(_a   ).type
           snake_case__						= pa_table.append_column(_a     ,		pa.array([None] * len(_a   )     ,		type=_a   )   )
         # 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(_a     ,		self.config.features.arrow_schema   )
       return pa_table







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:List[Any]   ):
       for file_idx, file in enumerate(itertools.chain.from_iterable(_a   )   ):
         # 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(_a     ,		encoding=self.config.encoding     ,		errors=self.config.encoding_errors   ) as f:
             snake_case__						= json.load(_a   )

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

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())
