






'''simple docstring'''




import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


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

if TYPE_CHECKING:
    import pyspark

@dataclass
class   _UpperCAmelCase     (	datasets.BuilderConfig		):




   """simple docstring"""

   snake_case       							=		None





def    __lowercase		(     __lowercase  ,    __lowercase  ,    )			->		Dict:

      '''simple docstring'''


      import pyspark

      def generate_fn():
            _A     	=      df.select("*"  ,    pyspark.sql.functions.spark_partition_id().alias("part_id"			)			)
            for partition_id in partition_order:
                  _A     	=      df_with_partition_id.select("*"			).where(F'''part_id = {partition_id}'''			).drop("part_id"			)
                  _A     	=      partition_df.collect()
                  _A     	=      0
                  for row in rows:
                        yield F'''{partition_id}_{row_id}''', row.asDict()
                        row_id += 1

      return generate_fn

class   _UpperCAmelCase     (	_BaseExamplesIterable		):




   """simple docstring"""

   def __init__(						self					:      Optional[Any] , __UpperCAmelCase					:      str , __UpperCAmelCase					:      Optional[Any]=None , ):





         '''simple docstring'''





         _A     	=      df
         _A     	=      partition_order or range(self.df.rdd.getNumPartitions()						)
         _A     	=      _generate_iterable_examples(self.df , self.partition_order						)

   def __iter__(						self					:      List[str]						):





         '''simple docstring'''





         yield from self.generate_examples_fn()

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      list(range(self.df.rdd.getNumPartitions()						)						)
         generator.shuffle(__UpperCAmelCase						)
         return SparkExamplesIterable(self.df , partition_order=__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Dict , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      List[str]						):





         '''simple docstring'''





         _A     	=      self.split_shard_indices_by_worker(__UpperCAmelCase , __UpperCAmelCase						)
         return SparkExamplesIterable(self.df , partition_order=__UpperCAmelCase						)

   @property
   def 		lowerCAmelCase					(						self					:      List[str]						):





         '''simple docstring'''





         return len(self.partition_order						)





class   _UpperCAmelCase     (	datasets.DatasetBuilder		):




   """simple docstring"""

   snake_case       							=		SparkConfig

   def __init__(						self					:      List[Any] , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      List[str] = None , __UpperCAmelCase					:      Optional[Any] = None , **__UpperCAmelCase					:      List[str] , ):





         '''simple docstring'''





         import pyspark

         _A     	=      pyspark.sql.SparkSession.builder.getOrCreate()
         _A     	=      df
         _A     	=      working_dir

         super().__init__(
             cache_dir=__UpperCAmelCase , config_name=str(self.df.semanticHash()						) , **__UpperCAmelCase , )

   def 		lowerCAmelCase					(						self					:      int						):





         '''simple docstring'''





         def create_cache_and_write_probe(__UpperCAmelCase					:      List[str]						):
               # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
               # already exist.
               os.makedirs(self._cache_dir , exist_ok=__UpperCAmelCase						)
               _A     	=      os.path.join(self._cache_dir , "fs_test" + uuid.uuida().hex						)
               # Opening the file in append mode will create a new file unless it already exists, in which case it will not
               # change the file contents.
               open(__UpperCAmelCase , "a"						)
               return [probe_file]

         if self._spark.conf.get("spark.master" , ""						).startswith("local"						):
               return

         # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
         # accessible to the driver.
         # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
         if self._cache_dir:
               _A     	=      (
                   self._spark.sparkContext.parallelize(range(1						) , 1						).mapPartitions(__UpperCAmelCase						).collect()
               )
               if os.path.isfile(probe[0]						):
                     return

         raise ValueError(
             "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"						)

   def 		lowerCAmelCase					(						self					:      str						):





         '''simple docstring'''





         return datasets.DatasetInfo(features=self.config.features						)

   def 		lowerCAmelCase					(						self					:      Optional[int] , __UpperCAmelCase					:      Dict						):





         '''simple docstring'''





         return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]

   def 		lowerCAmelCase					(						self					:      Optional[int] , __UpperCAmelCase					:      List[str]						):





         '''simple docstring'''





         import pyspark

         def get_arrow_batch_size(__UpperCAmelCase					:      List[str]						):
               for batch in it:
                     yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}						)

         _A     	=      self.df.count()
         _A     	=      df_num_rows if df_num_rows <= 100 else 100
         # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
         _A     	=      (
             self.df.limit(__UpperCAmelCase						)
             .repartition(1						)
             .mapInArrow(__UpperCAmelCase , "batch_bytes: long"						)
             .agg(pyspark.sql.functions.sum("batch_bytes"						).alias("sample_bytes"						)						)
             .collect()[0]
             .sample_bytes
             / sample_num_rows
         )
         _A     	=      approx_bytes_per_row * df_num_rows
         if approx_total_size > max_shard_size:
               # Make sure there is at least one row per partition.
               _A     	=      min(__UpperCAmelCase , int(approx_total_size / max_shard_size						)						)
               _A     	=      self.df.repartition(__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Optional[Any] , __UpperCAmelCase					:      Optional[Any] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      List[str] , ):





         '''simple docstring'''





         import pyspark

         _A     	=      ParquetWriter if file_format == "parquet" else ArrowWriter
         _A     	=      os.path.join(self._working_dir , os.path.basename(__UpperCAmelCase						)						) if self._working_dir else fpath
         _A     	=      file_format == "parquet"

         # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
         # pickling the SparkContext.
         _A     	=      self.config.features
         _A     	=      self._writer_batch_size
         _A     	=      self._fs.storage_options

         def write_arrow(__UpperCAmelCase					:      Optional[Any]						):
               # Within the same SparkContext, no two task attempts will share the same attempt ID.
               _A     	=      pyspark.TaskContext().taskAttemptId()
               _A     	=      next(__UpperCAmelCase , __UpperCAmelCase						)
               if first_batch is None:
                     # Some partitions might not receive any data.
                     return pa.RecordBatch.from_arrays(
                         [[task_id], [0], [0]] , names=["task_id", "num_examples", "num_bytes"] , )
               _A     	=      0
               _A     	=      writer_class(
                   features=__UpperCAmelCase , path=working_fpath.replace("SSSSS" , f'''{shard_id:05d}'''						).replace("TTTTT" , f'''{task_id:05d}'''						) , writer_batch_size=__UpperCAmelCase , storage_options=__UpperCAmelCase , embed_local_files=__UpperCAmelCase , )
               _A     	=      pa.Table.from_batches([first_batch]						)
               writer.write_table(__UpperCAmelCase						)
               for batch in it:
                     if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                           _A	,      _A     	=      writer.finalize()
                           writer.close()
                           yield pa.RecordBatch.from_arrays(
                               [[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )
                           shard_id += 1
                           _A     	=      writer_class(
                               features=writer._features , path=working_fpath.replace("SSSSS" , f'''{shard_id:05d}'''						).replace("TTTTT" , f'''{task_id:05d}'''						) , writer_batch_size=__UpperCAmelCase , storage_options=__UpperCAmelCase , embed_local_files=__UpperCAmelCase , )
                     _A     	=      pa.Table.from_batches([batch]						)
                     writer.write_table(__UpperCAmelCase						)

               if writer._num_bytes > 0:
                     _A	,      _A     	=      writer.finalize()
                     writer.close()
                     yield pa.RecordBatch.from_arrays(
                         [[task_id], [num_examples], [num_bytes]] , names=["task_id", "num_examples", "num_bytes"] , )

               if working_fpath != fpath:
                     for file in os.listdir(os.path.dirname(__UpperCAmelCase						)						):
                           _A     	=      os.path.join(os.path.dirname(__UpperCAmelCase						) , os.path.basename(__UpperCAmelCase						)						)
                           shutil.move(__UpperCAmelCase , __UpperCAmelCase						)

         _A     	=      (
             self.df.mapInArrow(__UpperCAmelCase , "task_id: long, num_examples: long, num_bytes: long"						)
             .groupBy("task_id"						)
             .agg(
                 pyspark.sql.functions.sum("num_examples"						).alias("total_num_examples"						) , pyspark.sql.functions.sum("num_bytes"						).alias("total_num_bytes"						) , pyspark.sql.functions.count("num_bytes"						).alias("num_shards"						) , pyspark.sql.functions.collect_list("num_examples"						).alias("shard_lengths"						) , )
             .collect()
         )
         for row in stats:
               yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      Any = "arrow" , __UpperCAmelCase					:      Optional[int] = None , __UpperCAmelCase					:      Union[str, Any] = None , **__UpperCAmelCase					:      Any , ):





         '''simple docstring'''





         self._validate_cache_dir()

         _A     	=      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
         self._repartition_df_if_needed(__UpperCAmelCase						)
         _A     	=      not is_remote_filesystem(self._fs						)
         _A     	=      os.path.join if is_local else posixpath.join

         _A     	=      "-TTTTT-SSSSS-of-NNNNN"
         _A     	=      f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
         _A     	=      path_join(self._output_dir , __UpperCAmelCase						)

         _A     	=      0
         _A     	=      0
         _A     	=      0
         _A     	=      []
         _A     	=      []

         for task_id, content in self._prepare_split_single(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase						):
               (
                   (
                   _A 
               )	,      (
                   _A 
               )	,      (
                   _A 
               )	,      (
                   _A 
               )	,       
               )     	=      content
               if num_bytes > 0:
                     total_num_examples += num_examples
                     total_num_bytes += num_bytes
                     total_shards += num_shards
                     task_id_and_num_shards.append((task_id, num_shards)						)
                     all_shard_lengths.extend(__UpperCAmelCase						)

         _A     	=      total_num_examples
         _A     	=      total_num_bytes

         # should rename everything at the end
         logger.debug(f'''Renaming {total_shards} shards.'''						)
         if total_shards > 1:
               _A     	=      all_shard_lengths

               # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
               # pickling error due to pickling the SparkContext.
               _A     	=      self._fs

               # use the -SSSSS-of-NNNNN pattern
               def _rename_shard(
                   __UpperCAmelCase					:      str , __UpperCAmelCase					:      List[str] , __UpperCAmelCase					:      List[str] , ):
                     rename(
                         __UpperCAmelCase , fpath.replace("SSSSS" , f'''{shard_id:05d}'''						).replace("TTTTT" , f'''{task_id:05d}'''						) , fpath.replace("TTTTT-SSSSS" , f'''{global_shard_id:05d}'''						).replace("NNNNN" , f'''{total_shards:05d}'''						) , )

               _A     	=      []
               _A     	=      0
               for i in range(len(__UpperCAmelCase						)						):
                     _A	,      _A     	=      task_id_and_num_shards[i]
                     for shard_id in range(__UpperCAmelCase						):
                           args.append([task_id, shard_id, global_shard_id]						)
                           global_shard_id += 1
               self._spark.sparkContext.parallelize(__UpperCAmelCase , len(__UpperCAmelCase						)						).map(lambda __UpperCAmelCase						: _rename_shard(*__UpperCAmelCase						)						).collect()
         else:
               # don't use any pattern
               _A     	=      0
               _A     	=      task_id_and_num_shards[0][0]
               self._rename(
                   fpath.replace("SSSSS" , f'''{shard_id:05d}'''						).replace("TTTTT" , f'''{task_id:05d}'''						) , fpath.replace(__UpperCAmelCase , ""						) , )

   def 		lowerCAmelCase					(						self					:      str , __UpperCAmelCase					:      int , ):





         '''simple docstring'''





         return SparkExamplesIterable(self.df						)







"""simple docstring"""


import math






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [True] * n
			lowerCamelCase_					=  False
			lowerCamelCase_					=  False
			lowerCamelCase_					=  True

			for i in range(3	,int(n**0.5 + 1						)	,2						):
						lowerCamelCase_					=  i * 2
						while index < n:
									lowerCamelCase_					=  False
									lowerCamelCase_					=  index + i

			lowerCamelCase_					=  [2]

			for i in range(3	,lowerCAmelCase__	,2						):
						if is_prime[i]:
									primes.append(lowerCAmelCase__						)

			return primes






def  lowercase						(	lowerCAmelCase__ = 999_966_663_333						):
			lowerCamelCase_					=  math.floor(math.sqrt(lowerCAmelCase__						)						) + 100
			lowerCamelCase_					=  prime_sieve(lowerCAmelCase__						)

			lowerCamelCase_					=  0
			lowerCamelCase_					=  0
			lowerCamelCase_					=  primes[prime_index]

			while (last_prime**2) <= limit:
						lowerCamelCase_					=  primes[prime_index + 1]

						lowerCamelCase_					=  last_prime**2
						lowerCamelCase_					=  next_prime**2

						# Get numbers divisible by lps(current)
						lowerCamelCase_					=  lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						lowerCamelCase_					=  upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						lowerCamelCase_					=  0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						lowerCamelCase_					=  next_prime
						prime_index += 1

			return matches_sum


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



