


'''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        lowerCAmelCase_  (     datasets.BuilderConfig	):
       __UpperCAmelCase					=None






def 					lowerCamelCase_ ( __UpperCamelCase					:       "pyspark.sql.DataFrame"							,    __UpperCamelCase					:       List[int]							,    )					->	int:







     """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        lowerCAmelCase_  (     _BaseExamplesIterable	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=None			,     )->      str:
            _A				=							df
            _A				=							partition_order or range(self.df.rdd.getNumPartitions()   )
            _A				=							_generate_iterable_examples(self.df			,     self.partition_order   )






       def __iter__( self   )->      Dict:
            yield from self.generate_examples_fn()






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      "SparkExamplesIterable":
            _A				=							list(range(self.df.rdd.getNumPartitions()   )   )
            generator.shuffle(_UpperCamelCase   )
            return SparkExamplesIterable(self.df			,     partition_order=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      "SparkExamplesIterable":
            _A				=							self.split_shard_indices_by_worker(_UpperCamelCase			,     _UpperCamelCase   )
            return SparkExamplesIterable(self.df			,     partition_order=_UpperCamelCase   )






       @property
       def   UpperCamelCase						( self   )->      int:
            return len(self.partition_order   )






class        lowerCAmelCase_  (     datasets.DatasetBuilder	):
       __UpperCAmelCase					=SparkConfig






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      Tuple:
            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   UpperCamelCase						( self   )->      Tuple:
            # Returns the path of the created file.
            def create_cache_and_write_probe(_UpperCamelCase   ):
                 # 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   UpperCamelCase						( self   )->      Union[str, Any]:
            return datasets.DatasetInfo(features=self.config.features   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      List[Any]:
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN   )]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            import pyspark

            def get_arrow_batch_size(_UpperCamelCase   ):
                 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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     )->      Iterable[Tuple[int, bool, Union[int, tuple]]]:
            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   ):
                 # 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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = "arrow"			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      int:
            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			,     _UpperCamelCase			,     _UpperCamelCase			,     ):
                      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   UpperCamelCase						( self			,     _UpperCamelCase			,     )->      SparkExamplesIterable:
            return SparkExamplesIterable(self.df   )









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	list:







     """simple docstring"""
     _A				=							int(__UpperCamelCase		)
     if n_element < 1:
          _A				=							ValueError('a should be a positive number'		)
          raise my_error

     _A				=							[1]
     _A		,				_A		,				_A				=							(0, 0, 0)
     _A				=							1
     while index < n_element:
          while hamming_list[i] * 2 <= hamming_list[-1]:
               i += 1
          while hamming_list[j] * 3 <= hamming_list[-1]:
               j += 1
          while hamming_list[k] * 5 <= hamming_list[-1]:
               k += 1
          hamming_list.append(
              min(hamming_list[i] * 2							,    hamming_list[j] * 3							,    hamming_list[k] * 5		)		)
          index += 1
     return hamming_list


if __name__ == "__main__":
    lowerCAmelCase						=					input("""Enter the last number (nth term) of the Hamming Number Series: """)
    print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
    lowerCAmelCase						=					hamming(int(n))
    print("""-----------------------------------------------------""")
    print(F'The list with nth numbers is: {hamming_numbers}')
    print("""-----------------------------------------------------""")





