





'''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


_A:					List[str]				=				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
    import pyspark






@dataclass
class UpperCAmelCase							(		datasets.BuilderConfig							):
    _A :			Optional[datasets.Features]						   =       None







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    )->     List[str]:
 import pyspark

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

 return generate_fn






class UpperCAmelCase							(		_BaseExamplesIterable							):



    def __init__(  self     ,			__A     ,			__A=None     ,			):
     __UpperCAmelCase					=   df
     __UpperCAmelCase					=   partition_order or range(self.df.rdd.getNumPartitions()					)
     __UpperCAmelCase					=   _generate_iterable_examples(self.df     ,			self.partition_order					)



    def __iter__(  self					):
     yield from self.generate_examples_fn()



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   list(range(self.df.rdd.getNumPartitions()					)					)
     generator.shuffle(__A					)
     return SparkExamplesIterable(self.df     ,			partition_order=__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.split_shard_indices_by_worker(__A     ,			__A					)
     return SparkExamplesIterable(self.df     ,			partition_order=__A					)




    @property
    def 					__lowerCamelCase						(  self					):
     return len(self.partition_order					)






class UpperCAmelCase							(		datasets.DatasetBuilder							):
    _A :			Union[str, Any]						   =       SparkConfig



    def __init__(  self     ,			__A     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     import pyspark

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

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



    def 					__lowerCamelCase						(  self					):
     # Returns the path of the created file.
     def create_cache_and_write_probe(__A					):
      # 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=__A					)
      __UpperCAmelCase					=   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(__A     ,			'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:
      __UpperCAmelCase					=   (
          self._spark.sparkContext.parallelize(range(1					)     ,			1					).mapPartitions(__A					).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					):
     return datasets.DatasetInfo(features=self.config.features					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]



    def 					__lowerCamelCase						(  self     ,			__A					):
     import pyspark

     def get_arrow_batch_size(__A					):
      for batch in it:
       yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}					)

     __UpperCAmelCase					=   self.df.count()
     __UpperCAmelCase					=   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.
     __UpperCAmelCase					=   (
         self.df.limit(__A					)
         .repartition(1					)
         .mapInArrow(__A     ,			'batch_bytes: long'					)
         .agg(pyspark.sql.functions.sum('batch_bytes'					).alias('sample_bytes'					)					)
         .collect()[0]
         .sample_bytes
         / sample_num_rows
     )
     __UpperCAmelCase					=   approx_bytes_per_row * df_num_rows
     if approx_total_size > max_shard_size:
      # Make sure there is at least one row per partition.
      __UpperCAmelCase					=   min(__A     ,			int(approx_total_size / max_shard_size					)					)
      __UpperCAmelCase					=   self.df.repartition(__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			):
     import pyspark

     __UpperCAmelCase					=   ParquetWriter if file_format == 'parquet' else ArrowWriter
     __UpperCAmelCase					=   os.path.join(self._working_dir     ,			os.path.basename(__A					)					) if self._working_dir else fpath
     __UpperCAmelCase					=   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.
     __UpperCAmelCase					=   self.config.features
     __UpperCAmelCase					=   self._writer_batch_size
     __UpperCAmelCase					=   self._fs.storage_options

     def write_arrow(__A					):
      # Within the same SparkContext, no two task attempts will share the same attempt ID.
      __UpperCAmelCase					=   pyspark.TaskContext().taskAttemptId()
      __UpperCAmelCase					=   next(__A     ,			__A					)
      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']     ,			)
      __UpperCAmelCase					=   0
      __UpperCAmelCase					=   writer_class(
          features=__A     ,			path=working_fpath.replace('SSSSS'     ,			f'{shard_id:05d}'					).replace('TTTTT'     ,			f'{task_id:05d}'					)     ,			writer_batch_size=__A     ,			storage_options=__A     ,			embed_local_files=__A     ,			)
      __UpperCAmelCase					=   pa.Table.from_batches([first_batch]					)
      writer.write_table(__A					)
      for batch in it:
       if max_shard_size is not None and writer._num_bytes >= max_shard_size:
        __UpperCAmelCase					,    __UpperCAmelCase					=   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
        __UpperCAmelCase					=   writer_class(
            features=writer._features     ,			path=working_fpath.replace('SSSSS'     ,			f'{shard_id:05d}'					).replace('TTTTT'     ,			f'{task_id:05d}'					)     ,			writer_batch_size=__A     ,			storage_options=__A     ,			embed_local_files=__A     ,			)
       __UpperCAmelCase					=   pa.Table.from_batches([batch]					)
       writer.write_table(__A					)

      if writer._num_bytes > 0:
       __UpperCAmelCase					,    __UpperCAmelCase					=   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(__A					)					):
        __UpperCAmelCase					=   os.path.join(os.path.dirname(__A					)     ,			os.path.basename(__A					)					)
        shutil.move(__A     ,			__A					)

     __UpperCAmelCase					=   (
         self.df.mapInArrow(__A     ,			'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     ,			__A     ,			__A = "arrow"     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     self._validate_cache_dir()

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

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

     __UpperCAmelCase					=   0
     __UpperCAmelCase					=   0
     __UpperCAmelCase					=   0
     __UpperCAmelCase					=   []
     __UpperCAmelCase					=   []

     for task_id, content in self._prepare_split_single(__A     ,			__A     ,			__A					):
      (
          (
          __UpperCAmelCase    
      )					,    (
          __UpperCAmelCase    
      )					,    (
          __UpperCAmelCase    
      )					,    (
          __UpperCAmelCase    
      )					,        
      )					=   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(__A					)

     __UpperCAmelCase					=   total_num_examples
     __UpperCAmelCase					=   total_num_bytes

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

      # use the -SSSSS-of-NNNNN pattern
      def _rename_shard(
          __A     ,			__A     ,			__A     ,			):
       rename(
           __A     ,			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}'					)     ,			)

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




    def 					__lowerCamelCase						(  self     ,			__A     ,			):
     return SparkExamplesIterable(self.df					)












'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 while True:
  __UpperCAmelCase					=   ['$'] * len(_lowerCAmelCase    )
  __UpperCAmelCase					=   []
  for i in range(len(_lowerCAmelCase    )    ):
   for j in range(i + 1       ,    len(_lowerCAmelCase    )    ):
    __UpperCAmelCase					=   compare_string(binary[i]       ,    binary[j]    )
    if k is False:
     __UpperCAmelCase					=   '*'
     __UpperCAmelCase					=   '*'
     temp.append('X'    )
  for i in range(len(_lowerCAmelCase    )    ):
   if checka[i] == "$":
    pi.append(binary[i]    )
  if len(_lowerCAmelCase    ) == 0:
   return pi
  __UpperCAmelCase					=   list(set(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]    )

  for i in range(len(chart[0]    )    ):
   if chart[rem][i] == 1:
    for j in range(len(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





