


'''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						):
 UpperCAmelCase				=					None


def        a__     (					UpperCamelCase_						:      "pyspark.sql.DataFrame",   UpperCamelCase_						:      List[int],   ):
   import pyspark

   def generate_fn():
      UpperCAmelCase__	:Optional[int]      =      df.select('''*''',   pyspark.sql.functions.spark_partition_id().alias('''part_id'''						)						)
      for partition_id in partition_order:
         UpperCAmelCase__	:str      =      df_with_partition_id.select('''*'''						).where(F'''part_id = {partition_id}'''						).drop('''part_id'''						)
         UpperCAmelCase__	:str      =      partition_df.collect()
         UpperCAmelCase__	:int      =      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							:	Tuple			,       __lowerCamelCase							:	"pyspark.sql.DataFrame"			,       __lowerCamelCase							:	Dict=None			,       ):
    UpperCAmelCase__	:Optional[Any]      =      df
    UpperCAmelCase__	:List[Any]      =      partition_order or range(self.df.rdd.getNumPartitions() )
    UpperCAmelCase__	:Optional[Any]      =      _generate_iterable_examples(self.df			,       self.partition_order )


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


 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	np.random.Generator ):
    UpperCAmelCase__	:int      =      list(range(self.df.rdd.getNumPartitions() ) )
    generator.shuffle(__lowerCamelCase )
    return SparkExamplesIterable(self.df			,       partition_order=__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	int ):
    UpperCAmelCase__	:str      =      self.split_shard_indices_by_worker(__lowerCamelCase			,       __lowerCamelCase )
    return SparkExamplesIterable(self.df			,       partition_order=__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    return len(self.partition_order )




class 				UpperCAmelCase			(			datasets.DatasetBuilder						):
 UpperCAmelCase				=					SparkConfig


 def __init__(      self							:	Tuple			,       __lowerCamelCase							:	"pyspark.sql.DataFrame"			,       __lowerCamelCase							:	str = None			,       __lowerCamelCase							:	str = None			,       **__lowerCamelCase							:	Any			,       ):
    import pyspark

    UpperCAmelCase__	:Dict      =      pyspark.sql.SparkSession.builder.getOrCreate()
    UpperCAmelCase__	:Optional[int]      =      df
    UpperCAmelCase__	:Any      =      working_dir

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    # Returns the path of the created file.
    def create_cache_and_write_probe(__lowerCamelCase							:	Dict ):
       # 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=__lowerCamelCase )
       UpperCAmelCase__	:Tuple      =      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(__lowerCamelCase			,       '''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__	:Optional[Any]      =      (
           self._spark.sparkContext.parallelize(range(1 )			,       1 ).mapPartitions(__lowerCamelCase ).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        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return datasets.DatasetInfo(features=self.config.features )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	datasets.download.download_manager.DownloadManager ):
    return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	Optional[int] ):
    import pyspark

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

    UpperCAmelCase__	:List[Any]      =      self.df.count()
    UpperCAmelCase__	:str      =      df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
    # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
    UpperCAmelCase__	:Optional[Any]      =      (
        self.df.limit(__lowerCamelCase )
        .repartition(1 )
        .mapInArrow(__lowerCamelCase			,       '''batch_bytes: long''' )
        .agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
        .collect()[0]
        .sample_bytes
        / sample_num_rows
    )
    UpperCAmelCase__	:Union[str, Any]      =      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__	:int      =      min(__lowerCamelCase			,       int(approx_total_size / max_shard_size ) )
       UpperCAmelCase__	:str      =      self.df.repartition(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int			,       ):
    import pyspark

    UpperCAmelCase__	:Optional[Any]      =      ParquetWriter if file_format == '''parquet''' else ArrowWriter
    UpperCAmelCase__	:Union[str, Any]      =      os.path.join(self._working_dir			,       os.path.basename(__lowerCamelCase ) ) if self._working_dir else fpath
    UpperCAmelCase__	:Tuple      =      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__	:List[Any]      =      self.config.features
    UpperCAmelCase__	:str      =      self._writer_batch_size
    UpperCAmelCase__	:Dict      =      self._fs.storage_options

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

       if writer._num_bytes > 0:
          UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      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(__lowerCamelCase ) ):
             UpperCAmelCase__	:Optional[int]      =      os.path.join(os.path.dirname(__lowerCamelCase )			,       os.path.basename(__lowerCamelCase ) )
             shutil.move(__lowerCamelCase			,       __lowerCamelCase )

    UpperCAmelCase__	:int      =      (
        self.df.mapInArrow(__lowerCamelCase			,       '''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        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	"datasets.SplitGenerator"			,       __lowerCamelCase							:	str = "arrow"			,       __lowerCamelCase							:	Optional[Union[str, int]] = None			,       __lowerCamelCase							:	Optional[int] = None			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    self._validate_cache_dir()

    UpperCAmelCase__	:Optional[Any]      =      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
    self._repartition_df_if_needed(__lowerCamelCase )
    UpperCAmelCase__	:Tuple      =      not is_remote_filesystem(self._fs )
    UpperCAmelCase__	:Optional[Any]      =      os.path.join if is_local else posixpath.join

    UpperCAmelCase__	:Dict      =      '''-TTTTT-SSSSS-of-NNNNN'''
    UpperCAmelCase__	:List[Any]      =      f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
    UpperCAmelCase__	:Union[str, Any]      =      path_join(self._output_dir			,       __lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      0
    UpperCAmelCase__	:List[str]      =      0
    UpperCAmelCase__	:Dict      =      0
    UpperCAmelCase__	:str      =      []
    UpperCAmelCase__	:Optional[Any]      =      []

    for task_id, content in self._prepare_split_single(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase ):
       (
           (
           UpperCAmelCase__
       )   ,						(
           UpperCAmelCase__
       )   ,						(
           UpperCAmelCase__
       )   ,						(
           UpperCAmelCase__
       )   ,						
       )	:Dict      =      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(__lowerCamelCase )

    UpperCAmelCase__	:Any      =      total_num_examples
    UpperCAmelCase__	:List[Any]      =      total_num_bytes

    # should rename everything at the end
    logger.debug(f'''Renaming {total_shards} shards.''' )
    if total_shards > 1:
       UpperCAmelCase__	:Optional[int]      =      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__	:int      =      self._fs

       # use the -SSSSS-of-NNNNN pattern
       def _rename_shard(
           __lowerCamelCase							:	int			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	int			,       ):
          rename(
              __lowerCamelCase			,       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__	:Optional[Any]      =      []
       UpperCAmelCase__	:Dict      =      0
       for i in range(len(__lowerCamelCase ) ):
          UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      task_id_and_num_shards[i]
          for shard_id in range(__lowerCamelCase ):
             args.append([task_id, shard_id, global_shard_id] )
             global_shard_id += 1
       self._spark.sparkContext.parallelize(__lowerCamelCase			,       len(__lowerCamelCase ) ).map(lambda __lowerCamelCase : _rename_shard(*__lowerCamelCase ) ).collect()
    else:
       # don't use any pattern
       UpperCAmelCase__	:List[str]      =      0
       UpperCAmelCase__	:int      =      task_id_and_num_shards[0][0]
       self._rename(
           fpath.replace('''SSSSS'''			,       f'''{shard_id:05d}''' ).replace('''TTTTT'''			,       f'''{task_id:05d}''' )			,       fpath.replace(__lowerCamelCase			,       '''''' )			,       )





 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	"datasets.SplitGenerator"			,       ):
    return SparkExamplesIterable(self.df )







'''simple docstring'''





def        a__     (					UpperCamelCase_						:      int | float | str						):
   try:
      UpperCAmelCase__	:Union[str, Any]      =      float(UpperCamelCase_						)
   except ValueError:
      raise ValueError('''Please enter a valid number'''						)
   UpperCAmelCase__	:List[str]      =      decimal - int(UpperCamelCase_						)
   if fractional_part == 0:
      return int(UpperCamelCase_						), 1
   else:
      UpperCAmelCase__	:List[Any]      =      len(str(UpperCamelCase_						).split('''.'''						)[1]						)
      UpperCAmelCase__	:Tuple      =      int(decimal * (10**number_of_frac_digits)						)
      UpperCAmelCase__	:int      =      10**number_of_frac_digits
      UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      denominator, numerator
      while True:
         UpperCAmelCase__	:Optional[Any]      =      dividend % divisor
         if remainder == 0:
            break
         UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      divisor, remainder
      UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      numerator / divisor, denominator / divisor
      return int(UpperCamelCase_						), int(UpperCamelCase_						)


if __name__ == "__main__":
    print(F'''{decimal_to_fraction(2) = }''')
    print(F'''{decimal_to_fraction(89.0) = }''')
    print(F'''{decimal_to_fraction("67") = }''')
    print(F'''{decimal_to_fraction("45.0") = }''')
    print(F'''{decimal_to_fraction(1.5) = }''')
    print(F'''{decimal_to_fraction("6.25") = }''')
    print(F'''{decimal_to_fraction("78td") = }''')



