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

if TYPE_CHECKING:
       import pyspark


@dataclass
class     UpperCAmelCase						(datasets.BuilderConfig  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[datasets.Features]						    = None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    )  ->  Union[str, Any]:
   import pyspark

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






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






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      list(range(self.df.rdd.getNumPartitions()							)							)
       generator.shuffle(_UpperCAmelCase							)
       return SparkExamplesIterable(self.df    ,			partition_order=_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      self.split_shard_indices_by_worker(_UpperCAmelCase    ,			_UpperCAmelCase							)
       return SparkExamplesIterable(self.df    ,			partition_order=_UpperCAmelCase							)






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



class     UpperCAmelCase						(datasets.DatasetBuilder  ):



    """simple docstring"""

    _UpperCAmelCase	:Any						    = SparkConfig






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

       lowercase__:   List[str]					=      pyspark.sql.SparkSession.builder.getOrCreate()
       lowercase__:   Optional[int]					=      df
       lowercase__:   Union[str, Any]					=      working_dir

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






    def _snake_case			(   self							):
       # 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							)
          lowercase__:   int					=      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:
          lowercase__:   Union[str, Any]					=      (
              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 _snake_case			(   self							):
       return datasets.DatasetInfo(features=self.config.features							)






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






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       import pyspark

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

       lowercase__:   Optional[int]					=      self.df.count()
       lowercase__:   Any					=      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.
       lowercase__:   Dict					=      (
           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
       )
       lowercase__:   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.
          lowercase__:   Dict					=      min(_UpperCAmelCase    ,			int(approx_total_size / max_shard_size							)							)
          lowercase__:   Dict					=      self.df.repartition(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			):
       import pyspark

       lowercase__:   int					=      ParquetWriter if file_format == '''parquet''' else ArrowWriter
       lowercase__:   List[Any]					=      os.path.join(self._working_dir    ,			os.path.basename(_UpperCAmelCase							)							) if self._working_dir else fpath
       lowercase__:   Dict					=      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.
       lowercase__:   int					=      self.config.features
       lowercase__:   List[Any]					=      self._writer_batch_size
       lowercase__:   Dict					=      self._fs.storage_options

       def write_arrow(_UpperCAmelCase							):
          # Within the same SparkContext, no two task attempts will share the same attempt ID.
          lowercase__:   int					=      pyspark.TaskContext().taskAttemptId()
          lowercase__:   Union[str, Any]					=      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''']    ,			)
          lowercase__:   List[Any]					=      0
          lowercase__:   List[Any]					=      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    ,			)
          lowercase__:   Any					=      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:
                lowercase__, lowercase__:   List[str]					=      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
                lowercase__:   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=_UpperCAmelCase    ,			storage_options=_UpperCAmelCase    ,			embed_local_files=_UpperCAmelCase    ,			)
             lowercase__:   Tuple					=      pa.Table.from_batches([batch]							)
             writer.write_table(_UpperCAmelCase							)

          if writer._num_bytes > 0:
             lowercase__, lowercase__:   str					=      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							)							):
                lowercase__:   Tuple					=      os.path.join(os.path.dirname(_UpperCAmelCase							)    ,			os.path.basename(_UpperCAmelCase							)							)
                shutil.move(_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   int					=      (
           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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = "arrow"    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       self._validate_cache_dir()

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

       lowercase__:   List[str]					=      '''-TTTTT-SSSSS-of-NNNNN'''
       lowercase__:   Optional[int]					=      F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
       lowercase__:   int					=      path_join(self._output_dir    ,			_UpperCAmelCase							)

       lowercase__:   Tuple					=      0
       lowercase__:   List[Any]					=      0
       lowercase__:   Tuple					=      0
       lowercase__:   List[Any]					=      []
       lowercase__:   List[str]					=      []

       for task_id, content in self._prepare_split_single(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
          (
              (
              lowercase__
          ), (
              lowercase__
          ), (
              lowercase__
          ), (
              lowercase__
          ), 
          ):   Union[str, Any]					=      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							)

       lowercase__:   List[Any]					=      total_num_examples
       lowercase__:   int					=      total_num_bytes

       # should rename everything at the end
       logger.debug(F"""Renaming {total_shards} shards."""							)
       if total_shards > 1:
          lowercase__:   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.
          lowercase__:   Any					=      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}"""							)    ,			)

          lowercase__:   Optional[int]					=      []
          lowercase__:   Union[str, Any]					=      0
          for i in range(len(_UpperCAmelCase							)							):
             lowercase__, lowercase__:   int					=      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
          lowercase__:   str					=      0
          lowercase__:   Optional[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(_UpperCAmelCase    ,			''''''							)    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			):
       return SparkExamplesIterable(self.df							)

"""simple docstring"""






from __future__ import annotations

from fractions import Fraction



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  bool:
   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[str]:
   lowercase__:   str					=      []
   lowercase__:   str					=      1_1
   lowercase__:   str					=      int('''1''' + '''0''' * digit_len )
   for num in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__UpperCAmelCase       ,    __UpperCAmelCase ):
               solutions.append(F"""{num}/{den}""" )
         den += 1
      num += 1
      lowercase__:   Dict					=      1_0
   return solutions



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 2 )  ->  int:
   lowercase__:   List[str]					=      1.0
   for fraction in fraction_list(__UpperCAmelCase ):
      lowercase__:   List[str]					=      Fraction(__UpperCAmelCase )
      result *= frac.denominator / frac.numerator
   return int(__UpperCAmelCase )


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