






'''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    __SCREAMING_SNAKE_CASE					(	datasets.BuilderConfig ):
 snake_case_     							=							None





def        _a(     UpperCamelCase__ :  Optional[int],		UpperCamelCase__ :  Union[str, Any],		):




      '''simple docstring'''






      import pyspark

      def generate_fn():
            SCREAMING_SNAKE_CASE__   : str													=df.select('''*''',		pyspark.sql.functions.spark_partition_id().alias('''part_id'''							)							)
            for partition_id in partition_order:
                  SCREAMING_SNAKE_CASE__   : List[Any]													=df_with_partition_id.select('''*'''							).where(f"part_id = {partition_id}"							).drop('''part_id'''							)
                  SCREAMING_SNAKE_CASE__   : Optional[int]													=partition_df.collect()
                  SCREAMING_SNAKE_CASE__   : List[Any]													=0
                  for row in rows:
                        yield f"{partition_id}_{row_id}", row.asDict()
                        row_id += 1

      return generate_fn






class    __SCREAMING_SNAKE_CASE					(	_BaseExamplesIterable ):




 def __init__(					self		:							Optional[int]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							Any=None    ,					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=df
       SCREAMING_SNAKE_CASE__   : int													=partition_order or range(self.df.rdd.getNumPartitions()					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=_generate_iterable_examples(self.df    ,					self.partition_order					)




 def __iter__(					self		:							Optional[Any]					)		->  str:
       yield from self.generate_examples_fn()




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							Optional[int]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : List[str]													=list(range(self.df.rdd.getNumPartitions()					)					)
       generator.shuffle(_UpperCAmelCase					)
       return SparkExamplesIterable(self.df    ,					partition_order=_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							Any    ,					__lowercase		:							Union[str, Any]					)		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__   : Dict													=self.split_shard_indices_by_worker(_UpperCAmelCase    ,					_UpperCAmelCase					)
       return SparkExamplesIterable(self.df    ,					partition_order=_UpperCAmelCase					)




 @property
 def    __magic_name__		(					self		:							Union[str, Any]					)		->  List[Any]:
       return len(self.partition_order					)






class    __SCREAMING_SNAKE_CASE					(	datasets.DatasetBuilder ):
 snake_case_     							=							SparkConfig




 def __init__(					self		:							Optional[Any]    ,					__lowercase		:							Optional[Any]    ,					__lowercase		:							Any = None    ,					__lowercase		:							Optional[Any] = None    ,					**__lowercase		:							str    ,					)		->  Dict:
       import pyspark

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

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




 def    __magic_name__		(					self		:							List[Any]					)		->  List[str]:
       # Returns the path of the created file.
       def create_cache_and_write_probe(__lowercase		:							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					)
             SCREAMING_SNAKE_CASE__   : 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:
             SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							Optional[Any]					)		->  List[Any]:
       return datasets.DatasetInfo(features=self.config.features					)




 def    __magic_name__		(					self		:							Union[str, Any]    ,					__lowercase		:							List[str]					)		->  int:
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							List[Any]					)		->  Union[str, Any]:
       import pyspark

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

       SCREAMING_SNAKE_CASE__   : Optional[int]													=self.df.count()
       SCREAMING_SNAKE_CASE__   : Any													=df_num_rows if df_num_rows <= 1_00 else 1_00
       # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
       SCREAMING_SNAKE_CASE__   : 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
       )
       SCREAMING_SNAKE_CASE__   : 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.
             SCREAMING_SNAKE_CASE__   : Dict													=min(_UpperCAmelCase    ,					int(approx_total_size / max_shard_size					)					)
             SCREAMING_SNAKE_CASE__   : Dict													=self.df.repartition(_UpperCAmelCase					)




 def    __magic_name__		(					self		:							List[Any]    ,					__lowercase		:							Optional[int]    ,					__lowercase		:							str    ,					__lowercase		:							Union[str, Any]    ,					)		->  Optional[Any]:
       import pyspark

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

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

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

       SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							str    ,					__lowercase		:							Dict    ,					__lowercase		:							Any = "arrow"    ,					__lowercase		:							str = None    ,					__lowercase		:							Tuple = None    ,					**__lowercase		:							str    ,					)		->  List[str]:
       self._validate_cache_dir()

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

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

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

       for task_id, content in self._prepare_split_single(_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase					):
             (
                 SCREAMING_SNAKE_CASE__ 
             )   : 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					)

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

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

             # use the -SSSSS-of-NNNNN pattern
             def _rename_shard(
                 __lowercase		:							str    ,					__lowercase		:							int    ,					__lowercase		:							int    ,					):
                   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}"					)    ,					)

             SCREAMING_SNAKE_CASE__   : Optional[int]													=[]
             SCREAMING_SNAKE_CASE__   : Union[str, Any]													=0
             for i in range(len(_UpperCAmelCase					)					):
                   SCREAMING_SNAKE_CASE__   : 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 __lowercase					: _rename_shard(*_UpperCAmelCase					)					).collect()
       else:
             # don't use any pattern
             SCREAMING_SNAKE_CASE__   : str													=0
             SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							Optional[Any]    ,					__lowercase		:							Dict    ,					)		->  str:
       return SparkExamplesIterable(self.df					)
"""simple docstring"""






__A			   = [
    (1_0_0_0, "M"),
    (9_0_0, "CM"),
    (5_0_0, "D"),
    (4_0_0, "CD"),
    (1_0_0, "C"),
    (9_0, "XC"),
    (5_0, "L"),
    (4_0, "XL"),
    (1_0, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   lowercase__:   Dict					=      {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
   lowercase__:   List[str]					=      0
   lowercase__:   List[Any]					=      0
   while place < len(__UpperCAmelCase ):
      if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Optional[Any]					=      []
   for arabic, roman in ROMAN:
      ((lowercase__), (lowercase__)):   Tuple					=      divmod(__UpperCAmelCase       ,    __UpperCAmelCase )
      result.append(roman * factor )
      if number == 0:
         break
   return "".join(__UpperCAmelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
