






'''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__				:						"pyspark.sql.DataFrame",	UpperCamelCase__				:						List[int],	):




      '''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[str]									=df_with_partition_id.select('''*'''							).where(f"part_id = {partition_id}"							).drop('''part_id'''							)
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=partition_df.collect()
                  SCREAMING_SNAKE_CASE__     :				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[Any]					,      __lowercase	:							"pyspark.sql.DataFrame"					,      __lowercase	:							Optional[int]=None					,      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				str									=df
       SCREAMING_SNAKE_CASE__     :				List[Any]									=partition_order or range(self.df.rdd.getNumPartitions()      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=_generate_iterable_examples(self.df					,      self.partition_order      )




 def __iter__(      self	:							Optional[int]      )	->						Tuple:
       yield from self.generate_examples_fn()




 def      __magic_name__	(      self	:							str					,      __lowercase	:							np.random.Generator      )	->						"SparkExamplesIterable":
       SCREAMING_SNAKE_CASE__     :				str									=list(range(self.df.rdd.getNumPartitions()      )      )
       generator.shuffle(__lowercase      )
       return SparkExamplesIterable(self.df					,      partition_order=__lowercase      )




 def      __magic_name__	(      self	:							str					,      __lowercase	:							int					,      __lowercase	:							int      )	->						"SparkExamplesIterable":
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.split_shard_indices_by_worker(__lowercase					,      __lowercase      )
       return SparkExamplesIterable(self.df					,      partition_order=__lowercase      )




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






class      __SCREAMING_SNAKE_CASE      (			datasets.DatasetBuilder				):
 snake_case_  							=							SparkConfig




 def __init__(      self	:							Tuple					,      __lowercase	:							"pyspark.sql.DataFrame"					,      __lowercase	:							str = None					,      __lowercase	:							str = None					,      **__lowercase	:							List[str]					,      )	->						Dict:
       import pyspark

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

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




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




 def      __magic_name__	(      self	:							str					,      __lowercase	:							datasets.download.download_manager.DownloadManager      )	->						List[str]:
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							Any      )	->						Tuple:
       import pyspark

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

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.df.count()
       SCREAMING_SNAKE_CASE__     :				Optional[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__     :				List[Any]									=(
           self.df.limit(__lowercase      )
           .repartition(1      )
           .mapInArrow(__lowercase					,      '''batch_bytes: long'''      )
           .agg(pyspark.sql.functions.sum('''batch_bytes'''      ).alias('''sample_bytes'''      )      )
           .collect()[0]
           .sample_bytes
           / sample_num_rows
       )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=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__     :				int									=min(__lowercase					,      int(approx_total_size / max_shard_size      )      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=self.df.repartition(__lowercase      )




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							str					,      __lowercase	:							str					,      __lowercase	:							int					,      )	->						Iterable[Tuple[int, bool, Union[int, tuple]]]:
       import pyspark

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

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

             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(__lowercase      )      ):
                         SCREAMING_SNAKE_CASE__     :				str									=os.path.join(os.path.dirname(__lowercase      )					,      os.path.basename(__lowercase      )      )
                         shutil.move(__lowercase					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
           self.df.mapInArrow(__lowercase					,      '''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	:							Union[str, Any]					,      __lowercase	:							"datasets.SplitGenerator"					,      __lowercase	:							str = "arrow"					,      __lowercase	:							Optional[Union[str, int]] = None					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							str					,      )	->						Any:
       self._validate_cache_dir()

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

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

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

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

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

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

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




 def      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							"datasets.SplitGenerator"					,      )	->						SparkExamplesIterable:
       return SparkExamplesIterable(self.df      )







'''simple docstring'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  _a(  UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						list[int],	UpperCamelCase__				:						int,	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=coefficient_matrix.shape
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=constant_matrix.shape

      if rowsa != colsa:
            SCREAMING_SNAKE_CASE__     :				Any									=f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if colsa != 1:
            SCREAMING_SNAKE_CASE__     :				str									=f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if rowsa != rowsa:
            SCREAMING_SNAKE_CASE__     :				str									=(
                '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
                f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if len(UpperCamelCase__							) != rowsa:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                '''Number of initial values must be equal to number of rows in coefficient '''
                f"matrix but received {len(UpperCamelCase__							)} and {rowsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if iterations <= 0:
            raise ValueError('''Iterations must be at least 1'''							)

      SCREAMING_SNAKE_CASE__     :				NDArray[floataa]									=np.concatenate(
          (coefficient_matrix, constant_matrix),	axis=1							)

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=table.shape

      strictly_diagonally_dominant(UpperCamelCase__							)

      # Iterates the whole matrix for given number of times
      for _ in range(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[str]									=[]
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0
                  for col in range(UpperCamelCase__							):
                        if col == row:
                              SCREAMING_SNAKE_CASE__     :				int									=table[row][col]
                        elif col == cols - 1:
                              SCREAMING_SNAKE_CASE__     :				Any									=table[row][col]
                        else:
                              temp += (-1) * table[row][col] * init_val[col]
                  SCREAMING_SNAKE_CASE__     :				int									=(temp + val) / denom
                  new_val.append(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=new_val

      return [float(UpperCamelCase__							) for i in new_val]





def  _a(  UpperCamelCase__				:						NDArray[floataa]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=table.shape

      SCREAMING_SNAKE_CASE__     :				Any									=True

      for i in range(0,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				int									=0
            for j in range(0,	cols - 1							):
                  if i == j:
                        continue
                  else:
                        total += table[i][j]

            if table[i][i] <= total:
                  raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''							)

      return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()