


"""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


__SCREAMING_SNAKE_CASE							:			Optional[Any]						=			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark





@dataclass
class    __A   (datasets.BuilderConfig):





							'''simple docstring'''

							__lowercase:						List[str]												=			None







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       )			-> Union[str, Any]:
							import pyspark

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

							return generate_fn





class    __A   (_BaseExamplesIterable):





							'''simple docstring'''

							def __init__( self							:					Optional[int]						,				UpperCAmelCase_							:					"pyspark.sql.DataFrame"						,				UpperCAmelCase_							:					List[Any]=None						,				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						df
														snake_case_							=						partition_order or range(self.df.rdd.getNumPartitions()				)
														snake_case_							=						_generate_iterable_examples(self.df						,				self.partition_order				)



							def __iter__( self							:					Dict				)    ->int:



														"""simple docstring"""



														yield from self.generate_examples_fn()



							def 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					np.random.Generator				)    ->"SparkExamplesIterable":



														"""simple docstring"""



														snake_case_							=						list(range(self.df.rdd.getNumPartitions()				)				)
														generator.shuffle(UpperCAmelCase_				)
														return SparkExamplesIterable(self.df						,				partition_order=UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Optional[Any]						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					int				)    ->"SparkExamplesIterable":



														"""simple docstring"""



														snake_case_							=						self.split_shard_indices_by_worker(UpperCAmelCase_						,				UpperCAmelCase_				)
														return SparkExamplesIterable(self.df						,				partition_order=UpperCAmelCase_				)



							@property
							def 						lowerCAmelCase				( self							:					str				)    ->int:



														"""simple docstring"""



														return len(self.partition_order				)





class    __A   (datasets.DatasetBuilder):





							'''simple docstring'''

							__lowercase:						Tuple												=			SparkConfig



							def __init__( self							:					Union[str, Any]						,				UpperCAmelCase_							:					"pyspark.sql.DataFrame"						,				UpperCAmelCase_							:					str = None						,				UpperCAmelCase_							:					str = None						,				**UpperCAmelCase_							:					int						,				)    ->Tuple:



														"""simple docstring"""



														import pyspark

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

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



							def 						lowerCAmelCase				( self							:					Tuple				)    ->Union[str, Any]:



														"""simple docstring"""



														def create_cache_and_write_probe(UpperCAmelCase_							:					int				):
																					# 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_				)
																					snake_case_							=						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:
																					snake_case_							=						(
																					    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 						lowerCAmelCase				( self							:					Any				)    ->List[Any]:



														"""simple docstring"""



														return datasets.DatasetInfo(features=self.config.features				)



							def 						lowerCAmelCase				( self							:					str						,				UpperCAmelCase_							:					datasets.download.download_manager.DownloadManager				)    ->Optional[int]:



														"""simple docstring"""



														return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]



							def 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					Tuple				)    ->Optional[Any]:



														"""simple docstring"""



														import pyspark

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

														snake_case_							=						self.df.count()
														snake_case_							=						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.
														snake_case_							=						(
														    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
														)
														snake_case_							=						approx_bytes_per_row * df_num_rows
														if approx_total_size > max_shard_size:
																					# Make sure there is at least one row per partition.
																					snake_case_							=						min(UpperCAmelCase_						,				int(approx_total_size / max_shard_size				)				)
																					snake_case_							=						self.df.repartition(UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Optional[int]						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					int						,				)    ->Iterable[Tuple[int, bool, Union[int, tuple]]]:



														"""simple docstring"""



														import pyspark

														snake_case_							=						ParquetWriter if file_format == """parquet""" else ArrowWriter
														snake_case_							=						os.path.join(self._working_dir						,				os.path.basename(UpperCAmelCase_				)				) if self._working_dir else fpath
														snake_case_							=						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.
														snake_case_							=						self.config.features
														snake_case_							=						self._writer_batch_size
														snake_case_							=						self._fs.storage_options

														def write_arrow(UpperCAmelCase_							:					Optional[int]				):
																					# Within the same SparkContext, no two task attempts will share the same attempt ID.
																					snake_case_							=						pyspark.TaskContext().taskAttemptId()
																					snake_case_							=						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"""]						,				)
																					snake_case_							=						0
																					snake_case_							=						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_						,				)
																					snake_case_							=						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:
																																			snake_case_							=						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
																																			snake_case_							=						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_						,				)
																												snake_case_							=						pa.Table.from_batches([batch]				)
																												writer.write_table(UpperCAmelCase_				)

																					if writer._num_bytes > 0:
																												snake_case_							=						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_				)				):
																																			snake_case_							=						os.path.join(os.path.dirname(UpperCAmelCase_				)						,				os.path.basename(UpperCAmelCase_				)				)
																																			shutil.move(UpperCAmelCase_						,				UpperCAmelCase_				)

														snake_case_							=						(
														    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 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					"datasets.SplitGenerator"						,				UpperCAmelCase_							:					str = "arrow"						,				UpperCAmelCase_							:					Optional[Union[str, int]] = None						,				UpperCAmelCase_							:					Optional[int] = None						,				**UpperCAmelCase_							:					List[str]						,				)    ->Optional[int]:



														"""simple docstring"""



														self._validate_cache_dir()

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

														snake_case_							=						"""-TTTTT-SSSSS-of-NNNNN"""
														snake_case_							=						F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
														snake_case_							=						path_join(self._output_dir						,				UpperCAmelCase_				)

														snake_case_							=						0
														snake_case_							=						0
														snake_case_							=						0
														snake_case_							=						[]
														snake_case_							=						[]

														for task_id, content in self._prepare_split_single(UpperCAmelCase_						,				UpperCAmelCase_						,				UpperCAmelCase_				):
																					(
																					    snake_case_							
																					)							=						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_				)

														snake_case_							=						total_num_examples
														snake_case_							=						total_num_bytes

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

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

																					snake_case_							=						[]
																					snake_case_							=						0
																					for i in range(len(UpperCAmelCase_				)				):
																												snake_case_							=						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
																					snake_case_							=						0
																					snake_case_							=						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 						lowerCAmelCase				( self							:					Dict						,				UpperCAmelCase_							:					"datasets.SplitGenerator"						,				)    ->SparkExamplesIterable:



														"""simple docstring"""



														return SparkExamplesIterable(self.df				)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for data in source_data:
    for i, el in enumerate(A__					):
      if len(A__					) < i + 1:
        data_lists.append([]					)
      data_lists[i].append(float(A__					)					)
  return data_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      list[float]			=   []
    # for weight 0 score is 1 - actual score
    if weight == 0:
      for item in dlist:
        try:
          score.append(1 - ((item - mind) / (maxd - mind))					)
        except ZeroDivisionError:
          score.append(1					)

    elif weight == 1:
      for item in dlist:
        try:
          score.append((item - mind) / (maxd - mind)					)
        except ZeroDivisionError:
          score.append(0					)

        # weight not 0 or 1
    else:
      lowerCAmelCase_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[float]			=   [0 for i in range(len(score_lists[0]					)					)]

  for slist in score_lists:
    for j, ele in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

  # append scores to source data
  for i, ele in enumerate(A__					):
    source_data[i].append(A__					)

  return source_data



