



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

if TYPE_CHECKING:
							import pyspark


@dataclass
class   __lowerCAmelCase  (		datasets.BuilderConfig   ):
					"""simple docstring"""






					A__					:		Optional[datasets.Features]      							=     None







def A    (							__UpperCamelCase			,						__UpperCamelCase			,						)    ->   Optional[int]:
			import pyspark

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

			return generate_fn


class   __lowerCAmelCase  (		_BaseExamplesIterable   ):
					"""simple docstring"""






					def __init__(  self       :							str					,       _snake_case       :							"pyspark.sql.DataFrame"					,       _snake_case       :							List[str]=None					,       ):






								"""simple docstring"""


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



					def __iter__(  self       :							Optional[int]       ):






								"""simple docstring"""


								yield from self.generate_examples_fn()



					def      _a				(  self       :							int					,       _snake_case       :							np.random.Generator       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							int					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     self.split_shard_indices_by_worker(_snake_case					,       _snake_case       )
								return SparkExamplesIterable(self.df					,       partition_order=_snake_case       )



					@property
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								return len(self.partition_order       )







class   __lowerCAmelCase  (		datasets.DatasetBuilder   ):
					"""simple docstring"""






					A__					:		Optional[int]      							=     SparkConfig



					def __init__(  self       :							Optional[Any]					,       _snake_case       :							"pyspark.sql.DataFrame"					,       _snake_case       :							str = None					,       _snake_case       :							str = None					,       **_snake_case       :							Optional[int]					,       ):






								"""simple docstring"""


								import pyspark

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

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



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								def create_cache_and_write_probe(_snake_case       :							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=_snake_case       )
											A__	  =     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(_snake_case					,       '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:
											A__	  =     (
											    self._spark.sparkContext.parallelize(range(1       )					,       1       ).mapPartitions(_snake_case       ).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      _a				(  self       :							str       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							datasets.download.download_manager.DownloadManager       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								import pyspark

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

								A__	  =     self.df.count()
								A__	  =     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.
								A__	  =     (
								    self.df.limit(_snake_case       )
								    .repartition(1       )
								    .mapInArrow(_snake_case					,       'batch_bytes: long'       )
								    .agg(pyspark.sql.functions.sum('batch_bytes'       ).alias('sample_bytes'       )       )
								    .collect()[0]
								    .sample_bytes
								    / sample_num_rows
								)
								A__	  =     approx_bytes_per_row * df_num_rows
								if approx_total_size > max_shard_size:
											# Make sure there is at least one row per partition.
											A__	  =     min(_snake_case					,       int(approx_total_size / max_shard_size       )       )
											A__	  =     self.df.repartition(_snake_case       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							str					,       _snake_case       :							str					,       _snake_case       :							int					,       ):






								"""simple docstring"""


								import pyspark

								A__	  =     ParquetWriter if file_format == 'parquet' else ArrowWriter
								A__	  =     os.path.join(self._working_dir					,       os.path.basename(_snake_case       )       ) if self._working_dir else fpath
								A__	  =     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.
								A__	  =     self.config.features
								A__	  =     self._writer_batch_size
								A__	  =     self._fs.storage_options

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

											if writer._num_bytes > 0:
														A__  ,			A__	  =     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(_snake_case       )       ):
																	A__	  =     os.path.join(os.path.dirname(_snake_case       )					,       os.path.basename(_snake_case       )       )
																	shutil.move(_snake_case					,       _snake_case       )

								A__	  =     (
								    self.df.mapInArrow(_snake_case					,       '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      _a				(  self       :							List[Any]					,       _snake_case       :							"datasets.SplitGenerator"					,       _snake_case       :							str = "arrow"					,       _snake_case       :							Optional[Union[str, int]] = None					,       _snake_case       :							Optional[int] = None					,       **_snake_case       :							Dict					,       ):






								"""simple docstring"""


								self._validate_cache_dir()

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

								A__	  =     '-TTTTT-SSSSS-of-NNNNN'
								A__	  =     F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
								A__	  =     path_join(self._output_dir					,       _snake_case       )

								A__	  =     0
								A__	  =     0
								A__	  =     0
								A__	  =     []
								A__	  =     []

								for task_id, content in self._prepare_split_single(_snake_case					,       _snake_case					,       _snake_case       ):
											(
											    (
											    A__
											)  ,			(
											    A__
											)  ,			(
											    A__
											)  ,			(
											    A__
											)  ,			
											)	  =     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(_snake_case       )

								A__	  =     total_num_examples
								A__	  =     total_num_bytes

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

											# use the -SSSSS-of-NNNNN pattern
											def _rename_shard(
											    _snake_case       :							int					,       _snake_case       :							int					,       _snake_case       :							int					,       ):
														rename(
														    _snake_case					,       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}'''       )					,       )

											A__	  =     []
											A__	  =     0
											for i in range(len(_snake_case       )       ):
														A__  ,			A__	  =     task_id_and_num_shards[i]
														for shard_id in range(_snake_case       ):
																	args.append([task_id, shard_id, global_shard_id]       )
																	global_shard_id += 1
											self._spark.sparkContext.parallelize(_snake_case					,       len(_snake_case       )       ).map(lambda _snake_case       : _rename_shard(*_snake_case       )       ).collect()
								else:
											# don't use any pattern
											A__	  =     0
											A__	  =     task_id_and_num_shards[0][0]
											self._rename(
											    fpath.replace('SSSSS'					,       F'''{shard_id:05d}'''       ).replace('TTTTT'					,       F'''{task_id:05d}'''       )					,       fpath.replace(_snake_case					,       ''       )					,       )



					def      _a				(  self       :							List[Any]					,       _snake_case       :							"datasets.SplitGenerator"					,       ):






								"""simple docstring"""


								return SparkExamplesIterable(self.df       )









def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     0
			A__	  =     len(__UpperCamelCase       ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__UpperCamelCase       ):
									return None

						A__	  =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												A__	  =     left
												A__	  =     point
									elif point > right:
												A__	  =     right
												A__	  =     point
									else:
												if item < current_item:
															A__	  =     point - 1
												else:
															A__	  =     point + 1
			return None


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			A__	  =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__UpperCamelCase       ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			elif point > right:
						return interpolation_search_by_recursion(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						point - 1       )
						else:
									return interpolation_search_by_recursion(
									    __UpperCamelCase			,						__UpperCamelCase			,						point + 1			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   List[str]:
			if collection != sorted(__UpperCamelCase       ):
						raise ValueError('Collection must be ascending sorted'       )
			return True


if __name__ == "__main__":
							import sys
							SCREAMING_SNAKE_CASE__   			=   0
							if debug == 1:
														SCREAMING_SNAKE_CASE__   			=   [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
														SCREAMING_SNAKE_CASE__   			=   6_7

							SCREAMING_SNAKE_CASE__   			=   interpolation_search(collection, target)
							if result is not None:
														print(f'{target} found at positions: {result}')
							else:
														print('''Not found''')




