


"""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__							:				Union[str, Any]          =	datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark




@dataclass
class 					UpperCamelCase_      (					datasets.BuilderConfig):

	"""simple docstring"""





	snake_case__							:    List[str]          =     None




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_    ,		):




					'''simple docstring'''


					import pyspark

					def generate_fn():
										__SCREAMING_SNAKE_CASE		  =							df.select("*"    ,		pyspark.sql.functions.spark_partition_id().alias("part_id"  )  )
										for partition_id in partition_order:
															__SCREAMING_SNAKE_CASE		  =							df_with_partition_id.select("*"  ).where(f"""part_id = {partition_id}"""  ).drop("part_id"  )
															__SCREAMING_SNAKE_CASE		  =							partition_df.collect()
															__SCREAMING_SNAKE_CASE		  =							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     :							Any     ,     UpperCAmelCase__     :							List[str]     ,     UpperCAmelCase__     :							Tuple=None     ,     )			->      List[str]:
						__SCREAMING_SNAKE_CASE		  =							df
						__SCREAMING_SNAKE_CASE		  =							partition_order or range(self.df.rdd.getNumPartitions()  )
						__SCREAMING_SNAKE_CASE		  =							_generate_iterable_examples(self.df     ,     self.partition_order  )




	def __iter__(					self     :							Any  )			->      Dict:
						yield from self.generate_examples_fn()




	def     UpperCAmelCase_ (					self     :							Union[str, Any]     ,     UpperCAmelCase__     :							Optional[int]  )			->      "SparkExamplesIterable":
						__SCREAMING_SNAKE_CASE		  =							list(range(self.df.rdd.getNumPartitions()  )  )
						generator.shuffle(lowerCamelCase_  )
						return SparkExamplesIterable(self.df     ,     partition_order=lowerCamelCase_  )




	def     UpperCAmelCase_ (					self     :							Union[str, Any]     ,     UpperCAmelCase__     :							int     ,     UpperCAmelCase__     :							List[Any]  )			->      "SparkExamplesIterable":
						__SCREAMING_SNAKE_CASE		  =							self.split_shard_indices_by_worker(lowerCamelCase_     ,     lowerCamelCase_  )
						return SparkExamplesIterable(self.df     ,     partition_order=lowerCamelCase_  )




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




class 					UpperCamelCase_      (					datasets.DatasetBuilder):

	"""simple docstring"""





	snake_case__							:    List[str]          =     SparkConfig




	def __init__(					self     :							str     ,     UpperCAmelCase__     :							int     ,     UpperCAmelCase__     :							Optional[int] = None     ,     UpperCAmelCase__     :							Any = None     ,     **UpperCAmelCase__     :							int     ,     )			->      List[Any]:
						import pyspark

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

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




	def     UpperCAmelCase_ (					self     :							Optional[Any]  )			->      str:
						# Returns the path of the created file.
						def create_cache_and_write_probe(UpperCAmelCase__     :							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=lowerCamelCase_  )
											__SCREAMING_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(lowerCamelCase_     ,     "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		  =							(
											    self._spark.sparkContext.parallelize(range(1  )     ,     1  ).mapPartitions(lowerCamelCase_  ).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     UpperCAmelCase_ (					self     :							Dict  )			->      int:
						return datasets.DatasetInfo(features=self.config.features  )




	def     UpperCAmelCase_ (					self     :							Optional[int]     ,     UpperCAmelCase__     :							Optional[int]  )			->      List[Any]:
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN  )]




	def     UpperCAmelCase_ (					self     :							Optional[Any]     ,     UpperCAmelCase__     :							Any  )			->      Union[str, Any]:
						import pyspark

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

						__SCREAMING_SNAKE_CASE		  =							self.df.count()
						__SCREAMING_SNAKE_CASE		  =							df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
						# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
						__SCREAMING_SNAKE_CASE		  =							(
						    self.df.limit(lowerCamelCase_  )
						    .repartition(1  )
						    .mapInArrow(lowerCamelCase_     ,     "batch_bytes: long"  )
						    .agg(pyspark.sql.functions.sum("batch_bytes"  ).alias("sample_bytes"  )  )
						    .collect()[0]
						    .sample_bytes
						    / sample_num_rows
						)
						__SCREAMING_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.
											__SCREAMING_SNAKE_CASE		  =							min(lowerCamelCase_     ,     int(approx_total_size / max_shard_size  )  )
											__SCREAMING_SNAKE_CASE		  =							self.df.repartition(lowerCamelCase_  )




	def     UpperCAmelCase_ (					self     :							Optional[Any]     ,     UpperCAmelCase__     :							Tuple     ,     UpperCAmelCase__     :							int     ,     UpperCAmelCase__     :							List[str]     ,     )			->      Iterable[Tuple[int, bool, Union[int, tuple]]]:
						import pyspark

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

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

											if writer._num_bytes > 0:
																__SCREAMING_SNAKE_CASE  ,    __SCREAMING_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(lowerCamelCase_  )  ):
																					__SCREAMING_SNAKE_CASE		  =							os.path.join(os.path.dirname(lowerCamelCase_  )     ,     os.path.basename(lowerCamelCase_  )  )
																					shutil.move(lowerCamelCase_     ,     lowerCamelCase_  )

						__SCREAMING_SNAKE_CASE		  =							(
						    self.df.mapInArrow(lowerCamelCase_     ,     "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     UpperCAmelCase_ (					self     :							Any     ,     UpperCAmelCase__     :							Union[str, Any]     ,     UpperCAmelCase__     :							Tuple = "arrow"     ,     UpperCAmelCase__     :							Optional[Any] = None     ,     UpperCAmelCase__     :							Union[str, Any] = None     ,     **UpperCAmelCase__     :							Optional[Any]     ,     )			->      int:
						self._validate_cache_dir()

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

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

						__SCREAMING_SNAKE_CASE		  =							0
						__SCREAMING_SNAKE_CASE		  =							0
						__SCREAMING_SNAKE_CASE		  =							0
						__SCREAMING_SNAKE_CASE		  =							[]
						__SCREAMING_SNAKE_CASE		  =							[]

						for task_id, content in self._prepare_split_single(lowerCamelCase_     ,     lowerCamelCase_     ,     lowerCamelCase_  ):
											(
											    (
											    __SCREAMING_SNAKE_CASE			
											)  ,    (
											    __SCREAMING_SNAKE_CASE			
											)  ,    (
											    __SCREAMING_SNAKE_CASE			
											)  ,    (
											    __SCREAMING_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(lowerCamelCase_  )

						__SCREAMING_SNAKE_CASE		  =							total_num_examples
						__SCREAMING_SNAKE_CASE		  =							total_num_bytes

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

											# use the -SSSSS-of-NNNNN pattern
											def _rename_shard(
											    UpperCAmelCase__     :							Any     ,     UpperCAmelCase__     :							Union[str, Any]     ,     UpperCAmelCase__     :							Tuple     ,     ):
																rename(
																    lowerCamelCase_     ,     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		  =							[]
											__SCREAMING_SNAKE_CASE		  =							0
											for i in range(len(lowerCamelCase_  )  ):
																__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE		  =							task_id_and_num_shards[i]
																for shard_id in range(lowerCamelCase_  ):
																					args.append([task_id, shard_id, global_shard_id]  )
																					global_shard_id += 1
											self._spark.sparkContext.parallelize(lowerCamelCase_     ,     len(lowerCamelCase_  )  ).map(lambda UpperCAmelCase__  : _rename_shard(*lowerCamelCase_  )  ).collect()
						else:
											# don't use any pattern
											__SCREAMING_SNAKE_CASE		  =							0
											__SCREAMING_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(lowerCamelCase_     ,     ""  )     ,     )




	def     UpperCAmelCase_ (					self     :							str     ,     UpperCAmelCase__     :							Any     ,     )			->      SparkExamplesIterable:
						return SparkExamplesIterable(self.df  )



"""simple docstring"""


def   _A					(	_a	:						Optional[int]     ,   _a	:						str				):



      """simple docstring"""






      A				  =		0
      A				  =		len(_a				) - 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(_a				):
                  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					(	_a	:						Any     ,   _a	:						List[str]     ,   _a	:						Dict     ,   _a	:						List[str]				):



      """simple docstring"""






      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(_a				):
            return None

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



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      if collection != sorted(_a				):
            raise ValueError("""Collection must be ascending sorted"""				)
      return True


if __name__ == "__main__":
  import sys
  UpperCAmelCase													=0
  if debug == 1:
    UpperCAmelCase													=[10, 30, 40, 45, 50, 66, 77, 93]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit("Sequence must be ascending sorted to apply interpolation search")
    UpperCAmelCase													=67

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




