





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							:			List[str]         =			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark







@dataclass
class      lowerCamelCase(						datasets.BuilderConfig	):
     '''simple docstring'''


     __magic_name__   =			None





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     )							-> Tuple:




    """simple docstring"""




    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				,     snake_case_				,     snake_case_=None				,     ):
         _A     =							df
         _A     =							partition_order or range(self.df.rdd.getNumPartitions()							)
         _A     =							_generate_iterable_examples(self.df				,     self.partition_order							)




     def __iter__(    self							):
         yield from self.generate_examples_fn()




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							list(range(self.df.rdd.getNumPartitions()							)							)
         generator.shuffle(snake_case_							)
         return SparkExamplesIterable(self.df				,     partition_order=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							self.split_shard_indices_by_worker(snake_case_				,     snake_case_							)
         return SparkExamplesIterable(self.df				,     partition_order=snake_case_							)




     @property
     def        lowerCAmelCase__		(    self							):
         return len(self.partition_order							)







class      lowerCamelCase(						datasets.DatasetBuilder	):
     '''simple docstring'''


     __magic_name__   =			SparkConfig




     def __init__(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         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        lowerCAmelCase__		(    self							):
         # Returns the path of the created file.
         def create_cache_and_write_probe(snake_case_							):
             # 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        lowerCAmelCase__		(    self							):
         return datasets.DatasetInfo(features=self.config.features							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN							)]




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         import pyspark

         def get_arrow_batch_size(snake_case_							):
             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 <= 100 else 100
         # 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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         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_							):
             # 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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = "arrow"				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         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_				,     snake_case_				,     snake_case_				,     ):
                 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        lowerCAmelCase__		(    self				,     snake_case_				,     ):
         return SparkExamplesIterable(self.df							)












from collections.abc import Callable





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    _A     =							a
    _A     =							b
    if function(_SCREAMING_SNAKE_CASE				) == 0:  # one of the a or b is a root for the function
        return a
    elif function(_SCREAMING_SNAKE_CASE				) == 0:
        return b
    elif (
        function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) > 0
    ):  # if none of these are root and they are both positive or negative,
        # then this algorithm can't find the root
        raise ValueError('could not find root in given interval.'				)
    else:
        _A     =							start + (end - start) / 2.0
        while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
            if function(_SCREAMING_SNAKE_CASE				) == 0:
                return mid
            elif function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) < 0:
                _A     =							mid
            else:
                _A     =							mid
            _A     =							start + (end - start) / 2.0
        return mid





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return x**3 - 2 * x - 5


if __name__ == "__main__":
 print(bisection(f, 1, 1_000))

 import doctest

 doctest.testmod()





