





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


_lowerCamelCase :    int  							= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark


@dataclass
class 						lowerCamelCase       (datasets.BuilderConfig      ):







       """simple docstring"""




       UpperCAmelCase_      						=   None




def _a (  SCREAMING_SNAKE_CASE__      :  "pyspark.sql.DataFrame"			,				SCREAMING_SNAKE_CASE__      :  List[int]			,				)	->    Optional[Any]:

       '''simple docstring'''






       import pyspark

       def generate_fn():
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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__       :      Dict     =						partition_df.collect()
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						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     :					Tuple,			_UpperCAmelCase     :					"pyspark.sql.DataFrame",			_UpperCAmelCase     :					List[Any]=None,			)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						df
              SCREAMING_SNAKE_CASE__       :      Tuple     =						partition_order or range(self.df.rdd.getNumPartitions()						)
              SCREAMING_SNAKE_CASE__       :      str     =						_generate_iterable_examples(self.df,			self.partition_order						)






       def __iter__( self     :					Dict						)				->			Dict:





              """simple docstring"""
              yield from self.generate_examples_fn()






       def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					np.random.Generator						)				->			"SparkExamplesIterable":





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						list(range(self.df.rdd.getNumPartitions()						)						)
              generator.shuffle(_UpperCAmelCase						)
              return SparkExamplesIterable(self.df,			partition_order=_UpperCAmelCase						)






       def  A_     ( self     :					Any,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					int						)				->			"SparkExamplesIterable":





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.split_shard_indices_by_worker(_UpperCAmelCase,			_UpperCAmelCase						)
              return SparkExamplesIterable(self.df,			partition_order=_UpperCAmelCase						)






       @property
       def  A_     ( self     :					Any						)				->			int:





              """simple docstring"""
              return len(self.partition_order						)


class 						lowerCamelCase       (datasets.DatasetBuilder      ):







       """simple docstring"""




       UpperCAmelCase_      						=   SparkConfig






       def __init__( self     :					Dict,			_UpperCAmelCase     :					"pyspark.sql.DataFrame",			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					Any,			)				->			Optional[Any]:





              """simple docstring"""
              import pyspark

              SCREAMING_SNAKE_CASE__       :      str     =						pyspark.sql.SparkSession.builder.getOrCreate()
              SCREAMING_SNAKE_CASE__       :      int     =						df
              SCREAMING_SNAKE_CASE__       :      Dict     =						working_dir

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






       def  A_     ( self     :					List[Any]						)				->			List[Any]:





              """simple docstring"""
              # Returns the path of the created file.
              def create_cache_and_write_probe(_UpperCAmelCase     :					Tuple						):
                     # 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						)
                     SCREAMING_SNAKE_CASE__       :      Union[str, 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(_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:
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						(
                         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  A_     ( self     :					str						)				->			str:





              """simple docstring"""
              return datasets.DatasetInfo(features=self.config.features						)






       def  A_     ( self     :					List[str],			_UpperCAmelCase     :					datasets.download.download_manager.DownloadManager						)				->			int:





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






       def  A_     ( self     :					Dict,			_UpperCAmelCase     :					str						)				->			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]}						)

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.df.count()
              SCREAMING_SNAKE_CASE__       :      int     =						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__       :      Union[str, Any]     =						(
                  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
              )
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						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__       :      Any     =						min(_UpperCAmelCase,			int(approx_total_size / max_shard_size						)						)
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.df.repartition(_UpperCAmelCase						)






       def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					int,			)				->			Iterable[Tuple[int, bool, Union[int, tuple]]]:





              """simple docstring"""
              import pyspark

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

              def write_arrow(_UpperCAmelCase     :					Union[str, Any]						):
                     # Within the same SparkContext, no two task attempts will share the same attempt ID.
                     SCREAMING_SNAKE_CASE__       :      Any     =						pyspark.TaskContext().taskAttemptId()
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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"],			)
                     SCREAMING_SNAKE_CASE__       :      int     =						0
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						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,			)
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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:
                                   SCREAMING_SNAKE_CASE__       :      Any     =						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__       :      Optional[int]     =						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,			)
                            SCREAMING_SNAKE_CASE__       :      str     =						pa.Table.from_batches([batch]						)
                            writer.write_table(_UpperCAmelCase						)

                     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(_UpperCAmelCase						)						):
                                   SCREAMING_SNAKE_CASE__       :      Dict     =						os.path.join(os.path.dirname(_UpperCAmelCase						),			os.path.basename(_UpperCAmelCase						)						)
                                   shutil.move(_UpperCAmelCase,			_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Any     =						(
                  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  A_     ( self     :					List[str],			_UpperCAmelCase     :					"datasets.SplitGenerator",			_UpperCAmelCase     :					str = "arrow",			_UpperCAmelCase     :					Optional[Union[str, int]] = None,			_UpperCAmelCase     :					Optional[int] = None,			**_UpperCAmelCase     :					Any,			)				->			int:





              """simple docstring"""
              self._validate_cache_dir()

              SCREAMING_SNAKE_CASE__       :      Dict     =						convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
              self._repartition_df_if_needed(_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      Any     =						not is_remote_filesystem(self._fs						)
              SCREAMING_SNAKE_CASE__       :      Tuple     =						os.path.join if is_local else posixpath.join

              SCREAMING_SNAKE_CASE__       :      int     =						"-TTTTT-SSSSS-of-NNNNN"
              SCREAMING_SNAKE_CASE__       :      Tuple     =						F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
              SCREAMING_SNAKE_CASE__       :      Dict     =						path_join(self._output_dir,			_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						0
              SCREAMING_SNAKE_CASE__       :      str     =						0
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0
              SCREAMING_SNAKE_CASE__       :      Any     =						[]
              SCREAMING_SNAKE_CASE__       :      str     =						[]

              for task_id, content in self._prepare_split_single(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						):
                     (
                         SCREAMING_SNAKE_CASE__
                     )       :      Tuple     =						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						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						total_num_examples
              SCREAMING_SNAKE_CASE__       :      List[str]     =						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(
                         _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}'''						),			)

                     SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
                     for i in range(len(_UpperCAmelCase						)						):
                            SCREAMING_SNAKE_CASE__       :      Dict     =						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
                     SCREAMING_SNAKE_CASE__       :      List[str]     =						0
                     SCREAMING_SNAKE_CASE__       :      List[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(_UpperCAmelCase,			""						),			)






       def  A_     ( self     :					Any,			_UpperCAmelCase     :					"datasets.SplitGenerator",			)				->			SparkExamplesIterable:





              """simple docstring"""
              return SparkExamplesIterable(self.df						)













from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														else:
																					raise ZeroDivisionError("Could not find root"       ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess       ) < precision:
																					return next_guess

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(f"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(f"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    '''The root of log(y) - 1 = 0 is ''',
		    f"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    '''The root of exp(x) - 1 = 0 is''',
		    f"{newton_raphson('exp(x) - 1', 1_0, precision=0.005)}",
		)

		# Find root of cos(x)
		print(f"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






