





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


UpperCAmelCase						:   Tuple			  =     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark





@dataclass
class 						_A(					datasets.BuilderConfig   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[datasets.Features]					    =	None







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

					def generate_fn():
										__A	:						str       =				df.select('*'					,						pyspark.sql.functions.spark_partition_id().alias('part_id'  )  )
										for partition_id in partition_order:
															__A	:						int       =				df_with_partition_id.select('*'  ).where(F"""part_id = {partition_id}"""  ).drop('part_id'  )
															__A	:						Optional[Any]       =				partition_df.collect()
															__A	:						Optional[Any]       =				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     ,					_A     ,					_A=None     ,					):
									__A	:						Union[str, Any]       =				df
									__A	:						Any       =				partition_order or range(self.df.rdd.getNumPartitions()     )
									__A	:						Union[str, Any]       =				_generate_iterable_examples(self.df     ,					self.partition_order     )



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



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Optional[int]       =				list(range(self.df.rdd.getNumPartitions()     )     )
									generator.shuffle(_A     )
									return SparkExamplesIterable(self.df     ,					partition_order=_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				self.split_shard_indices_by_worker(_A     ,					_A     )
									return SparkExamplesIterable(self.df     ,					partition_order=_A     )





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





class 						_A(					datasets.DatasetBuilder   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	SparkConfig



				def __init__(			self     ,					_A     ,					_A = None     ,					_A = None     ,					**_A     ,					):
									import pyspark

									__A	:						Any       =				pyspark.sql.SparkSession.builder.getOrCreate()
									__A	:						List[Any]       =				df
									__A	:						Optional[int]       =				working_dir

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



				def 					UpperCAmelCase_		(			self     ):
									# Returns the path of the created file.
									def create_cache_and_write_probe(_A     ):
														# 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=_A     )
														__A	:						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(_A     ,					'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	:						List[str]       =				(
														    self._spark.sparkContext.parallelize(range(1     )     ,					1     ).mapPartitions(_A     ).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     ):
									return datasets.DatasetInfo(features=self.config.features     )



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



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									import pyspark

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

									__A	:						Any       =				self.df.count()
									__A	:						List[str]       =				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	:						str       =				(
									    self.df.limit(_A     )
									    .repartition(1     )
									    .mapInArrow(_A     ,					'batch_bytes: long'     )
									    .agg(pyspark.sql.functions.sum('batch_bytes'     ).alias('sample_bytes'     )     )
									    .collect()[0]
									    .sample_bytes
									    / sample_num_rows
									)
									__A	:						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.
														__A	:						Optional[Any]       =				min(_A     ,					int(approx_total_size / max_shard_size     )     )
														__A	:						str       =				self.df.repartition(_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					):
									import pyspark

									__A	:						Tuple       =				ParquetWriter if file_format == 'parquet' else ArrowWriter
									__A	:						Optional[Any]       =				os.path.join(self._working_dir     ,					os.path.basename(_A     )     ) if self._working_dir else fpath
									__A	:						Dict       =				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	:						int       =				self.config.features
									__A	:						List[Any]       =				self._writer_batch_size
									__A	:						int       =				self._fs.storage_options

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

														if writer._num_bytes > 0:
																			__A    ,				__A	:						Union[str, Any]       =				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(_A     )     ):
																								__A	:						List[Any]       =				os.path.join(os.path.dirname(_A     )     ,					os.path.basename(_A     )     )
																								shutil.move(_A     ,					_A     )

									__A	:						List[str]       =				(
									    self.df.mapInArrow(_A     ,					'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     ,					_A     ,					_A = "arrow"     ,					_A = None     ,					_A = None     ,					**_A     ,					):
									self._validate_cache_dir()

									__A	:						Dict       =				convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE     )
									self._repartition_df_if_needed(_A     )
									__A	:						List[Any]       =				not is_remote_filesystem(self._fs     )
									__A	:						Union[str, Any]       =				os.path.join if is_local else posixpath.join

									__A	:						List[str]       =				'-TTTTT-SSSSS-of-NNNNN'
									__A	:						Any       =				F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
									__A	:						str       =				path_join(self._output_dir     ,					_A     )

									__A	:						Any       =				0
									__A	:						List[str]       =				0
									__A	:						Optional[Any]       =				0
									__A	:						Tuple       =				[]
									__A	:						Optional[Any]       =				[]

									for task_id, content in self._prepare_split_single(_A     ,					_A     ,					_A     ):
														(
														    (
														    __A
														)    ,				(
														    __A
														)    ,				(
														    __A
														)    ,				(
														    __A
														)    ,				
														)	:						List[str]       =				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(_A     )

									__A	:						Optional[int]       =				total_num_examples
									__A	:						Tuple       =				total_num_bytes

									# should rename everything at the end
									logger.debug(F"""Renaming {total_shards} shards."""     )
									if total_shards > 1:
														__A	:						Any       =				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	:						Union[str, Any]       =				self._fs

														# use the -SSSSS-of-NNNNN pattern
														def _rename_shard(
														    _A     ,					_A     ,					_A     ,					):
																			rename(
																			    _A     ,					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	:						List[Any]       =				[]
														__A	:						List[Any]       =				0
														for i in range(len(_A     )     ):
																			__A    ,				__A	:						Tuple       =				task_id_and_num_shards[i]
																			for shard_id in range(_A     ):
																								args.append([task_id, shard_id, global_shard_id]     )
																								global_shard_id += 1
														self._spark.sparkContext.parallelize(_A     ,					len(_A     )     ).map(lambda _A     : _rename_shard(*_A     )     ).collect()
									else:
														# don't use any pattern
														__A	:						Union[str, Any]       =				0
														__A	:						Dict       =				task_id_and_num_shards[0][0]
														self._rename(
														    fpath.replace('SSSSS'     ,					F"""{shard_id:05d}"""     ).replace('TTTTT'     ,					F"""{task_id:05d}"""     )     ,					fpath.replace(_A     ,					''     )     ,					)





				def 					UpperCAmelCase_		(			self     ,					_A     ,					):
									return SparkExamplesIterable(self.df     )











import colorsys

from PIL import Image  # type: ignore







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					__A	:						List[str]       =				x
					__A	:						str       =				y
					for step in range(a  ):  # noqa: B007
										__A	:						Union[str, Any]       =				a * a - b * b + x
										__A	:						Optional[int]       =				2 * a * b + y
										__A	:						List[str]       =				a_new

										# divergence happens for all complex number with an absolute value
										# greater than 4
										if a * a + b * b > 4:
															break
					return step / (max_step - 1)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return (2_55, 2_55, 2_55)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 2_55  ) for i in colorsys.hsv_to_rgb(a					,						1					,						1  )  )







def  _SCREAMING_SNAKE_CASE				(   a = 8_00					,						a = 6_00					,						a = -0.6					,						a = 0					,						a = 3.2					,						a = 50					,						a = True					,						)      ->      Image.Image:
					__A	:						str       =				Image.new('RGB'					,						(image_width, image_height)  )
					__A	:						Dict       =				img.load()

					# loop through the image-coordinates
					for image_x in range(a  ):
										for image_y in range(a  ):
															# determine the figure-coordinates based on the image-coordinates
															__A	:						Dict       =				figure_width / image_width * image_height
															__A	:						Union[str, Any]       =				figure_center_x + (image_x / image_width - 0.5) * figure_width
															__A	:						Optional[Any]       =				figure_center_y + (image_y / image_height - 0.5) * figure_height

															__A	:						Union[str, Any]       =				get_distance(a					,						a					,						a  )

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				__A	:						Optional[Any]       =				get_color_coded_rgb(a  )
															else:
																				__A	:						Dict       =				get_black_and_white_rgb(a  )

					return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				UpperCAmelCase						:   str			  =     get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()




