

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


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

if TYPE_CHECKING:
  import pyspark





@dataclass
class  __lowerCAmelCase     (					datasets.BuilderConfig							):
  _a									=  None
def      a      (			A__	:   Dict      ,   A__	:   List[Any]      ,   )    ->      Union[str, Any]:





      """simple docstring"""



      import pyspark

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

      return generate_fn





class  __lowerCAmelCase     (					_BaseExamplesIterable							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=None							,   )  -> List[str]:
        '''simple docstring'''






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




  def __iter__(				self					)  -> Optional[int]:
        '''simple docstring'''






        yield from self.generate_examples_fn()




  def   A__     (				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.split_shard_indices_by_worker(_A							,   _A					)
        return SparkExamplesIterable(self.df							,   partition_order=_A					)




  @property
  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        return len(self.partition_order					)





class  __lowerCAmelCase     (					datasets.DatasetBuilder							):
  _a									=  SparkConfig




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> List[str]:
        '''simple docstring'''






        import pyspark

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

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




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        def create_cache_and_write_probe(lowerCAmelCase					):
              # 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					)
              _lowercase	       =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:
              _lowercase	       =(
                  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   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        import pyspark

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

        _lowercase	       =self.df.count()
        _lowercase	       =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.
        _lowercase	       =(
            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
        )
        _lowercase	       =approx_bytes_per_row * df_num_rows
        if approx_total_size > max_shard_size:
              # Make sure there is at least one row per partition.
              _lowercase	       =min(_A							,   int(approx_total_size / max_shard_size					)					)
              _lowercase	       =self.df.repartition(_A					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   )  -> List[str]:
        '''simple docstring'''






        import pyspark

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

        def write_arrow(lowerCAmelCase					):
              # Within the same SparkContext, no two task attempts will share the same attempt ID.
              _lowercase	       =pyspark.TaskContext().taskAttemptId()
              _lowercase	       =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']							,   )
              _lowercase	       =0
              _lowercase	       =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							,   )
              _lowercase	       =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:
                          _lowercase	       =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
                          _lowercase	       =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							,   )
                    _lowercase	       =pa.Table.from_batches([batch]					)
                    writer.write_table(_A					)

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

        _lowercase	       =(
            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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = "arrow"							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> str:
        '''simple docstring'''






        self._validate_cache_dir()

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

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

        _lowercase	       =0
        _lowercase	       =0
        _lowercase	       =0
        _lowercase	       =[]
        _lowercase	       =[]

        for task_id, content in self._prepare_split_single(_A							,   _A							,   _A					):
              (
                  _lowercase				
              )	       =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					)

        _lowercase	       =total_num_examples
        _lowercase	       =total_num_bytes

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

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

              _lowercase	       =[]
              _lowercase	       =0
              for i in range(len(_A					)					):
                    _lowercase	       =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 lowerCAmelCase					: _rename_shard(*_A					)					).collect()
        else:
              # don't use any pattern
              _lowercase	       =0
              _lowercase	       =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   A__     (				self							,   lowerCAmelCase							,   )  -> Tuple:
        '''simple docstring'''






        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()




