




'''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


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

if TYPE_CHECKING:
			import pyspark
@dataclass
class 		SCREAMING_SNAKE_CASE			(     datasets.BuilderConfig							):




				'''simple docstring'''





				__UpperCamelCase     				=   None






def       lowerCAmelCase_     (					__A						:							Optional[Any] ,			__A						:							str ,			):




			'''simple docstring'''


			import pyspark

			def generate_fn():
						snake_case:       List[str]        =       df.select('*' ,			pyspark.sql.functions.spark_partition_id().alias('part_id' ) )
						for partition_id in partition_order:
									snake_case:       Tuple        =       df_with_partition_id.select('*' ).where(f"""part_id = {partition_id}""" ).drop('part_id' )
									snake_case:       Optional[int]        =       partition_df.collect()
									snake_case:       int        =       0
									for row in rows:
												yield f"""{partition_id}_{row_id}""", row.asDict()
												row_id += 1

			return generate_fn
class 		SCREAMING_SNAKE_CASE			(     _BaseExamplesIterable							):




				'''simple docstring'''





				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=None  ,						):
							'''simple docstring'''







							snake_case:       Dict        =       df
							snake_case:       List[str]        =       partition_order or range(self.df.rdd.getNumPartitions()				)
							snake_case:       int        =       _generate_iterable_examples(self.df  ,						self.partition_order				)







				def __iter__(   self				):
							'''simple docstring'''







							yield from self.generate_examples_fn()







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Dict        =       list(range(self.df.rdd.getNumPartitions()				)				)
							generator.shuffle(SCREAMING_SNAKE_CASE__				)
							return SparkExamplesIterable(self.df  ,						partition_order=SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)
							return SparkExamplesIterable(self.df  ,						partition_order=SCREAMING_SNAKE_CASE__				)







				@property
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							return len(self.partition_order				)






class 		SCREAMING_SNAKE_CASE			(     datasets.DatasetBuilder							):




				'''simple docstring'''





				__UpperCamelCase     				=   SparkConfig







				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = None  ,						SCREAMING_SNAKE_CASE__ = None  ,						**SCREAMING_SNAKE_CASE__  ,						):
							'''simple docstring'''







							import pyspark

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

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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							def create_cache_and_write_probe(SCREAMING_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=SCREAMING_SNAKE_CASE__				)
										snake_case:       str        =       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(SCREAMING_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:
										snake_case:       Dict        =       (
										    self._spark.sparkContext.parallelize(range(1				)  ,						1				).mapPartitions(SCREAMING_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        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							import pyspark

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

							snake_case:       str        =       self.df.count()
							snake_case:       Any        =       df_num_rows if df_num_rows <= 1_00 else 1_00
							# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
							snake_case:       Any        =       (
							    self.df.limit(SCREAMING_SNAKE_CASE__				)
							    .repartition(1				)
							    .mapInArrow(SCREAMING_SNAKE_CASE__  ,						'batch_bytes: long'				)
							    .agg(pyspark.sql.functions.sum('batch_bytes'				).alias('sample_bytes'				)				)
							    .collect()[0]
							    .sample_bytes
							    / sample_num_rows
							)
							snake_case:       Dict        =       approx_bytes_per_row * df_num_rows
							if approx_total_size > max_shard_size:
										# Make sure there is at least one row per partition.
										snake_case:       Tuple        =       min(SCREAMING_SNAKE_CASE__  ,						int(approx_total_size / max_shard_size				)				)
										snake_case:       Any        =       self.df.repartition(SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						):
							'''simple docstring'''







							import pyspark

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

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

										if writer._num_bytes > 0:
													snake_case:       List[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(SCREAMING_SNAKE_CASE__				)				):
																snake_case:       Optional[int]        =       os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__				)  ,						os.path.basename(SCREAMING_SNAKE_CASE__				)				)
																shutil.move(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)

							snake_case:       Tuple        =       (
							    self.df.mapInArrow(SCREAMING_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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = "arrow"  ,						SCREAMING_SNAKE_CASE__ = None  ,						SCREAMING_SNAKE_CASE__ = None  ,						**SCREAMING_SNAKE_CASE__  ,						):
							'''simple docstring'''







							self._validate_cache_dir()

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

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

							snake_case:       List[Any]        =       0
							snake_case:       Dict        =       0
							snake_case:       int        =       0
							snake_case:       Any        =       []
							snake_case:       Optional[int]        =       []

							for task_id, content in self._prepare_split_single(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
										(
										    snake_case					
										):       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(SCREAMING_SNAKE_CASE__				)

							snake_case:       Any        =       total_num_examples
							snake_case:       Union[str, Any]        =       total_num_bytes

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

										# use the -SSSSS-of-NNNNN pattern
										def _rename_shard(
										    SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						):
													rename(
													    SCREAMING_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}"""				)  ,						)

										snake_case:       int        =       []
										snake_case:       Tuple        =       0
										for i in range(len(SCREAMING_SNAKE_CASE__				)				):
													snake_case:       Dict        =       task_id_and_num_shards[i]
													for shard_id in range(SCREAMING_SNAKE_CASE__				):
																args.append([task_id, shard_id, global_shard_id]				)
																global_shard_id += 1
										self._spark.sparkContext.parallelize(SCREAMING_SNAKE_CASE__  ,						len(SCREAMING_SNAKE_CASE__				)				).map(lambda SCREAMING_SNAKE_CASE__				: _rename_shard(*SCREAMING_SNAKE_CASE__				)				).collect()
							else:
										# don't use any pattern
										snake_case:       Optional[Any]        =       0
										snake_case:       int        =       task_id_and_num_shards[0][0]
										self._rename(
										    fpath.replace('SSSSS'  ,						F"""{shard_id:05d}"""				).replace('TTTTT'  ,						F"""{task_id:05d}"""				)  ,						fpath.replace(SCREAMING_SNAKE_CASE__  ,						''				)  ,						)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						):
							'''simple docstring'''







							return SparkExamplesIterable(self.df				)





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     int       =       len(lowercase__						)
				# We need to create solution object to save path.
				_lowerCamelCase	:     Tuple       =       [[0 for _ in range(lowercase__						)] for _ in range(lowercase__						)]
				_lowerCamelCase	:     Optional[Any]       =       run_maze(lowercase__				,       0				,       0				,       lowercase__						)
				if solved:
								print('\n'.join(str(lowercase__						) for row in solutions						)						)
				else:
								print('No solution exists!'						)
				return solved







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       len(lowercase__						)
				# Final check point.
				if i == j == (size - 1):
								_lowerCamelCase	:     Optional[Any]       =       1
								return True

				_lowerCamelCase	:     List[str]       =       (not i < 0) and (not j < 0)  # Check lower bounds
				_lowerCamelCase	:     List[str]       =       (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								_lowerCamelCase	:     List[Any]       =       (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												_lowerCamelCase	:     Union[str, Any]       =       1

												# check for directions
												if (
												    run_maze(lowercase__				,       i + 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j + 1				,       lowercase__						)
												    or run_maze(lowercase__				,       i - 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j - 1				,       lowercase__						)
												):
																return True

												_lowerCamelCase	:     Dict       =       0
												return False
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()