





"""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


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

if TYPE_CHECKING:
						import pyspark





@dataclass
class        _lowerCamelCase      (       datasets.BuilderConfig ):
						UpperCAmelCase_ =			None



def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):




		"""simple docstring"""


		import pyspark

		def generate_fn():
				UpperCamelCase   =	df.select("*"  ,	pyspark.sql.functions.spark_partition_id().alias("part_id"    )    )
				for partition_id in partition_order:
						UpperCamelCase   =	df_with_partition_id.select("*"    ).where(F"part_id = {partition_id}"    ).drop("part_id"    )
						UpperCamelCase   =	partition_df.collect()
						UpperCamelCase   =	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   ,							__a   ,							__a=None   ,							) ->   int:
								UpperCamelCase   =	df
								UpperCamelCase   =	partition_order or range(self.df.rdd.getNumPartitions()		)
								UpperCamelCase   =	_generate_iterable_examples(self.df   ,							self.partition_order		)


						def __iter__(self		) ->   Optional[int]:
								yield from self.generate_examples_fn()


						def     snake_case_   (self   ,							__a		) ->   "SparkExamplesIterable":
								UpperCamelCase   =	list(range(self.df.rdd.getNumPartitions()		)		)
								generator.shuffle(__a		)
								return SparkExamplesIterable(self.df   ,							partition_order=__a		)


						def     snake_case_   (self   ,							__a   ,							__a		) ->   "SparkExamplesIterable":
								UpperCamelCase   =	self.split_shard_indices_by_worker(__a   ,							__a		)
								return SparkExamplesIterable(self.df   ,							partition_order=__a		)




						@property
						def     snake_case_   (self		) ->   int:
								return len(self.partition_order		)





class        _lowerCamelCase      (       datasets.DatasetBuilder ):
						UpperCAmelCase_ =			SparkConfig


						def __init__(self   ,							__a   ,							__a = None   ,							__a = None   ,							**__a   ,							) ->   Union[str, Any]:
								import pyspark

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

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


						def     snake_case_   (self		) ->   Optional[int]:
								# 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		)
										UpperCamelCase   =	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:
										UpperCamelCase   =	(
										    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     snake_case_   (self		) ->   Optional[Any]:
								return datasets.DatasetInfo(features=self.config.features		)


						def     snake_case_   (self   ,							__a		) ->   Any:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]


						def     snake_case_   (self   ,							__a		) ->   Union[str, Any]:
								import pyspark

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

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


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							) ->   Iterable[Tuple[int, bool, Union[int, tuple]]]:
								import pyspark

								UpperCamelCase   =	ParquetWriter if file_format == "parquet" else ArrowWriter
								UpperCamelCase   =	os.path.join(self._working_dir   ,							os.path.basename(__a		)		) if self._working_dir else fpath
								UpperCamelCase   =	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.
								UpperCamelCase   =	self.config.features
								UpperCamelCase   =	self._writer_batch_size
								UpperCamelCase   =	self._fs.storage_options

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

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

								UpperCamelCase   =	(
								    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     snake_case_   (self   ,							__a   ,							__a = "arrow"   ,							__a = None   ,							__a = None   ,							**__a   ,							) ->   List[Any]:
								self._validate_cache_dir()

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

								UpperCamelCase   =	"-TTTTT-SSSSS-of-NNNNN"
								UpperCamelCase   =	F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
								UpperCamelCase   =	path_join(self._output_dir   ,							__a		)

								UpperCamelCase   =	0
								UpperCamelCase   =	0
								UpperCamelCase   =	0
								UpperCamelCase   =	[]
								UpperCamelCase   =	[]

								for task_id, content in self._prepare_split_single(__a   ,							__a   ,							__a		):
										(
										    (
										    UpperCamelCase				
										)   ,				(
										    UpperCamelCase				
										)   ,				(
										    UpperCamelCase				
										)   ,				(
										    UpperCamelCase				
										)   ,								
										)   =	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		)

								UpperCamelCase   =	total_num_examples
								UpperCamelCase   =	total_num_bytes

								# should rename everything at the end
								logger.debug(F"Renaming {total_shards} shards."		)
								if total_shards > 1:
										UpperCamelCase   =	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.
										UpperCamelCase   =	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}"		)   ,							)

										UpperCamelCase   =	[]
										UpperCamelCase   =	0
										for i in range(len(__a		)		):
												UpperCamelCase   ,				UpperCamelCase   =	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
										UpperCamelCase   =	0
										UpperCamelCase   =	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     snake_case_   (self   ,							__a   ,							) ->   SparkExamplesIterable:
								return SparkExamplesIterable(self.df		)













"""simple docstring"""





import string


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in range(len(string.ascii_uppercase    )    ):
				UpperCamelCase   =	""
				for symbol in message:
						if symbol in string.ascii_uppercase:
								UpperCamelCase   =	string.ascii_uppercase.find(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	num - key
								if num < 0:
										UpperCamelCase   =	num + len(string.ascii_uppercase    )
								UpperCamelCase   =	translated + string.ascii_uppercase[num]
						else:
								UpperCamelCase   =	translated + symbol
				print(F"Decryption using Key #{key}: {translated}"    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	input("Encrypted message: "    )
		UpperCamelCase   =	message.upper()
		decrypt(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






