'''simple docstring'''




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   str:
							lowercase_      :    Optional[int]       		= """"""
							for ch in key:
														if ch == " " or ch not in key_no_dups and ch.isalpha():
																					key_no_dups += ch
							return key_no_dups




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   dict[str, str]:
							lowercase_      :    Optional[Any]       		= [chr(i + 65				) for i in range(26				)]
							# Remove duplicate characters from key
							lowercase_      :    str       		= remove_duplicates(key.upper()				)
							lowercase_      :    Optional[int]       		= len(UpperCAmelCase__				)
							# First fill cipher with key characters
							lowercase_      :    List[str]       		= {alphabet[i]: char for i, char in enumerate(UpperCAmelCase__				)}
							# Then map remaining characters in alphabet to
							# the alphabet from the beginning
							for i in range(len(UpperCAmelCase__				)   ,   26				):
														lowercase_      :    Optional[Any]       		= alphabet[i - offset]
														# Ensure we are not mapping letters to letters previously mapped
														while char in key:
																					offset -= 1
																					lowercase_      :    List[str]       		= alphabet[i - offset]
														lowercase_      :    List[str]       		= char
							return cipher_alphabet




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      dict[str, str]				)    ->   str:
							return "".join(cipher_map.get(UpperCAmelCase__   ,   UpperCAmelCase__				) for ch in message.upper()				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      dict[str, str]				)    ->   str:
							lowercase_      :    Optional[int]       		= {v: k for k, v in cipher_map.items()}
							return "".join(rev_cipher_map.get(UpperCAmelCase__   ,   UpperCAmelCase__				) for ch in message.upper()				)




def 							lowerCamelCase			(   )    ->   None:
							lowercase_      :    Tuple       		= input("""Enter message to encode or decode: """				).strip()
							lowercase_      :    Optional[int]       		= input("""Enter keyword: """				).strip()
							lowercase_      :    Tuple       		= input("""Encipher or decipher? E/D:"""				).strip()[0].lower()
							try:
														lowercase_      :    int       		= {"""e""": encipher, """d""": decipher}[option]
							except KeyError:
														raise KeyError("""invalid input option"""				)
							lowercase_      :    List[Any]       		= create_cipher_map(UpperCAmelCase__				)
							print(func(UpperCAmelCase__   ,   UpperCAmelCase__				)				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






'''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


_lowercase		:				str	       =		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark





@dataclass
class 					__magic_name__		(					datasets.BuilderConfig):
			UpperCamelCase__       = None




def 							lowerCamelCase			(   UpperCAmelCase__ :      "pyspark.sql.DataFrame"   ,   UpperCAmelCase__ :      List[int]   ,   )    ->   str:
							import pyspark

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

							return generate_fn





class 					__magic_name__		(					_BaseExamplesIterable):







			def __init__(			self       :						int       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						Optional[int]=None       ,    ):
										lowercase_      :    Dict       		= df
										lowercase_      :    Optional[Any]       		= partition_order or range(self.df.rdd.getNumPartitions()					)
										lowercase_      :    Optional[Any]       		= _generate_iterable_examples(self.df       ,    self.partition_order					)







			def __iter__(			self       :						List[Any]					):
										yield from self.generate_examples_fn()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.random.Generator					):
										lowercase_      :    str       		= list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										lowercase_      :    str       		= self.split_shard_indices_by_worker(lowercase_       ,    lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return len(self.partition_order					)





class 					__magic_name__		(					datasets.DatasetBuilder):
			UpperCamelCase__       = SparkConfig







			def __init__(			self       :						Tuple       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						str = None       ,    lowercase_       :						str = None       ,    **lowercase_       :						str       ,    ):
										import pyspark

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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase_       :						str					):
																	# 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=lowercase_					)
																	lowercase_      :    List[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(lowercase_       ,    """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_      :    str       		= (
																	    self._spark.sparkContext.parallelize(range(1					)       ,    1					).mapPartitions(lowercase_					).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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return datasets.DatasetInfo(features=self.config.features					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						datasets.download.download_manager.DownloadManager					):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										import pyspark

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

										lowercase_      :    Union[str, Any]       		= self.df.count()
										lowercase_      :    Union[str, Any]       		= 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_      :    Any       		= (
										    self.df.limit(lowercase_					)
										    .repartition(1					)
										    .mapInArrow(lowercase_       ,    """batch_bytes: long"""					)
										    .agg(pyspark.sql.functions.sum("""batch_bytes"""					).alias("""sample_bytes"""					)					)
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										lowercase_      :    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.
																	lowercase_      :    Any       		= min(lowercase_       ,    int(approx_total_size / max_shard_size					)					)
																	lowercase_      :    Any       		= self.df.repartition(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						int       ,    ):
										import pyspark

										lowercase_      :    Any       		= ParquetWriter if file_format == """parquet""" else ArrowWriter
										lowercase_      :    Dict       		= os.path.join(self._working_dir       ,    os.path.basename(lowercase_					)					) if self._working_dir else fpath
										lowercase_      :    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.
										lowercase_      :    Tuple       		= self.config.features
										lowercase_      :    Any       		= self._writer_batch_size
										lowercase_      :    List[str]       		= self._fs.storage_options

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

																	if writer._num_bytes > 0:
																								lowercase_		,		lowercase_      :    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(lowercase_					)					):
																															lowercase_      :    Optional[Any]       		= os.path.join(os.path.dirname(lowercase_					)       ,    os.path.basename(lowercase_					)					)
																															shutil.move(lowercase_       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= (
										    self.df.mapInArrow(lowercase_       ,    """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 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						"datasets.SplitGenerator"       ,    lowercase_       :						str = "arrow"       ,    lowercase_       :						Optional[Union[str, int]] = None       ,    lowercase_       :						Optional[int] = None       ,    **lowercase_       :						List[str]       ,    ):
										self._validate_cache_dir()

										lowercase_      :    int       		= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(lowercase_					)
										lowercase_      :    Tuple       		= not is_remote_filesystem(self._fs					)
										lowercase_      :    int       		= os.path.join if is_local else posixpath.join

										lowercase_      :    Dict       		= """-TTTTT-SSSSS-of-NNNNN"""
										lowercase_      :    Dict       		= f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
										lowercase_      :    Optional[int]       		= path_join(self._output_dir       ,    lowercase_					)

										lowercase_      :    Any       		= 0
										lowercase_      :    Tuple       		= 0
										lowercase_      :    int       		= 0
										lowercase_      :    Dict       		= []
										lowercase_      :    Union[str, Any]       		= []

										for task_id, content in self._prepare_split_single(lowercase_       ,    lowercase_       ,    lowercase_					):
																	(
																	    (
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		
																	)      :    Union[str, Any]       		= 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(lowercase_					)

										lowercase_      :    List[str]       		= total_num_examples
										lowercase_      :    int       		= total_num_bytes

										# should rename everything at the end
										logger.debug(f'''Renaming {total_shards} shards.'''					)
										if total_shards > 1:
																	lowercase_      :    Tuple       		= 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_      :    Dict       		= self._fs

																	# use the -SSSSS-of-NNNNN pattern
																	def _rename_shard(
																	    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    ):
																								rename(
																								    lowercase_       ,    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_      :    Union[str, Any]       		= []
																	lowercase_      :    Tuple       		= 0
																	for i in range(len(lowercase_					)					):
																								lowercase_		,		lowercase_      :    List[Any]       		= task_id_and_num_shards[i]
																								for shard_id in range(lowercase_					):
																															args.append([task_id, shard_id, global_shard_id]					)
																															global_shard_id += 1
																	self._spark.sparkContext.parallelize(lowercase_       ,    len(lowercase_					)					).map(lambda lowercase_					: _rename_shard(*lowercase_					)					).collect()
										else:
																	# don't use any pattern
																	lowercase_      :    List[str]       		= 0
																	lowercase_      :    Optional[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(lowercase_       ,    """"""					)       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						"datasets.SplitGenerator"       ,    ):
										return SparkExamplesIterable(self.df					)





