
import argparse
import struct
import unittest





class     __UpperCamelCase :






     """simple docstring"""







     def __init__(				self							:							Union[str, Any]     ,						_A							:							bytes ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		data

       # Initialize hash values
       __SCREAMING_SNAKE_CASE      :	List[str]					=		[
           0x6A_09_E6_67,
           0xBB_67_AE_85,
           0x3C_6E_F3_72,
           0xA5_4F_F5_3A,
           0x51_0E_52_7F,
           0x9B_05_68_8C,
           0x1F_83_D9_AB,
           0x5B_E0_CD_19,
       ]

       # Initialize round constants
       __SCREAMING_SNAKE_CASE      :	List[str]					=		[
           0x42_8A_2F_98,
           0x71_37_44_91,
           0xB5_C0_FB_CF,
           0xE9_B5_DB_A5,
           0x39_56_C2_5B,
           0x59_F1_11_F1,
           0x92_3F_82_A4,
           0xAB_1C_5E_D5,
           0xD8_07_AA_98,
           0x12_83_5B_01,
           0x24_31_85_BE,
           0x55_0C_7D_C3,
           0x72_BE_5D_74,
           0x80_DE_B1_FE,
           0x9B_DC_06_A7,
           0xC1_9B_F1_74,
           0xE4_9B_69_C1,
           0xEF_BE_47_86,
           0x0F_C1_9D_C6,
           0x24_0C_A1_CC,
           0x2D_E9_2C_6F,
           0x4A_74_84_AA,
           0x5C_B0_A9_DC,
           0x76_F9_88_DA,
           0x98_3E_51_52,
           0xA8_31_C6_6D,
           0xB0_03_27_C8,
           0xBF_59_7F_C7,
           0xC6_E0_0B_F3,
           0xD5_A7_91_47,
           0x06_CA_63_51,
           0x14_29_29_67,
           0x27_B7_0A_85,
           0x2E_1B_21_38,
           0x4D_2C_6D_FC,
           0x53_38_0D_13,
           0x65_0A_73_54,
           0x76_6A_0A_BB,
           0x81_C2_C9_2E,
           0x92_72_2C_85,
           0xA2_BF_E8_A1,
           0xA8_1A_66_4B,
           0xC2_4B_8B_70,
           0xC7_6C_51_A3,
           0xD1_92_E8_19,
           0xD6_99_06_24,
           0xF4_0E_35_85,
           0x10_6A_A0_70,
           0x19_A4_C1_16,
           0x1E_37_6C_08,
           0x27_48_77_4C,
           0x34_B0_BC_B5,
           0x39_1C_0C_B3,
           0x4E_D8_AA_4A,
           0x5B_9C_CA_4F,
           0x68_2E_6F_F3,
           0x74_8F_82_EE,
           0x78_A5_63_6F,
           0x84_C8_78_14,
           0x8C_C7_02_08,
           0x90_BE_FF_FA,
           0xA4_50_6C_EB,
           0xBE_F9_A3_F7,
           0xC6_71_78_F2,
       ]

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.preprocessing(self.data )
       self.final_hash()



     @staticmethod
     def  UpperCAmelCase__							(				_A							:							bytes ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		B'''\x80''' + (B'''\x00''' * (63 - (len(_A ) + 8) % 64))
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		struct.pack('''>Q'''     ,						(len(_A ) * 8) )
       return data + padding + big_endian_integer



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		[
           self.preprocessed_data[x : x + 64]
           for x in range(0     ,						len(self.preprocessed_data )     ,						64 )
       ]

       for block in self.blocks:
         # Convert the given block into a list of 4 byte integers
         __SCREAMING_SNAKE_CASE      :	Dict					=		list(struct.unpack('''>16L'''     ,						_A ) )
         # add 48 0-ed integers
         words += [0] * 48

         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		self.hashes

         for index in range(0     ,						64 ):
           if index > 15:
             # modify the zero-ed indexes at the end of the array
             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(
                 self.ror(words[index - 15]     ,						7 )
                 ^ self.ror(words[index - 15]     ,						18 )
                 ^ (words[index - 15] >> 3)
             )
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(
                 self.ror(words[index - 2]     ,						17 )
                 ^ self.ror(words[index - 2]     ,						19 )
                 ^ (words[index - 2] >> 10)
             )

             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(
                 words[index - 16] + sa + words[index - 7] + sa
             ) % 0x1_00_00_00_00

           # Compression
           __SCREAMING_SNAKE_CASE      :	Any					=		self.ror(_A     ,						6 ) ^ self.ror(_A     ,						11 ) ^ self.ror(_A     ,						25 )
           __SCREAMING_SNAKE_CASE      :	int					=		(e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
           __SCREAMING_SNAKE_CASE      :	List[str]					=		(
               h + sa + ch + self.round_constants[index] + words[index]
           ) % 0x1_00_00_00_00
           __SCREAMING_SNAKE_CASE      :	Dict					=		self.ror(_A     ,						2 ) ^ self.ror(_A     ,						13 ) ^ self.ror(_A     ,						22 )
           __SCREAMING_SNAKE_CASE      :	str					=		(a & b) ^ (a & c) ^ (b & c)
           __SCREAMING_SNAKE_CASE      :	Dict					=		(sa + maj) % 0x1_00_00_00_00

           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[Any]					=		(
               g,
               f,
               e,
               ((d + tempa) % 0x1_00_00_00_00),
               c,
               b,
               a,
               ((tempa + tempa) % 0x1_00_00_00_00),
           )

         __SCREAMING_SNAKE_CASE      :	Tuple					=		[a, b, c, d, e, f, g, h]

         # Modify final values
         __SCREAMING_SNAKE_CASE      :	List[str]					=		[
             ((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
             for index, element in enumerate(self.hashes )
         ]

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		''''''.join([hex(_A )[2:].zfill(8 ) for value in self.hashes] )




     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							int     ,						_A							:							int ):







       """simple docstring"""




       return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)






class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       import hashlib

       __SCREAMING_SNAKE_CASE      :	Tuple					=		bytes('''Test String'''     ,						'''utf-8''' )
       self.assertEqual(SHAaaa(_A ).hash     ,						hashlib.shaaaa(_A ).hexdigest() )






def 							a__					(							):





  """simple docstring"""
  import doctest

  doctest.testmod()

  __SCREAMING_SNAKE_CASE      :	Any					=		argparse.ArgumentParser()
  parser.add_argument(
      '''-s'''       ,			'''--string'''       ,			dest='''input_string'''       ,			default='''Hello World!! Welcome to Cryptography'''       ,			help='''Hash the string'''       ,			)
  parser.add_argument(
      '''-f'''       ,			'''--file'''       ,			dest='''input_file'''       ,			help='''Hash contents of a file'''							)

  __SCREAMING_SNAKE_CASE      :	int					=		parser.parse_args()

  __SCREAMING_SNAKE_CASE      :	Tuple					=		args.input_string

  # hash input should be a bytestring
  if args.input_file:
    with open(args.input_file       ,			'''rb'''							) as f:
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		f.read()
  else:
    __SCREAMING_SNAKE_CASE      :	str					=		bytes(snake_case       ,			'''utf-8'''							)

  print(SHAaaa(snake_case							).hash							)


if __name__ == "__main__":
   main()



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


A_			:					Any    			=     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
	import pyspark



@dataclass
class        _a		(datasets.BuilderConfig							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[datasets.Features]    				=			None



def 		UpperCamelCase   (lowercase_:					"pyspark.sql.DataFrame"       , lowercase_:					List[int]       , )	-> Dict:
	import pyspark

	def generate_fn():
		A__				:       List[str]	  =						df.select("""*"""       , pyspark.sql.functions.spark_partition_id().alias("""part_id"""   )   )
		for partition_id in partition_order:
			A__				:       List[Any]	  =						df_with_partition_id.select("""*"""   ).where(f"""part_id = {partition_id}"""   ).drop("""part_id"""   )
			A__				:       Optional[int]	  =						partition_df.collect()
			A__				:       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__				:       List[str]	  =						df
						A__				:       Optional[int]	  =						partition_order or range(self.df.rdd.getNumPartitions()     )
						A__				:       Tuple	  =						_generate_iterable_examples(self.df       ,			self.partition_order     )






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






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						list(range(self.df.rdd.getNumPartitions()     )     )
						generator.shuffle(A__     )
						return SparkExamplesIterable(self.df       ,			partition_order=A__     )






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






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



class        _a		(datasets.DatasetBuilder							):


					'''simple docstring'''






					UpperCAmelCase__:  Union[str, Any]    				=			SparkConfig






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

						A__				:       Dict	  =						pyspark.sql.SparkSession.builder.getOrCreate()
						A__				:       int	  =						df
						A__				:       Any	  =						working_dir

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






					def     __A     (					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__				:       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(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__				:       int	  =						(
							    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     ):
						return datasets.DatasetInfo(features=self.config.features     )






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






					def     __A     (					self       ,			A__     ):
						import pyspark

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

						A__				:       Dict	  =						self.df.count()
						A__				:       List[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.
						A__				:       Union[str, Any]	  =						(
						    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__				:       str	  =						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__				:       Dict	  =						min(A__       ,			int(approx_total_size / max_shard_size     )     )
							A__				:       int	  =						self.df.repartition(A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			):
						import pyspark

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

						def write_arrow(A__     ):
							# Within the same SparkContext, no two task attempts will share the same attempt ID.
							A__				:       str	  =						pyspark.TaskContext().taskAttemptId()
							A__				:       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__				:       List[Any]	  =						0
							A__				:       Optional[Any]	  =						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__				:       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"""]       ,			)
									shard_id += 1
									A__				:       Tuple	  =						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__				:       Optional[int]	  =						pa.Table.from_batches([batch]     )
								writer.write_table(A__     )

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

						A__				:       Tuple	  =						(
						    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       ,			A__       ,			A__ = "arrow"       ,			A__ = None       ,			A__ = None       ,			**A__       ,			):
						self._validate_cache_dir()

						A__				:       Union[str, Any]	  =						convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE     )
						self._repartition_df_if_needed(A__     )
						A__				:       Any	  =						not is_remote_filesystem(self._fs     )
						A__				:       Optional[int]	  =						os.path.join if is_local else posixpath.join

						A__				:       Dict	  =						"""-TTTTT-SSSSS-of-NNNNN"""
						A__				:       Any	  =						F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
						A__				:       Any	  =						path_join(self._output_dir       ,			A__     )

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

						for task_id, content in self._prepare_split_single(A__       ,			A__       ,			A__     ):
							(
							    (
							    A__
							)  ,  (
							    A__
							)  ,  (
							    A__
							)  ,  (
							    A__
							)  ,  
							)				:       List[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(A__     )

						A__				:       Optional[int]	  =						total_num_examples
						A__				:       Union[str, Any]	  =						total_num_bytes

						# should rename everything at the end
						logger.debug(F"""Renaming {total_shards} shards."""     )
						if total_shards > 1:
							A__				:       int	  =						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__				:       Dict	  =						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__				:       Union[str, Any]	  =						0
							for i in range(len(A__     )     ):
								A__  ,  A__				:       Optional[int]	  =						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__				:       List[Any]	  =						0
							A__				:       List[str]	  =						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       ,			A__       ,			):
						return SparkExamplesIterable(self.df     )



