






'''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    :      List[str]  			=		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
							import pyspark




@dataclass
class 		UpperCamelCase__    (datasets.BuilderConfig			):




		'''simple docstring'''





		_UpperCamelCase				  =					None



def 						A__			(				__lowerCAmelCase		:			"pyspark.sql.DataFrame"   ,    __lowerCAmelCase		:			List[int]   ,    ):
						import pyspark

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

						return generate_fn




class 		UpperCamelCase__    (_BaseExamplesIterable			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  df
								lowerCamelCase__         =  partition_order or range(self.df.rdd.getNumPartitions()       )
								lowerCamelCase__         =  _generate_iterable_examples(self.df   ,self.partition_order       )






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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  list(range(self.df.rdd.getNumPartitions()       )       )
								generator.shuffle(_lowerCAmelCase       )
								return SparkExamplesIterable(self.df   ,partition_order=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.split_shard_indices_by_worker(_lowerCAmelCase   ,_lowerCAmelCase       )
								return SparkExamplesIterable(self.df   ,partition_order=_lowerCAmelCase       )






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




class 		UpperCamelCase__    (datasets.DatasetBuilder			):




		'''simple docstring'''





		_UpperCamelCase				  =					SparkConfig






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

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

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






		def 			UpperCamelCase_					(      self       ):
								# Returns the path of the created file.
								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=_lowerCAmelCase       )
														lowerCamelCase__         =  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(_lowerCAmelCase   ,"""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:
														lowerCamelCase__         =  (
														    self._spark.sparkContext.parallelize(range(1       )   ,1       ).mapPartitions(_lowerCAmelCase       ).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       ):
								return datasets.DatasetInfo(features=self.config.features       )






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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								import pyspark

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

								lowerCamelCase__         =  self.df.count()
								lowerCamelCase__         =  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.
								lowerCamelCase__         =  (
								    self.df.limit(_lowerCAmelCase       )
								    .repartition(1       )
								    .mapInArrow(_lowerCAmelCase   ,"""batch_bytes: long"""       )
								    .agg(pyspark.sql.functions.sum("""batch_bytes"""       ).alias("""sample_bytes"""       )       )
								    .collect()[0]
								    .sample_bytes
								    / sample_num_rows
								)
								lowerCamelCase__         =  approx_bytes_per_row * df_num_rows
								if approx_total_size > max_shard_size:
														# Make sure there is at least one row per partition.
														lowerCamelCase__         =  min(_lowerCAmelCase   ,int(approx_total_size / max_shard_size       )       )
														lowerCamelCase__         =  self.df.repartition(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,):
								import pyspark

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

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

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

								lowerCamelCase__         =  (
								    self.df.mapInArrow(_lowerCAmelCase   ,"""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   ,_lowerCAmelCase   ,_lowerCAmelCase = "arrow"   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								self._validate_cache_dir()

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

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

								lowerCamelCase__         =  0
								lowerCamelCase__         =  0
								lowerCamelCase__         =  0
								lowerCamelCase__         =  []
								lowerCamelCase__         =  []

								for task_id, content in self._prepare_split_single(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
														(
														    (
														    lowerCamelCase__
														)       ,			(
														    lowerCamelCase__
														)       ,			(
														    lowerCamelCase__
														)       ,			(
														    lowerCamelCase__
														)       ,			
														)         =  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(_lowerCAmelCase       )

								lowerCamelCase__         =  total_num_examples
								lowerCamelCase__         =  total_num_bytes

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

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

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,):
								return SparkExamplesIterable(self.df       )









'''simple docstring'''







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase    :      List[Any]  			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					GPTSwaTokenizer
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					True
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase   ,eos_token="""<unk>"""   ,bos_token="""<unk>"""   ,pad_token="""<unk>"""       )

								tokenizer.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  """This is a test"""
								lowerCamelCase__         =  """This is a test"""
								return input_text, output_text






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """<s>"""
								lowerCamelCase__         =  1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase       )   ,_lowerCAmelCase       )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  list(self.get_tokenizer().get_vocab().keys()       )

								self.assertEqual(vocab_keys[0]   ,"""<unk>"""       )
								self.assertEqual(vocab_keys[1]   ,"""<s>"""       )
								self.assertEqual(vocab_keys[-1]   ,"""j"""       )
								self.assertEqual(len(_lowerCAmelCase       )   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								self.assertEqual(self.get_tokenizer().vocab_size   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer.tokenize("""This is a test"""       )
								self.assertListEqual(_lowerCAmelCase   ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )   ,[4_65, 2_87, 2_65, 6_31, 8_42]       )

								lowerCamelCase__         =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   ,)
								# fmt: on

								lowerCamelCase__         =  tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )
								self.assertListEqual(
								    _lowerCAmelCase   ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]   ,)

								lowerCamelCase__         =  tokenizer.convert_ids_to_tokens(_lowerCAmelCase       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]       )
								# fmt: on






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )
								lowerCamelCase__         =  ["""This is a test""", """I was born in 92000, and this is falsé."""]
								lowerCamelCase__         =  [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [
								    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
								    """Hey there, how are you doing this fine day?""",
								    """This is a text with a trailing spaces followed by a dot     .""",
								    """Häj sväjs lillebrör! =)""",
								    """Det är inget fel på Mr. Cool""",
								]

								# fmt: off
								lowerCamelCase__         =  {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCAmelCase   ,model_name="""AI-Sweden/gpt-sw3-126m"""   ,sequences=_lowerCAmelCase   ,)

