


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       :       int						       =				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
       import pyspark

@dataclass
class       __magic_name__	(     datasets.BuilderConfig):
     A:		Optional[datasets.Features]			=      None



def 			_a  (				SCREAMING_SNAKE_CASE	:				"pyspark.sql.DataFrame"    , SCREAMING_SNAKE_CASE	:				List[int]    , ):







 """simple docstring"""



 import pyspark

 def generate_fn():
  UpperCamelCase__					:    Union[str, Any]          =   df.select('''*'''    , pyspark.sql.functions.spark_partition_id().alias('''part_id'''     )     )
  for partition_id in partition_order:
   UpperCamelCase__					:    Dict          =   df_with_partition_id.select('''*'''     ).where(F"part_id = {partition_id}"     ).drop('''part_id'''     )
   UpperCamelCase__					:    int          =   partition_df.collect()
   UpperCamelCase__					:    int          =   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							:     Tuple							,	lowerCamelCase__							:     "pyspark.sql.DataFrame"							,	lowerCamelCase__							:     List[Any]=None							,	)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   df
      UpperCamelCase__					:    Dict          =   partition_order or range(self.df.rdd.getNumPartitions()			)
      UpperCamelCase__					:    Dict          =   _generate_iterable_examples(self.df							,	self.partition_order			)


     def __iter__(      self							:     Union[str, Any]			)  ->      List[str]:





      '''simple docstring'''






      yield from self.generate_examples_fn()


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     np.random.Generator			)  ->      "SparkExamplesIterable":





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   list(range(self.df.rdd.getNumPartitions()			)			)
      generator.shuffle(lowerCamelCase__			)
      return SparkExamplesIterable(self.df							,	partition_order=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      "SparkExamplesIterable":





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.split_shard_indices_by_worker(lowerCamelCase__							,	lowerCamelCase__			)
      return SparkExamplesIterable(self.df							,	partition_order=lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      int:





      '''simple docstring'''






      return len(self.partition_order			)



class       __magic_name__	(     datasets.DatasetBuilder):
     A:		List[Any]			=      SparkConfig


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     "pyspark.sql.DataFrame"							,	lowerCamelCase__							:     str = None							,	lowerCamelCase__							:     str = None							,	**lowerCamelCase__							:     Dict							,	)  ->      Tuple:





      '''simple docstring'''






      import pyspark

      UpperCamelCase__					:    List[str]          =   pyspark.sql.SparkSession.builder.getOrCreate()
      UpperCamelCase__					:    Tuple          =   df
      UpperCamelCase__					:    Any          =   working_dir

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      Dict:





      '''simple docstring'''






      def create_cache_and_write_probe(lowerCamelCase__							:     List[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=lowerCamelCase__			)
       UpperCamelCase__					:    Optional[Any]          =   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:
       UpperCamelCase__					:    Optional[Any]          =   (
           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							:     int			)  ->      List[str]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     datasets.download.download_manager.DownloadManager			)  ->      Dict:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Any:





      '''simple docstring'''






      import pyspark

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

      UpperCamelCase__					:    str          =   self.df.count()
      UpperCamelCase__					:    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.
      UpperCamelCase__					:    List[Any]          =   (
          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
      )
      UpperCamelCase__					:    List[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.
       UpperCamelCase__					:    Any          =   min(lowerCamelCase__							,	int(approx_total_size / max_shard_size			)			)
       UpperCamelCase__					:    Tuple          =   self.df.repartition(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	)  ->      Iterable[Tuple[int, bool, Union[int, tuple]]]:





      '''simple docstring'''






      import pyspark

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

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

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

      UpperCamelCase__					:    Union[str, Any]          =   (
          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							:     Optional[Any]							,	lowerCamelCase__							:     "datasets.SplitGenerator"							,	lowerCamelCase__							:     str = "arrow"							,	lowerCamelCase__							:     Optional[Union[str, int]] = None							,	lowerCamelCase__							:     Optional[int] = None							,	**lowerCamelCase__							:     Dict							,	)  ->      Optional[Any]:





      '''simple docstring'''






      self._validate_cache_dir()

      UpperCamelCase__					:    Optional[Any]          =   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE			)
      self._repartition_df_if_needed(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   not is_remote_filesystem(self._fs			)
      UpperCamelCase__					:    List[str]          =   os.path.join if is_local else posixpath.join

      UpperCamelCase__					:    Tuple          =   '''-TTTTT-SSSSS-of-NNNNN'''
      UpperCamelCase__					:    Optional[int]          =   F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
      UpperCamelCase__					:    List[str]          =   path_join(self._output_dir							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   0
      UpperCamelCase__					:    Union[str, Any]          =   0
      UpperCamelCase__					:    Dict          =   0
      UpperCamelCase__					:    Dict          =   []
      UpperCamelCase__					:    List[str]          =   []

      for task_id, content in self._prepare_split_single(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			):
       (
           UpperCamelCase__				
       )					:    Tuple          =   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__			)

      UpperCamelCase__					:    Tuple          =   total_num_examples
      UpperCamelCase__					:    Any          =   total_num_bytes

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

       # use the -SSSSS-of-NNNNN pattern
       def _rename_shard(
           lowerCamelCase__							:     int							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int							,	):
        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}"			)							,	)

       UpperCamelCase__					:    Optional[int]          =   []
       UpperCamelCase__					:    Dict          =   0
       for i in range(len(lowerCamelCase__			)			):
        UpperCamelCase__					:    List[str]          =   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
       UpperCamelCase__					:    Tuple          =   0
       UpperCamelCase__					:    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(lowerCamelCase__							,	''''''			)							,	)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     "datasets.SplitGenerator"							,	)  ->      SparkExamplesIterable:





      '''simple docstring'''






      return SparkExamplesIterable(self.df			)






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   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							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 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], [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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


