
"""simple docstring"""



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 UpperCAmelCase__          = 'pt'
elif is_tf_available():
 UpperCAmelCase__          = 'tf'
else:
 UpperCAmelCase__          = 'jax'

class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     __a						=					PerceiverTokenizer
     __a						=					False







     def      lowercase	(							self			:			Union[str, Any]			):
       super().setUp()
       _snake_case					     =       PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname			)







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''			)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Optional[Any]			):
       return self.tokenizer_class.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			List[str]=False    ,      _lowerCamelCase			:			Optional[int]=20    ,      _lowerCamelCase			:			List[str]=5			):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for Perceiver because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       _snake_case					     =       []
       for i in range(len(_lowerCamelCase			)			):
         try:
           _snake_case					     =       tokenizer.decode([i]    ,      clean_up_tokenization_spaces=_lowerCamelCase			)
         except UnicodeDecodeError:
           pass
         toks.append((i, tok)			)

       _snake_case					     =       list(filter(lambda _lowerCamelCase			: re.match(R'''^[ a-zA-Z]+$'''    ,      t[1]			)    ,      _lowerCamelCase			)			)
       _snake_case					     =       list(filter(lambda _lowerCamelCase			: [t[0]] == tokenizer.encode(t[1]    ,      add_special_tokens=_lowerCamelCase			)    ,      _lowerCamelCase			)			)
       if max_length is not None and len(_lowerCamelCase			) > max_length:
         _snake_case					     =       toks[:max_length]
       if min_length is not None and len(_lowerCamelCase			) < min_length and len(_lowerCamelCase			) > 0:
         while len(_lowerCamelCase			) < min_length:
           _snake_case					     =       toks + toks
        # toks_str = [t[1] for t in toks]
       _snake_case					     =       [t[0] for t in toks]

       # Ensure consistency
       _snake_case					     =       tokenizer.decode(_lowerCamelCase    ,      clean_up_tokenization_spaces=_lowerCamelCase			)
       if " " not in output_txt and len(_lowerCamelCase			) > 1:
         _snake_case					     =       (
             tokenizer.decode([toks_ids[0]]    ,      clean_up_tokenization_spaces=_lowerCamelCase			)
             + ''' '''
             + tokenizer.decode(toks_ids[1:]    ,      clean_up_tokenization_spaces=_lowerCamelCase			)
         )
       if with_prefix_space:
         _snake_case					     =       ''' ''' + output_txt
       _snake_case					     =       tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
       return output_txt, output_ids







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.perceiver_tokenizer
       _snake_case					     =       '''Unicode €.'''
       _snake_case					     =       tokenizer(_lowerCamelCase			)
       _snake_case					     =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
       self.assertEqual(encoded['''input_ids''']    ,      _lowerCamelCase			)

       # decoding
       _snake_case					     =       tokenizer.decode(_lowerCamelCase			)
       self.assertEqual(_lowerCamelCase    ,      '''[CLS]Unicode €.[SEP]'''			)

       _snake_case					     =       tokenizer('''e è é ê ë'''			)
       _snake_case					     =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
       self.assertEqual(encoded['''input_ids''']    ,      _lowerCamelCase			)
       # decoding
       _snake_case					     =       tokenizer.decode(_lowerCamelCase			)
       self.assertEqual(_lowerCamelCase    ,      '''[CLS]e è é ê ë[SEP]'''			)

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''			)			)    ,      '''[CLS]e è é ê ë[SEP]'''			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.perceiver_tokenizer
       _snake_case					     =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       # fmt: off
       _snake_case					     =       [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
       # fmt: on
       _snake_case					     =       tokenizer(_lowerCamelCase    ,      padding=_lowerCamelCase    ,      return_tensors=_lowerCamelCase			)
       self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)

       if FRAMEWORK != "jax":
         _snake_case					     =       list(batch.input_ids.numpy()[0]			)
       else:
         _snake_case					     =       list(batch.input_ids.tolist()[0]			)

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)

       self.assertEqual((2, 38)    ,      batch.input_ids.shape			)
       self.assertEqual((2, 38)    ,      batch.attention_mask.shape			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       self.perceiver_tokenizer
       _snake_case					     =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       _snake_case					     =       tokenizer(_lowerCamelCase    ,      padding=_lowerCamelCase    ,      return_tensors=_lowerCamelCase			)
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn('''input_ids'''    ,      _lowerCamelCase			)
       self.assertIn('''attention_mask'''    ,      _lowerCamelCase			)
       self.assertNotIn('''decoder_input_ids'''    ,      _lowerCamelCase			)
       self.assertNotIn('''decoder_attention_mask'''    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.perceiver_tokenizer
       _snake_case					     =       [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       _snake_case					     =       tokenizer(
           text_target=_lowerCamelCase    ,      max_length=32    ,      padding='''max_length'''    ,      truncation=_lowerCamelCase    ,      return_tensors=_lowerCamelCase			)
       self.assertEqual(32    ,      targets['''input_ids'''].shape[1]			)







     def      lowercase	(							self			:			List[Any]			):
       # safety check on max_len default value so we are sure the test works
       _snake_case					     =       self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
           self.assertNotEqual(tokenizer.model_max_length    ,      42			)

        # Now let's start the test
       _snake_case					     =       self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
           # Isolate this from the other tests because we save additional tokens/etc
           _snake_case					     =       tempfile.mkdtemp()

           _snake_case					     =       ''' He is very happy, UNwant\u00E9d,running'''
           _snake_case					     =       tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
           tokenizer.save_pretrained(_lowerCamelCase			)

           _snake_case					     =       tokenizer.__class__.from_pretrained(_lowerCamelCase			)
           _snake_case					     =       after_tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
           self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)

           shutil.rmtree(_lowerCamelCase			)

       _snake_case					     =       self.get_tokenizers(model_max_length=42			)
       for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
           # Isolate this from the other tests because we save additional tokens/etc
           _snake_case					     =       tempfile.mkdtemp()

           _snake_case					     =       ''' He is very happy, UNwant\u00E9d,running'''
           tokenizer.add_tokens(['''bim''', '''bambam''']			)
           _snake_case					     =       tokenizer.additional_special_tokens
           additional_special_tokens.append('''new_additional_special_token'''			)
           tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}			)
           _snake_case					     =       tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
           tokenizer.save_pretrained(_lowerCamelCase			)

           _snake_case					     =       tokenizer.__class__.from_pretrained(_lowerCamelCase			)
           _snake_case					     =       after_tokenizer.encode(_lowerCamelCase    ,      add_special_tokens=_lowerCamelCase			)
           self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)
           self.assertIn('''new_additional_special_token'''    ,      after_tokenizer.additional_special_tokens			)
           self.assertEqual(after_tokenizer.model_max_length    ,      42			)

           _snake_case					     =       tokenizer.__class__.from_pretrained(_lowerCamelCase    ,      model_max_length=43			)
           self.assertEqual(tokenizer.model_max_length    ,      43			)

           shutil.rmtree(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       []
       if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())			)

       if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())			)

       for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(_lowerCamelCase			)

           with open(os.path.join(_lowerCamelCase    ,      '''special_tokens_map.json'''			)    ,      encoding='''utf-8'''			) as json_file:
             _snake_case					     =       json.load(_lowerCamelCase			)

           with open(os.path.join(_lowerCamelCase    ,      '''tokenizer_config.json'''			)    ,      encoding='''utf-8'''			) as json_file:
             _snake_case					     =       json.load(_lowerCamelCase			)

           _snake_case					     =       [f'''<extra_id_{i}>''' for i in range(125			)]

           _snake_case					     =       added_tokens_extra_ids + [
               '''an_additional_special_token'''
           ]
           _snake_case					     =       added_tokens_extra_ids + [
               '''an_additional_special_token'''
           ]

           with open(os.path.join(_lowerCamelCase    ,      '''special_tokens_map.json'''			)    ,      '''w'''    ,      encoding='''utf-8'''			) as outfile:
             json.dump(_lowerCamelCase    ,      _lowerCamelCase			)
           with open(os.path.join(_lowerCamelCase    ,      '''tokenizer_config.json'''			)    ,      '''w'''    ,      encoding='''utf-8'''			) as outfile:
             json.dump(_lowerCamelCase    ,      _lowerCamelCase			)

           # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
           # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
           # "special_tokens_map.json" files
           _snake_case					     =       tokenizer_class.from_pretrained(
               _lowerCamelCase    ,      )
           self.assertIn(
               '''an_additional_special_token'''    ,      tokenizer_without_change_in_init.additional_special_tokens			)
           self.assertEqual(
               ['''an_additional_special_token''']    ,      tokenizer_without_change_in_init.convert_ids_to_tokens(
                   tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']			)			)    ,      )

           # Now we test that we can change the value of additional_special_tokens in the from_pretrained
           _snake_case					     =       added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''    ,      lstrip=_lowerCamelCase			)]
           _snake_case					     =       tokenizer_class.from_pretrained(
               _lowerCamelCase    ,      additional_special_tokens=_lowerCamelCase    ,      )

           self.assertIn('''a_new_additional_special_token'''    ,      tokenizer.additional_special_tokens			)
           self.assertEqual(
               ['''a_new_additional_special_token''']    ,      tokenizer.convert_ids_to_tokens(
                   tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']			)			)    ,      )







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([178]			)    ,      '''�'''			)







     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			Any			):
       pass







     def      lowercase	(							self			:			str			):
       pass







     def      lowercase	(							self			:			int			):
       pass







     def      lowercase	(							self			:			Any			):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       _snake_case					     =       self.get_tokenizers(fast=_lowerCamelCase    ,      do_lower_case=_lowerCamelCase			)
       for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
           _snake_case					     =       ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
           _snake_case					     =       tokenizer.convert_tokens_to_string(_lowerCamelCase			)

           self.assertIsInstance(_lowerCamelCase    ,      _lowerCamelCase			)





"""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__          = datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark

@dataclass
class       lowerCAmelCase__				(						datasets.BuilderConfig	):
     __a						=					None






def 					_UpperCAmelCase      ( __lowerCamelCase   :       "pyspark.sql.DataFrame" ,					__lowerCamelCase   :       List[int] ,					)     ->					Optional[int]:
  import pyspark

  def generate_fn():
    _snake_case					     =       df.select('''*''' ,					pyspark.sql.functions.spark_partition_id().alias('''part_id'''		)		)
    for partition_id in partition_order:
      _snake_case					     =       df_with_partition_id.select('''*'''		).where(f'''part_id = {partition_id}'''		).drop('''part_id'''		)
      _snake_case					     =       partition_df.collect()
      _snake_case					     =       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			:			Optional[int]    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			List[Any]=None    ,      ):
       _snake_case					     =       df
       _snake_case					     =       partition_order or range(self.df.rdd.getNumPartitions()			)
       _snake_case					     =       _generate_iterable_examples(self.df    ,      self.partition_order			)







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







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			np.random.Generator			):
       _snake_case					     =       list(range(self.df.rdd.getNumPartitions()			)			)
       generator.shuffle(_lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       self.split_shard_indices_by_worker(_lowerCamelCase    ,      _lowerCamelCase			)
       return SparkExamplesIterable(self.df    ,      partition_order=_lowerCamelCase			)







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






class       lowerCAmelCase__				(						datasets.DatasetBuilder	):
     __a						=					SparkConfig







     def __init__(							self			:			str    ,      _lowerCamelCase			:			"pyspark.sql.DataFrame"    ,      _lowerCamelCase			:			str = None    ,      _lowerCamelCase			:			str = None    ,      **_lowerCamelCase			:			List[str]    ,      ):
       import pyspark

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

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







     def      lowercase	(							self			:			str			):
       # Returns the path of the created file.
       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			)
         _snake_case					     =       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:
         _snake_case					     =       (
             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      lowercase	(							self			:			Dict			):
       return datasets.DatasetInfo(features=self.config.features			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			datasets.download.download_manager.DownloadManager			):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]			):
       import pyspark

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

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







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			int    ,      ):
       import pyspark

       _snake_case					     =       ParquetWriter if file_format == '''parquet''' else ArrowWriter
       _snake_case					     =       os.path.join(self._working_dir    ,      os.path.basename(_lowerCamelCase			)			) if self._working_dir else fpath
       _snake_case					     =       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.
       _snake_case					     =       self.config.features
       _snake_case					     =       self._writer_batch_size
       _snake_case					     =       self._fs.storage_options

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

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

       _snake_case					     =       (
           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      lowercase	(							self			:			int    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      _lowerCamelCase			:			str = "arrow"    ,      _lowerCamelCase			:			Optional[Union[str, int]] = None    ,      _lowerCamelCase			:			Optional[int] = None    ,      **_lowerCamelCase			:			List[Any]    ,      ):
       self._validate_cache_dir()

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

       _snake_case					     =       '''-TTTTT-SSSSS-of-NNNNN'''
       _snake_case					     =       f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
       _snake_case					     =       path_join(self._output_dir    ,      _lowerCamelCase			)

       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       0
       _snake_case					     =       []
       _snake_case					     =       []

       for task_id, content in self._prepare_split_single(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			):
         (
             (
             _snake_case
         )     ,					(
             _snake_case
         )     ,					(
             _snake_case
         )     ,					(
             _snake_case
         )     ,					
         )					     =       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			)

       _snake_case					     =       total_num_examples
       _snake_case					     =       total_num_bytes

       # should rename everything at the end
       logger.debug(f'''Renaming {total_shards} shards.'''			)
       if total_shards > 1:
         _snake_case					     =       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.
         _snake_case					     =       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}'''			)    ,      )

         _snake_case					     =       []
         _snake_case					     =       0
         for i in range(len(_lowerCamelCase			)			):
           _snake_case     ,					_snake_case					     =       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
         _snake_case					     =       0
         _snake_case					     =       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      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			"datasets.SplitGenerator"    ,      ):
       return SparkExamplesIterable(self.df			)



