import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					SCREAMING_SNAKE_CASE__   =					"platform"
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class 					lowerCAmelCase_      :



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  PegasusConfig
				_lowerCAmelCase       :      Optional[Any]     					=  {}
				_lowerCAmelCase       :      int     					=  """gelu"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=5						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=20						,      lowerCAmelCase=2						,      lowerCAmelCase=1						,      lowerCAmelCase=0						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size

										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     eos_token_id
										snake_case        =     pad_token_id
										snake_case        =     bos_token_id

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.seq_length - 1]						,      self.vocab_size      ).clip(3						,      self.vocab_size      )
										snake_case        =     np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )						,      1      )
										snake_case        =     np.concatenate([input_ids, eos_tensor]						,      axis=1      )

										snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

										snake_case        =     self.config_cls(
										    vocab_size=self.vocab_size						,      d_model=self.hidden_size						,      encoder_layers=self.num_hidden_layers						,      decoder_layers=self.num_hidden_layers						,      encoder_attention_heads=self.num_attention_heads						,      decoder_attention_heads=self.num_attention_heads						,      encoder_ffn_dim=self.intermediate_size						,      decoder_ffn_dim=self.intermediate_size						,      dropout=self.hidden_dropout_prob						,      attention_dropout=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      eos_token_ids=[2]						,      bos_token_id=self.bos_token_id						,      pad_token_id=self.pad_token_id						,      decoder_start_token_id=self.pad_token_id						,      **self.config_updates						,      )
										snake_case        =     prepare_pegasus_inputs_dict(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
										return config, inputs_dict

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     20
										snake_case        =     model_class_name(lowerCAmelCase      )

										snake_case        =     model.encode(inputs_dict['input_ids']      )

										snake_case   ,snake_case        =     (
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										snake_case        =     model.init_cache(decoder_input_ids.shape[0]						,      lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,      dtype='i4'      )

										snake_case        =     jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]						,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,      )
										snake_case        =     model.decode(
										    decoder_input_ids[:, :-1]						,      lowerCAmelCase						,      decoder_attention_mask=lowerCAmelCase						,      past_key_values=lowerCAmelCase						,      decoder_position_ids=lowerCAmelCase						,      )

										snake_case        =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,      dtype='i4'      )
										snake_case        =     model.decode(
										    decoder_input_ids[:, -1:]						,      lowerCAmelCase						,      decoder_attention_mask=lowerCAmelCase						,      past_key_values=outputs_cache.past_key_values						,      decoder_position_ids=lowerCAmelCase						,      )

										snake_case        =     model.decode(lowerCAmelCase						,      lowerCAmelCase      )

										snake_case        =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
										self.parent.assertTrue(diff < 1E-3						,      msg=F"""Max diff is {diff}"""      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     20
										snake_case        =     model_class_name(lowerCAmelCase      )

										snake_case        =     model.encode(inputs_dict['input_ids']      )

										snake_case   ,snake_case        =     (
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										snake_case        =     jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
										    ]						,      axis=-1						,      )

										snake_case        =     model.init_cache(decoder_input_ids.shape[0]						,      lowerCAmelCase						,      lowerCAmelCase      )
										snake_case        =     jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]						,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,      )

										snake_case        =     model.decode(
										    decoder_input_ids[:, :-1]						,      lowerCAmelCase						,      decoder_attention_mask=lowerCAmelCase						,      past_key_values=lowerCAmelCase						,      decoder_position_ids=lowerCAmelCase						,      )
										snake_case        =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,      dtype='i4'      )
										snake_case        =     model.decode(
										    decoder_input_ids[:, -1:]						,      lowerCAmelCase						,      past_key_values=outputs_cache.past_key_values						,      decoder_attention_mask=lowerCAmelCase						,      decoder_position_ids=lowerCAmelCase						,      )

										snake_case        =     model.decode(lowerCAmelCase						,      lowerCAmelCase						,      decoder_attention_mask=lowerCAmelCase      )

										snake_case        =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
										self.parent.assertTrue(diff < 1E-3						,      msg=F"""Max diff is {diff}"""      )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str=None	,     _UpperCamelCase    :			Dict=None	,     )   ->			Optional[Any]:





						"""simple docstring"""






						if attention_mask is None:
												snake_case        =     np.not_equal(_UpperCamelCase	,     config.pad_token_id   ).astype(np.inta   )
						if decoder_attention_mask is None:
												snake_case        =     np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape	,     dtype=np.inta   ),
												        np.not_equal(decoder_input_ids[:, 1:]	,     config.pad_token_id   ).astype(np.inta   ),
												    ]	,     axis=-1	,     )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}



@require_flax
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Dict     					=  (
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				_lowerCAmelCase       :      Any     					=  (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				_lowerCAmelCase       :      Optional[Any]     					=  True
				_lowerCAmelCase       :      int     					=  False
				_lowerCAmelCase       :      Tuple     					=  False
				_lowerCAmelCase       :      Dict     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     FlaxPegasusModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__      ):
																						snake_case        =     self._prepare_for_class(lowerCAmelCase						,      lowerCAmelCase      )
																						snake_case        =     model_class(lowerCAmelCase      )

																						@jax.jit
																						def encode_jitted(lowerCAmelCase						,      lowerCAmelCase=None						,      **lowerCAmelCase      ):
																												return model.encode(input_ids=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )

																						with self.subTest('JIT Enabled'      ):
																												snake_case        =     encode_jitted(**lowerCAmelCase      ).to_tuple()

																						with self.subTest('JIT Disabled'      ):
																												with jax.disable_jit():
																																		snake_case        =     encode_jitted(**lowerCAmelCase      ).to_tuple()

																						self.assertEqual(len(lowerCAmelCase      )						,      len(lowerCAmelCase      )      )
																						for jitted_output, output in zip(lowerCAmelCase						,      lowerCAmelCase      ):
																												self.assertEqual(jitted_output.shape						,      output.shape      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__      ):
																						snake_case        =     model_class(lowerCAmelCase      )
																						snake_case        =     model.encode(inputs_dict['input_ids']						,      inputs_dict['attention_mask']      )

																						snake_case        =     {
																						    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																						    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																						    'encoder_outputs': encoder_outputs,
																						}

																						@jax.jit
																						def decode_jitted(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):
																												return model.decode(
																												    decoder_input_ids=lowerCAmelCase						,      decoder_attention_mask=lowerCAmelCase						,      encoder_outputs=lowerCAmelCase						,      )

																						with self.subTest('JIT Enabled'      ):
																												snake_case        =     decode_jitted(**lowerCAmelCase      ).to_tuple()

																						with self.subTest('JIT Disabled'      ):
																												with jax.disable_jit():
																																		snake_case        =     decode_jitted(**lowerCAmelCase      ).to_tuple()

																						self.assertEqual(len(lowerCAmelCase      )						,      len(lowerCAmelCase      )      )
																						for jitted_output, output in zip(lowerCAmelCase						,      lowerCAmelCase      ):
																												self.assertEqual(jitted_output.shape						,      output.shape      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_class_name in self.all_model_classes:
																snake_case        =     model_class_name.from_pretrained('google/pegasus-large'						,      from_pt=lowerCAmelCase      )
																snake_case        =     np.ones((1, 1)      )
																snake_case        =     model(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'      )
										snake_case        =     PegasusTokenizer.from_pretrained('google/pegasus-xsum'      )

										snake_case        =     [
										    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
										    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
										]

										snake_case        =     [
										    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
										    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
										]

										snake_case        =     tokenizer(lowerCAmelCase						,      return_tensors='np'						,      truncation=lowerCAmelCase						,      max_length=5_12						,      padding=lowerCAmelCase      )
										snake_case        =     model.generate(**lowerCAmelCase						,      num_beams=2      ).sequences
										snake_case        =     tokenizer.batch_decode(lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase      )
										assert tgt_text == decoded



"""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


SCREAMING_SNAKE_CASE__   =					datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark



@dataclass
class 					lowerCAmelCase_      (     datasets.BuilderConfig   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[datasets.Features]     					=  None





def 				lowerCAmelCase__      (     _UpperCamelCase    :			"pyspark.sql.DataFrame"	,     _UpperCamelCase    :			List[int]	,     )   ->			Dict:





						"""simple docstring"""






						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   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										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      ):






										"""simple docstring"""

										yield from self.generate_examples_fn()

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     list(range(self.df.rdd.getNumPartitions()      )      )
										generator.shuffle(lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.split_shard_indices_by_worker(lowerCAmelCase						,      lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return len(self.partition_order      )





class 					lowerCAmelCase_      (     datasets.DatasetBuilder   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[str]     					=  SparkConfig

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										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 						snake_case	(			self      ):






										"""simple docstring"""

										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      )
																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 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										import pyspark

										def get_arrow_batch_size(lowerCAmelCase      ):
																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 <= 1_00 else 1_00
										# 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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										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      ):
																# 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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = "arrow"						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										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						,      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}"""      )						,      )

																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 						snake_case	(			self						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										return SparkExamplesIterable(self.df      )


