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
   a_    					=       'platform'
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class 		_lowercase						:
 lowercase				     =   PegasusConfig
 lowercase				     =   {}
 lowercase				     =   'gelu'

 def __init__( self	:							Union[str, Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Union[str, Any]=1_3   ,      snake_case	:							int=7   ,      snake_case	:							int=True   ,      snake_case	:							Union[str, Any]=False   ,      snake_case	:							Tuple=9_9   ,      snake_case	:							List[str]=3_2   ,      snake_case	:							Tuple=5   ,      snake_case	:							Dict=4   ,      snake_case	:							Optional[Any]=3_7   ,      snake_case	:							Union[str, Any]=0.1   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							Dict=2_0   ,      snake_case	:							Optional[Any]=2   ,      snake_case	:							List[Any]=1   ,      snake_case	:							Any=0   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					parent
  UpperCamelCase_					:						Optional[int] =					batch_size
  UpperCamelCase_					:						Optional[Any] =					seq_length
  UpperCamelCase_					:						Tuple =					is_training
  UpperCamelCase_					:						Union[str, Any] =					use_labels
  UpperCamelCase_					:						List[str] =					vocab_size
  UpperCamelCase_					:						Optional[int] =					hidden_size
  UpperCamelCase_					:						Optional[int] =					num_hidden_layers
  UpperCamelCase_					:						str =					num_attention_heads
  UpperCamelCase_					:						List[Any] =					intermediate_size

  UpperCamelCase_					:						List[str] =					hidden_dropout_prob
  UpperCamelCase_					:						int =					attention_probs_dropout_prob
  UpperCamelCase_					:						List[Any] =					max_position_embeddings
  UpperCamelCase_					:						Tuple =					eos_token_id
  UpperCamelCase_					:						Optional[int] =					pad_token_id
  UpperCamelCase_					:						Tuple =					bos_token_id

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						int:

  """simple docstring"""





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

  UpperCamelCase_					:						Optional[int] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)

  UpperCamelCase_					:						Optional[int] =					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   ,      )
  UpperCamelCase_					:						Tuple =					prepare_pegasus_inputs_dict(snake_case   ,      snake_case   ,      snake_case			)
  return config, inputs_dict

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Tuple   ,      snake_case	:							Optional[int]   ,      snake_case	:							Dict			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					2_0
  UpperCamelCase_					:						List[str] =					model_class_name(snake_case			)

  UpperCamelCase_					:						List[Any] =					model.encode(inputs_dict['input_ids']			)

  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					(
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

  UpperCamelCase_					:						List[str] =					model.init_cache(decoder_input_ids.shape[0]   ,      snake_case   ,      snake_case			)
  UpperCamelCase_					:						List[str] =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,      dtype='i4'			)

  UpperCamelCase_					:						Optional[int] =					jnp.broadcast_to(
      jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )
  UpperCamelCase_					:						List[Any] =					model.decode(
      decoder_input_ids[:, :-1]   ,      snake_case   ,      decoder_attention_mask=snake_case   ,      past_key_values=snake_case   ,      decoder_position_ids=snake_case   ,      )

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

  UpperCamelCase_					:						Union[str, Any] =					model.decode(snake_case   ,      snake_case			)

  UpperCamelCase_					:						str =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							str   ,      snake_case	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					2_0
  UpperCamelCase_					:						Optional[Any] =					model_class_name(snake_case			)

  UpperCamelCase_					:						Dict =					model.encode(inputs_dict['input_ids']			)

  UpperCamelCase_, UpperCamelCase_					:						Dict =					(
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

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

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

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

  UpperCamelCase_					:						List[Any] =					model.decode(snake_case   ,      snake_case   ,      decoder_attention_mask=snake_case			)

  UpperCamelCase_					:						str =					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    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int=None  ,  lowerCamelCase					:     Dict=None  ,  ):
 if attention_mask is None:
  UpperCamelCase_					:						Optional[Any] =					np.not_equal(lowerCamelCase  ,  config.pad_token_id	).astype(np.inta	)
 if decoder_attention_mask is None:
  UpperCamelCase_					:						Optional[Any] =					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 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   (
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 lowercase				     =   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 lowercase				     =   True
 lowercase				     =   False
 lowercase				     =   False
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					FlaxPegasusModelTester(self			)
  UpperCamelCase_					:						Optional[int] =					ConfigTester(self   ,      config_class=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  self.config_tester.run_common_tests()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





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

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

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[str] =					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(snake_case   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs_for_common()

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

    @jax.jit
    def encode_jitted(snake_case	:							Any   ,      snake_case	:							Optional[Any]=None   ,      **snake_case	:							Optional[int]			):
     return model.encode(input_ids=snake_case   ,      attention_mask=snake_case			)

    with self.subTest('JIT Enabled'			):
     UpperCamelCase_					:						Optional[int] =					encode_jitted(**snake_case			).to_tuple()

    with self.subTest('JIT Disabled'			):
     with jax.disable_jit():
      UpperCamelCase_					:						List[Any] =					encode_jitted(**snake_case			).to_tuple()

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[str] =					self.model_tester.prepare_config_and_inputs_for_common()

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

    UpperCamelCase_					:						str =					{
        '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(snake_case	:							Optional[Any]   ,      snake_case	:							int   ,      snake_case	:							Optional[Any]			):
     return model.decode(
         decoder_input_ids=snake_case   ,      decoder_attention_mask=snake_case   ,      encoder_outputs=snake_case   ,      )

    with self.subTest('JIT Enabled'			):
     UpperCamelCase_					:						Union[str, Any] =					decode_jitted(**snake_case			).to_tuple()

    with self.subTest('JIT Disabled'			):
     with jax.disable_jit():
      UpperCamelCase_					:						Optional[Any] =					decode_jitted(**snake_case			).to_tuple()

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

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





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


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'			)
  UpperCamelCase_					:						Any =					PegasusTokenizer.from_pretrained('google/pegasus-xsum'			)

  UpperCamelCase_					:						int =					[
      ' 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!" ',
  ]

  UpperCamelCase_					:						Union[str, Any] =					[
      '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.',
  ]

  UpperCamelCase_					:						Any =					tokenizer(snake_case   ,      return_tensors='np'   ,      truncation=snake_case   ,      max_length=5_1_2   ,      padding=snake_case			)
  UpperCamelCase_					:						str =					model.generate(**snake_case   ,      num_beams=2			).sequences
  UpperCamelCase_					:						List[Any] =					tokenizer.batch_decode(snake_case   ,      skip_special_tokens=snake_case			)
  assert tgt_text == decoded




import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


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

if TYPE_CHECKING:
   import pyspark





@dataclass
class 		_lowercase						(					datasets.BuilderConfig       ):
 lowercase				     =   None





def    __lowercase     (						lowerCamelCase					:     "pyspark.sql.DataFrame"  ,  lowerCamelCase					:     List[int]  ,  ):
 import pyspark

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

 return generate_fn





class 		_lowercase						(					_BaseExamplesIterable       ):

 def __init__( self	:							Optional[int]   ,      snake_case	:							"pyspark.sql.DataFrame"   ,      snake_case	:							Tuple=None   ,      )   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					df
  UpperCamelCase_					:						int =					partition_order or range(self.df.rdd.getNumPartitions()			)
  UpperCamelCase_					:						Optional[Any] =					_generate_iterable_examples(self.df   ,      self.partition_order			)

 def __iter__( self	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  yield from self.generate_examples_fn()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							np.random.Generator			)   ->						"SparkExamplesIterable":

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					list(range(self.df.rdd.getNumPartitions()			)			)
  generator.shuffle(snake_case			)
  return SparkExamplesIterable(self.df   ,      partition_order=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							int   ,      snake_case	:							int			)   ->						"SparkExamplesIterable":

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.split_shard_indices_by_worker(snake_case   ,      snake_case			)
  return SparkExamplesIterable(self.df   ,      partition_order=snake_case			)


 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						int:

  """simple docstring"""





  return len(self.partition_order			)





class 		_lowercase						(					datasets.DatasetBuilder       ):
 lowercase				     =   SparkConfig

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

  """simple docstring"""





  import pyspark

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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  def create_cache_and_write_probe(snake_case	:							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=snake_case			)
   UpperCamelCase_					:						Tuple =					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(snake_case   ,      '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_					:						Tuple =					(
       self._spark.sparkContext.parallelize(range(1			)   ,      1			).mapPartitions(snake_case			).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 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Union[str, Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							datasets.download.download_manager.DownloadManager			)   ->						Optional[int]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  import pyspark

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

  UpperCamelCase_					:						List[str] =					self.df.count()
  UpperCamelCase_					:						Union[str, Any] =					df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
  # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
  UpperCamelCase_					:						str =					(
      self.df.limit(snake_case			)
      .repartition(1			)
      .mapInArrow(snake_case   ,      'batch_bytes: long'			)
      .agg(pyspark.sql.functions.sum('batch_bytes'			).alias('sample_bytes'			)			)
      .collect()[0]
      .sample_bytes
      / sample_num_rows
  )
  UpperCamelCase_					:						Optional[int] =					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_					:						Optional[Any] =					min(snake_case   ,      int(approx_total_size / max_shard_size			)			)
   UpperCamelCase_					:						int =					self.df.repartition(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str   ,      snake_case	:							str   ,      snake_case	:							int   ,      )   ->						Iterable[Tuple[int, bool, Union[int, tuple]]]:

  """simple docstring"""





  import pyspark

  UpperCamelCase_					:						List[Any] =					ParquetWriter if file_format == 'parquet' else ArrowWriter
  UpperCamelCase_					:						List[str] =					os.path.join(self._working_dir   ,      os.path.basename(snake_case			)			) if self._working_dir else fpath
  UpperCamelCase_					:						Union[str, Any] =					file_format == 'parquet'

  # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
  # pickling the SparkContext.
  UpperCamelCase_					:						Union[str, Any] =					self.config.features
  UpperCamelCase_					:						Any =					self._writer_batch_size
  UpperCamelCase_					:						Dict =					self._fs.storage_options

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

   if writer._num_bytes > 0:
    UpperCamelCase_, UpperCamelCase_					:						str =					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(snake_case			)			):
     UpperCamelCase_					:						Dict =					os.path.join(os.path.dirname(snake_case			)   ,      os.path.basename(snake_case			)			)
     shutil.move(snake_case   ,      snake_case			)

  UpperCamelCase_					:						int =					(
      self.df.mapInArrow(snake_case   ,      '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 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							"datasets.SplitGenerator"   ,      snake_case	:							str = "arrow"   ,      snake_case	:							Optional[Union[str, int]] = None   ,      snake_case	:							Optional[int] = None   ,      **snake_case	:							Any   ,      )   ->						int:

  """simple docstring"""





  self._validate_cache_dir()

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

  UpperCamelCase_					:						Optional[int] =					'-TTTTT-SSSSS-of-NNNNN'
  UpperCamelCase_					:						Dict =					f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
  UpperCamelCase_					:						int =					path_join(self._output_dir   ,      snake_case			)

  UpperCamelCase_					:						int =					0
  UpperCamelCase_					:						Optional[int] =					0
  UpperCamelCase_					:						Union[str, Any] =					0
  UpperCamelCase_					:						Optional[Any] =					[]
  UpperCamelCase_					:						Any =					[]

  for task_id, content in self._prepare_split_single(snake_case   ,      snake_case   ,      snake_case			):
   (
       (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), (
       UpperCamelCase_
   ), 
   )					:						Optional[Any] =					content
   if num_bytes > 0:
    total_num_examples += num_examples
    total_num_bytes += num_bytes
    total_shards += num_shards
    task_id_and_num_shards.append((task_id, num_shards)			)
    all_shard_lengths.extend(snake_case			)

  UpperCamelCase_					:						Optional[Any] =					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[Any] =					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_					:						int =					self._fs

   # use the -SSSSS-of-NNNNN pattern
   def _rename_shard(
       snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							int   ,      ):
    rename(
        snake_case   ,      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_					:						Any =					[]
   UpperCamelCase_					:						Optional[int] =					0
   for i in range(len(snake_case			)			):
    UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					task_id_and_num_shards[i]
    for shard_id in range(snake_case			):
     args.append([task_id, shard_id, global_shard_id]			)
     global_shard_id += 1
   self._spark.sparkContext.parallelize(snake_case   ,      len(snake_case			)			).map(lambda snake_case			: _rename_shard(*snake_case			)			).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(snake_case   ,      ''			)   ,      )


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							"datasets.SplitGenerator"   ,      )   ->						SparkExamplesIterable:

  """simple docstring"""





  return SparkExamplesIterable(self.df			)



