'''simple docstring'''




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor





class 					__magic_name__		:







			def __init__(			self       :						Any       ,    lowercase_       :						Dict       ,    lowercase_       :						Dict=13       ,    lowercase_       :						int=30       ,    lowercase_       :						List[Any]=2       ,    lowercase_       :						List[str]=3       ,    lowercase_       :						Tuple=True       ,    lowercase_       :						List[str]=True       ,    lowercase_       :						Tuple=32       ,    lowercase_       :						Union[str, Any]=5       ,    lowercase_       :						Tuple=4       ,    lowercase_       :						Optional[int]=37       ,    lowercase_       :						int="gelu"       ,    lowercase_       :						List[str]=0.1       ,    lowercase_       :						List[str]=0.1       ,    lowercase_       :						Optional[Any]=10       ,    lowercase_       :						Optional[Any]=0.02       ,    lowercase_       :						List[Any]=3       ,    lowercase_       :						str=0.6       ,    lowercase_       :						List[str]=None       ,    ):
										lowercase_      :    Union[str, Any]       		= parent
										lowercase_      :    Any       		= batch_size
										lowercase_      :    Optional[Any]       		= image_size
										lowercase_      :    str       		= patch_size
										lowercase_      :    Union[str, Any]       		= num_channels
										lowercase_      :    List[str]       		= is_training
										lowercase_      :    Tuple       		= use_labels
										lowercase_      :    str       		= hidden_size
										lowercase_      :    Optional[Any]       		= num_hidden_layers
										lowercase_      :    List[str]       		= num_attention_heads
										lowercase_      :    str       		= intermediate_size
										lowercase_      :    List[str]       		= hidden_act
										lowercase_      :    Tuple       		= hidden_dropout_prob
										lowercase_      :    List[str]       		= attention_probs_dropout_prob
										lowercase_      :    Union[str, Any]       		= type_sequence_label_size
										lowercase_      :    int       		= initializer_range
										lowercase_      :    List[Any]       		= mask_ratio
										lowercase_      :    Optional[int]       		= scope

										# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
										# (we add 1 for the [CLS] token)
										lowercase_      :    Union[str, Any]       		= (image_size // patch_size) ** 2
										lowercase_      :    str       		= int(math.ceil((1 - mask_ratio) * (num_patches + 1)					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Optional[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowercase_      :    Union[str, Any]       		= None
										if self.use_labels:
																	lowercase_      :    List[str]       		= ids_tensor([self.batch_size]       ,    self.type_sequence_label_size					)

										lowercase_      :    List[Any]       		= self.get_config()

										return config, pixel_values, labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										return ViTMAEConfig(
										    image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    intermediate_size=self.intermediate_size       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    is_decoder=lowercase_       ,    initializer_range=self.initializer_range       ,    mask_ratio=self.mask_ratio       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						Any       ,    lowercase_       :						str					):
										lowercase_      :    Optional[Any]       		= ViTMAEModel(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[str]       		= model(lowercase_					)
										self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Any       ,    lowercase_       :						int					):
										lowercase_      :    List[str]       		= ViTMAEForPreTraining(lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Union[str, Any]       		= model(lowercase_					)
										lowercase_      :    Tuple       		= (self.image_size // self.patch_size) ** 2
										lowercase_      :    Tuple       		= self.patch_size**2 * self.num_channels
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, num_patches, expected_num_channels)					)

										# test greyscale images
										lowercase_      :    Any       		= 1
										lowercase_      :    int       		= ViTMAEForPreTraining(lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    Dict       		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowercase_      :    Optional[int]       		= model(lowercase_					)
										lowercase_      :    List[str]       		= self.patch_size**2
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, num_patches, expected_num_channels)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    List[Any]       		= self.prepare_config_and_inputs()
										lowercase_      :    Union[str, Any]       		= config_and_inputs
										lowercase_      :    Optional[int]       		= {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			UpperCamelCase__       = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    str       		= ViTMAEModelTester(self					)
										lowercase_      :    Any       		= ConfigTester(self       ,    config_class=lowercase_       ,    has_text_modality=lowercase_       ,    hidden_size=37					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										self.config_tester.run_common_tests()







			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    str       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    Optional[int]       		= model_class(lowercase_					)
																	self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)					)
																	lowercase_      :    Dict       		= model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(lowercase_       ,    nn.Linear					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    Tuple       		= model_class(lowercase_					)
																	lowercase_      :    List[str]       		= inspect.signature(model.forward					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase_      :    Dict       		= [*signature.parameters.keys()]

																	lowercase_      :    Optional[int]       		= ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						List[str]       ,    lowercase_       :						str					):
										# make masks reproducible
										np.random.seed(2					)

										lowercase_      :    List[str]       		= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2					)
										lowercase_      :    List[Any]       		= np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
										lowercase_      :    Union[str, Any]       		= torch.from_numpy(lowercase_					)

										# Add `noise` argument.
										# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
										lowercase_      :    Optional[Any]       		= pt_noise

										super().check_pt_tf_models(lowercase_       ,    lowercase_       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[Any]       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    Union[str, Any]       		= model_class(lowercase_					)
																	model.to(lowercase_					)
																	model.eval()
																	# make random mask reproducible
																	torch.manual_seed(2					)
																	with torch.no_grad():
																								lowercase_      :    Dict       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)					)

																	lowercase_      :    List[str]       		= outputs[0].cpu().numpy()
																	lowercase_      :    int       		= 0

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								model.save_pretrained(lowercase_					)
																								lowercase_      :    Any       		= model_class.from_pretrained(lowercase_					)
																								model.to(lowercase_					)
																								# make random mask reproducible
																								torch.manual_seed(2					)
																								with torch.no_grad():
																															lowercase_      :    Any       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)					)

																								# Make sure we don't have nans
																								lowercase_      :    int       		= after_outputs[0].cpu().numpy()
																								lowercase_      :    Optional[int]       		= 0
																								lowercase_      :    List[Any]       		= np.amax(np.abs(out_a - out_a					)					)
																								self.assertLessEqual(lowercase_       ,    1E-5					)







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										pass







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										pass







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										pass







			@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										pass







			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										pass







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :    str       		= ViTMAEModel.from_pretrained(lowercase_					)
																	self.assertIsNotNone(lowercase_					)




def 							lowerCamelCase    (   )    ->   Optional[int]:
							lowercase_      :    Optional[Any]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





@require_torch
@require_vision
class 					__magic_name__		(					unittest.TestCase):







			@cached_property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										# make random mask reproducible across the PT and TF model
										np.random.seed(2					)

										lowercase_      :    Dict       		= ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					).to(lowercase_					)

										lowercase_      :    Tuple       		= self.default_image_processor
										lowercase_      :    int       		= prepare_img()
										lowercase_      :    Dict       		= image_processor(images=lowercase_       ,    return_tensors="""pt"""					).to(lowercase_					)

										# prepare a noise vector that will be also used for testing the TF model
										# (this way we can ensure that the PT and TF models operate on the same inputs)
										lowercase_      :    Union[str, Any]       		= ViTMAEConfig()
										lowercase_      :    Union[str, Any]       		= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
										lowercase_      :    Union[str, Any]       		= np.random.uniform(size=(1, num_patches)					)

										# forward pass
										with torch.no_grad():
																	lowercase_      :    Any       		= model(**lowercase_       ,    noise=torch.from_numpy(lowercase_					).to(device=lowercase_					)					)

										# verify the logits
										lowercase_      :    List[Any]       		= torch.Size((1, 196, 768)					)
										self.assertEqual(outputs.logits.shape       ,    lowercase_					)

										lowercase_      :    str       		= torch.tensor(
										    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,    expected_slice.to(lowercase_					)       ,    atol=1E-4					)					)






'''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


_lowercase		:				str	       =		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark





@dataclass
class 					__magic_name__		(					datasets.BuilderConfig):
			UpperCamelCase__       = None




def 							lowerCamelCase			(   UpperCAmelCase__ :      "pyspark.sql.DataFrame"   ,   UpperCAmelCase__ :      List[int]   ,   )    ->   str:
							import pyspark

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

							return generate_fn





class 					__magic_name__		(					_BaseExamplesIterable):







			def __init__(			self       :						int       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						Optional[int]=None       ,    ):
										lowercase_      :    Dict       		= df
										lowercase_      :    Optional[Any]       		= partition_order or range(self.df.rdd.getNumPartitions()					)
										lowercase_      :    Optional[Any]       		= _generate_iterable_examples(self.df       ,    self.partition_order					)







			def __iter__(			self       :						List[Any]					):
										yield from self.generate_examples_fn()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.random.Generator					):
										lowercase_      :    str       		= list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										lowercase_      :    str       		= self.split_shard_indices_by_worker(lowercase_       ,    lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







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





class 					__magic_name__		(					datasets.DatasetBuilder):
			UpperCamelCase__       = SparkConfig







			def __init__(			self       :						Tuple       ,    lowercase_       :						"pyspark.sql.DataFrame"       ,    lowercase_       :						str = None       ,    lowercase_       :						str = None       ,    **lowercase_       :						str       ,    ):
										import pyspark

										lowercase_      :    str       		= pyspark.sql.SparkSession.builder.getOrCreate()
										lowercase_      :    Optional[int]       		= df
										lowercase_      :    List[str]       		= working_dir

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase_       :						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=lowercase_					)
																	lowercase_      :    List[str]       		= 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(lowercase_       ,    """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:
																	lowercase_      :    str       		= (
																	    self._spark.sparkContext.parallelize(range(1					)       ,    1					).mapPartitions(lowercase_					).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       :						Union[str, Any]					):
										return datasets.DatasetInfo(features=self.config.features					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						datasets.download.download_manager.DownloadManager					):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										import pyspark

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

										lowercase_      :    Union[str, Any]       		= self.df.count()
										lowercase_      :    Union[str, Any]       		= df_num_rows if df_num_rows <= 100 else 100
										# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
										lowercase_      :    Any       		= (
										    self.df.limit(lowercase_					)
										    .repartition(1					)
										    .mapInArrow(lowercase_       ,    """batch_bytes: long"""					)
										    .agg(pyspark.sql.functions.sum("""batch_bytes"""					).alias("""sample_bytes"""					)					)
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										lowercase_      :    List[Any]       		= approx_bytes_per_row * df_num_rows
										if approx_total_size > max_shard_size:
																	# Make sure there is at least one row per partition.
																	lowercase_      :    Any       		= min(lowercase_       ,    int(approx_total_size / max_shard_size					)					)
																	lowercase_      :    Any       		= self.df.repartition(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						int       ,    ):
										import pyspark

										lowercase_      :    Any       		= ParquetWriter if file_format == """parquet""" else ArrowWriter
										lowercase_      :    Dict       		= os.path.join(self._working_dir       ,    os.path.basename(lowercase_					)					) if self._working_dir else fpath
										lowercase_      :    Optional[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.
										lowercase_      :    Tuple       		= self.config.features
										lowercase_      :    Any       		= self._writer_batch_size
										lowercase_      :    List[str]       		= self._fs.storage_options

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

																	if writer._num_bytes > 0:
																								lowercase_		,		lowercase_      :    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(lowercase_					)					):
																															lowercase_      :    Optional[Any]       		= os.path.join(os.path.dirname(lowercase_					)       ,    os.path.basename(lowercase_					)					)
																															shutil.move(lowercase_       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= (
										    self.df.mapInArrow(lowercase_       ,    """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       :						Dict       ,    lowercase_       :						"datasets.SplitGenerator"       ,    lowercase_       :						str = "arrow"       ,    lowercase_       :						Optional[Union[str, int]] = None       ,    lowercase_       :						Optional[int] = None       ,    **lowercase_       :						List[str]       ,    ):
										self._validate_cache_dir()

										lowercase_      :    int       		= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(lowercase_					)
										lowercase_      :    Tuple       		= not is_remote_filesystem(self._fs					)
										lowercase_      :    int       		= os.path.join if is_local else posixpath.join

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

										lowercase_      :    Any       		= 0
										lowercase_      :    Tuple       		= 0
										lowercase_      :    int       		= 0
										lowercase_      :    Dict       		= []
										lowercase_      :    Union[str, Any]       		= []

										for task_id, content in self._prepare_split_single(lowercase_       ,    lowercase_       ,    lowercase_					):
																	(
																	    (
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		
																	)      :    Union[str, 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(lowercase_					)

										lowercase_      :    List[str]       		= total_num_examples
										lowercase_      :    int       		= total_num_bytes

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

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

																	lowercase_      :    Union[str, Any]       		= []
																	lowercase_      :    Tuple       		= 0
																	for i in range(len(lowercase_					)					):
																								lowercase_		,		lowercase_      :    List[Any]       		= task_id_and_num_shards[i]
																								for shard_id in range(lowercase_					):
																															args.append([task_id, shard_id, global_shard_id]					)
																															global_shard_id += 1
																	self._spark.sparkContext.parallelize(lowercase_       ,    len(lowercase_					)					).map(lambda lowercase_					: _rename_shard(*lowercase_					)					).collect()
										else:
																	# don't use any pattern
																	lowercase_      :    List[str]       		= 0
																	lowercase_      :    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(lowercase_       ,    """"""					)       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						"datasets.SplitGenerator"       ,    ):
										return SparkExamplesIterable(self.df					)





