from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	"DiagonalGaussianDistribution"



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=True






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = 3   ,				SCREAMING_SNAKE_CASE_ = ("DownEncoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = ("UpDecoderBlock2D",)   ,				SCREAMING_SNAKE_CASE_ = (64,)   ,				SCREAMING_SNAKE_CASE_ = 1   ,				SCREAMING_SNAKE_CASE_ = "silu"   ,				SCREAMING_SNAKE_CASE_ = 4   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 32   ,				SCREAMING_SNAKE_CASE_ = 0.18215   ,				)		->      Optional[int]:





       '''simple docstring'''





       super().__init__()

       # pass init params to Encoder
       UpperCamelCase__										=      Encoder(
           in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				down_block_types=SCREAMING_SNAKE_CASE_   ,				block_out_channels=SCREAMING_SNAKE_CASE_   ,				layers_per_block=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				norm_num_groups=SCREAMING_SNAKE_CASE_   ,				double_z=SCREAMING_SNAKE_CASE_   ,				)

       # pass init params to Decoder
       UpperCamelCase__										=      Decoder(
           in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				up_block_types=SCREAMING_SNAKE_CASE_   ,				block_out_channels=SCREAMING_SNAKE_CASE_   ,				layers_per_block=SCREAMING_SNAKE_CASE_   ,				norm_num_groups=SCREAMING_SNAKE_CASE_   ,				act_fn=SCREAMING_SNAKE_CASE_   ,				)

       UpperCamelCase__										=      nn.Convad(2 * latent_channels   ,				2 * latent_channels   ,				1	)
       UpperCamelCase__										=      nn.Convad(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				1	)

       UpperCamelCase__										=      False
       UpperCamelCase__										=      False

       # only relevant if vae tiling is enabled
       UpperCamelCase__										=      self.config.sample_size
       UpperCamelCase__										=      (
           self.config.sample_size[0]
           if isinstance(self.config.sample_size   ,				(list, tuple)	)
           else self.config.sample_size
       )
       UpperCamelCase__										=      int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
       UpperCamelCase__										=      0.25






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Dict:





       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE_   ,				(Encoder, Decoder)	):
            UpperCamelCase__										=      value






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      use_tiling






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       self.enable_tiling(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      True






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





       '''simple docstring'''





       UpperCamelCase__										=      False






  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 		_a					(self	)		->      Dict[str, AttentionProcessor]:





       '''simple docstring'''





       UpperCamelCase__										=      {}

       def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            if hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_processor'''	):
                 UpperCamelCase__										=      module.processor

            for sub_name, child in module.named_children():
                 fn_recursive_add_processors(F"{name}.{sub_name}"   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            return processors

       for name, module in self.named_children():
            fn_recursive_add_processors(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       return processors






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      len(self.attn_processors.keys()	)

       if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) != count:
            raise ValueError(
                F"A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_	)} does not match the"
                F" number of attention layers: {count}. Please make sure to pass {count} processor classes."	)

       def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            if hasattr(SCREAMING_SNAKE_CASE_   ,				'''set_processor'''	):
                 if not isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                      module.set_processor(SCREAMING_SNAKE_CASE_	)
                 else:
                      module.set_processor(processor.pop(F"{name}.processor"	)	)

            for sub_name, child in module.named_children():
                 fn_recursive_attn_processor(F"{name}.{sub_name}"   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       for name, module in self.named_children():
            fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.set_attn_processor(AttnProcessor()	)






  @apply_forward_hook
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





       '''simple docstring'''





       if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            return self.tiled_encode(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

       if self.use_slicing and x.shape[0] > 1:
            UpperCamelCase__										=      [self.encoder(SCREAMING_SNAKE_CASE_	) for x_slice in x.split(1	)]
            UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      self.encoder(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.quant_conv(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





       if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
            return self.tiled_decode(SCREAMING_SNAKE_CASE_   ,				return_dict=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






  @apply_forward_hook
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





       if self.use_slicing and z.shape[0] > 1:
            UpperCamelCase__										=      [self._decode(SCREAMING_SNAKE_CASE_	).sample for z_slice in z.split(1	)]
            UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      self._decode(SCREAMING_SNAKE_CASE_	).sample

       if not return_dict:
            return (decoded,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      min(a.shape[2]   ,				b.shape[2]   ,				SCREAMING_SNAKE_CASE_	)
       for y in range(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      min(a.shape[3]   ,				b.shape[3]   ,				SCREAMING_SNAKE_CASE_	)
       for x in range(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      AutoencoderKLOutput:





       '''simple docstring'''





       UpperCamelCase__										=      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
       UpperCamelCase__										=      int(self.tile_latent_min_size * self.tile_overlap_factor	)
       UpperCamelCase__										=      self.tile_latent_min_size - blend_extent

       # Split the image into 512x512 tiles and encode them separately.
       UpperCamelCase__										=      []
       for i in range(0   ,				x.shape[2]   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j in range(0   ,				x.shape[3]   ,				SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                 UpperCamelCase__										=      self.encoder(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      self.quant_conv(SCREAMING_SNAKE_CASE_	)
                 row.append(SCREAMING_SNAKE_CASE_	)
            rows.append(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      []
       for i, row in enumerate(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j, tile in enumerate(SCREAMING_SNAKE_CASE_	):
                 # blend the above tile and the left tile
                 # to the current tile and add the current tile to the result row
                 if i > 0:
                      UpperCamelCase__										=      self.blend_v(rows[i - 1][j]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 if j > 0:
                      UpperCamelCase__										=      self.blend_h(row[j - 1]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 result_row.append(tile[:, :, :row_limit, :row_limit]	)
            result_rows.append(torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=3	)	)

       UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
       UpperCamelCase__										=      DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE_	)

       if not return_dict:
            return (posterior,)

       return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = True	)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





       UpperCamelCase__										=      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
       UpperCamelCase__										=      int(self.tile_sample_min_size * self.tile_overlap_factor	)
       UpperCamelCase__										=      self.tile_sample_min_size - blend_extent

       # Split z into overlapping 64x64 tiles and decode them separately.
       # The tiles have an overlap to avoid seams between tiles.
       UpperCamelCase__										=      []
       for i in range(0   ,				z.shape[2]   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j in range(0   ,				z.shape[3]   ,				SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                 UpperCamelCase__										=      self.post_quant_conv(SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      self.decoder(SCREAMING_SNAKE_CASE_	)
                 row.append(SCREAMING_SNAKE_CASE_	)
            rows.append(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      []
       for i, row in enumerate(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      []
            for j, tile in enumerate(SCREAMING_SNAKE_CASE_	):
                 # blend the above tile and the left tile
                 # to the current tile and add the current tile to the result row
                 if i > 0:
                      UpperCamelCase__										=      self.blend_v(rows[i - 1][j]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 if j > 0:
                      UpperCamelCase__										=      self.blend_h(row[j - 1]   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 result_row.append(tile[:, :, :row_limit, :row_limit]	)
            result_rows.append(torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=3	)	)

       UpperCamelCase__										=      torch.cat(SCREAMING_SNAKE_CASE_   ,				dim=2	)
       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Union[DecoderOutput, torch.FloatTensor]:





       '''simple docstring'''





       UpperCamelCase__										=      sample
       UpperCamelCase__										=      self.encode(SCREAMING_SNAKE_CASE_	).latent_dist
       if sample_posterior:
            UpperCamelCase__										=      posterior.sample(generator=SCREAMING_SNAKE_CASE_	)
       else:
            UpperCamelCase__										=      posterior.mode()
       UpperCamelCase__										=      self.decode(SCREAMING_SNAKE_CASE_	).sample

       if not return_dict:
            return (dec,)

       return DecoderOutput(sample=SCREAMING_SNAKE_CASE_	)




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


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

if TYPE_CHECKING:
     import pyspark



@dataclass
class     _A   (    datasets.BuilderConfig							):
  SCREAMING_SNAKE_CASE_	:	Optional[datasets.Features]						=None







def  __UpperCamelCase    (						A ,							A ,							):
     import pyspark

     def generate_fn():
          UpperCamelCase__										=      df.select('''*''' ,							pyspark.sql.functions.spark_partition_id().alias('''part_id'''							)							)
          for partition_id in partition_order:
               UpperCamelCase__										=      df_with_partition_id.select('''*'''							).where(f"part_id = {partition_id}"							).drop('''part_id'''							)
               UpperCamelCase__										=      partition_df.collect()
               UpperCamelCase__										=      0
               for row in rows:
                    yield f"{partition_id}_{row_id}", row.asDict()
                    row_id += 1

     return generate_fn



class     _A   (    _BaseExamplesIterable							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      Any:





       '''simple docstring'''





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






  def __iter__(self	)		->      int:





       '''simple docstring'''





       yield from self.generate_examples_fn()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





       '''simple docstring'''





       UpperCamelCase__										=      self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       return SparkExamplesIterable(self.df   ,				partition_order=SCREAMING_SNAKE_CASE_	)






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return len(self.partition_order	)



class     _A   (    datasets.DatasetBuilder							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=SparkConfig






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





       '''simple docstring'''





       import pyspark

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

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






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





       '''simple docstring'''





       def create_cache_and_write_probe(SCREAMING_SNAKE_CASE_	):
            # 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=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      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(SCREAMING_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__										=      (
                self._spark.sparkContext.parallelize(range(1	)   ,				1	).mapPartitions(SCREAMING_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 		_a					(self	)		->      Any:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       import pyspark

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      Iterable[Tuple[int, bool, Union[int, tuple]]]:





       '''simple docstring'''





       import pyspark

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

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

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

       UpperCamelCase__										=      (
           self.df.mapInArrow(SCREAMING_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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "arrow"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[int]:





       '''simple docstring'''





       self._validate_cache_dir()

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

       UpperCamelCase__										=      '''-TTTTT-SSSSS-of-NNNNN'''
       UpperCamelCase__										=      F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
       UpperCamelCase__										=      path_join(self._output_dir   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      []
       UpperCamelCase__										=      []

       for task_id, content in self._prepare_split_single(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            (
                (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       
            )										=      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(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      total_num_examples
       UpperCamelCase__										=      total_num_bytes

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

            # use the -SSSSS-of-NNNNN pattern
            def _rename_shard(
                SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				):
                 rename(
                     SCREAMING_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__										=      []
            UpperCamelCase__										=      0
            for i in range(len(SCREAMING_SNAKE_CASE_	)	):
                 UpperCamelCase__						,       UpperCamelCase__										=      task_id_and_num_shards[i]
                 for shard_id in range(SCREAMING_SNAKE_CASE_	):
                      args.append([task_id, shard_id, global_shard_id]	)
                      global_shard_id += 1
            self._spark.sparkContext.parallelize(SCREAMING_SNAKE_CASE_   ,				len(SCREAMING_SNAKE_CASE_	)	).map(lambda SCREAMING_SNAKE_CASE_	: _rename_shard(*SCREAMING_SNAKE_CASE_	)	).collect()
       else:
            # don't use any pattern
            UpperCamelCase__										=      0
            UpperCamelCase__										=      task_id_and_num_shards[0][0]
            self._rename(
                fpath.replace('''SSSSS'''   ,				F"{shard_id:05d}"	).replace('''TTTTT'''   ,				F"{task_id:05d}"	)   ,				fpath.replace(SCREAMING_SNAKE_CASE_   ,				''''''	)   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				)		->      SparkExamplesIterable:





       '''simple docstring'''





       return SparkExamplesIterable(self.df	)



