





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


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

if TYPE_CHECKING:
						import pyspark
@dataclass
class        snake_case_		(			datasets.BuilderConfig):
		lowerCamelCase    :Optional[datasets.Features]						=     None


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       )   ->     Optional[Any]:
		import pyspark

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

		return generate_fn
class        snake_case_		(			_BaseExamplesIterable):







		def __init__(		self			,     __lowercase			,     __lowercase=None			,     )				->   Tuple:
				lowerCamelCase   : Dict								=df
				lowerCamelCase   : str								=partition_order or range(self.df.rdd.getNumPartitions()   )
				lowerCamelCase   : Dict								=_generate_iterable_examples(self.df			,     self.partition_order   )







		def __iter__(		self   )				->   Any:
				yield from self.generate_examples_fn()







		def 			__lowercase   (		self			,     __lowercase   )				->   "SparkExamplesIterable":
				lowerCamelCase   : List[str]								=list(range(self.df.rdd.getNumPartitions()   )   )
				generator.shuffle(__lowercase   )
				return SparkExamplesIterable(self.df			,     partition_order=__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   "SparkExamplesIterable":
				lowerCamelCase   : Dict								=self.split_shard_indices_by_worker(__lowercase			,     __lowercase   )
				return SparkExamplesIterable(self.df			,     partition_order=__lowercase   )







		@property
		def 			__lowercase   (		self   )				->   int:
				return len(self.partition_order   )

class        snake_case_		(			datasets.DatasetBuilder):
		lowerCamelCase    :Tuple						=     SparkConfig







		def __init__(		self			,     __lowercase			,     __lowercase = None			,     __lowercase = None			,     **__lowercase			,     )				->   Dict:
				import pyspark

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

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







		def 			__lowercase   (		self   )				->   Optional[Any]:
				# Returns the path of the created file.
				def create_cache_and_write_probe(__lowercase   ):
						# 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   )
						lowerCamelCase   : 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:
						lowerCamelCase   : List[Any]								=(
						    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 			__lowercase   (		self   )				->   Optional[int]:
				return datasets.DatasetInfo(features=self.config.features   )







		def 			__lowercase   (		self			,     __lowercase   )				->   int:
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN   )]







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				import pyspark

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

				lowerCamelCase   : Union[str, Any]								=self.df.count()
				lowerCamelCase   : List[str]								=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.
				lowerCamelCase   : List[str]								=(
				    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
				)
				lowerCamelCase   : Dict								=approx_bytes_per_row * df_num_rows
				if approx_total_size > max_shard_size:
						# Make sure there is at least one row per partition.
						lowerCamelCase   : Optional[Any]								=min(__lowercase			,     int(approx_total_size / max_shard_size   )   )
						lowerCamelCase   : Any								=self.df.repartition(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     )				->   Iterable[Tuple[int, bool, Union[int, tuple]]]:
				import pyspark

				lowerCamelCase   : Dict								=ParquetWriter if file_format == '''parquet''' else ArrowWriter
				lowerCamelCase   : List[Any]								=os.path.join(self._working_dir			,     os.path.basename(__lowercase   )   ) if self._working_dir else fpath
				lowerCamelCase   : str								=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.
				lowerCamelCase   : Tuple								=self.config.features
				lowerCamelCase   : int								=self._writer_batch_size
				lowerCamelCase   : Union[str, Any]								=self._fs.storage_options

				def write_arrow(__lowercase   ):
						# Within the same SparkContext, no two task attempts will share the same attempt ID.
						lowerCamelCase   : List[str]								=pyspark.TaskContext().taskAttemptId()
						lowerCamelCase   : int								=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''']			,     )
						lowerCamelCase   : Any								=0
						lowerCamelCase   : 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			,     )
						lowerCamelCase   : List[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:
										lowerCamelCase   : List[Any]								=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
										lowerCamelCase   : Tuple								=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			,     )
								lowerCamelCase   : Dict								=pa.Table.from_batches([batch]   )
								writer.write_table(__lowercase   )

						if writer._num_bytes > 0:
								lowerCamelCase   : Dict								=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   )   ):
										lowerCamelCase   : Tuple								=os.path.join(os.path.dirname(__lowercase   )			,     os.path.basename(__lowercase   )   )
										shutil.move(__lowercase			,     __lowercase   )

				lowerCamelCase   : List[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 			__lowercase   (		self			,     __lowercase			,     __lowercase = "arrow"			,     __lowercase = None			,     __lowercase = None			,     **__lowercase			,     )				->   str:
				self._validate_cache_dir()

				lowerCamelCase   : Dict								=convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE   )
				self._repartition_df_if_needed(__lowercase   )
				lowerCamelCase   : str								=not is_remote_filesystem(self._fs   )
				lowerCamelCase   : int								=os.path.join if is_local else posixpath.join

				lowerCamelCase   : int								='''-TTTTT-SSSSS-of-NNNNN'''
				lowerCamelCase   : List[Any]								=F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
				lowerCamelCase   : int								=path_join(self._output_dir			,     __lowercase   )

				lowerCamelCase   : Optional[int]								=0
				lowerCamelCase   : Any								=0
				lowerCamelCase   : Tuple								=0
				lowerCamelCase   : Any								=[]
				lowerCamelCase   : Optional[int]								=[]

				for task_id, content in self._prepare_split_single(__lowercase			,     __lowercase			,     __lowercase   ):
						(
						    lowerCamelCase   
						)   : List[str]								=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   )

				lowerCamelCase   : Optional[int]								=total_num_examples
				lowerCamelCase   : int								=total_num_bytes

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

						# use the -SSSSS-of-NNNNN pattern
						def _rename_shard(
						    __lowercase			,     __lowercase			,     __lowercase			,     ):
								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}"   )			,     )

						lowerCamelCase   : Union[str, Any]								=[]
						lowerCamelCase   : int								=0
						for i in range(len(__lowercase   )   ):
								lowerCamelCase   : Dict								=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
						lowerCamelCase   : Union[str, Any]								=0
						lowerCamelCase   : Union[str, 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 			__lowercase   (		self			,     __lowercase			,     )				->   SparkExamplesIterable:
				return SparkExamplesIterable(self.df   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_							=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

				snake_case_							=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

