
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    :    Optional[Any]         =     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark


@dataclass
class     __SCREAMING_SNAKE_CASE					(   datasets.BuilderConfig):
						_SCREAMING_SNAKE_CASE	:       Optional[datasets.Features]   =					None







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

						"""simple docstring"""



						import pyspark

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

						return generate_fn


class     __SCREAMING_SNAKE_CASE					(   _BaseExamplesIterable):






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






												"""simple docstring"""
												lowerCAmelCase__    						=						df
												lowerCAmelCase__    						=						partition_order or range(self.df.rdd.getNumPartitions()   )
												lowerCAmelCase__    						=						_generate_iterable_examples(self.df     ,						self.partition_order   )






						def __iter__(	self   ):






												"""simple docstring"""
												yield from self.generate_examples_fn()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						list(range(self.df.rdd.getNumPartitions()   )   )
												generator.shuffle(_UpperCamelCase   )
												return SparkExamplesIterable(self.df     ,						partition_order=_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.split_shard_indices_by_worker(_UpperCamelCase     ,						_UpperCamelCase   )
												return SparkExamplesIterable(self.df     ,						partition_order=_UpperCamelCase   )







						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return len(self.partition_order   )




class     __SCREAMING_SNAKE_CASE					(   datasets.DatasetBuilder):
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					SparkConfig






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






												"""simple docstring"""
												import pyspark

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

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






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Returns the path of the created file.
												def create_cache_and_write_probe(_UpperCamelCase   ):
																		# 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=_UpperCamelCase   )
																		lowerCAmelCase__    						=						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(_UpperCamelCase     ,						'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__    						=						(
																		    self._spark.sparkContext.parallelize(range(1   )     ,						1   ).mapPartitions(_UpperCamelCase   ).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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return datasets.DatasetInfo(features=self.config.features   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												import pyspark

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

												lowerCAmelCase__    						=						self.df.count()
												lowerCAmelCase__    						=						df_num_rows if df_num_rows <= 1_00 else 1_00
												# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
												lowerCAmelCase__    						=						(
												    self.df.limit(_UpperCamelCase   )
												    .repartition(1   )
												    .mapInArrow(_UpperCamelCase     ,						'batch_bytes: long'   )
												    .agg(pyspark.sql.functions.sum('batch_bytes'   ).alias('sample_bytes'   )   )
												    .collect()[0]
												    .sample_bytes
												    / sample_num_rows
												)
												lowerCAmelCase__    						=						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__    						=						min(_UpperCamelCase     ,						int(approx_total_size / max_shard_size   )   )
																		lowerCAmelCase__    						=						self.df.repartition(_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												import pyspark

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

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

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

												lowerCAmelCase__    						=						(
												    self.df.mapInArrow(_UpperCamelCase     ,						'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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = "arrow"     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												self._validate_cache_dir()

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

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

												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						[]
												lowerCAmelCase__    						=						[]

												for task_id, content in self._prepare_split_single(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):
																		(
																		    (
																		    lowerCAmelCase__       
																		)						,       (
																		    lowerCAmelCase__       
																		)						,       (
																		    lowerCAmelCase__       
																		)						,       (
																		    lowerCAmelCase__       
																		)						,              
																		)    						=						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(_UpperCamelCase   )

												lowerCAmelCase__    						=						total_num_examples
												lowerCAmelCase__    						=						total_num_bytes

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

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







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												return SparkExamplesIterable(self.df   )













from typing import TYPE_CHECKING

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


__snake_case    :    Dict         =     {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Union[str, Any]         =     ["""SpeechEncoderDecoderModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    str         =     ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
					from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
					import sys

					__snake_case    :    Tuple         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





