
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


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark





@dataclass
class 							UpperCAmelCase				(  datasets.BuilderConfig  ):




	'''simple docstring'''





	snake_case_    =					None





def 						UpperCAmelCase      (     a_		,       a_		,       )		->     Tuple:







				"""simple docstring"""



				import pyspark

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

				return generate_fn





class 							UpperCAmelCase				(  _BaseExamplesIterable  ):




	'''simple docstring'''





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






	def __iter__(	self :			str			):
					yield from self.generate_examples_fn()






	def     UpperCamelCase_  (	self :			Tuple							,A :			np.random.Generator			):
					__A             =			list(range(self.df.rdd.getNumPartitions()			)			)
					generator.shuffle(__snake_case			)
					return SparkExamplesIterable(self.df							,partition_order=__snake_case			)






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			int			):
					__A             =			self.split_shard_indices_by_worker(__snake_case							,__snake_case			)
					return SparkExamplesIterable(self.df							,partition_order=__snake_case			)







	@property
	def     UpperCamelCase_  (	self :			int			):
					return len(self.partition_order			)





class 							UpperCAmelCase				(  datasets.DatasetBuilder  ):




	'''simple docstring'''





	snake_case_    =					SparkConfig






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

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

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






	def     UpperCamelCase_  (	self :			Dict			):
					# Returns the path of the created file.
					def create_cache_and_write_probe(A :			Tuple			):
									# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
									# already exist.
									os.makedirs(self._cache_dir							,exist_ok=__snake_case			)
									__A             =			os.path.join(self._cache_dir							,"fs_test" + uuid.uuida().hex			)
									# Opening the file in append mode will create a new file unless it already exists, in which case it will not
									# change the file contents.
									open(__snake_case							,"a"			)
									return [probe_file]

					if self._spark.conf.get("spark.master"							,""			).startswith("local"			):
									return

					# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
					# accessible to the driver.
					# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
					if self._cache_dir:
									__A             =			(
									    self._spark.sparkContext.parallelize(range(1			)							,1			).mapPartitions(__snake_case			).collect()
									)
									if os.path.isfile(probe[0]			):
													return

					raise ValueError(
					    "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return datasets.DatasetInfo(features=self.config.features			)






	def     UpperCamelCase_  (	self :			Tuple							,A :			datasets.download.download_manager.DownloadManager			):
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[str]			):
					import pyspark

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

					__A             =			self.df.count()
					__A             =			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.
					__A             =			(
					    self.df.limit(__snake_case			)
					    .repartition(1			)
					    .mapInArrow(__snake_case							,"batch_bytes: long"			)
					    .agg(pyspark.sql.functions.sum("batch_bytes"			).alias("sample_bytes"			)			)
					    .collect()[0]
					    .sample_bytes
					    / sample_num_rows
					)
					__A             =			approx_bytes_per_row * df_num_rows
					if approx_total_size > max_shard_size:
									# Make sure there is at least one row per partition.
									__A             =			min(__snake_case							,int(approx_total_size / max_shard_size			)			)
									__A             =			self.df.repartition(__snake_case			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			str							,A :			str							,A :			int							,):
					import pyspark

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

					def write_arrow(A :			str			):
									# Within the same SparkContext, no two task attempts will share the same attempt ID.
									__A             =			pyspark.TaskContext().taskAttemptId()
									__A             =			next(__snake_case							,__snake_case			)
									if first_batch is None:
													# Some partitions might not receive any data.
													return pa.RecordBatch.from_arrays(
													    [[task_id], [0], [0]]							,names=["task_id", "num_examples", "num_bytes"]							,)
									__A             =			0
									__A             =			writer_class(
									    features=__snake_case							,path=working_fpath.replace("SSSSS"							,f'''{shard_id:05d}'''			).replace("TTTTT"							,f'''{task_id:05d}'''			)							,writer_batch_size=__snake_case							,storage_options=__snake_case							,embed_local_files=__snake_case							,)
									__A             =			pa.Table.from_batches([first_batch]			)
									writer.write_table(__snake_case			)
									for batch in it:
													if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																	__A             =			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
																	__A             =			writer_class(
																	    features=writer._features							,path=working_fpath.replace("SSSSS"							,f'''{shard_id:05d}'''			).replace("TTTTT"							,f'''{task_id:05d}'''			)							,writer_batch_size=__snake_case							,storage_options=__snake_case							,embed_local_files=__snake_case							,)
													__A             =			pa.Table.from_batches([batch]			)
													writer.write_table(__snake_case			)

									if writer._num_bytes > 0:
													__A             =			writer.finalize()
													writer.close()
													yield pa.RecordBatch.from_arrays(
													    [[task_id], [num_examples], [num_bytes]]							,names=["task_id", "num_examples", "num_bytes"]							,)

									if working_fpath != fpath:
													for file in os.listdir(os.path.dirname(__snake_case			)			):
																	__A             =			os.path.join(os.path.dirname(__snake_case			)							,os.path.basename(__snake_case			)			)
																	shutil.move(__snake_case							,__snake_case			)

					__A             =			(
					    self.df.mapInArrow(__snake_case							,"task_id: long, num_examples: long, num_bytes: long"			)
					    .groupBy("task_id"			)
					    .agg(
					        pyspark.sql.functions.sum("num_examples"			).alias("total_num_examples"			)							,pyspark.sql.functions.sum("num_bytes"			).alias("total_num_bytes"			)							,pyspark.sql.functions.count("num_bytes"			).alias("num_shards"			)							,pyspark.sql.functions.collect_list("num_examples"			).alias("shard_lengths"			)							,)
					    .collect()
					)
					for row in stats:
									yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






	def     UpperCamelCase_  (	self :			Dict							,A :			"datasets.SplitGenerator"							,A :			str = "arrow"							,A :			Optional[Union[str, int]] = None							,A :			Optional[int] = None							,**A :			Tuple							,):
					self._validate_cache_dir()

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

					__A             =			'-TTTTT-SSSSS-of-NNNNN'
					__A             =			f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
					__A             =			path_join(self._output_dir							,__snake_case			)

					__A             =			0
					__A             =			0
					__A             =			0
					__A             =			[]
					__A             =			[]

					for task_id, content in self._prepare_split_single(__snake_case							,__snake_case							,__snake_case			):
									(
									    __A     
									)             =			content
									if num_bytes > 0:
													total_num_examples += num_examples
													total_num_bytes += num_bytes
													total_shards += num_shards
													task_id_and_num_shards.append((task_id, num_shards)			)
													all_shard_lengths.extend(__snake_case			)

					__A             =			total_num_examples
					__A             =			total_num_bytes

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

									# use the -SSSSS-of-NNNNN pattern
									def _rename_shard(
									    A :			int							,A :			int							,A :			int							,):
													rename(
													    __snake_case							,fpath.replace("SSSSS"							,f'''{shard_id:05d}'''			).replace("TTTTT"							,f'''{task_id:05d}'''			)							,fpath.replace("TTTTT-SSSSS"							,f'''{global_shard_id:05d}'''			).replace("NNNNN"							,f'''{total_shards:05d}'''			)							,)

									__A             =			[]
									__A             =			0
									for i in range(len(__snake_case			)			):
													__A             =			task_id_and_num_shards[i]
													for shard_id in range(__snake_case			):
																	args.append([task_id, shard_id, global_shard_id]			)
																	global_shard_id += 1
									self._spark.sparkContext.parallelize(__snake_case							,len(__snake_case			)			).map(lambda A			: _rename_shard(*__snake_case			)			).collect()
					else:
									# don't use any pattern
									__A             =			0
									__A             =			task_id_and_num_shards[0][0]
									self._rename(
									    fpath.replace("SSSSS"							,f'''{shard_id:05d}'''			).replace("TTTTT"							,f'''{task_id:05d}'''			)							,fpath.replace(__snake_case							,""			)							,)







	def     UpperCamelCase_  (	self :			str							,A :			"datasets.SplitGenerator"							,):
					return SparkExamplesIterable(self.df			)


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					if self.audio_column not in features:
									raise ValueError(f'''Column {self.audio_column} is not present in features.'''			)
					if not isinstance(features[self.audio_column]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			int			):
					return {self.audio_column: "audio", self.transcription_column: "transcription"}
