






"""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


SCREAMING_SNAKE_CASE						:						List[str]					   =       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark





@dataclass
class       snake_case_       (    datasets.BuilderConfig       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[Any]   			=					None





def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,):
				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       snake_case_       (    _BaseExamplesIterable       ):



	"""simple docstring"""

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


					"""simple docstring"""

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


	def __iter__(      self						):


					"""simple docstring"""

					yield from self.generate_examples_fn()


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       self.split_shard_indices_by_worker(__lowerCAmelCase  ,					__lowerCAmelCase						)
					return SparkExamplesIterable(self.df  ,					partition_order=__lowerCAmelCase						)






	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.partition_order						)





class       snake_case_       (    datasets.DatasetBuilder       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							int   			=					SparkConfig


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


					"""simple docstring"""

					import pyspark

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

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					def create_cache_and_write_probe(__a						):
									# 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=__lowerCAmelCase						)
									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(__lowerCAmelCase  ,					'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(__lowerCAmelCase						).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  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					import pyspark

					def get_arrow_batch_size(__a						):
									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 <= 100 else 100
					# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
					A__							 =       (
					    self.df.limit(__lowerCAmelCase						)
					    .repartition(1						)
					    .mapInArrow(__lowerCAmelCase  ,					'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(__lowerCAmelCase  ,					int(approx_total_size / max_shard_size						)						)
									A__							 =       self.df.repartition(__lowerCAmelCase						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					):


					"""simple docstring"""

					import pyspark

					A__							 =       ParquetWriter if file_format == 'parquet' else ArrowWriter
					A__							 =       os.path.join(self._working_dir  ,					os.path.basename(__lowerCAmelCase						)						) 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						):
									# Within the same SparkContext, no two task attempts will share the same attempt ID.
									A__							 =       pyspark.TaskContext().taskAttemptId()
									A__							 =       next(__lowerCAmelCase  ,					__lowerCAmelCase						)
									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=__lowerCAmelCase  ,					path=working_fpath.replace('SSSSS'  ,					f'''{shard_id:05d}'''						).replace('TTTTT'  ,					f'''{task_id:05d}'''						)  ,					writer_batch_size=__lowerCAmelCase  ,					storage_options=__lowerCAmelCase  ,					embed_local_files=__lowerCAmelCase  ,					)
									A__							 =       pa.Table.from_batches([first_batch]						)
									writer.write_table(__lowerCAmelCase						)
									for batch in it:
													if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																	A__ ,			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=__lowerCAmelCase  ,					storage_options=__lowerCAmelCase  ,					embed_local_files=__lowerCAmelCase  ,					)
													A__							 =       pa.Table.from_batches([batch]						)
													writer.write_table(__lowerCAmelCase						)

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

					A__							 =       (
					    self.df.mapInArrow(__lowerCAmelCase  ,					'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  ,					__a  ,					__a = "arrow"  ,					__a = None  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					self._validate_cache_dir()

					A__							 =       convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
					self._repartition_df_if_needed(__lowerCAmelCase						)
					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  ,					__lowerCAmelCase						)

					A__							 =       0
					A__							 =       0
					A__							 =       0
					A__							 =       []
					A__							 =       []

					for task_id, content in self._prepare_split_single(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase						):
									(
									    (
									    A__			
									) ,			(
									    A__			
									) ,			(
									    A__			
									) ,			(
									    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(__lowerCAmelCase						)

					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  ,					__a  ,					__a  ,					):
													rename(
													    __lowerCAmelCase  ,					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(__lowerCAmelCase						)						):
													A__ ,			A__							 =       task_id_and_num_shards[i]
													for shard_id in range(__lowerCAmelCase						):
																	args.append([task_id, shard_id, global_shard_id]						)
																	global_shard_id += 1
									self._spark.sparkContext.parallelize(__lowerCAmelCase  ,					len(__lowerCAmelCase						)						).map(lambda __a						: _rename_shard(*__lowerCAmelCase						)						).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(__lowerCAmelCase  ,					''						)  ,					)






	def       _UpperCAmelCase						(      self  ,					__a  ,					):


					"""simple docstring"""

					return SparkExamplesIterable(self.df						)










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


