




'''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							:   Any      		= datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark




@dataclass
class  _snake_case		(	datasets.BuilderConfig							):
			lowerCAmelCase_     :      Optional[datasets.Features]						       =    None


def      UpperCamelCase_(	snake_case							:       "pyspark.sql.DataFrame"							,				snake_case							:       List[int]							,				):

							'''simple docstring'''






							import pyspark

							def generate_fn():
														snake_case_						=      df.select("*"							,				pyspark.sql.functions.spark_partition_id().alias("part_id"     )     )
														for partition_id in partition_order:
																					snake_case_						=      df_with_partition_id.select("*"     ).where(f'part_id = {partition_id}'     ).drop("part_id"     )
																					snake_case_						=      partition_df.collect()
																					snake_case_						=      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				,							a__				,							a__=None				,							)				->				Any:

										'''simple docstring'''




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




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

										'''simple docstring'''




										yield from self.generate_examples_fn()




			def    lowerCAmelCase__				(					self				,							a__							)				->				"SparkExamplesIterable":

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__				,							a__							)				->				"SparkExamplesIterable":

										'''simple docstring'''




										snake_case_						=      self.split_shard_indices_by_worker(a__				,							a__							)
										return SparkExamplesIterable(self.df				,							partition_order=a__							)




			@property
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										return len(self.partition_order							)




class  _snake_case		(	datasets.DatasetBuilder							):
			lowerCAmelCase_     :      Dict						       =    SparkConfig




			def __init__(					self				,							a__				,							a__ = None				,							a__ = None				,							**a__				,							)				->				str:

										'''simple docstring'''




										import pyspark

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

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




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

										'''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=a__							)
																	snake_case_						=      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(a__				,							"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:
																	snake_case_						=      (
																	    self._spark.sparkContext.parallelize(range(1							)				,							1							).mapPartitions(a__							).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    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__							)				->				Optional[Any]:

										'''simple docstring'''




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




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

										'''simple docstring'''




										import pyspark

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

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




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

										'''simple docstring'''




										import pyspark

										snake_case_						=      ParquetWriter if file_format == "parquet" else ArrowWriter
										snake_case_						=      os.path.join(self._working_dir				,							os.path.basename(a__							)							) if self._working_dir else fpath
										snake_case_						=      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.
										snake_case_						=      self.config.features
										snake_case_						=      self._writer_batch_size
										snake_case_						=      self._fs.storage_options

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

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

										snake_case_						=      (
										    self.df.mapInArrow(a__				,							"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    lowerCAmelCase__				(					self				,							a__				,							a__ = "arrow"				,							a__ = None				,							a__ = None				,							**a__				,							)				->				int:

										'''simple docstring'''




										self._validate_cache_dir()

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

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

										snake_case_						=      0
										snake_case_						=      0
										snake_case_						=      0
										snake_case_						=      []
										snake_case_						=      []

										for task_id, content in self._prepare_split_single(a__				,							a__				,							a__							):
																	(
																	    (
																	    snake_case_
																	)					,      (
																	    snake_case_
																	)					,      (
																	    snake_case_
																	)					,      (
																	    snake_case_
																	)					,      
																	)						=      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(a__							)

										snake_case_						=      total_num_examples
										snake_case_						=      total_num_bytes

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

																	# use the -SSSSS-of-NNNNN pattern
																	def _rename_shard(
																	    a__				,							a__				,							a__				,							):
																								rename(
																								    a__				,							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}'							)				,							)

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




			def    lowerCAmelCase__				(					self				,							a__				,							)				->				SparkExamplesIterable:

										'''simple docstring'''




										return SparkExamplesIterable(self.df							)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


