

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


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

if TYPE_CHECKING:
			import pyspark






@dataclass
class _UpperCamelCase				(				datasets.BuilderConfig ):
			'''simple docstring'''







			lowerCamelCase__							     =None

def 			lowerCamelCase__			(       _a				,      _a				,      ):
	import pyspark

	def generate_fn():
		SCREAMING_SNAKE_CASE :   str					  =							df.select("*"				,      pyspark.sql.functions.spark_partition_id().alias("part_id"))
		for partition_id in partition_order:
			SCREAMING_SNAKE_CASE :   int					  =							df_with_partition_id.select("*").where(f"part_id = {partition_id}").drop("part_id")
			SCREAMING_SNAKE_CASE :   Tuple					  =							partition_df.collect()
			SCREAMING_SNAKE_CASE :   int					  =							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				:			Dict   ,   a				:			"pyspark.sql.DataFrame"   ,   a				:			str=None   ,   )				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							df
				SCREAMING_SNAKE_CASE :   Any					  =							partition_order or range(self.df.rdd.getNumPartitions()						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							_generate_iterable_examples(self.df   ,   self.partition_order						)
			def __iter__(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				yield from self.generate_examples_fn()
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			np.random.Generator						)				->							"SparkExamplesIterable":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							list(range(self.df.rdd.getNumPartitions()						)						)
				generator.shuffle(a						)
				return SparkExamplesIterable(self.df   ,   partition_order=a						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			int   ,   a				:			int						)				->							"SparkExamplesIterable":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.split_shard_indices_by_worker(a   ,   a						)
				return SparkExamplesIterable(self.df   ,   partition_order=a						)






			@property
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				return len(self.partition_order						)






class _UpperCamelCase				(				datasets.DatasetBuilder ):
			'''simple docstring'''







			lowerCamelCase__							     =SparkConfig
			def __init__(      self				:			Tuple   ,   a				:			"pyspark.sql.DataFrame"   ,   a				:			str = None   ,   a				:			str = None   ,   **a				:			Optional[int]   ,   )				->							Tuple:

				"""simple docstring"""

				import pyspark

				SCREAMING_SNAKE_CASE :   Tuple					  =							pyspark.sql.SparkSession.builder.getOrCreate()
				SCREAMING_SNAKE_CASE :   int					  =							df
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							working_dir

				super().__init__(
				    cache_dir=a   ,   config_name=str(self.df.semanticHash()						)   ,   **a   ,   )
			def 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				def create_cache_and_write_probe(a				:			Optional[int]						):
					# 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						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							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:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(
					    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 				__UpperCamelCase			(      self				:			Any						)				->							Dict:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			datasets.download.download_manager.DownloadManager						)				->							Any:

				"""simple docstring"""

				return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Union[str, Any]						)				->							Optional[Any]:

				"""simple docstring"""

				import pyspark

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

				SCREAMING_SNAKE_CASE :   Tuple					  =							self.df.count()
				SCREAMING_SNAKE_CASE :   int					  =							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.
				SCREAMING_SNAKE_CASE :   str					  =							(
				    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
				)
				SCREAMING_SNAKE_CASE :   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.
					SCREAMING_SNAKE_CASE :   Tuple					  =							min(a   ,   int(approx_total_size / max_shard_size						)						)
					SCREAMING_SNAKE_CASE :   Tuple					  =							self.df.repartition(a						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			str   ,   a				:			str   ,   a				:			int   ,   )				->							Iterable[Tuple[int, bool, Union[int, tuple]]]:

				"""simple docstring"""

				import pyspark

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

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

					if writer._num_bytes > 0:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							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						)						):
							SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							os.path.join(os.path.dirname(a						)   ,   os.path.basename(a						)						)
							shutil.move(a   ,   a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							(
				    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 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			"datasets.SplitGenerator"   ,   a				:			str = "arrow"   ,   a				:			Optional[Union[str, int]] = None   ,   a				:			Optional[int] = None   ,   **a				:			str   ,   )				->							str:

				"""simple docstring"""

				self._validate_cache_dir()

				SCREAMING_SNAKE_CASE :   int					  =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE						)
				self._repartition_df_if_needed(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							not is_remote_filesystem(self._fs						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							os.path.join if is_local else posixpath.join

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"-TTTTT-SSSSS-of-NNNNN"
				SCREAMING_SNAKE_CASE :   Dict					  =							F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
				SCREAMING_SNAKE_CASE :   Dict					  =							path_join(self._output_dir   ,   a						)

				SCREAMING_SNAKE_CASE :   int					  =							0
				SCREAMING_SNAKE_CASE :   str					  =							0
				SCREAMING_SNAKE_CASE :   List[Any]					  =							0
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[]
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[]

				for task_id, content in self._prepare_split_single(a   ,   a   ,   a						):
					(
					    (
					    SCREAMING_SNAKE_CASE
					)  ,(
					    SCREAMING_SNAKE_CASE
					)  ,(
					    SCREAMING_SNAKE_CASE
					)  ,(
					    SCREAMING_SNAKE_CASE
					)  ,
					) :   Optional[Any]					  =							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						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							total_num_examples
				SCREAMING_SNAKE_CASE :   str					  =							total_num_bytes

				# should rename everything at the end
				logger.debug(F"Renaming {total_shards} shards."						)
				if total_shards > 1:
					SCREAMING_SNAKE_CASE :   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.
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self._fs

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

					SCREAMING_SNAKE_CASE :   Tuple					  =							[]
					SCREAMING_SNAKE_CASE :   str					  =							0
					for i in range(len(a						)						):
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							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
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							0
					SCREAMING_SNAKE_CASE :   Optional[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(a   ,   ""						)   ,   )






			def 				__UpperCamelCase			(      self				:			int   ,   a				:			"datasets.SplitGenerator"   ,   )				->							SparkExamplesIterable:

				"""simple docstring"""

				return SparkExamplesIterable(self.df						)



'''simple docstring'''



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


A__						:   Tuple				=    logging.get_logger(__name__)

A__						:   Dict				=    {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     'rwkv'
    lowerCamelCase    :   Any					=     {'max_position_embeddings': 'context_length'}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_77     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=40_96     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						vocab_size
         __lowerCamelCase  :      Tuple														=						context_length
         __lowerCamelCase  :      str														=						hidden_size
         __lowerCamelCase  :      List[str]														=						num_hidden_layers
         __lowerCamelCase  :      Any														=						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __lowerCamelCase  :      Optional[int]														=						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __lowerCamelCase  :      Optional[Any]														=						layer_norm_epsilon
         __lowerCamelCase  :      int														=						rescale_every
         __lowerCamelCase  :      Tuple														=						use_cache

         __lowerCamelCase  :      int														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						eos_token_id

         super().__init__(
             tie_word_embeddings=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






