'''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


lowerCAmelCase_	:		Dict			=     datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
			import pyspark




@dataclass
class 		SCREAMING_SNAKE_CASE     (					datasets.BuilderConfig	):







				'''simple docstring'''
				UpperCAmelCase__							=							None





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,)     ->			List[Any]:




						'''simple docstring'''
						import pyspark

						def generate_fn():
												_UpperCamelCase			:			List[Any]   			=						df.select("*"     ,pyspark.sql.functions.spark_partition_id().alias("part_id"					)					)
												for partition_id in partition_order:
																		_UpperCamelCase			:			Tuple   			=						df_with_partition_id.select("*"					).where(f'''part_id = {partition_id}'''					).drop("part_id"					)
																		_UpperCamelCase			:			Optional[int]   			=						partition_df.collect()
																		_UpperCamelCase			:			int   			=						0
																		for row in rows:
																								yield f'''{partition_id}_{row_id}''', row.asDict()
																								row_id += 1

						return generate_fn




class 		SCREAMING_SNAKE_CASE     (					_BaseExamplesIterable	):







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						"pyspark.sql.DataFrame"       ,		lowercase__					:						Union[str, Any]=None       ,		)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						df
										_UpperCamelCase			:			Any   			=						partition_order or range(self.df.rdd.getNumPartitions()					)
										_UpperCamelCase			:			List[Any]   			=						_generate_iterable_examples(self.df       ,		self.partition_order					)





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


										'''simple docstring'''




										yield from self.generate_examples_fn()





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						np.random.Generator					)							->"SparkExamplesIterable":


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase__					)
										return SparkExamplesIterable(self.df       ,		partition_order=lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						int       ,		lowercase__					:						int					)							->"SparkExamplesIterable":


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						self.split_shard_indices_by_worker(lowercase__       ,		lowercase__					)
										return SparkExamplesIterable(self.df       ,		partition_order=lowercase__					)







				@property
				def  snake_case__				(						self					:						Any					)							->int:


										'''simple docstring'''




										return len(self.partition_order					)





class 		SCREAMING_SNAKE_CASE     (					datasets.DatasetBuilder	):







				'''simple docstring'''
				UpperCAmelCase__							=							SparkConfig





				def __init__(						self					:						Any       ,		lowercase__					:						"pyspark.sql.DataFrame"       ,		lowercase__					:						str = None       ,		lowercase__					:						str = None       ,		**lowercase__					:						Dict       ,		)							->Optional[Any]:


										'''simple docstring'''




										import pyspark

										_UpperCamelCase			:			Any   			=						pyspark.sql.SparkSession.builder.getOrCreate()
										_UpperCamelCase			:			List[str]   			=						df
										_UpperCamelCase			:			Union[str, Any]   			=						working_dir

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





				def  snake_case__				(						self					:						Any					)							->int:


										'''simple docstring'''




										def create_cache_and_write_probe(lowercase__					:						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=lowercase__					)
																_UpperCamelCase			:			Dict   			=						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(lowercase__       ,		"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:
																_UpperCamelCase			:			int   			=						(
																    self._spark.sparkContext.parallelize(range(1					)       ,		1					).mapPartitions(lowercase__					).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  snake_case__				(						self					:						Union[str, Any]					)							->Union[str, Any]:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						datasets.download.download_manager.DownloadManager					)							->Any:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						str					)							->str:


										'''simple docstring'''




										import pyspark

										def get_arrow_batch_size(lowercase__					:						Union[str, Any]					):
																for batch in it:
																						yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}					)

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





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						str       ,		lowercase__					:						str       ,		lowercase__					:						int       ,		)							->Iterable[Tuple[int, bool, Union[int, tuple]]]:


										'''simple docstring'''




										import pyspark

										_UpperCamelCase			:			List[Any]   			=						ParquetWriter if file_format == "parquet" else ArrowWriter
										_UpperCamelCase			:			Tuple   			=						os.path.join(self._working_dir       ,		os.path.basename(lowercase__					)					) if self._working_dir else fpath
										_UpperCamelCase			:			List[str]   			=						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.
										_UpperCamelCase			:			List[Any]   			=						self.config.features
										_UpperCamelCase			:			Optional[int]   			=						self._writer_batch_size
										_UpperCamelCase			:			int   			=						self._fs.storage_options

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

																if writer._num_bytes > 0:
																						_UpperCamelCase			,       _UpperCamelCase			:			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(lowercase__					)					):
																												_UpperCamelCase			:			List[Any]   			=						os.path.join(os.path.dirname(lowercase__					)       ,		os.path.basename(lowercase__					)					)
																												shutil.move(lowercase__       ,		lowercase__					)

										_UpperCamelCase			:			Dict   			=						(
										    self.df.mapInArrow(lowercase__       ,		"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  snake_case__				(						self					:						int       ,		lowercase__					:						"datasets.SplitGenerator"       ,		lowercase__					:						str = "arrow"       ,		lowercase__					:						Optional[Union[str, int]] = None       ,		lowercase__					:						Optional[int] = None       ,		**lowercase__					:						Dict       ,		)							->str:


										'''simple docstring'''




										self._validate_cache_dir()

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

										_UpperCamelCase			:			Tuple   			=						"-TTTTT-SSSSS-of-NNNNN"
										_UpperCamelCase			:			Tuple   			=						f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
										_UpperCamelCase			:			Any   			=						path_join(self._output_dir       ,		lowercase__					)

										_UpperCamelCase			:			Union[str, Any]   			=						0
										_UpperCamelCase			:			Union[str, Any]   			=						0
										_UpperCamelCase			:			Optional[Any]   			=						0
										_UpperCamelCase			:			Optional[Any]   			=						[]
										_UpperCamelCase			:			Tuple   			=						[]

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

										_UpperCamelCase			:			Dict   			=						total_num_examples
										_UpperCamelCase			:			int   			=						total_num_bytes

										# should rename everything at the end
										logger.debug(f'''Renaming {total_shards} shards.'''					)
										if total_shards > 1:
																_UpperCamelCase			:			Dict   			=						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.
																_UpperCamelCase			:			List[Any]   			=						self._fs

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

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







				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						"datasets.SplitGenerator"       ,		)							->SparkExamplesIterable:


										'''simple docstring'''




										return SparkExamplesIterable(self.df					)



'''simple docstring'''





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[Any]			=     argparse.ArgumentParser(
			    description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
			)
			parser.add_argument(
			    """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
			)
			parser.add_argument(
			    """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
			)
			parser.add_argument("""--vocab_size""", default=3_0522, type=int)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

			logger.info("""Counting occurrences for MLM.""")
			lowerCAmelCase_	:		str			=     Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			lowerCAmelCase_	:		Any			=     [0] * args.vocab_size
			for k, v in counter.items():
						lowerCAmelCase_	:		Optional[Any]			=     v

			logger.info(f"""Dump to {args.token_counts_dump}""")
			with open(args.token_counts_dump, """wb""") as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


