'''simple docstring'''




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_lowercase		:				Optional[Any]	       =		[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

_lowercase		:				List[Any]	       =		[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]




def 							lowerCamelCase			(   )    ->   List[str]:
							lowercase_      :    str       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bootstrap_aggregation=UpperCAmelCase__   ,   rouge_keys=["""rouge2""", """rougeL"""]				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    int       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bootstrap_aggregation=UpperCAmelCase__   ,   rouge_keys=["""rouge2"""]				)
							assert (
							    pd.DataFrame(no_aggregation["""rouge2"""]				).fmeasure.mean()
							    == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]				).fmeasure.mean()
							)




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Tuple       		= """rougeLsum"""
							lowercase_      :    Optional[Any]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=[k]				)[k]
							lowercase_      :    Optional[Any]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=[k]				)[k]
							assert score > score_no_sep




def 							lowerCamelCase			(   )    ->   List[Any]:
							lowercase_      :    Optional[int]       		= ["""rouge1""", """rouge2""", """rougeL"""]
							lowercase_      :    Tuple       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=UpperCAmelCase__				)
							lowercase_      :    Tuple       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=UpperCAmelCase__				)
							assert score_sep == score_no_sep




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Union[str, Any]       		= [
							    """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
							    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
							]
							lowercase_      :    List[str]       		= [
							    """Margot Frank, died in 1945, a month earlier than previously thought.""",
							    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
							    """ the final seconds on board Flight 9525.""",
							]
							assert calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__				) == calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__				)




def 							lowerCamelCase			(   )    ->   Union[str, Any]:
							lowercase_      :    Optional[Any]       		= [
							    """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
							]
							lowercase_      :    List[Any]       		= [
							    """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
							]

							lowercase_      :    Optional[int]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   rouge_keys=["""rougeLsum"""]   ,   newline_sep=UpperCAmelCase__				)["""rougeLsum"""]
							lowercase_      :    List[str]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   rouge_keys=["""rougeLsum"""]				)["""rougeLsum"""]
							assert new_score > prev_score




def 							lowerCamelCase			(   )    ->   Tuple:
							lowercase_      :    Optional[int]       		= Path("""examples/seq2seq/test_data/wmt_en_ro"""				)
							lowercase_      :    List[Any]       		= calculate_rouge_path(data_dir.joinpath("""test.source"""				)   ,   data_dir.joinpath("""test.target"""				)				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= calculate_rouge_path(
							    data_dir.joinpath("""test.source"""				)   ,   data_dir.joinpath("""test.target"""				)   ,   bootstrap_aggregation=UpperCAmelCase__				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)






'''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


_lowercase		:				str	       =		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark





@dataclass
class 					__magic_name__		(					datasets.BuilderConfig):
			UpperCamelCase__       = None




def 							lowerCamelCase			(   UpperCAmelCase__ :      "pyspark.sql.DataFrame"   ,   UpperCAmelCase__ :      List[int]   ,   )    ->   str:
							import pyspark

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

							return generate_fn





class 					__magic_name__		(					_BaseExamplesIterable):







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







			def __iter__(			self       :						List[Any]					):
										yield from self.generate_examples_fn()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						np.random.Generator					):
										lowercase_      :    str       		= list(range(self.df.rdd.getNumPartitions()					)					)
										generator.shuffle(lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										lowercase_      :    str       		= self.split_shard_indices_by_worker(lowercase_       ,    lowercase_					)
										return SparkExamplesIterable(self.df       ,    partition_order=lowercase_					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return len(self.partition_order					)





class 					__magic_name__		(					datasets.DatasetBuilder):
			UpperCamelCase__       = SparkConfig







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

										lowercase_      :    str       		= pyspark.sql.SparkSession.builder.getOrCreate()
										lowercase_      :    Optional[int]       		= df
										lowercase_      :    List[str]       		= working_dir

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										# Returns the path of the created file.
										def create_cache_and_write_probe(lowercase_       :						str					):
																	# 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_					)
																	lowercase_      :    List[str]       		= 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:
																	lowercase_      :    str       		= (
																	    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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										return datasets.DatasetInfo(features=self.config.features					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						datasets.download.download_manager.DownloadManager					):
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										import pyspark

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

										lowercase_      :    Union[str, Any]       		= self.df.count()
										lowercase_      :    Union[str, Any]       		= 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.
										lowercase_      :    Any       		= (
										    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
										)
										lowercase_      :    List[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.
																	lowercase_      :    Any       		= min(lowercase_       ,    int(approx_total_size / max_shard_size					)					)
																	lowercase_      :    Any       		= self.df.repartition(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						str       ,    lowercase_       :						int       ,    ):
										import pyspark

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

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

																	if writer._num_bytes > 0:
																								lowercase_		,		lowercase_      :    str       		= 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_					)					):
																															lowercase_      :    Optional[Any]       		= os.path.join(os.path.dirname(lowercase_					)       ,    os.path.basename(lowercase_					)					)
																															shutil.move(lowercase_       ,    lowercase_					)

										lowercase_      :    Union[str, Any]       		= (
										    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 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						"datasets.SplitGenerator"       ,    lowercase_       :						str = "arrow"       ,    lowercase_       :						Optional[Union[str, int]] = None       ,    lowercase_       :						Optional[int] = None       ,    **lowercase_       :						List[str]       ,    ):
										self._validate_cache_dir()

										lowercase_      :    int       		= convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
										self._repartition_df_if_needed(lowercase_					)
										lowercase_      :    Tuple       		= not is_remote_filesystem(self._fs					)
										lowercase_      :    int       		= os.path.join if is_local else posixpath.join

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

										lowercase_      :    Any       		= 0
										lowercase_      :    Tuple       		= 0
										lowercase_      :    int       		= 0
										lowercase_      :    Dict       		= []
										lowercase_      :    Union[str, Any]       		= []

										for task_id, content in self._prepare_split_single(lowercase_       ,    lowercase_       ,    lowercase_					):
																	(
																	    (
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		(
																	    lowercase_
																	)		,		
																	)      :    Union[str, 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_					)

										lowercase_      :    List[str]       		= total_num_examples
										lowercase_      :    int       		= total_num_bytes

										# should rename everything at the end
										logger.debug(f'''Renaming {total_shards} shards.'''					)
										if total_shards > 1:
																	lowercase_      :    Tuple       		= 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.
																	lowercase_      :    Dict       		= 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}'''					)       ,    )

																	lowercase_      :    Union[str, Any]       		= []
																	lowercase_      :    Tuple       		= 0
																	for i in range(len(lowercase_					)					):
																								lowercase_		,		lowercase_      :    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
																	lowercase_      :    List[str]       		= 0
																	lowercase_      :    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(lowercase_       ,    """"""					)       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						"datasets.SplitGenerator"       ,    ):
										return SparkExamplesIterable(self.df					)





