


'''simple docstring'''






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCAmelCase					       =							logging.get_logger(__name__)




@dataclass
class 	a__       :





							'''simple docstring'''






							lowercase__    :     str					=   field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
							lowercase__    :     str					=   field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
							lowercase__    :     int					=   field(
							    default=1_2_8       ,       metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,       )
							lowercase__    :     bool					=   field(
							    default=a__       ,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			self.task_name.lower()




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Any					=   "train"
							lowercase__    :     Optional[int]					=   "dev"
							lowercase__    :     List[Any]					=   "test"




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     GlueDataTrainingArguments
							lowercase__    :     str
							lowercase__    :     List[InputFeatures]







							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = Split.train      ,					lowerCamelCase_ = None      ,					) ->							Any:
														warnings.warn(
														    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
														    '''library. You can have a look at this example script for pointers: '''
														    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''      ,					lowerCamelCase_      ,					)
														lowerCAmelCase__      =			args
														lowerCAmelCase__      =			glue_processors[args.task_name]()
														lowerCAmelCase__      =			glue_output_modes[args.task_name]
														if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																					try:
																												lowerCAmelCase__      =			Split[mode]
																					except KeyError:
																												raise KeyError('''mode is not a valid split name'''     )
        # Load data features from cache or dataset file
														lowerCAmelCase__      =			os.path.join(
														    cache_dir if cache_dir is not None else args.data_dir      ,					F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""      ,					)
														lowerCAmelCase__      =			self.processor.get_labels()
														if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
														    "RobertaTokenizer",
														    "RobertaTokenizerFast",
														    "XLMRobertaTokenizer",
														    "BartTokenizer",
														    "BartTokenizerFast",
														):
																					# HACK(label indices are swapped in RoBERTa pretrained model)
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			label_list[2], label_list[1]
														lowerCAmelCase__      =			label_list

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														lowerCAmelCase__      =			cached_features_file + '''.lock'''
														with FileLock(lowerCamelCase_     ):
																					if os.path.exists(lowerCamelCase_     ) and not args.overwrite_cache:
																												lowerCAmelCase__      =			time.time()
																												lowerCAmelCase__      =			torch.load(lowerCamelCase_     )
																												logger.info(
																												    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,					time.time() - start     )
																					else:
																												logger.info(F"""Creating features from dataset file at {args.data_dir}"""     )

																												if mode == Split.dev:
																																			lowerCAmelCase__      =			self.processor.get_dev_examples(args.data_dir     )
																												elif mode == Split.test:
																																			lowerCAmelCase__      =			self.processor.get_test_examples(args.data_dir     )
																												else:
																																			lowerCAmelCase__      =			self.processor.get_train_examples(args.data_dir     )
																												if limit_length is not None:
																																			lowerCAmelCase__      =			examples[:limit_length]
																												lowerCAmelCase__      =			glue_convert_examples_to_features(
																												    lowerCamelCase_      ,					lowerCamelCase_      ,					max_length=args.max_seq_length      ,					label_list=lowerCamelCase_      ,					output_mode=self.output_mode      ,					)
																												lowerCAmelCase__      =			time.time()
																												torch.save(self.features      ,					lowerCamelCase_     )
																												# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																												logger.info(
																												    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""     )







							def __len__(	self     ) ->							Tuple:
														return len(self.features     )







							def __getitem__(	self      ,					lowerCamelCase_     ) ->							InputFeatures:
														return self.features[i]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return self.label_list



'''simple docstring'''






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def       _snake_case     (  A				,					A )		->					List[Any]:
							lowerCAmelCase__      =			[]
							for part_id in partition_order:
														lowerCAmelCase__      =			df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
														for row_idx, row in enumerate(A ):
																					expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
							return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
							# that each partition can hold 2 rows.
							spark_builder._repartition_df_if_needed(max_shard_size=16 )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[int]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(2 )
							lowerCAmelCase__      =			[1, 0]
							lowerCAmelCase__      =			_generate_iterable_examples(A				,					A )  # Reverse the partitions.
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					A )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(1 )
							lowerCAmelCase__      =			SparkExamplesIterable(A )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(A ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Union[str, Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch('''numpy.random.Generator''' ) as generator_mock:
														lowerCAmelCase__      =			lambda A : x.reverse()
														lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[2, 1, 0] )

														lowerCAmelCase__      =			SparkExamplesIterable(A ).shuffle_data_sources(A )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(A ):
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
																					assert row_id == expected_row_id
																					assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=0				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=1				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# Choose a small max_shard_size for maximum partitioning.
							spark_builder._repartition_df_if_needed(max_shard_size=1 )
							# The new number of partitions should not be greater than the number of rows.
							assert spark_builder.df.rdd.getNumPartitions() == 100