


"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     =  get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					XLMRobertaTokenizer
				_lowerCAmelCase      :      Tuple      							=					XLMRobertaTokenizerFast
				_lowerCAmelCase      :      int      							=					True
				_lowerCAmelCase      :      Tuple      							=					True



				def   lowerCAmelCase__		(						self     ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(UpperCAmelCase__       ,		keep_accents=UpperCAmelCase__     )
								tokenizer.save_pretrained(self.tmpdirname     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"<pad>"
								SCREAMING_SNAKE_CASE__     							=				1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__     )       ,		UpperCAmelCase__     )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__     )       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				list(self.get_tokenizer().get_vocab().keys()     )

								self.assertEqual(vocab_keys[0]       ,		"<s>"     )
								self.assertEqual(vocab_keys[1]       ,		"<pad>"     )
								self.assertEqual(vocab_keys[-1]       ,		"<mask>"     )
								self.assertEqual(len(UpperCAmelCase__     )       ,		1002     )



				def   lowerCAmelCase__		(						self     ):
								self.assertEqual(self.get_tokenizer().vocab_size       ,		1002     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(UpperCAmelCase__       ,		keep_accents=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize("This is a test"     )
								self.assertListEqual(UpperCAmelCase__       ,		["▁This", "▁is", "▁a", "▁t", "est"]     )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )       ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,		)

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize("I was born in 92000, and this is falsé."     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]       ,		)
								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]       ,		)

								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_ids_to_tokens(UpperCAmelCase__     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]       ,		)



				def   lowerCAmelCase__		(						self     ):
								if not self.test_slow_tokenizer:
												# as we don't have a slow version, we can't compare the outputs between slow and fast versions
												return

								SCREAMING_SNAKE_CASE__     							=				(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																SCREAMING_SNAKE_CASE__     							=				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__       ,		**UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained(UpperCAmelCase__       ,		**UpperCAmelCase__     )

																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it save with the same files + the tokenizer.json file for the fast one
																self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
																SCREAMING_SNAKE_CASE__     							=				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
																self.assertSequenceEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )
																				# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																				# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(UpperCAmelCase__     )

																# Save tokenizer rust, legacy_format=True
																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__       ,		legacy_format=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it save with the same files
																self.assertSequenceEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

																shutil.rmtree(UpperCAmelCase__     )

																# Save tokenizer rust, legacy_format=False
																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__       ,		legacy_format=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it saved the tokenizer.json file
																self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

																shutil.rmtree(UpperCAmelCase__     )



				@cached_property
				def   lowerCAmelCase__		(						self     ):
								return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"     )



				def   lowerCAmelCase__		(						self     ):
								with tempfile.NamedTemporaryFile() as f:
												shutil.copyfile(UpperCAmelCase__       ,		f.name     )
												SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(f.name       ,		keep_accents=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				pickle.dumps(UpperCAmelCase__     )
								pickle.loads(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								if not self.test_rust_tokenizer:
												return

								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()
								SCREAMING_SNAKE_CASE__     							=				self.get_rust_tokenizer()

								SCREAMING_SNAKE_CASE__     							=				"I was born in 92000, and this is falsé."

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.tokenize(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.encode(UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				self.get_rust_tokenizer()
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.encode(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"Hello World!"
								SCREAMING_SNAKE_CASE__     							=				[0, 3_5378, 6661, 38, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(UpperCAmelCase__       ,		self.big_tokenizer.encode(UpperCAmelCase__     )     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				(
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								SCREAMING_SNAKE_CASE__     							=				[
								    0,
								    3293,
								    83,
								    10,
								    4552,
								    4989,
								    7986,
								    678,
								    10,
								    5915,
								    111,
								    17_9459,
								    12_4850,
								    4,
								    6044,
								    237,
								    12,
								    6,
								    5,
								    6,
								    4,
								    6780,
								    705,
								    15,
								    1388,
								    44,
								    378,
								    1_0114,
								    711,
								    152,
								    20,
								    6,
								    5,
								    2_2376,
								    642,
								    1221,
								    1_5190,
								    3_4153,
								    450,
								    5608,
								    959,
								    1119,
								    5_7702,
								    136,
								    186,
								    47,
								    1098,
								    2_9367,
								    47,
								    # 4426, # What fairseq tokenizes from "<unk>": "_<"
								    # 3678, # What fairseq tokenizes from "<unk>": "unk"
								    # 2740, # What fairseq tokenizes from "<unk>": ">"
								    3,  # What we tokenize from "<unk>": "<unk>"
								    6,  # Residue from the tokenization: an extra sentencepiece underline
								    4,
								    6044,
								    237,
								    6284,
								    5_0901,
								    528,
								    31,
								    90,
								    34,
								    927,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(UpperCAmelCase__       ,		self.big_tokenizer.encode(UpperCAmelCase__     )     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCAmelCase__       ,		model_name="xlm-roberta-base"       ,		revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"       ,		)




"""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


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
				_lowerCAmelCase      :      str      							=					field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
				_lowerCAmelCase      :      int      							=					field(
				    default=128       ,  metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								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"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				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}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				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)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																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:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ 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     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
