





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCamelCase_							=     get_logger(__name__)






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[Any]      ):
		return TestCommand(
		    args.dataset   ,				args.name   ,				args.cache_dir   ,				args.data_dir   ,				args.all_configs   ,				args.save_info or args.save_infos   ,				args.ignore_verifications   ,				args.force_redownload   ,				args.clear_cache   ,				)




class 					UpperCamelCase_    (       lowercase_      ):
							lowerCamelCase_          =   False  # to tell pytest it's not a test class


							@staticmethod
							def        _snake_case					(							__A      :ArgumentParser						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= parser.add_parser("""test"""							,			help="""Test dataset implementation."""						)
									test_parser.add_argument("""--name"""							,			type=UpperCamelCase_							,			default=UpperCamelCase_							,			help="""Dataset processing name"""						)
									test_parser.add_argument(
									    """--cache_dir"""							,			type=UpperCamelCase_							,			default=UpperCamelCase_							,			help="""Cache directory where the datasets are stored."""							,			)
									test_parser.add_argument(
									    """--data_dir"""							,			type=UpperCamelCase_							,			default=UpperCamelCase_							,			help="""Can be used to specify a manual directory to get the files from."""							,			)
									test_parser.add_argument("""--all_configs"""							,			action="""store_true"""							,			help="""Test all dataset configurations"""						)
									test_parser.add_argument(
									    """--save_info"""							,			action="""store_true"""							,			help="""Save the dataset infos in the dataset card (README.md)"""						)
									test_parser.add_argument(
									    """--ignore_verifications"""							,			action="""store_true"""							,			help="""Run the test without checksums and splits checks."""							,			)
									test_parser.add_argument("""--force_redownload"""							,			action="""store_true"""							,			help="""Force dataset redownload"""						)
									test_parser.add_argument(
									    """--clear_cache"""							,			action="""store_true"""							,			help="""Remove downloaded files and cached datasets after each config test"""							,			)
									# aliases
									test_parser.add_argument("""--save_infos"""							,			action="""store_true"""							,			help="""alias to save_info"""						)
									test_parser.add_argument("""dataset"""							,			type=UpperCamelCase_							,			help="""Name of the dataset to download"""						)
									test_parser.set_defaults(func=_test_command_factory						)


							def __init__(							self      :str							,			__A      :str							,			__A      :str							,			__A      :str							,			__A      :str							,			__A      :bool							,			__A      :bool							,			__A      :bool							,			__A      :bool							,			__A      :bool							,			)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= dataset
									SCREAMING_SNAKE_CASE__								= name
									SCREAMING_SNAKE_CASE__								= cache_dir
									SCREAMING_SNAKE_CASE__								= data_dir
									SCREAMING_SNAKE_CASE__								= all_configs
									SCREAMING_SNAKE_CASE__								= save_infos
									SCREAMING_SNAKE_CASE__								= ignore_verifications
									SCREAMING_SNAKE_CASE__								= force_redownload
									SCREAMING_SNAKE_CASE__								= clear_cache
									if clear_cache and not cache_dir:
											print(
											    """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
											    """The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
											    """Please provide a --cache_dir that will be used to test the dataset script."""						)
											exit(1						)
									if save_infos:
											SCREAMING_SNAKE_CASE__								= True


							def        _snake_case					(							self      :Dict						)				->				Optional[Any]:



									"""simple docstring"""



									fl_logger().setLevel(UpperCamelCase_						)
									if self._name is not None and self._all_configs:
											print("""Both parameters `config` and `all_configs` can't be used at once."""						)
											exit(1						)
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= self._dataset, self._name
									SCREAMING_SNAKE_CASE__								= dataset_module_factory(UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__								= import_main_class(module.module_path						)
									SCREAMING_SNAKE_CASE__								= len(builder_cls.BUILDER_CONFIGS						) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

									def get_builders() -> Generator[DatasetBuilder, None, None]:
											if self._all_configs and builder_cls.BUILDER_CONFIGS:
													for i, config in enumerate(builder_cls.BUILDER_CONFIGS						):
															if "config_name" in module.builder_kwargs:
																	yield builder_cls(
																	    cache_dir=self._cache_dir							,			data_dir=self._data_dir							,			**module.builder_kwargs							,			)
															else:
																	yield builder_cls(
																	    config_name=config.name							,			cache_dir=self._cache_dir							,			data_dir=self._data_dir							,			**module.builder_kwargs							,			)
											else:
													if "config_name" in module.builder_kwargs:
															yield builder_cls(cache_dir=self._cache_dir							,			data_dir=self._data_dir							,			**module.builder_kwargs						)
													else:
															yield builder_cls(
															    config_name=UpperCamelCase_							,			cache_dir=self._cache_dir							,			data_dir=self._data_dir							,			**module.builder_kwargs							,			)

									for j, builder in enumerate(get_builders()						):
											print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''						)
											SCREAMING_SNAKE_CASE__								= os.path.exists(
											    os.path.join(builder.get_imported_module_dir()							,			datasets.config.DATASETDICT_INFOS_FILENAME						)						)  # record checksums only if we need to update a (deprecated) dataset_infos.json
											builder.download_and_prepare(
											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
											    if not self._force_redownload
											    else DownloadMode.FORCE_REDOWNLOAD							,			verification_mode=VerificationMode.NO_CHECKS
											    if self._ignore_verifications
											    else VerificationMode.ALL_CHECKS							,			try_from_hf_gcs=UpperCamelCase_							,			)
											builder.as_dataset()
											if self._save_infos:
													builder._save_infos()

											# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
											# The dataset_infos are saved in the YAML part of the README.md

											# Let's move it to the original directory of the dataset script, to allow the user to
											# upload them on S3 at the same time afterwards.
											if self._save_infos:
													SCREAMING_SNAKE_CASE__								= os.path.join(builder_cls.get_imported_module_dir()							,			"""README.md"""						)
													SCREAMING_SNAKE_CASE__								= Path(UpperCamelCase_						).name + """.py"""
													SCREAMING_SNAKE_CASE__								= os.path.join(UpperCamelCase_							,			UpperCamelCase_						)
													if os.path.isfile(UpperCamelCase_						):
															SCREAMING_SNAKE_CASE__								= os.path.dirname(UpperCamelCase_						)
													elif os.path.isfile(UpperCamelCase_						):
															SCREAMING_SNAKE_CASE__								= path
													elif os.path.isdir(UpperCamelCase_						):  # for local directories containing only data files
															SCREAMING_SNAKE_CASE__								= path
													else:  # in case of a remote dataset
															SCREAMING_SNAKE_CASE__								= None
															print(f'''Dataset card saved at {dataset_readme_path}'''						)

													# Move dataset_info back to the user
													if dataset_dir is not None:
															SCREAMING_SNAKE_CASE__								= os.path.join(UpperCamelCase_							,			"""README.md"""						)
															copyfile(UpperCamelCase_							,			UpperCamelCase_						)
															print(f'''Dataset card saved at {user_dataset_readme_path}'''						)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
											if self._clear_cache:
													if os.path.isdir(builder._cache_dir						):
															logger.warning(f'''Clearing cache at {builder._cache_dir}'''						)
															rmtree(builder._cache_dir						)
													SCREAMING_SNAKE_CASE__								= os.path.join(self._cache_dir							,			datasets.config.DOWNLOADED_DATASETS_DIR						)
													if os.path.isdir(UpperCamelCase_						):
															logger.warning(f'''Clearing cache at {download_dir}'''						)
															rmtree(UpperCamelCase_						)

									print("""Test successful."""						)






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece




class 					UpperCamelCase_    (       unittest.TestCase      ):


							def        _snake_case					(							self      :str							,			__A      :str						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= list(tokenizer_class.max_model_input_sizes.keys()						)
									for model_name in sa_models[:1]:
											SCREAMING_SNAKE_CASE__								= tokenizer_class.from_pretrained(__A						)
											self.assertIsNotNone(__A						)
											self.assertIsInstance(__A							,			__A						)
											self.assertIsInstance(__A							,			__A						)

											for special_tok in tokenizer.all_special_tokens:
													self.assertIsInstance(__A							,			__A						)
													SCREAMING_SNAKE_CASE__								= tokenizer.convert_tokens_to_ids(__A						)
													self.assertIsInstance(__A							,			__A						)


							def        _snake_case					(							self      :List[str]							,			__A      :BatchEncoding							,			__A      :Optional[Callable] = None						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pickle.dumps(__A						)
									self.assertIsNotNone(__A						)

									SCREAMING_SNAKE_CASE__								= pickle.loads(__A						)

									# Ensure is_fast is correctly restored
									self.assertEqual(be_restored.is_fast							,			be_original.is_fast						)

									# Ensure encodings are potentially correctly restored
									if be_original.is_fast:
											self.assertIsNotNone(be_restored.encodings						)
									else:
											self.assertIsNone(be_restored.encodings						)

									# Ensure the keys are the same
									for original_v, restored_v in zip(be_original.values()							,			be_restored.values()						):
											if equal_op:
													self.assertTrue(equal_op(__A							,			__A						)						)
											else:
													self.assertEqual(__A							,			__A						)


							@slow
							def        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__A						)


							def        _snake_case					(							self      :List[Any]						)				->				Dict:



									"""simple docstring"""



									self.assertEqual(TensorType("""tf"""						)							,			TensorType.TENSORFLOW						)
									self.assertEqual(TensorType("""pt"""						)							,			TensorType.PYTORCH						)
									self.assertEqual(TensorType("""np"""						)							,			TensorType.NUMPY						)


							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				List[Any]:



									"""simple docstring"""



									import numpy as np

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									# Python no tensor
									with self.subTest("""BatchEncoding (Python, return_tensors=None)"""						):
											self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""						)						)

									with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.NUMPY						)							,			np.array_equal						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""						):
											self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""						)						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.NUMPY						)							,			np.array_equal						)


							@require_tf
							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__A      :Union[str, Any]							,			__A      :Any						):
											return tf.reduce_all(tf.equal(__A							,			__A						)						)

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.TENSORFLOW						)							,			__A						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.TENSORFLOW						)							,			__A						)


							@require_torch
							@require_tokenizers
							def        _snake_case					(							self      :List[Any]						)				->				int:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.PYTORCH						)							,			torch.equal						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.PYTORCH						)							,			torch.equal						)


							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""Python Tokenizer"""						):
											self.assertFalse(tokenizer_p("""Small example to_encode"""						).is_fast						)

									with self.subTest("""Rust Tokenizer"""						):
											self.assertTrue(tokenizer_r("""Small example to_encode"""						).is_fast						)


							@require_tokenizers
							def        _snake_case					(							self      :List[str]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= tokenizer_r(["""Test""", """\xad""", """test"""]							,			is_split_into_words=__A						)

									self.assertEqual(encoded.word_to_tokens(0						)							,			TokenSpan(start=1							,			end=2						)						)
									self.assertEqual(encoded.word_to_tokens(1						)							,			__A						)
									self.assertEqual(encoded.word_to_tokens(2						)							,			TokenSpan(start=2							,			end=3						)						)


							def        _snake_case					(							self      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_torch
							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_tf
							def        _snake_case					(							self      :Optional[Any]						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_flax
							def        _snake_case					(							self      :Dict						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							def        _snake_case					(							self      :List[Any]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": np.array([0, 1, 2]						)}, {"""input_ids""": np.array([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			np.ndarray						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""np"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			np.ndarray						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_torch
							def        _snake_case					(							self      :int						)				->				Dict:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": torch.tensor([0, 1, 2]						)}, {"""input_ids""": torch.tensor([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			torch.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""pt"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			torch.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_tf
							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									import tensorflow as tf

									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": tf.constant([0, 1, 2]						)}, {"""input_ids""": tf.constant([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			tf.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""tf"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			tf.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_tokenizers
							def        _snake_case					(							self      :Tuple						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= Tokenizer(WordPiece(unk_token="""[UNK]"""						)						)
									PreTrainedTokenizerFast(tokenizer_object=__A						)


							@require_tokenizers
							def        _snake_case					(							self      :Any						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= Tokenizer(WordPiece(unk_token="""[UNK]"""						)						)
									with tempfile.TemporaryDirectory() as tmpdirname:
											bert_tokenizer.save(os.path.join(__A							,			"""tokenizer.json"""						)						)
											PreTrainedTokenizerFast(tokenizer_file=os.path.join(__A							,			"""tokenizer.json"""						)						)