






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def    UpperCamelCase__  (   UpperCAmelCase    ,	**UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							return RunBeamCommand(
							    args.dataset    ,	args.name    ,	args.cache_dir    ,	args.beam_pipeline_options    ,	args.data_dir    ,	args.all_configs    ,	args.save_info or args.save_infos    ,	args.ignore_verifications    ,	args.force_redownload    ,	**snake_case_    ,	)







class      UpperCamelCase_							(				UpperCamelCase       ):




						@staticmethod
						def      snake_case__(    lowercase       )			->						Union[str, Any]:
													_a  :    Union[str, Any]			=						parser.add_parser('''run_beam'''		,	help='''Run a Beam dataset processing pipeline'''       )
													run_beam_parser.add_argument('''dataset'''		,	type=__SCREAMING_SNAKE_CASE		,	help='''Name of the dataset to download'''       )
													run_beam_parser.add_argument('''--name'''		,	type=__SCREAMING_SNAKE_CASE		,	default=__SCREAMING_SNAKE_CASE		,	help='''Dataset config name'''       )
													run_beam_parser.add_argument(
													    '''--cache_dir'''		,	type=__SCREAMING_SNAKE_CASE		,	default=__SCREAMING_SNAKE_CASE		,	help='''Cache directory where the datasets are stored'''		,	)
													run_beam_parser.add_argument(
													    '''--beam_pipeline_options'''		,	type=__SCREAMING_SNAKE_CASE		,	default=''''''		,	help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''		,	)
													run_beam_parser.add_argument(
													    '''--data_dir'''		,	type=__SCREAMING_SNAKE_CASE		,	default=__SCREAMING_SNAKE_CASE		,	help='''Can be used to specify a manual directory to get the files from'''		,	)
													run_beam_parser.add_argument('''--all_configs'''		,	action='''store_true'''		,	help='''Test all dataset configurations'''       )
													run_beam_parser.add_argument('''--save_info'''		,	action='''store_true'''		,	help='''Save the dataset infos file'''       )
													run_beam_parser.add_argument(
													    '''--ignore_verifications'''		,	action='''store_true'''		,	help='''Run the test without checksums and splits checks'''       )
													run_beam_parser.add_argument('''--force_redownload'''		,	action='''store_true'''		,	help='''Force dataset redownload'''       )
													# aliases
													run_beam_parser.add_argument('''--save_infos'''		,	action='''store_true'''		,	help='''alias for save_info'''       )
													run_beam_parser.set_defaults(func=__SCREAMING_SNAKE_CASE       )




						def __init__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						Optional[Any]:
													_a  :    Union[str, Any]			=						dataset
													_a  :    List[Any]			=						name
													_a  :    int			=						cache_dir
													_a  :    str			=						beam_pipeline_options
													_a  :    List[str]			=						data_dir
													_a  :    int			=						all_configs
													_a  :    Any			=						save_infos
													_a  :    Union[str, Any]			=						ignore_verifications
													_a  :    Optional[int]			=						force_redownload
													_a  :    Union[str, Any]			=						config_kwargs







						def      snake_case__(    self       )			->						str:
													import apache_beam as beam

													if self._name is not None and self._all_configs:
																				print('''Both parameters `name` and `all_configs` can\'t be used at once.'''       )
																				exit(1       )
													_a  :    List[Any]			=						self._dataset, self._name
													_a  :    str			=						dataset_module_factory(__SCREAMING_SNAKE_CASE       )
													_a  :    str			=						import_main_class(dataset_module.module_path       )
													_a  :    List[DatasetBuilder]			=						[]
													if self._beam_pipeline_options:
																				_a  :    Optional[int]			=						beam.options.pipeline_options.PipelineOptions(
																				    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(''','''       ) if opt]       )
													else:
																				_a  :    List[Any]			=						None
													if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
																				for builder_config in builder_cls.BUILDER_CONFIGS:
																											builders.append(
																											    builder_cls(
																											        config_name=builder_config.name		,	data_dir=self._data_dir		,	hash=dataset_module.hash		,	beam_options=__SCREAMING_SNAKE_CASE		,	cache_dir=self._cache_dir		,	base_path=dataset_module.builder_kwargs.get('''base_path'''       )		,	)       )
													else:
																				builders.append(
																				    builder_cls(
																				        config_name=__SCREAMING_SNAKE_CASE		,	data_dir=self._data_dir		,	beam_options=__SCREAMING_SNAKE_CASE		,	cache_dir=self._cache_dir		,	base_path=dataset_module.builder_kwargs.get('''base_path'''       )		,	**self._config_kwargs		,	)       )

													for builder in builders:
																				builder.download_and_prepare(
																				    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
																				    if not self._force_redownload
																				    else DownloadMode.FORCE_REDOWNLOAD		,	download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )		,	verification_mode=VerificationMode.NO_CHECKS
																				    if self._ignore_verifications
																				    else VerificationMode.ALL_CHECKS		,	try_from_hf_gcs=__SCREAMING_SNAKE_CASE		,	)
																				if self._save_infos:
																											builder._save_infos()

													print('''Apache beam run successful.'''       )

													# If save_infos=True, the dataset infos file is created next to the loaded module file.
													# 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:
																				_a  :    Dict			=						os.path.join(builder_cls.get_imported_module_dir()		,	config.DATASETDICT_INFOS_FILENAME       )

																				_a  :    Union[str, Any]			=						Path(__SCREAMING_SNAKE_CASE       ).name + '.py'

																				_a  :    Dict			=						os.path.join(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE       )
																				if os.path.isfile(__SCREAMING_SNAKE_CASE       ):
																											_a  :    Tuple			=						os.path.dirname(__SCREAMING_SNAKE_CASE       )
																				elif os.path.isfile(__SCREAMING_SNAKE_CASE       ):
																											_a  :    Optional[Any]			=						path
																				else:  # in case of a remote dataset
																											print(F'Dataset Infos file saved at {dataset_infos_path}'       )
																											exit(1       )

																				# Move datasetinfo back to the user
																				_a  :    List[Any]			=						os.path.join(__SCREAMING_SNAKE_CASE		,	config.DATASETDICT_INFOS_FILENAME       )
																				copyfile(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE       )
																				print(F'Dataset Infos file saved at {user_dataset_infos_path}'       )

'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






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

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


