code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  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 	__lowerCamelCase (  UpperCAmelCase_				,							**UpperCAmelCase_       )      ->str:
  	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				,							**UpperCAmelCase_				,							)
  
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  
  
  
  
  
  	@staticmethod
  	def      _snake_case      (		UpperCamelCase_		)			->		Any:
  		snake_case__  					=      parser.add_parser('run_beam'			,					help='Run a Beam dataset processing pipeline'		)
  		run_beam_parser.add_argument('dataset'			,					type=UpperCamelCase_			,					help='Name of the dataset to download'		)
  		run_beam_parser.add_argument('--name'			,					type=UpperCamelCase_			,					default=UpperCamelCase_			,					help='Dataset config name'		)
  		run_beam_parser.add_argument(
  		    '--cache_dir'			,					type=UpperCamelCase_			,					default=UpperCamelCase_			,					help='Cache directory where the datasets are stored'			,					)
  		run_beam_parser.add_argument(
  		    '--beam_pipeline_options'			,					type=UpperCamelCase_			,					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=UpperCamelCase_			,					default=UpperCamelCase_			,					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=UpperCamelCase_		)
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					**UpperCamelCase_			,					)			->		Dict:
  		snake_case__  					=      dataset
  		snake_case__  					=      name
  		snake_case__  					=      cache_dir
  		snake_case__  					=      beam_pipeline_options
  		snake_case__  					=      data_dir
  		snake_case__  					=      all_configs
  		snake_case__  					=      save_infos
  		snake_case__  					=      ignore_verifications
  		snake_case__  					=      force_redownload
  		snake_case__  					=      config_kwargs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		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		)
  		snake_case__					,     snake_case__  					=      self._dataset, self._name
  		snake_case__  					=      dataset_module_factory(UpperCamelCase_		)
  		snake_case__  					=      import_main_class(dataset_module.module_path		)
  		snake_case__  					=      []
  		if self._beam_pipeline_options:
  			snake_case__  					=      beam.options.pipeline_options.PipelineOptions(
  			    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','		) if opt]		)
  		else:
  			snake_case__  					=      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=UpperCamelCase_			,					cache_dir=self._cache_dir			,					base_path=dataset_module.builder_kwargs.get('base_path'		)			,					)		)
  		else:
  			builders.append(
  			    builder_cls(
  			        config_name=UpperCamelCase_			,					data_dir=self._data_dir			,					beam_options=UpperCamelCase_			,					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=UpperCamelCase_			,					)
  			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:
  			snake_case__  					=      os.path.join(builder_cls.get_imported_module_dir()			,					config.DATASETDICT_INFOS_FILENAME		)
  
  			snake_case__  					=      Path(UpperCamelCase_		).name + '.py'
  
  			snake_case__  					=      os.path.join(UpperCamelCase_			,					UpperCamelCase_		)
  			if os.path.isfile(UpperCamelCase_		):
  				snake_case__  					=      os.path.dirname(UpperCamelCase_		)
  			elif os.path.isfile(UpperCamelCase_		):
  				snake_case__  					=      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
  			snake_case__  					=      os.path.join(UpperCamelCase_			,					config.DATASETDICT_INFOS_FILENAME		)
  			copyfile(UpperCamelCase_			,					UpperCamelCase_		)
  			print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''		)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  