





'''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}'''		)










'''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)



