

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							(		a__       ,					**a__) ->    Union[str, Any]:

			'''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       ,					**SCREAMING_SNAKE_CASE_       ,					)



class    A__(a_    ):
						"""simple docstring"""






						@staticmethod
						def 	UpperCamelCase__			(  _lowercase  )      ->				str:
									a_		:		Optional[int]				    =				parser.add_parser("""run_beam"""       ,     help="""Run a Beam dataset processing pipeline"""  )
									run_beam_parser.add_argument("""dataset"""       ,     type=__A       ,     help="""Name of the dataset to download"""  )
									run_beam_parser.add_argument("""--name"""       ,     type=__A       ,     default=__A       ,     help="""Dataset config name"""  )
									run_beam_parser.add_argument(
									    """--cache_dir"""       ,     type=__A       ,     default=__A       ,     help="""Cache directory where the datasets are stored"""       ,     )
									run_beam_parser.add_argument(
									    """--beam_pipeline_options"""       ,     type=__A       ,     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=__A       ,     default=__A       ,     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=__A  )





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





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									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_		:		Tuple				    =				self._dataset, self._name
									a_		:		Any				    =				dataset_module_factory(__A  )
									a_		:		Tuple				    =				import_main_class(dataset_module.module_path  )
									a_		:		List[DatasetBuilder]				    =				[]
									if self._beam_pipeline_options:
												a_		:		Optional[Any]				    =				beam.options.pipeline_options.PipelineOptions(
												    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(""","""  ) if opt]  )
									else:
												a_		:		int				    =				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=__A       ,     cache_dir=self._cache_dir       ,     base_path=dataset_module.builder_kwargs.get("""base_path"""  )       ,     )  )
									else:
												builders.append(
												    builder_cls(
												        config_name=__A       ,     data_dir=self._data_dir       ,     beam_options=__A       ,     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=__A       ,     )
												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_		:		Any				    =				os.path.join(builder_cls.get_imported_module_dir()       ,     config.DATASETDICT_INFOS_FILENAME  )

												a_		:		List[str]				    =				Path(__A  ).name + ".py"

												a_		:		Tuple				    =				os.path.join(__A       ,     __A  )
												if os.path.isfile(__A  ):
															a_		:		str				    =				os.path.dirname(__A  )
												elif os.path.isfile(__A  ):
															a_		:		Optional[int]				    =				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_		:		Union[str, Any]				    =				os.path.join(__A       ,     config.DATASETDICT_INFOS_FILENAME  )
												copyfile(__A       ,     __A  )
												print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''  )





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	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:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	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)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


