


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_														=      {"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YosoForMaskedLM",
		    "YosoForMultipleChoice",
		    "YosoForQuestionAnswering",
		    "YosoForSequenceClassification",
		    "YosoForTokenClassification",
		    "YosoLayer",
		    "YosoModel",
		    "YosoPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yoso import (
				    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YosoForMaskedLM,
				    YosoForMultipleChoice,
				    YosoForQuestionAnswering,
				    YosoForSequenceClassification,
				    YosoForTokenClassification,
				    YosoLayer,
				    YosoModel,
				    YosoPreTrainedModel,
				)


else:
		import sys

		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure)






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      )->		Tuple:







					'''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				,**UpperCAmelCase				,)






class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			@staticmethod
			def _lowercase (	lowerCAmelCase_     :     ArgumentParser					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       parser.add_parser('''run_beam'''							,							help='''Run a Beam dataset processing pipeline'''					)
								run_beam_parser.add_argument('''dataset'''							,							type=lowerCAmelCase_							,							help='''Name of the dataset to download'''					)
								run_beam_parser.add_argument('''--name'''							,							type=lowerCAmelCase_							,							default=lowerCAmelCase_							,							help='''Dataset config name'''					)
								run_beam_parser.add_argument(
								    '''--cache_dir'''							,							type=lowerCAmelCase_							,							default=lowerCAmelCase_							,							help='''Cache directory where the datasets are stored'''							,							)
								run_beam_parser.add_argument(
								    '''--beam_pipeline_options'''							,							type=lowerCAmelCase_							,							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=lowerCAmelCase_							,							default=lowerCAmelCase_							,							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=lowerCAmelCase_					)

			def __init__(	self     :     Any							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							**lowerCAmelCase_     :     Any							,							)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       dataset
								SCREAMING_SNAKE_CASE_	      =       name
								SCREAMING_SNAKE_CASE_	      =       cache_dir
								SCREAMING_SNAKE_CASE_	      =       beam_pipeline_options
								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_	      =       config_kwargs




			def _lowercase (	self     :     List[str]					)					->					int:




								"""simple docstring"""






								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					)
								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self._dataset, self._name
								SCREAMING_SNAKE_CASE_	      =       dataset_module_factory(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       import_main_class(dataset_module.module_path					)
								SCREAMING_SNAKE_CASE_	      =       []
								if self._beam_pipeline_options:
													SCREAMING_SNAKE_CASE_	      =       beam.options.pipeline_options.PipelineOptions(
													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''					) if opt]					)
								else:
													SCREAMING_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=lowerCAmelCase_							,							cache_dir=self._cache_dir							,							base_path=dataset_module.builder_kwargs.get('''base_path'''					)							,							)					)
								else:
													builders.append(
													    builder_cls(
													        config_name=lowerCAmelCase_							,							data_dir=self._data_dir							,							beam_options=lowerCAmelCase_							,							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=lowerCAmelCase_							,							)
													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:
													SCREAMING_SNAKE_CASE_	      =       os.path.join(builder_cls.get_imported_module_dir()							,							config.DATASETDICT_INFOS_FILENAME					)

													SCREAMING_SNAKE_CASE_	      =       Path(lowerCAmelCase_					).name + '''.py'''

													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							lowerCAmelCase_					)
													if os.path.isfile(lowerCAmelCase_					):
																		SCREAMING_SNAKE_CASE_	      =       os.path.dirname(lowerCAmelCase_					)
													elif os.path.isfile(lowerCAmelCase_					):
																		SCREAMING_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
													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							config.DATASETDICT_INFOS_FILENAME					)
													copyfile(lowerCAmelCase_							,							lowerCAmelCase_					)
													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)


