


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      (			__magic_name__  :							Union[str, Any]					,					**__magic_name__  :							List[Any]  )     ->						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					,					**__lowerCAmelCase					,					)







class 							A					( A__							):
					'''simple docstring'''






					@staticmethod
					def 						lowerCamelCase__  (_UpperCAmelCase					:		ArgumentParser	)		->     List[Any]:




							"""simple docstring"""


							lowercase__      =	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					:		Optional[Any]   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		str   ,      _UpperCAmelCase					:		bool   ,      _UpperCAmelCase					:		bool   ,      _UpperCAmelCase					:		bool   ,      _UpperCAmelCase					:		bool   ,      **_UpperCAmelCase					:		Union[str, Any]   ,      )		->     int:




							"""simple docstring"""


							lowercase__      =	dataset
							lowercase__      =	name
							lowercase__      =	cache_dir
							lowercase__      =	beam_pipeline_options
							lowercase__      =	data_dir
							lowercase__      =	all_configs
							lowercase__      =	save_infos
							lowercase__      =	ignore_verifications
							lowercase__      =	force_redownload
							lowercase__      =	config_kwargs


					def 						lowerCamelCase__  (self					:		int	)		->     List[Any]:




							"""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	)
							lowercase__      =	self._dataset, self._name
							lowercase__      =	dataset_module_factory(_UpperCAmelCase	)
							lowercase__      =	import_main_class(dataset_module.module_path	)
							lowercase__      =	[]
							if self._beam_pipeline_options:
									lowercase__      =	beam.options.pipeline_options.PipelineOptions(
									    flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(""","""	) if opt]	)
							else:
									lowercase__      =	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:
									lowercase__      =	os.path.join(builder_cls.get_imported_module_dir()   ,      config.DATASETDICT_INFOS_FILENAME	)

									lowercase__      =	Path(_UpperCAmelCase	).name + ".py"

									lowercase__      =	os.path.join(_UpperCAmelCase   ,      _UpperCAmelCase	)
									if os.path.isfile(_UpperCAmelCase	):
											lowercase__      =	os.path.dirname(_UpperCAmelCase	)
									elif os.path.isfile(_UpperCAmelCase	):
											lowercase__      =	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
									lowercase__      =	os.path.join(_UpperCAmelCase   ,      config.DATASETDICT_INFOS_FILENAME	)
									copyfile(_UpperCAmelCase   ,      _UpperCAmelCase	)
									print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''	)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

