

"""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     :							Dict      ,							**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     __a   (      snake_case__    ):

			"""simple docstring"""

			@staticmethod
			def    _lowerCAmelCase			(							lowercase_     :					ArgumentParser						):
				UpperCamelCase__		:    Optional[Any]         =parser.add_parser('''run_beam'''						,  help='''Run a Beam dataset processing pipeline'''						)
				run_beam_parser.add_argument('''dataset'''						,  type=lowercase_						,  help='''Name of the dataset to download'''						)
				run_beam_parser.add_argument('''--name'''						,  type=lowercase_						,  default=lowercase_						,  help='''Dataset config name'''						)
				run_beam_parser.add_argument(
				    '''--cache_dir'''						,  type=lowercase_						,  default=lowercase_						,  help='''Cache directory where the datasets are stored'''						,  )
				run_beam_parser.add_argument(
				    '''--beam_pipeline_options'''						,  type=lowercase_						,  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=lowercase_						,  default=lowercase_						,  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=lowercase_						)






			def __init__(							self     :					Union[str, Any]						,  lowercase_     :					str						,  lowercase_     :					str						,  lowercase_     :					str						,  lowercase_     :					str						,  lowercase_     :					str						,  lowercase_     :					bool						,  lowercase_     :					bool						,  lowercase_     :					bool						,  lowercase_     :					bool						,  **lowercase_     :					Tuple						,  ):
				UpperCamelCase__		:    Optional[int]         =dataset
				UpperCamelCase__		:    int         =name
				UpperCamelCase__		:    Union[str, Any]         =cache_dir
				UpperCamelCase__		:    int         =beam_pipeline_options
				UpperCamelCase__		:    List[str]         =data_dir
				UpperCamelCase__		:    str         =all_configs
				UpperCamelCase__		:    Optional[Any]         =save_infos
				UpperCamelCase__		:    Any         =ignore_verifications
				UpperCamelCase__		:    str         =force_redownload
				UpperCamelCase__		:    Any         =config_kwargs






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

					UpperCamelCase__		:    List[str]         =Path(lowercase_						).name + '''.py'''

					UpperCamelCase__		:    Dict         =os.path.join(lowercase_						,  lowercase_						)
					if os.path.isfile(lowercase_						):
						UpperCamelCase__		:    int         =os.path.dirname(lowercase_						)
					elif os.path.isfile(lowercase_						):
						UpperCamelCase__		:    str         =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
					UpperCamelCase__		:    Tuple         =os.path.join(lowercase_						,  config.DATASETDICT_INFOS_FILENAME						)
					copyfile(lowercase_						,  lowercase_						)
					print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''						)





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

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


