

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 						__SCREAMING_SNAKE_CASE						(   A_		,  **A_				):
	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		,  **A_		,  )







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					@staticmethod
					def  __lowerCAmelCase				( lowercase_    :       ArgumentParser	):
						lowerCAmelCase__			:		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    :       Optional[Any]     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       bool     ,lowercase_    :       bool     ,lowercase_    :       bool     ,lowercase_    :       bool     ,**lowercase_    :       List[str]     ,):
						lowerCAmelCase__			:		List[str]						       =			dataset
						lowerCAmelCase__			:		Optional[Any]						       =			name
						lowerCAmelCase__			:		Union[str, Any]						       =			cache_dir
						lowerCAmelCase__			:		Tuple						       =			beam_pipeline_options
						lowerCAmelCase__			:		str						       =			data_dir
						lowerCAmelCase__			:		Any						       =			all_configs
						lowerCAmelCase__			:		Optional[int]						       =			save_infos
						lowerCAmelCase__			:		str						       =			ignore_verifications
						lowerCAmelCase__			:		int						       =			force_redownload
						lowerCAmelCase__			:		Union[str, Any]						       =			config_kwargs







					def  __lowerCAmelCase				( self    :       int	):
						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	)
						lowerCAmelCase__			:		Union[str, Any]						       =			self._dataset, self._name
						lowerCAmelCase__			:		Union[str, Any]						       =			dataset_module_factory(lowercase_	)
						lowerCAmelCase__			:		List[str]						       =			import_main_class(dataset_module.module_path	)
						lowerCAmelCase__			:		List[DatasetBuilder]						       =			[]
						if self._beam_pipeline_options:
							lowerCAmelCase__			:		Union[str, Any]						       =			beam.options.pipeline_options.PipelineOptions(
							    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(''','''	) if opt]	)
						else:
							lowerCAmelCase__			:		Dict						       =			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:
							lowerCAmelCase__			:		int						       =			os.path.join(builder_cls.get_imported_module_dir()     ,config.DATASETDICT_INFOS_FILENAME	)

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

							lowerCAmelCase__			:		int						       =			os.path.join(lowercase_     ,lowercase_	)
							if os.path.isfile(lowercase_	):
								lowerCAmelCase__			:		List[Any]						       =			os.path.dirname(lowercase_	)
							elif os.path.isfile(lowercase_	):
								lowerCAmelCase__			:		Union[str, Any]						       =			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
							lowerCAmelCase__			:		Optional[int]						       =			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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase						:   Union[str, Any]							   =	{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Union[str, Any]							   =	['''LayoutLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Dict							   =	[
							    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LayoutLMForMaskedLM''',
							    '''LayoutLMForSequenceClassification''',
							    '''LayoutLMForTokenClassification''',
							    '''LayoutLMForQuestionAnswering''',
							    '''LayoutLMModel''',
							    '''LayoutLMPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   str							   =	[
							    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFLayoutLMForMaskedLM''',
							    '''TFLayoutLMForSequenceClassification''',
							    '''TFLayoutLMForTokenClassification''',
							    '''TFLayoutLMForQuestionAnswering''',
							    '''TFLayoutLMMainLayer''',
							    '''TFLayoutLMModel''',
							    '''TFLayoutLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

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




