

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       snake_case__    (       lowerCAmelCase_,   **lowerCAmelCase_      ):





     """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_	(							lowerCamelCase_					):



      """simple docstring"""







      @staticmethod
      def 	_lowerCAmelCase					(				snake_case					:							ArgumentParser    ):
           SCREAMING_SNAKE_CASE										=parser.add_parser('run_beam'      ,help='Run a Beam dataset processing pipeline'    )
           run_beam_parser.add_argument('dataset'      ,type=snake_case      ,help='Name of the dataset to download'    )
           run_beam_parser.add_argument('--name'      ,type=snake_case      ,default=snake_case      ,help='Dataset config name'    )
           run_beam_parser.add_argument(
               '--cache_dir'      ,type=snake_case      ,default=snake_case      ,help='Cache directory where the datasets are stored'      ,)
           run_beam_parser.add_argument(
               '--beam_pipeline_options'      ,type=snake_case      ,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=snake_case      ,default=snake_case      ,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=snake_case    )
      def __init__(				self					:							Optional[Any]      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							bool      ,snake_case					:							bool      ,snake_case					:							bool      ,snake_case					:							bool      ,**snake_case					:							Tuple      ,):
           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 	_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    )
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self._dataset, self._name
           SCREAMING_SNAKE_CASE										=dataset_module_factory(snake_case    )
           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=snake_case      ,cache_dir=self._cache_dir      ,base_path=dataset_module.builder_kwargs.get('base_path'    )      ,)    )
           else:
                builders.append(
                    builder_cls(
                        config_name=snake_case      ,data_dir=self._data_dir      ,beam_options=snake_case      ,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=snake_case      ,)
                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(snake_case    ).name + '.py'

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




from typing import TYPE_CHECKING

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


_lowerCamelCase								={
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=["XLMProphetNetTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=[
     "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
     "XLMProphetNetForCausalLM",
     "XLMProphetNetForConditionalGeneration",
     "XLMProphetNetModel",
     "XLMProphetNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_prophetnet import (
      XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMProphetNetDecoder,
      XLMProphetNetEncoder,
      XLMProphetNetForCausalLM,
      XLMProphetNetForConditionalGeneration,
      XLMProphetNetModel,
      XLMProphetNetPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase								=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

