from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case			      =    {
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MarianForCausalLM""",
         """MarianModel""",
         """MarianMTModel""",
         """MarianPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    ["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    ["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

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_ (				__lowerCAmelCase		,					**__lowerCAmelCase						)->  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 			__snake_case   (     lowerCamelCase__   ):
 @staticmethod
 def 	UpperCAmelCase__       (  snake_case__						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =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		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   **snake_case__		,   )	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : str      =dataset
       UpperCAmelCase    : Optional[Any]      =name
       UpperCAmelCase    : str      =cache_dir
       UpperCAmelCase    : Union[str, Any]      =beam_pipeline_options
       UpperCAmelCase    : List[str]      =data_dir
       UpperCAmelCase    : int      =all_configs
       UpperCAmelCase    : List[Any]      =save_infos
       UpperCAmelCase    : Dict      =ignore_verifications
       UpperCAmelCase    : Optional[int]      =force_redownload
       UpperCAmelCase    : Optional[int]      =config_kwargs






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

             UpperCAmelCase    : List[str]      =Path(snake_case__						).name + '''.py'''

             UpperCAmelCase    : Tuple      =os.path.join(snake_case__		,   snake_case__						)
             if os.path.isfile(snake_case__						):
                   UpperCAmelCase    : Dict      =os.path.dirname(snake_case__						)
             elif os.path.isfile(snake_case__						):
                   UpperCAmelCase    : 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
             UpperCAmelCase    : Tuple      =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}'''						)
