code: 
  
  
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile, rmtree
  from typing import Generator
  
  import datasets.config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.filelock import logger as fl_logger
  from datasets.utils.info_utils import VerificationMode
  from datasets.utils.logging import ERROR, get_logger
  
  
  __lowerCamelCase  				=					get_logger(__name__)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> int:
    return TestCommand(
        args.dataset						,				args.name						,				args.cache_dir						,				args.data_dir						,				args.all_configs						,				args.save_info or args.save_infos						,				args.ignore_verifications						,				args.force_redownload						,				args.clear_cache						,				)
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			False  # to tell pytest it's not a test class
   @staticmethod
   def lowercase_		(						a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parser.add_parser('''test'''	,    help='''Test dataset implementation.'''					)
     test_parser.add_argument('''--name'''	,    type=a	,    default=a	,    help='''Dataset processing name'''					)
     test_parser.add_argument(
         '''--cache_dir'''	,    type=a	,    default=a	,    help='''Cache directory where the datasets are stored.'''	,    )
     test_parser.add_argument(
         '''--data_dir'''	,    type=a	,    default=a	,    help='''Can be used to specify a manual directory to get the files from.'''	,    )
     test_parser.add_argument('''--all_configs'''	,    action='''store_true'''	,    help='''Test all dataset configurations'''					)
     test_parser.add_argument(
         '''--save_info'''	,    action='''store_true'''	,    help='''Save the dataset infos in the dataset card (README.md)'''					)
     test_parser.add_argument(
         '''--ignore_verifications'''	,    action='''store_true'''	,    help='''Run the test without checksums and splits checks.'''	,    )
     test_parser.add_argument('''--force_redownload'''	,    action='''store_true'''	,    help='''Force dataset redownload'''					)
     test_parser.add_argument(
         '''--clear_cache'''	,    action='''store_true'''	,    help='''Remove downloaded files and cached datasets after each config test'''	,    )
     # aliases
     test_parser.add_argument('''--save_infos'''	,    action='''store_true'''	,    help='''alias to save_info'''					)
     test_parser.add_argument('''dataset'''	,    type=a	,    help='''Name of the dataset to download'''					)
     test_parser.set_defaults(func=_test_command_factory					)
   def __init__(						self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    )	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    dataset
     _A           =    name
     _A           =    cache_dir
     _A           =    data_dir
     _A           =    all_configs
     _A           =    save_infos
     _A           =    ignore_verifications
     _A           =    force_redownload
     _A           =    clear_cache
     if clear_cache and not cache_dir:
       print(
           '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
           '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
           '''Please provide a --cache_dir that will be used to test the dataset script.'''					)
       exit(1					)
     if save_infos:
       _A           =    True
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     fl_logger().setLevel(a					)
     if self._name is not None and self._all_configs:
       print('''Both parameters `config` and `all_configs` can\'t be used at once.'''					)
       exit(1					)
     _A    ,   _A           =    self._dataset, self._name
     _A           =    dataset_module_factory(a					)
     _A           =    import_main_class(module.module_path					)
     _A           =    len(builder_cls.BUILDER_CONFIGS					) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1
  
     def get_builders() -> Generator[DatasetBuilder, None, None]:
       if self._all_configs and builder_cls.BUILDER_CONFIGS:
         for i, config in enumerate(builder_cls.BUILDER_CONFIGS					):
           if "config_name" in module.builder_kwargs:
             yield builder_cls(
                 cache_dir=self._cache_dir	,    data_dir=self._data_dir	,    **module.builder_kwargs	,    )
           else:
             yield builder_cls(
                 config_name=config.name	,    cache_dir=self._cache_dir	,    data_dir=self._data_dir	,    **module.builder_kwargs	,    )
       else:
         if "config_name" in module.builder_kwargs:
           yield builder_cls(cache_dir=self._cache_dir	,    data_dir=self._data_dir	,    **module.builder_kwargs					)
         else:
           yield builder_cls(
               config_name=a	,    cache_dir=self._cache_dir	,    data_dir=self._data_dir	,    **module.builder_kwargs	,    )
  
     for j, builder in enumerate(get_builders()					):
       print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''					)
       _A           =    os.path.exists(
           os.path.join(builder.get_imported_module_dir()	,    datasets.config.DATASETDICT_INFOS_FILENAME					)					)  # record checksums only if we need to update a (deprecated) dataset_infos.json
       builder.download_and_prepare(
           download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
           if not self._force_redownload
           else DownloadMode.FORCE_REDOWNLOAD	,    verification_mode=VerificationMode.NO_CHECKS
           if self._ignore_verifications
           else VerificationMode.ALL_CHECKS	,    try_from_hf_gcs=a	,    )
       builder.as_dataset()
       if self._save_infos:
         builder._save_infos()
  
       # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
       # The dataset_infos are saved in the YAML part of the README.md
  
       # 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:
         _A           =    os.path.join(builder_cls.get_imported_module_dir()	,    '''README.md'''					)
         _A           =    Path(a					).name + '''.py'''
         _A           =    os.path.join(a	,    a					)
         if os.path.isfile(a					):
           _A           =    os.path.dirname(a					)
         elif os.path.isfile(a					):
           _A           =    path
         elif os.path.isdir(a					):  # for local directories containing only data files
           _A           =    path
         else:  # in case of a remote dataset
           _A           =    None
           print(f'''Dataset card saved at {dataset_readme_path}'''					)
  
         # Move dataset_info back to the user
         if dataset_dir is not None:
           _A           =    os.path.join(a	,    '''README.md'''					)
           copyfile(a	,    a					)
           print(f'''Dataset card saved at {user_dataset_readme_path}'''					)
  
              # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
       if self._clear_cache:
         if os.path.isdir(builder._cache_dir					):
           logger.warning(f'''Clearing cache at {builder._cache_dir}'''					)
           rmtree(builder._cache_dir					)
         _A           =    os.path.join(self._cache_dir	,    datasets.config.DOWNLOADED_DATASETS_DIR					)
         if os.path.isdir(a					):
           logger.warning(f'''Clearing cache at {download_dir}'''					)
           rmtree(a					)
  
     print('''Test successful.'''					)
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output