code: 
  
  
  
  '''simple docstring'''
  
  
  
  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
  
  
  a__						:			Optional[Any]										=			get_logger(__name__)
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   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        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				False  # to tell pytest it's not a test class
  
  
  
   @staticmethod
   def    __lowerCamelCase				(  lowercase			)  ->							Tuple:
    __UpperCamelCase				      =  parser.add_parser("""test"""		,	help="""Test dataset implementation."""			)
    test_parser.add_argument("""--name"""		,	type=lowercase		,	default=lowercase		,	help="""Dataset processing name"""			)
    test_parser.add_argument(
        """--cache_dir"""		,	type=lowercase		,	default=lowercase		,	help="""Cache directory where the datasets are stored."""		,	)
    test_parser.add_argument(
        """--data_dir"""		,	type=lowercase		,	default=lowercase		,	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=lowercase		,	help="""Name of the dataset to download"""			)
    test_parser.set_defaults(func=_test_command_factory			)
  
  
  
   def __init__(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Tuple:
    __UpperCamelCase				      =  dataset
    __UpperCamelCase				      =  name
    __UpperCamelCase				      =  cache_dir
    __UpperCamelCase				      =  data_dir
    __UpperCamelCase				      =  all_configs
    __UpperCamelCase				      =  save_infos
    __UpperCamelCase				      =  ignore_verifications
    __UpperCamelCase				      =  force_redownload
    __UpperCamelCase				      =  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:
     __UpperCamelCase				      =  True
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    fl_logger().setLevel(lowercase			)
    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			)
    __UpperCamelCase      ,		__UpperCamelCase				      =  self._dataset, self._name
    __UpperCamelCase				      =  dataset_module_factory(lowercase			)
    __UpperCamelCase				      =  import_main_class(module.module_path			)
    __UpperCamelCase				      =  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=lowercase		,	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})"			)
     __UpperCamelCase				      =  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=lowercase		,	)
     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:
      __UpperCamelCase				      =  os.path.join(builder_cls.get_imported_module_dir()		,	"""README.md"""			)
      __UpperCamelCase				      =  Path(lowercase			).name + """.py"""
      __UpperCamelCase				      =  os.path.join(lowercase		,	lowercase			)
      if os.path.isfile(lowercase			):
       __UpperCamelCase				      =  os.path.dirname(lowercase			)
      elif os.path.isfile(lowercase			):
       __UpperCamelCase				      =  path
      elif os.path.isdir(lowercase			):  # for local directories containing only data files
       __UpperCamelCase				      =  path
      else:  # in case of a remote dataset
       __UpperCamelCase				      =  None
       print(f"Dataset card saved at {dataset_readme_path}"			)
  
      # Move dataset_info back to the user
      if dataset_dir is not None:
       __UpperCamelCase				      =  os.path.join(lowercase		,	"""README.md"""			)
       copyfile(lowercase		,	lowercase			)
       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			)
      __UpperCamelCase				      =  os.path.join(self._cache_dir		,	datasets.config.DOWNLOADED_DATASETS_DIR			)
      if os.path.isdir(lowercase			):
       logger.warning(f"Clearing cache at {download_dir}"			)
       rmtree(lowercase			)
  
    print("""Test successful."""			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  