





"""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


lowerCAmelCase:	Any							      =get_logger(__name__)




def 			__snake_case						(      __A    )		->				Optional[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 lowerCamelCase__  (    lowerCAmelCase_				):
   __UpperCAmelCase        =					False  # to tell pytest it's not a test class



   @staticmethod
   def    _UpperCAmelCase	(       snake_case	) ->    int:







      """simple docstring"""
      lowercase    :			Dict  =       parser.add_parser("""test"""      ,   help="""Test dataset implementation."""	)
      test_parser.add_argument("""--name"""      ,   type=lowerCamelCase_      ,   default=lowerCamelCase_      ,   help="""Dataset processing name"""	)
      test_parser.add_argument(
          """--cache_dir"""      ,   type=lowerCamelCase_      ,   default=lowerCamelCase_      ,   help="""Cache directory where the datasets are stored."""      ,   )
      test_parser.add_argument(
          """--data_dir"""      ,   type=lowerCamelCase_      ,   default=lowerCamelCase_      ,   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=lowerCamelCase_      ,   help="""Name of the dataset to download"""	)
      test_parser.set_defaults(func=_test_command_factory	)



   def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   ) ->    int:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       dataset
      lowercase    :			List[Any]  =       name
      lowercase    :			Optional[int]  =       cache_dir
      lowercase    :			Optional[int]  =       data_dir
      lowercase    :			Union[str, Any]  =       all_configs
      lowercase    :			Dict  =       save_infos
      lowercase    :			Dict  =       ignore_verifications
      lowercase    :			Optional[int]  =       force_redownload
      lowercase    :			str  =       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:
         lowercase    :			Dict  =       True





   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      fl_logger().setLevel(lowerCamelCase_	)
      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	)
      lowercase  ,					lowercase    :			Union[str, Any]  =       self._dataset, self._name
      lowercase    :			Tuple  =       dataset_module_factory(lowerCamelCase_	)
      lowercase    :			Dict  =       import_main_class(module.module_path	)
      lowercase    :			Tuple  =       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=lowerCamelCase_      ,   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})'''	)
         lowercase    :			int  =       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=lowerCamelCase_      ,   )
         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:
            lowercase    :			int  =       os.path.join(builder_cls.get_imported_module_dir()      ,   """README.md"""	)
            lowercase    :			List[Any]  =       Path(lowerCamelCase_	).name + """.py"""
            lowercase    :			List[Any]  =       os.path.join(lowerCamelCase_      ,   lowerCamelCase_	)
            if os.path.isfile(lowerCamelCase_	):
               lowercase    :			str  =       os.path.dirname(lowerCamelCase_	)
            elif os.path.isfile(lowerCamelCase_	):
               lowercase    :			Optional[Any]  =       path
            elif os.path.isdir(lowerCamelCase_	):  # for local directories containing only data files
               lowercase    :			str  =       path
            else:  # in case of a remote dataset
               lowercase    :			Tuple  =       None
               print(f'''Dataset card saved at {dataset_readme_path}'''	)

            # Move dataset_info back to the user
            if dataset_dir is not None:
               lowercase    :			Dict  =       os.path.join(lowerCamelCase_      ,   """README.md"""	)
               copyfile(lowerCamelCase_      ,   lowerCamelCase_	)
               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	)
            lowercase    :			int  =       os.path.join(self._cache_dir      ,   datasets.config.DOWNLOADED_DATASETS_DIR	)
            if os.path.isdir(lowerCamelCase_	):
               logger.warning(f'''Clearing cache at {download_dir}'''	)
               rmtree(lowerCamelCase_	)

      print("""Test successful."""	)









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


