

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


UpperCAmelCase__	:       List[Any]			      =      get_logger(__name__)






def    __lowercase (       _A     )       ->  Optional[Any]:
      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     a__    (				UpperCamelCase_    ):







  """simple docstring"""




  UpperCAmelCase__ :	List[Any]         =False  # to tell pytest it's not a test class


  @staticmethod
  def 	_lowercase (     UpperCAmelCase__      :	str						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				parser.add_parser("""test"""			,			help="""Test dataset implementation."""						)
        test_parser.add_argument("""--name"""			,			type=__SCREAMING_SNAKE_CASE			,			default=__SCREAMING_SNAKE_CASE			,			help="""Dataset processing name"""						)
        test_parser.add_argument(
            """--cache_dir"""			,			type=__SCREAMING_SNAKE_CASE			,			default=__SCREAMING_SNAKE_CASE			,			help="""Cache directory where the datasets are stored."""			,			)
        test_parser.add_argument(
            """--data_dir"""			,			type=__SCREAMING_SNAKE_CASE			,			default=__SCREAMING_SNAKE_CASE			,			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=__SCREAMING_SNAKE_CASE			,			help="""Name of the dataset to download"""						)
        test_parser.set_defaults(func=_test_command_factory						)


  def __init__(     self      :	List[Any]			,			UpperCAmelCase__      :	List[str]			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	List[str]			,			)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				dataset
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				name
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				cache_dir
        SCREAMING_SNAKE_CASE	: Any					       =				data_dir
        SCREAMING_SNAKE_CASE	: Tuple					       =				all_configs
        SCREAMING_SNAKE_CASE	: str					       =				save_infos
        SCREAMING_SNAKE_CASE	: List[Any]					       =				ignore_verifications
        SCREAMING_SNAKE_CASE	: str					       =				force_redownload
        SCREAMING_SNAKE_CASE	: List[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:
              SCREAMING_SNAKE_CASE	: Any					       =				True




  def 	_lowercase (     self      :	Dict						)   ->str:
        """simple docstring"""







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

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

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







import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class     a__    (				tf.keras.layers.Layer    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	List			,			UpperCAmelCase__      :	bool			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	str = "longest"			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	int = 5_1_2			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			**UpperCAmelCase__      :	Optional[int]			,			)   ->Union[str, Any]:
        """simple docstring"""







        super().__init__()
        if use_fast_bert_tokenizer:
              SCREAMING_SNAKE_CASE	: Any					       =				FastBertTokenizer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case_nfd_strip_accents=UpperCAmelCase__			,			**UpperCAmelCase__						)
        else:
              SCREAMING_SNAKE_CASE	: Any					       =				tf.lookup.StaticVocabularyTable(
                  tf.lookup.KeyValueTensorInitializer(
                      keys=UpperCAmelCase__			,			key_dtype=tf.string			,			values=tf.range(tf.size(UpperCAmelCase__			,			out_type=tf.intaa						)			,			dtype=tf.intaa						)			,			value_dtype=tf.intaa			,			)			,			num_oov_buckets=1			,			)
              SCREAMING_SNAKE_CASE	: Dict					       =				BertTokenizerLayer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case=UpperCAmelCase__			,			**UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Any					       =				vocab_list
        SCREAMING_SNAKE_CASE	: int					       =				do_lower_case
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				cls_token_id or vocab_list.index("""[CLS]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				sep_token_id or vocab_list.index("""[SEP]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				pad_token_id or vocab_list.index("""[PAD]"""						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				ShrinkLongestTrimmer(max_length - 3			,			axis=1						)  # Allow room for special tokens
        SCREAMING_SNAKE_CASE	: Tuple					       =				max_length
        SCREAMING_SNAKE_CASE	: List[str]					       =				padding
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				truncation
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				pad_to_multiple_of
        SCREAMING_SNAKE_CASE	: Dict					       =				return_token_type_ids
        SCREAMING_SNAKE_CASE	: str					       =				return_attention_mask


  @classmethod
  def 	_lowercase (     cls      :	Optional[int]			,			UpperCAmelCase__      :	"PreTrainedTokenizerBase"			,			**UpperCAmelCase__      :	Tuple						)   ->str:  # noqa: F821
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""do_lower_case"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""cls_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				kwargs.pop("""sep_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        SCREAMING_SNAKE_CASE	: int					       =				kwargs.pop("""pad_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        SCREAMING_SNAKE_CASE	: List[str]					       =				tokenizer.get_vocab()
        SCREAMING_SNAKE_CASE	: List[str]					       =				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,			key=lambda UpperCAmelCase__						: x[1]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				[entry[0] for entry in vocab]
        return cls(
            vocab_list=UpperCAmelCase__			,			do_lower_case=UpperCAmelCase__			,			cls_token_id=UpperCAmelCase__			,			sep_token_id=UpperCAmelCase__			,			pad_token_id=UpperCAmelCase__			,			**UpperCAmelCase__			,			)


  @classmethod
  def 	_lowercase (     cls      :	str			,			UpperCAmelCase__      :	Union[str, os.PathLike]			,			*UpperCAmelCase__      :	Dict			,			**UpperCAmelCase__      :	int						)   ->Any:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE	: List[str]					       =				BertTokenizer.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				BertTokenizerFast.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        return cls.from_tokenizer(UpperCAmelCase__			,			**UpperCAmelCase__						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	List[str]						)   ->List[str]:
        """simple docstring"""







        if self.do_lower_case:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				case_fold_utfa(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: int					       =				self.tf_tokenizer.tokenize(UpperCAmelCase__						)
        return tokens.merge_dims(1			,			-1						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	Optional[int]=None			,			UpperCAmelCase__      :	Optional[Any]=None			,			UpperCAmelCase__      :	Union[str, Any]=None			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	int=None			,			UpperCAmelCase__      :	Tuple=None			,			)   ->Any:
        """simple docstring"""







        if padding is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.padding
        if padding not in ("longest", "max_length"):
              raise ValueError("""Padding must be either 'longest' or 'max_length'!"""						)
        if max_length is not None and text_pair is not None:
              # Because we have to instantiate a Trimmer to do it properly
              raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""						)
        if max_length is None:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.max_length
        if truncation is None:
              SCREAMING_SNAKE_CASE	: str					       =				self.truncation
        if pad_to_multiple_of is None:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.pad_to_multiple_of
        if return_token_type_ids is None:
              SCREAMING_SNAKE_CASE	: Any					       =				self.return_token_type_ids
        if return_attention_mask is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.return_attention_mask
        if not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: str					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None and not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: int					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None:
              if text.shape.rank > 1:
                    raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""						)
              if text_pair.shape.rank > 1:
                    raise ValueError("""text_pair should not be multidimensional!"""						)
        if text.shape.rank == 2:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Dict					       =				text[:, 0], text[:, 1]
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
        if text_pair is None:  # Unpaired text
              if truncation:
                    SCREAMING_SNAKE_CASE	: int					       =				text[:, : max_length - 2]  # Allow room for special tokens
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				combine_segments(
                  (text,)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        else:  # Paired text
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
              if truncation:
                    SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				self.paired_trimmer.trim([text, text_pair]						)
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: str					       =				combine_segments(
                  (text, text_pair)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        if padding == "longest":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				input_ids.bounding_shape(axis=1						)
              if pad_to_multiple_of is not None:
                    # No ceiling division in tensorflow, so we negate floordiv instead
                    SCREAMING_SNAKE_CASE	: Any					       =				pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,			UpperCAmelCase__						))
        else:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				max_length

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				pad_model_inputs(UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
        SCREAMING_SNAKE_CASE	: Any					       =				{"""input_ids""": input_ids}
        if return_attention_mask:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				attention_mask
        if return_token_type_ids:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				pad_model_inputs(
                  UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
              SCREAMING_SNAKE_CASE	: List[str]					       =				token_type_ids
        return output




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }




