




'''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				     = get_logger(__name__)

def       __lowerCAmelCase (   snake_case__						):
			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		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    False  # to tell pytest it's not a test class
				@staticmethod
				def 	a_	(_UpperCAmelCase      )     ->	Any:
							__UpperCamelCase				:      Union[str, Any]									=       parser.add_parser("test"			,  help="Test dataset implementation."      )
							test_parser.add_argument("--name"			,  type=_UpperCAmelCase			,  default=_UpperCAmelCase			,  help="Dataset processing name"      )
							test_parser.add_argument(
							    "--cache_dir"			,  type=_UpperCAmelCase			,  default=_UpperCAmelCase			,  help="Cache directory where the datasets are stored."			,  )
							test_parser.add_argument(
							    "--data_dir"			,  type=_UpperCAmelCase			,  default=_UpperCAmelCase			,  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=_UpperCAmelCase			,  help="Name of the dataset to download"      )
							test_parser.set_defaults(func=_test_command_factory      )
				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )     ->	int:
							__UpperCamelCase				:      int									=       dataset
							__UpperCamelCase				:      str									=       name
							__UpperCamelCase				:      Union[str, Any]									=       cache_dir
							__UpperCamelCase				:      Optional[int]									=       data_dir
							__UpperCamelCase				:      Any									=       all_configs
							__UpperCamelCase				:      Union[str, Any]									=       save_infos
							__UpperCamelCase				:      Any									=       ignore_verifications
							__UpperCamelCase				:      Tuple									=       force_redownload
							__UpperCamelCase				:      Any									=       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				:      str									=       True





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

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

							print("Test successful."      )









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



