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    				=		get_logger(__name__)

def      lowercase_							(     _lowerCamelCase				:			List[str]):
    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_			(						snake_case_			):
   __A      :						int   =							False  # to tell pytest it's not a test class






   @staticmethod
   def  __UpperCamelCase	( lowercase_							:	Any    )   ->		str:
       lowercase__					:  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							:	List[str]      ,		lowercase_							:	Dict      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Union[str, Any]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	int      ,		lowercase_							:	str      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	str      ,		lowercase_							:	Tuple      ,		)   ->		List[str]:
       lowercase__					:  Optional[int]          =							dataset
       lowercase__					:  str          =							name
       lowercase__					:  List[Any]          =							cache_dir
       lowercase__					:  str          =							data_dir
       lowercase__					:  Optional[Any]          =							all_configs
       lowercase__					:  Dict          =							save_infos
       lowercase__					:  int          =							ignore_verifications
       lowercase__					:  Optional[int]          =							force_redownload
       lowercase__					:  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:
           lowercase__					:  str          =							True







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

               # Move dataset_info back to the user
               if dataset_dir is not None:
                   lowercase__					:  Optional[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    )
               lowercase__					:  int          =							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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
