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


lowercase__      :    Any          =     get_logger(__name__)

def        SCREAMING_SNAKE_CASE_					(						snake_case__ :    List[Any]							)					->     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        lowercase_  (  lowercase_						):



	"""simple docstring"""





	UpperCAmelCase_  : Union[str, Any]       =    False  # to tell pytest it's not a test class


	@staticmethod
	def       SCREAMING_SNAKE_CASE_    (							__SCREAMING_SNAKE_CASE    )						->int:
								lowerCAmelCase     		=		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		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      )						->Tuple:
								lowerCAmelCase     		=		dataset
								lowerCAmelCase     		=		name
								lowerCAmelCase     		=		cache_dir
								lowerCAmelCase     		=		data_dir
								lowerCAmelCase     		=		all_configs
								lowerCAmelCase     		=		save_infos
								lowerCAmelCase     		=		ignore_verifications
								lowerCAmelCase     		=		force_redownload
								lowerCAmelCase     		=		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:
															lowerCAmelCase     		=		True




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

																						# Move dataset_info back to the user
																						if dataset_dir is not None:
																													lowerCAmelCase     		=		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    )
																						lowerCAmelCase     		=		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.'''    )




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



