





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowerCAmelCase_    =    [
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]






def 					lowerCamelCase_						(    _UpperCamelCase=True			)		->						List[str]:






				"""simple docstring"""


				if with_config:
								return [
								    {
								        "testcase_name": d["dataset"] + "/" + d["config_name"],
								        "dataset": d["dataset"],
								        "config_name": d["config_name"],
								    }
								    for d in DATASETS_ON_HF_GCP
								]
				else:
								return [
								    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
								]



@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=A__					)					)
class  __lowerCAmelCase	(      A__					):
			lowerCamelCase_					:					Union[str, Any]	      =				None
			lowerCamelCase_					:					Optional[Any]	      =				None






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							with TemporaryDirectory() as tmp_dir:
											snake_case_						:   Dict           =  dataset_module_factory(__A ,							cache_dir=__A   )

											snake_case_						:   List[str]           =  import_main_class(dataset_module.module_path ,							dataset=__A   )

											snake_case_						:   DatasetBuilder           =  builder_cls(
											    cache_dir=__A ,							config_name=__A ,							hash=dataset_module.hash ,							)

											snake_case_						:   Any           =  """/""".join(
											    [
											        HF_GCP_BASE_URL,
											        builder_instance._relative_data_dir(with_hash=__A   ).replace(os.sep ,							'''/'''   ),
											        config.DATASET_INFO_FILENAME,
											    ]   )
											snake_case_						:   Dict           =  cached_path(__A ,							cache_dir=__A   )
											self.assertTrue(os.path.exists(__A   )   )






@pytest.mark.integration
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Dict           =  tmp_path_factory.mktemp('''test_hf_gcp'''			) / """test_wikipedia_simple"""
				snake_case_						:   List[Any]           =  dataset_module_factory('''wikipedia'''			,    cache_dir=lowercase__			)
				snake_case_						:   Optional[int]           =  import_main_class(dataset_module.module_path			)
				snake_case_						:   DatasetBuilder           =  builder_cls(
				    cache_dir=lowercase__			,    config_name='''20220301.frr'''			,    hash=dataset_module.hash			,    )
				# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
				snake_case_						:   Optional[int]           =  None
				builder_instance.download_and_prepare()
				snake_case_						:   Tuple           =  builder_instance.as_dataset()
				assert ds






@pytest.mark.integration
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   Dict           =  dataset_module_factory('''wikipedia'''			,    cache_dir=lowercase__			)
				snake_case_						:   Any           =  import_main_class(dataset_module.module_path			,    dataset=lowercase__			)
				snake_case_						:   DatasetBuilder           =  builder_cls(
				    cache_dir=lowercase__			,    config_name='''20220301.frr'''			,    hash=dataset_module.hash			,    )
				snake_case_						:   Dict           =  builder_instance.as_streaming_dataset()
				assert ds
				assert isinstance(lowercase__			,    lowercase__			)
				assert "train" in ds
				assert isinstance(ds['''train''']			,    lowercase__			)
				assert next(iter(ds['''train''']			)			)







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  xgboost(_UpperCamelCase			,    _UpperCamelCase			)

				# Display the confusion matrix of the classifier with both training and test sets
				ConfusionMatrixDisplay.from_estimator(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    display_labels=_UpperCamelCase			,    cmap='''Blues'''			,    normalize='''true'''			,    )
				plt.title('''Normalized Confusion Matrix - IRIS Dataset'''			)
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()
