


'''simple docstring'''






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


__UpperCAmelCase					       =							[
    {'''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       _snake_case     (  A=True )		->					Optional[Any]:
							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 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Dict					=   None
							lowercase__    :     Optional[int]					=   None







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							int:
														with TemporaryDirectory() as tmp_dir:
																					lowerCAmelCase__      =			dataset_module_factory(lowerCamelCase_      ,					cache_dir=lowerCamelCase_     )

																					lowerCAmelCase__      =			import_main_class(dataset_module.module_path      ,					dataset=lowerCamelCase_     )

																					lowerCAmelCase__      =			builder_cls(
																					    cache_dir=lowerCamelCase_      ,					config_name=lowerCamelCase_      ,					hash=dataset_module.hash      ,					)

																					lowerCAmelCase__      =			'''/'''.join(
																					    [
																					        HF_GCP_BASE_URL,
																					        builder_instance._relative_data_dir(with_hash=lowerCamelCase_     ).replace(os.sep      ,					'''/'''     ),
																					        config.DATASET_INFO_FILENAME,
																					    ]     )
																					lowerCAmelCase__      =			cached_path(lowerCamelCase_      ,					cache_dir=lowerCamelCase_     )
																					self.assertTrue(os.path.exists(lowerCamelCase_     )     )


@pytest.mark.integration
def       _snake_case     (  A )		->					Optional[Any]:
							lowerCAmelCase__      =			tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
							lowerCAmelCase__      =			dataset_module_factory('''wikipedia'''				,					cache_dir=A )
							lowerCAmelCase__      =			import_main_class(dataset_module.module_path )
							lowerCAmelCase__      =			builder_cls(
							    cache_dir=A				,					config_name='''20220301.frr'''				,					hash=dataset_module.hash				,					)
							# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
							lowerCAmelCase__      =			None
							builder_instance.download_and_prepare()
							lowerCAmelCase__      =			builder_instance.as_dataset()
							assert ds






@pytest.mark.integration
def       _snake_case     (  A )		->					int:
							lowerCAmelCase__      =			dataset_module_factory('''wikipedia'''				,					cache_dir=A )
							lowerCAmelCase__      =			import_main_class(dataset_module.module_path				,					dataset=A )
							lowerCAmelCase__      =			builder_cls(
							    cache_dir=A				,					config_name='''20220301.frr'''				,					hash=dataset_module.hash				,					)
							lowerCAmelCase__      =			builder_instance.as_streaming_dataset()
							assert ds
							assert isinstance(A				,					A )
							assert "train" in ds
							assert isinstance(ds['''train''']				,					A )
							assert next(iter(ds['''train'''] ) )



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()