






"""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


_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       __UpperCamelCase						(		snake_case__=True       ):
	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=_SCREAMING_SNAKE_CASE    )    )
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					int 			=					None
	_A							:					Tuple 			=					None





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		with TemporaryDirectory() as tmp_dir:
			A_		:    str						     = dataset_module_factory(lowerCAmelCase_  ,  cache_dir=lowerCAmelCase_				)

			A_		:    int						     = import_main_class(dataset_module.module_path  ,  dataset=lowerCAmelCase_				)

			A_		:    DatasetBuilder						     = builder_cls(
			    cache_dir=lowerCAmelCase_  ,  config_name=lowerCAmelCase_  ,  hash=dataset_module.hash  ,  )

			A_		:    List[Any]						     = """/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=lowerCAmelCase_				).replace(os.sep  ,  """/"""				),
			        config.DATASET_INFO_FILENAME,
			    ]				)
			A_		:    Optional[int]						     = cached_path(lowerCAmelCase_  ,  cache_dir=lowerCAmelCase_				)
			self.assertTrue(os.path.exists(lowerCAmelCase_				)				)






@pytest.mark.integration
def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
	A_		:    int						     = dataset_module_factory("""wikipedia""" , cache_dir=snake_case__       )
	A_		:    List[Any]						     = import_main_class(dataset_module.module_path       )
	A_		:    DatasetBuilder						     = builder_cls(
	    cache_dir=snake_case__ , config_name="""20220301.frr""" , hash=dataset_module.hash , )
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	A_		:    Any						     = None
	builder_instance.download_and_prepare()
	A_		:    List[str]						     = builder_instance.as_dataset()
	assert ds






@pytest.mark.integration
def       __UpperCamelCase						(		snake_case__       ):
	A_		:    int						     = dataset_module_factory("""wikipedia""" , cache_dir=snake_case__       )
	A_		:    Tuple						     = import_main_class(dataset_module.module_path , dataset=snake_case__       )
	A_		:    DatasetBuilder						     = builder_cls(
	    cache_dir=snake_case__ , config_name="""20220301.frr""" , hash=dataset_module.hash , )
	A_		:    Tuple						     = builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(snake_case__ , snake_case__       )
	assert "train" in ds
	assert isinstance(ds["""train"""] , snake_case__       )
	assert next(iter(ds["""train"""]       )       )









"""simple docstring"""
import numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , input_matrix.conj().T       )

	# Set convergence to False. Will define convergence when we exceed max_iterations
	# or when we have small changes from one iteration to next.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

	while not convergence:
		# Multiple matrix by the vector.
		A_		:    Dict						     = np.dot(snake_case__ , snake_case__       )
		# Normalize the resulting output vector.
		A_		:    str						     = w / np.linalg.norm(snake_case__       )
		# Find rayleigh quotient
		# (faster than usual b/c we know vector is normalized already)
		A_		:    str						     = vector.conj().T if is_complex else vector.T
		A_		:    Optional[Any]						     = np.dot(snake_case__ , np.dot(snake_case__ , snake_case__       )       )

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = eigen_vectors[:, -1]

		# Check our implementation and numpy gives close answers.
		assert np.abs(eigen_value - eigen_value_max       ) <= 1E-6
		# Take absolute values element wise of each eigenvector.
		# as they are only unique to a minus sign.
		assert np.linalg.norm(np.abs(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

