
"""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


_snake_case          =						[
    {'''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  (    SCREAMING_SNAKE_CASE:					List[str]=True						):






							"""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=UpperCAmelCase							)							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[str]							=					None
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					None


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[Any]  )		->  str:







											"""simple docstring"""




											with TemporaryDirectory() as tmp_dir:
																		_lowerCAmelCase  	=			dataset_module_factory(UpperCAmelCase_				,     cache_dir=UpperCAmelCase_  )

																		_lowerCAmelCase  	=			import_main_class(dataset_module.module_path				,     dataset=UpperCAmelCase_  )

																		_lowerCAmelCase  	=			builder_cls(
																		    cache_dir=UpperCAmelCase_				,     config_name=UpperCAmelCase_				,     hash=dataset_module.hash				,     )

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






@pytest.mark.integration
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			tmp_path_factory.mktemp('test_hf_gcp'						) / 'test_wikipedia_simple'
							_lowerCAmelCase  	=			dataset_module_factory('wikipedia'     ,  cache_dir=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			import_main_class(dataset_module.module_path						)
							_lowerCAmelCase  	=			builder_cls(
							    cache_dir=SCREAMING_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
							_lowerCAmelCase  	=			None
							builder_instance.download_and_prepare()
							_lowerCAmelCase  	=			builder_instance.as_dataset()
							assert ds






@pytest.mark.integration
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_module_factory('wikipedia'     ,  cache_dir=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			import_main_class(dataset_module.module_path     ,  dataset=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			builder_cls(
							    cache_dir=SCREAMING_SNAKE_CASE     ,  config_name='20220301.frr'     ,  hash=dataset_module.hash     ,  )
							_lowerCAmelCase  	=			builder_instance.as_streaming_dataset()
							assert ds
							assert isinstance(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							assert "train" in ds
							assert isinstance(ds['train']     ,  SCREAMING_SNAKE_CASE						)
							assert next(iter(ds['train']						)						)








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]						):
														for y in range(image.shape[0]						):
																					_lowerCAmelCase  	=			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_lowerCAmelCase  	=			int(summation > 0						)
							return output


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






