





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_a				:	List[str]           =       imread(r'digital_image_processing/image_data/lena_small.jpg')
_a				:	Optional[Any]           =       cvtColor(img, COLOR_BGR2GRAY)



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       cn.convert_to_negative(_lowerCAmelCase				)
      # assert negative_img array for at least one True
      assert negative_img.any()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(_lowerCAmelCase							,    110				)				).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       canny.gen_gaussian_kernel(9							,    sigma=1.4				)
      # Assert ambiguous array
      assert resp.all()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       imread("""digital_image_processing/image_data/lena_small.jpg"""							,    0				)
      # assert ambiguous array for all == True
      assert canny_img.all()
      __lowerCamelCase				   =       canny.canny(_lowerCAmelCase				)
      # assert canny array for at least one True
      assert canny_array.any()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      assert gg.gaussian_filter(_lowerCAmelCase							,    5							,    sigma=0.9				).all()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
      __lowerCamelCase				   =       conv.img_convolve(_lowerCAmelCase							,    _lowerCAmelCase				).astype(_lowerCAmelCase				)
      assert res.any()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      assert med.median_filter(_lowerCAmelCase							,    3				).any()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       sob.sobel_filter(_lowerCAmelCase				)
      assert grad.any() and theta.any()



def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       sp.make_sepia(_lowerCAmelCase							,    20				)
      assert sepia.all()



def  UpperCamelCase__     (   _A:    Optional[int] = "digital_image_processing/image_data/lena_small.jpg"				):




      '''simple docstring'''







      __lowerCamelCase				   =       bs.Burkes(imread(_lowerCAmelCase							,    1				)							,    120				)
      burkes.process()
      assert burkes.output_img.any()



def  UpperCamelCase__     (   _A:    List[str] = "digital_image_processing/image_data/lena_small.jpg"							,    ):




      '''simple docstring'''







      __lowerCamelCase				   =       rs.NearestNeighbour(imread(_lowerCAmelCase							,    1				)							,    400							,    200				)
      nn.process()
      assert nn.output.any()






def  UpperCamelCase__     (   ):




      '''simple docstring'''







      __lowerCamelCase				   =       """digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      __lowerCamelCase				   =       imread(_lowerCAmelCase							,    0				)

      # Test for get_neighbors_pixel function() return not None
      __lowerCamelCase				   =       0
      __lowerCamelCase				   =       0
      __lowerCamelCase				   =       image[x_coordinate][y_coordinate]

      __lowerCamelCase				   =       lbp.get_neighbors_pixel(
          _lowerCAmelCase							,    _lowerCAmelCase							,    _lowerCAmelCase							,    _lowerCAmelCase				)

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      __lowerCamelCase				   =       np.zeros((image.shape[0], image.shape[1])				)

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0							,    image.shape[0]				):
            for j in range(0							,    image.shape[1]				):
                  __lowerCamelCase				   =       lbp.local_binary_value(_lowerCAmelCase							,    _lowerCAmelCase							,    _lowerCAmelCase				)

      assert lbp_image.any()











'''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


__a  			=						[
    {"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(							_lowerCAmelCase=True						)						->					Dict:
      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        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						None
    lowercase   =						None




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Optional[int]		):
          with TemporaryDirectory() as tmp_dir:
                snake_case__    :       Dict       		= dataset_module_factory(snake_case_					,	cache_dir=snake_case_		)

                snake_case__    :       Optional[int]       		= import_main_class(dataset_module.module_path					,	dataset=snake_case_		)

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

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





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Optional[int]       		= tmp_path_factory.mktemp("""test_hf_gcp"""						) / """test_wikipedia_simple"""
      snake_case__    :       Dict       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       Dict       		= import_main_class(dataset_module.module_path						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  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__    :       Any       		= None
      builder_instance.download_and_prepare()
      snake_case__    :       Union[str, Any]       		= builder_instance.as_dataset()
      assert ds





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					List[str]:
      snake_case__    :       Optional[int]       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       List[str]       		= import_main_class(dataset_module.module_path	,  dataset=_lowerCAmelCase						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  config_name="""20220301.frr"""	,  hash=dataset_module.hash	,  )
      snake_case__    :       Any       		= builder_instance.as_streaming_dataset()
      assert ds
      assert isinstance(_lowerCAmelCase	,  _lowerCAmelCase						)
      assert "train" in ds
      assert isinstance(ds["""train"""]	,  _lowerCAmelCase						)
      assert next(iter(ds["""train"""]						)						)
