
from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       k_size // 2
  __magic_name__     ,    __magic_name__				     =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  __magic_name__				     =       1 / (2 * pi * sigma) * exp(-(square(A_					) + square(A_					)) / (2 * square(A_					))					)
  return g

def a__    (       A_,     A_,     A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       image.shape[0], image.shape[1]
  # dst image height and width
  __magic_name__				     =       height - k_size + 1
  __magic_name__				     =       width - k_size + 1

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  __magic_name__				     =       zeros((dst_height * dst_width, k_size * k_size)					)
  __magic_name__				     =       0
  for i, j in product(range(A_					),     range(A_					)					):
    __magic_name__				     =       ravel(image[i : i + k_size, j : j + k_size]					)
    __magic_name__				     =       window
    row += 1

  #  turn the kernel into shape(k*k, 1)
  __magic_name__				     =       gen_gaussian_kernel(A_,     A_					)
  __magic_name__				     =       ravel(A_					)

  # reshape and get the dst image
  __magic_name__				     =       dot(A_,     A_					).reshape(A_,     A_					).astype(A_					)

  return dst


if __name__ == "__main__":
    # read original image
    __lowerCAmelCase			:     List[str]           =   imread(R'../image_data/lena.jpg')
    # turn image in gray scale value
    __lowerCAmelCase			:     int           =   cvtColor(img, COLOR_BGR2GRAY)

    # get values with two different mask size
    __lowerCAmelCase			:     Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
    __lowerCAmelCase			:     Union[str, Any]           =   gaussian_filter(gray, 5, sigma=0.8)

    # show result images
    imshow('gaussian filter with 3x3 mask', gaussianaxa)
    imshow('gaussian filter with 5x5 mask', gaussianaxa)
    waitKey()



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			:     Any           =   [
    {'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 a__    (       A_=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=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

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







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       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
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       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"""]					)					)

