


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       assert isinstance(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''keep_in_memory'''							,			[False, True]       )
def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		tmp_path / '''cache'''
       UpperCamelCase    :	Optional[Any]   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              UpperCamelCase    :	Optional[int]   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_							,			keep_in_memory=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_dataset(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


@pytest.mark.parametrize(
    '''features'''							,			[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]							,			)
def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		tmp_path / '''cache'''
       UpperCamelCase    :	Tuple   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCamelCase    :	Optional[int]   	=		features.copy() if features else default_expected_features
       UpperCamelCase    :	Optional[int]   	=		(
           Features({feature: Value(SCREAMING_SNAKE_CASE_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       UpperCamelCase    :	Optional[int]   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			features=SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_dataset(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


@pytest.mark.parametrize(
    '''features'''							,			[
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]							,			)
def      a	(							SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		tmp_path / '''cache'''
       UpperCamelCase    :	List[Any]   	=		{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
       UpperCamelCase    :	Dict   	=		features.copy() if features else default_expected_features
       UpperCamelCase    :	Optional[int]   	=		(
           Features({feature: Value(SCREAMING_SNAKE_CASE_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       UpperCamelCase    :	Optional[Any]   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			features=SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       assert isinstance(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_3", "col_1", "col_2"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype


def      a	(							SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
       UpperCamelCase    :	Union[str, Any]   	=		features.copy()
       UpperCamelCase    :	List[str]   	=		(
           Features({feature: Value(SCREAMING_SNAKE_CASE_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       UpperCamelCase    :	Dict   	=		tmp_path / '''cache'''
       UpperCamelCase    :	Tuple   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			features=SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       assert isinstance(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_2", "col_3", "col_1"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''split'''							,			[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		tmp_path / '''cache'''
       UpperCamelCase    :	List[Any]   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCamelCase    :	int   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_							,			split=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_dataset(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       assert dataset.split == split if split else "train"


@pytest.mark.parametrize('''path_type'''							,			[str, list]       )
def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       if issubclass(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	List[Any]   	=		jsonl_path
       elif issubclass(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	Optional[Any]   	=		[jsonl_path]
       UpperCamelCase    :	Optional[Any]   	=		tmp_path / '''cache'''
       UpperCamelCase    :	str   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCamelCase    :	Any   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_dataset(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		List[Any]=("train",)       ):






       """simple docstring"""





       assert isinstance(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       for split in splits:
              UpperCamelCase    :	List[Any]   	=		dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('''keep_in_memory'''							,			[False, True]       )
def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		tmp_path / '''cache'''
       UpperCamelCase    :	Optional[int]   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              UpperCamelCase    :	str   	=		JsonDatasetReader({'''train''': jsonl_path}							,			cache_dir=SCREAMING_SNAKE_CASE_							,			keep_in_memory=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_datasetdict(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


@pytest.mark.parametrize(
    '''features'''							,			[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]							,			)
def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		tmp_path / '''cache'''
       UpperCamelCase    :	Union[str, Any]   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCamelCase    :	List[str]   	=		features.copy() if features else default_expected_features
       UpperCamelCase    :	Union[str, Any]   	=		(
           Features({feature: Value(SCREAMING_SNAKE_CASE_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       UpperCamelCase    :	Union[str, Any]   	=		JsonDatasetReader({'''train''': jsonl_path}							,			features=SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_datasetdict(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


@pytest.mark.parametrize('''split'''							,			[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def      a	(							SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       if split:
              UpperCamelCase    :	Optional[int]   	=		{split: jsonl_path}
       else:
              UpperCamelCase    :	Tuple   	=		'''train'''
              UpperCamelCase    :	int   	=		{'''train''': jsonl_path, '''test''': jsonl_path}
       UpperCamelCase    :	Tuple   	=		tmp_path / '''cache'''
       UpperCamelCase    :	int   	=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       UpperCamelCase    :	Optional[Any]   	=		JsonDatasetReader(SCREAMING_SNAKE_CASE_							,			cache_dir=SCREAMING_SNAKE_CASE_       ).read()
       _check_json_datasetdict(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





       return json.load(SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       return [json.loads(SCREAMING_SNAKE_CASE_       ) for line in buffer]



class    UpperCAmelCase_	:
       '''simple docstring'''






       @pytest.mark.parametrize('''lines, load_json_function''' ,					[(True, load_json_lines), (False, load_json)]						)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with io.BytesIO() as buffer:
                     JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					lines=__SCREAMING_SNAKE_CASE						).write()
                     buffer.seek(0						)
                     UpperCamelCase    :	Tuple   	=		load_json_function(__SCREAMING_SNAKE_CASE						)
              assert isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              assert isinstance(exported_content[0] ,					__SCREAMING_SNAKE_CASE						)
              assert len(__SCREAMING_SNAKE_CASE						) == 10






       @pytest.mark.parametrize(
           '''orient, container, keys, len_at''' ,					[
               ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
               ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
               ('''index''', dict, set('''0123456789'''						), None),
               ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
               ('''values''', list, None, None),
               ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
           ] ,					)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with io.BytesIO() as buffer:
                     JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					lines=__SCREAMING_SNAKE_CASE ,					orient=__SCREAMING_SNAKE_CASE						).write()
                     buffer.seek(0						)
                     UpperCamelCase    :	Optional[int]   	=		load_json(__SCREAMING_SNAKE_CASE						)
              assert isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              if keys:
                     if container is dict:
                            assert exported_content.keys() == keys
                     else:
                            assert exported_content[0].keys() == keys
              else:
                     assert not hasattr(__SCREAMING_SNAKE_CASE ,					'''keys'''						) and not hasattr(exported_content[0] ,					'''keys'''						)
              if len_at:
                     assert len(exported_content[len_at]						) == 10
              else:
                     assert len(__SCREAMING_SNAKE_CASE						) == 10






       @pytest.mark.parametrize('''lines, load_json_function''' ,					[(True, load_json_lines), (False, load_json)]						)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with io.BytesIO() as buffer:
                     JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					lines=__SCREAMING_SNAKE_CASE ,					num_proc=2						).write()
                     buffer.seek(0						)
                     UpperCamelCase    :	str   	=		load_json_function(__SCREAMING_SNAKE_CASE						)
              assert isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              assert isinstance(exported_content[0] ,					__SCREAMING_SNAKE_CASE						)
              assert len(__SCREAMING_SNAKE_CASE						) == 10






       @pytest.mark.parametrize(
           '''orient, container, keys, len_at''' ,					[
               ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
               ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
               ('''index''', dict, set('''0123456789'''						), None),
               ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
               ('''values''', list, None, None),
               ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
           ] ,					)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with io.BytesIO() as buffer:
                     JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					lines=__SCREAMING_SNAKE_CASE ,					orient=__SCREAMING_SNAKE_CASE ,					num_proc=2						).write()
                     buffer.seek(0						)
                     UpperCamelCase    :	List[str]   	=		load_json(__SCREAMING_SNAKE_CASE						)
              assert isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              if keys:
                     if container is dict:
                            assert exported_content.keys() == keys
                     else:
                            assert exported_content[0].keys() == keys
              else:
                     assert not hasattr(__SCREAMING_SNAKE_CASE ,					'''keys'''						) and not hasattr(exported_content[0] ,					'''keys'''						)
              if len_at:
                     assert len(exported_content[len_at]						) == 10
              else:
                     assert len(__SCREAMING_SNAKE_CASE						) == 10






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with pytest.raises(__SCREAMING_SNAKE_CASE						):
                     with io.BytesIO() as buffer:
                            JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					num_proc=0						)






       @pytest.mark.parametrize('''compression, extension''' ,					[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]						)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		tmp_path_factory.mktemp('''data'''						) / f"""test.json.{extension}"""
              UpperCamelCase    :	Any   	=		str(shared_datadir / f"""test_file.json.{extension}"""						)
              JsonDatasetWriter(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					compression=__SCREAMING_SNAKE_CASE						).write()

              with fsspec.open(__SCREAMING_SNAKE_CASE ,					'''rb''' ,					compression='''infer'''						) as f:
                     UpperCamelCase    :	Any   	=		f.read()
              with fsspec.open(__SCREAMING_SNAKE_CASE ,					'''rb''' ,					compression='''infer'''						) as f:
                     UpperCamelCase    :	Dict   	=		f.read()
              assert exported_content == original_content





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	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

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

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[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()

