





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					assert isinstance(a					,						a  )
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'					,						[False, True]  )
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      str:
					__A	:						int       =				tmp_path / 'cache'
					__A	:						Optional[Any]       =				{'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__A	:						List[Any]       =				TextDatasetReader(a					,						cache_dir=a					,						keep_in_memory=a  ).read()
					_check_text_dataset(a					,						a  )







@pytest.mark.parametrize(
    'features'					,						[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]					,						)
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      int:
					__A	:						Any       =				tmp_path / 'cache'
					__A	:						List[str]       =				{'text': 'string'}
					__A	:						List[Any]       =				features.copy() if features else default_expected_features
					__A	:						List[str]       =				(
					    Features({feature: Value(a  ) for feature, dtype in features.items()}  ) if features is not None else None
					)
					__A	:						Optional[int]       =				TextDatasetReader(a					,						features=a					,						cache_dir=a  ).read()
					_check_text_dataset(a					,						a  )







@pytest.mark.parametrize('split'					,						[None, NamedSplit('train'  ), 'train', 'test']  )
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Tuple:
					__A	:						Tuple       =				tmp_path / 'cache'
					__A	:						Union[str, Any]       =				{'text': 'string'}
					__A	:						Optional[int]       =				TextDatasetReader(a					,						cache_dir=a					,						split=a  ).read()
					_check_text_dataset(a					,						a  )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'					,						[str, list]  )
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      List[Any]:
					if issubclass(a					,						a  ):
										__A	:						Tuple       =				text_path
					elif issubclass(a					,						a  ):
										__A	:						Optional[Any]       =				[text_path]
					__A	:						List[Any]       =				tmp_path / 'cache'
					__A	:						Optional[int]       =				{'text': 'string'}
					__A	:						Tuple       =				TextDatasetReader(a					,						cache_dir=a  ).read()
					_check_text_dataset(a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a=("train",)  )      ->      Tuple:
					assert isinstance(a					,						a  )
					for split in splits:
										__A	:						Optional[Any]       =				dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'					,						[False, True]  )
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      List[str]:
					__A	:						Tuple       =				tmp_path / 'cache'
					__A	:						List[Any]       =				{'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__A	:						Tuple       =				TextDatasetReader({'train': text_path}					,						cache_dir=a					,						keep_in_memory=a  ).read()
					_check_text_datasetdict(a					,						a  )







@pytest.mark.parametrize(
    'features'					,						[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]					,						)
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      int:
					__A	:						Optional[Any]       =				tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__A	:						int       =				{'text': 'string'}
					__A	:						str       =				features.copy() if features else default_expected_features
					__A	:						str       =				(
					    Features({feature: Value(a  ) for feature, dtype in features.items()}  ) if features is not None else None
					)
					__A	:						int       =				TextDatasetReader({'train': text_path}					,						features=a					,						cache_dir=a  ).read()
					_check_text_datasetdict(a					,						a  )







@pytest.mark.parametrize('split'					,						[None, NamedSplit('train'  ), 'train', 'test']  )
def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Any:
					if split:
										__A	:						List[str]       =				{split: text_path}
					else:
										__A	:						List[Any]       =				'train'
										__A	:						int       =				{'train': text_path, 'test': text_path}
					__A	:						Optional[Any]       =				tmp_path / 'cache'
					__A	:						List[str]       =				{'text': 'string'}
					__A	:						int       =				TextDatasetReader(a					,						cache_dir=a  ).read()
					_check_text_datasetdict(a					,						a					,						splits=list(path.keys()  )  )
					assert all(dataset[split].split == split for split in path.keys()  )











import colorsys

from PIL import Image  # type: ignore







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					__A	:						List[str]       =				x
					__A	:						str       =				y
					for step in range(a  ):  # noqa: B007
										__A	:						Union[str, Any]       =				a * a - b * b + x
										__A	:						Optional[int]       =				2 * a * b + y
										__A	:						List[str]       =				a_new

										# divergence happens for all complex number with an absolute value
										# greater than 4
										if a * a + b * b > 4:
															break
					return step / (max_step - 1)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return (2_55, 2_55, 2_55)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 2_55  ) for i in colorsys.hsv_to_rgb(a					,						1					,						1  )  )







def  _SCREAMING_SNAKE_CASE				(   a = 8_00					,						a = 6_00					,						a = -0.6					,						a = 0					,						a = 3.2					,						a = 50					,						a = True					,						)      ->      Image.Image:
					__A	:						str       =				Image.new('RGB'					,						(image_width, image_height)  )
					__A	:						Dict       =				img.load()

					# loop through the image-coordinates
					for image_x in range(a  ):
										for image_y in range(a  ):
															# determine the figure-coordinates based on the image-coordinates
															__A	:						Dict       =				figure_width / image_width * image_height
															__A	:						Union[str, Any]       =				figure_center_x + (image_x / image_width - 0.5) * figure_width
															__A	:						Optional[Any]       =				figure_center_y + (image_y / image_height - 0.5) * figure_height

															__A	:						Union[str, Any]       =				get_distance(a					,						a					,						a  )

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				__A	:						Optional[Any]       =				get_color_coded_rgb(a  )
															else:
																				__A	:						Dict       =				get_black_and_white_rgb(a  )

					return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				UpperCAmelCase						:   str			  =     get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()




