



"""simple docstring"""






from manim import *



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= Rectangle(height=0.5 ,    width=0.5)
				__lowerCAmelCase   :						str				= Rectangle(height=0.46 ,    width=0.46).set_stroke(width=0)

				__lowerCAmelCase   :						Dict				= [mem.copy() for i in range(6)]
				__lowerCAmelCase   :						Union[str, Any]				= [mem.copy() for i in range(6)]
				__lowerCAmelCase   :						Any				= VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0)
				__lowerCAmelCase   :						Optional[int]				= VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0)
				__lowerCAmelCase   :						Dict				= VGroup(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0)
				__lowerCAmelCase   :						int				= Text("CPU" ,    font_size=24)
				__lowerCAmelCase   :						Optional[Any]				= Group(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0.5 ,    aligned_edge=_SCREAMING_SNAKE_CASE)
				cpu.move_to([-2.5, -0.5, 0])
				self.add(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= [mem.copy() for i in range(1)]
				__lowerCAmelCase   :						List[Any]				= VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0)
				__lowerCAmelCase   :						Optional[int]				= Text("GPU" ,    font_size=24)
				__lowerCAmelCase   :						Optional[int]				= Group(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0.5 ,    aligned_edge=_SCREAMING_SNAKE_CASE)
				gpu.align_to(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				gpu.set_x(gpu.get_x() - 1)

				self.add(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= [mem.copy() for i in range(6)]
				__lowerCAmelCase   :						str				= VGroup(*_SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0)

				__lowerCAmelCase   :						Dict				= Text("Model" ,    font_size=24)
				__lowerCAmelCase   :						Any				= Group(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE).arrange(_SCREAMING_SNAKE_CASE ,    buff=0.5 ,    aligned_edge=_SCREAMING_SNAKE_CASE)
				model.move_to([3, -1.0, 0])

				self.play(
				    Create(_SCREAMING_SNAKE_CASE ,    run_time=1) ,    Create(_SCREAMING_SNAKE_CASE ,    run_time=1) ,    Create(_SCREAMING_SNAKE_CASE ,    run_time=1) ,    )

				__lowerCAmelCase   :						List[Any]				= MarkupText(
				    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" ,    font_size=24 ,    )

				__lowerCAmelCase   :						int				= Square(side_length=2.2)
				key.move_to([-5, 2, 0])

				__lowerCAmelCase   :						Union[str, Any]				= MarkupText(
				    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,    font_size=18 ,    )

				key_text.move_to([-5, 2.4, 0])

				step_a.move_to([2, 2, 0])
				self.play(Write(_SCREAMING_SNAKE_CASE ,    run_time=2.5) ,    Write(_SCREAMING_SNAKE_CASE) ,    Write(_SCREAMING_SNAKE_CASE))

				self.add(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= []
				__lowerCAmelCase   :						int				= []
				__lowerCAmelCase   :						Optional[Any]				= []
				for i, rect in enumerate(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Optional[int]				= Rectangle(height=0.46 ,    width=0.46).set_stroke(width=0.0).set_fill(_SCREAMING_SNAKE_CASE ,    opacity=0.7)
							cpu_target.move_to(_SCREAMING_SNAKE_CASE)
							cpu_target.generate_target()
							__lowerCAmelCase   :						Optional[int]				= 0.46 / 4
							__lowerCAmelCase   :						Union[str, Any]				= 0.46 / 3

							if i == 0:
										cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT) ,    buff=0.02 ,    direction=_SCREAMING_SNAKE_CASE)
										cpu_target.target.set_x(cpu_target.target.get_x() + 0.1)
							elif i == 3:
										cpu_target.target.next_to(cpu_targs[0].target ,    direction=_SCREAMING_SNAKE_CASE ,    buff=0.0)
							else:
										cpu_target.target.next_to(cpu_targs[i - 1].target ,    direction=_SCREAMING_SNAKE_CASE ,    buff=0.0)
							cpu_targs.append(_SCREAMING_SNAKE_CASE)

							first_animations.append(rect.animate(run_time=0.5).set_stroke(_SCREAMING_SNAKE_CASE))
							second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE ,    run_time=1.5))

				self.play(*_SCREAMING_SNAKE_CASE)
				self.play(*_SCREAMING_SNAKE_CASE)

				self.wait()




"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			assert isinstance(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Dict				= tmp_path / "cache"
			__lowerCAmelCase   :						Union[str, 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():
						__lowerCAmelCase   :						Optional[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						Dict				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						Optional[int]				= ParquetDatasetReader(__snake_case      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,split=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)
			assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"      ,[str, list]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			if issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= parquet_path
			elif issubclass(__snake_case      ,__snake_case			):
						__lowerCAmelCase   :						List[Any]				= [parquet_path]
			__lowerCAmelCase   :						str				= tmp_path / "cache"
			__lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Tuple				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_dataset(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=("train",)			)		->     int:
			assert isinstance(__snake_case      ,__snake_case			)
			for split in splits:
						__lowerCAmelCase   :						str				= 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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			__lowerCAmelCase   :						Any				= tmp_path / "cache"
			__lowerCAmelCase   :						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():
						__lowerCAmelCase   :						str				= ParquetDatasetReader(
						    {"train": parquet_path}      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						List[Any]				= tmp_path / "cache"
			__lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						str				= features.copy() if features else default_expected_features
			__lowerCAmelCase   :						str				= (
			    Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
			)
			__lowerCAmelCase   :						str				= ParquetDatasetReader({"train": parquet_path}      ,features=__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
			if split:
						__lowerCAmelCase   :						Optional[int]				= {split: parquet_path}
			else:
						__lowerCAmelCase   :						str				= "train"
						__lowerCAmelCase   :						Optional[Any]				= {"train": parquet_path, "test": parquet_path}
			__lowerCAmelCase   :						Tuple				= tmp_path / "cache"
			__lowerCAmelCase   :						int				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			__lowerCAmelCase   :						Union[str, Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
			_check_parquet_datasetdict(__snake_case      ,__snake_case      ,splits=list(path.keys()			)			)
			assert all(dataset[split].split == split for split in path.keys()			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						int				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0
			__lowerCAmelCase   :						str				= pq.ParquetFile(tmp_path / "foo.parquet"			)
			__lowerCAmelCase   :						int				= pf.read()
			assert dataset.data.table == output_table


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= str(shared_datadir / "test_image_rgb.jpg"			)
			__lowerCAmelCase   :						Any				= {"image": [image_path]}
			__lowerCAmelCase   :						List[Any]				= Features({"image": Image()}			)
			__lowerCAmelCase   :						Tuple				= Dataset.from_dict(__snake_case      ,features=__snake_case			)
			__lowerCAmelCase   :						Dict				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
			assert writer.write() > 0

			__lowerCAmelCase   :						List[Any]				= Dataset.from_parquet(str(tmp_path / "foo.parquet"			)			)
			assert dataset.features == reloaded_dataset.features

			__lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(str(tmp_path / "foo.parquet"			)      ,streaming=__snake_case			).read()
			assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"      ,[
        (Features({"foo": Value("int32"			)}			), None),
        (Features({"image": Image(), "foo": Value("int32"			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected