


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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Any:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =features.copy() if features else default_expected_features
 _lowercase							      =(
     Features({feature: Value(__snake_case						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 if issubclass(__snake_case     ,						__snake_case						):
  _lowercase							      =parquet_path
 elif issubclass(__snake_case     ,						__snake_case						):
  _lowercase							      =[parquet_path]
 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =ParquetDatasetReader(__snake_case     ,						cache_dir=__snake_case						).read()
 _check_parquet_dataset(__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=("train",)						)     ->			Tuple:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						)
 for split in splits:
  _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =features.copy() if features else default_expected_features
 _lowercase							      =(
     Features({feature: Value(__snake_case						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 if split:
  _lowercase							      ={split: parquet_path}
 else:
  _lowercase							      ='''train'''
  _lowercase							      ={'''train''': parquet_path, '''test''': parquet_path}
 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =ParquetDatasetWriter(__snake_case     ,						tmp_path / '''foo.parquet'''						)
 assert writer.write() > 0
 _lowercase							      =pq.ParquetFile(tmp_path / '''foo.parquet'''						)
 _lowercase							      =pf.read()
 assert dataset.data.table == output_table
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =str(shared_datadir / '''test_image_rgb.jpg'''						)
 _lowercase							      ={'''image''': [image_path]}
 _lowercase							      =Features({'''image''': Image()}						)
 _lowercase							      =Dataset.from_dict(__snake_case     ,						features=__snake_case						)
 _lowercase							      =ParquetDatasetWriter(__snake_case     ,						tmp_path / '''foo.parquet'''						)
 assert writer.write() > 0

 _lowercase							      =Dataset.from_parquet(str(tmp_path / '''foo.parquet'''						)						)
 assert dataset.features == reloaded_dataset.features

 _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 assert get_writer_batch_size(__snake_case						) == expected









from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')