"""simple docstring"""





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						int			  =    test_file.split(os.path.sep  )
							if components[0:2] != ["tests", "models"]:
														raise ValueError(
														    """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
														    F"""{test_file} instead."""  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    components[-1]
							if not test_fn.endswith("""py"""  ):
														raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""  )
							if not test_fn.startswith("""test_modeling_"""  ):
														raise ValueError(
														    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""  )

							_SCREAMING_SNAKE_CASE		:						str			  =    components[:-1] + [test_fn.replace(""".py"""     ,	""""""  )]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    """.""".join(__SCREAMING_SNAKE_CASE  )

							return test_module_path



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						str			  =    get_module_path(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    importlib.import_module(__SCREAMING_SNAKE_CASE  )

							return test_module



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    []
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    get_test_module(__SCREAMING_SNAKE_CASE  )
							for attr in dir(__SCREAMING_SNAKE_CASE  ):
														if attr.endswith("""ModelTester"""  ):
																					tester_classes.append(getattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

    # sort with class names
							return sorted(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE  : x.__name__  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_test_module(__SCREAMING_SNAKE_CASE  )
							for attr in dir(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Dict			  =    getattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
														# `all_model_classes` is not empty (which also excludes other special classes).
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    getattr(__SCREAMING_SNAKE_CASE     ,	"""all_model_classes"""     ,	[]  )
														if len(__SCREAMING_SNAKE_CASE  ) > 0:
																					test_classes.append(__SCREAMING_SNAKE_CASE  )

    # sort with class names
							return sorted(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE  : x.__name__  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						str			  =    get_test_classes(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    set()
							for test_class in test_classes:
														model_classes.update(test_class.all_model_classes  )

							# sort with class names
							return sorted(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE  : x.__name__  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    test_class()
							if hasattr(__SCREAMING_SNAKE_CASE     ,	"""setUp"""  ):
														test.setUp()

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    None
							if hasattr(__SCREAMING_SNAKE_CASE     ,	"""model_tester"""  ):
														# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
														if test.model_tester is not None:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    test.model_tester.__class__

							return model_tester



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_test_classes(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
							for test_class in test_classes:
														if model_class in test_class.all_model_classes:
																					target_test_classes.append(__SCREAMING_SNAKE_CASE  )

    # sort with class names
							return sorted(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE  : x.__name__  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						int			  =    get_test_classes_for_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for test_class in test_classes:
														_SCREAMING_SNAKE_CASE		:						str			  =    get_model_tester_from_test_class(__SCREAMING_SNAKE_CASE  )
														if tester_class is not None:
																					tester_classes.append(__SCREAMING_SNAKE_CASE  )

    # sort with class names
							return sorted(__SCREAMING_SNAKE_CASE     ,	key=lambda __SCREAMING_SNAKE_CASE  : x.__name__  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_test_classes(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    {test_class: get_model_tester_from_test_class(__SCREAMING_SNAKE_CASE  ) for test_class in test_classes}
							return test_tester_mapping



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Any:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_model_classes(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    {
							    model_class: get_test_classes_for_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for model_class in model_classes
							}
							return model_test_mapping



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Any:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    get_model_classes(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {
							    model_class: get_tester_classes_for_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for model_class in model_classes
							}
							return model_to_tester_mapping



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														return o
							elif isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														return o.__name__
							elif isinstance(__SCREAMING_SNAKE_CASE     ,	(list, tuple)  ):
														return [to_json(__SCREAMING_SNAKE_CASE  ) for x in o]
							elif isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														return {to_json(__SCREAMING_SNAKE_CASE  ): to_json(__SCREAMING_SNAKE_CASE  ) for k, v in o.items()}
							else:
														return o



"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						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():
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						int			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						str			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						int			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	split=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""     ,	[str, list]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							if issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    parquet_path
							elif issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [parquet_path]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						str			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



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



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    pq.ParquetFile(tmp_path / """foo.parquet"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    pf.read()
							assert dataset.data.table == output_table



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

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

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(str(tmp_path / """foo.parquet"""  )     ,	streaming=__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							assert get_writer_batch_size(__SCREAMING_SNAKE_CASE  ) == expected


