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      ):
    '''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 lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:Optional[Any]         =						tmp_path / '''cache'''
    __UpperCamelCase	:List[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]         =						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      ):
    '''simple docstring'''







    __UpperCamelCase	:Union[str, Any]         =						tmp_path / '''cache'''
    __UpperCamelCase	:int         =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase	:Optional[Any]         =						features.copy() if features else default_expected_features
    __UpperCamelCase	:List[Any]         =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE      ) for feature, dtype in features.items()}      ) if features is not None else None
    )
    __UpperCamelCase	: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      ):
    '''simple docstring'''







    __UpperCamelCase	:List[str]         =						tmp_path / '''cache'''
    __UpperCamelCase	:int         =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase	:Union[str, Any]         =						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      ):
    '''simple docstring'''







    if issubclass(SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
        __UpperCamelCase	:Tuple         =						parquet_path
    elif issubclass(SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
        __UpperCamelCase	:Tuple         =						[parquet_path]
    __UpperCamelCase	:Tuple         =						tmp_path / '''cache'''
    __UpperCamelCase	:int         =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase	:Tuple         =						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",)      ):
    '''simple docstring'''







    assert isinstance(SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      )
    for split in splits:
        __UpperCamelCase	:Union[str, 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 lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:List[Any]         =						tmp_path / '''cache'''
    __UpperCamelCase	:Dict         =						{'''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	:Union[str, Any]         =						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      ):
    '''simple docstring'''







    __UpperCamelCase	:int         =						tmp_path / '''cache'''
    __UpperCamelCase	:Union[str, Any]         =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase	:Optional[int]         =						features.copy() if features else default_expected_features
    __UpperCamelCase	:int         =						(
        Features({feature: Value(SCREAMING_SNAKE_CASE      ) for feature, dtype in features.items()}      ) if features is not None else None
    )
    __UpperCamelCase	:Optional[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      ):
    '''simple docstring'''







    if split:
        __UpperCamelCase	:Tuple         =						{split: parquet_path}
    else:
        __UpperCamelCase	:List[str]         =						'''train'''
        __UpperCamelCase	:Tuple         =						{'''train''': parquet_path, '''test''': parquet_path}
    __UpperCamelCase	:Any         =						tmp_path / '''cache'''
    __UpperCamelCase	:List[str]         =						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    __UpperCamelCase	:List[str]         =						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      ):
    '''simple docstring'''







    __UpperCamelCase	:List[str]         =						ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,     tmp_path / '''foo.parquet'''      )
    assert writer.write() > 0
    __UpperCamelCase	:List[Any]         =						pq.ParquetFile(tmp_path / '''foo.parquet'''      )
    __UpperCamelCase	:List[Any]         =						pf.read()
    assert dataset.data.table == output_table



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:Tuple         =						str(shared_datadir / '''test_image_rgb.jpg'''      )
    __UpperCamelCase	:Dict         =						{'''image''': [image_path]}
    __UpperCamelCase	:List[Any]         =						Features({'''image''': Image()}      )
    __UpperCamelCase	:Union[str, Any]         =						Dataset.from_dict(SCREAMING_SNAKE_CASE    ,     features=SCREAMING_SNAKE_CASE      )
    __UpperCamelCase	:Dict         =						ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,     tmp_path / '''foo.parquet'''      )
    assert writer.write() > 0

    __UpperCamelCase	:Optional[Any]         =						Dataset.from_parquet(str(tmp_path / '''foo.parquet'''      )      )
    assert dataset.features == reloaded_dataset.features

    __UpperCamelCase	:Optional[int]         =						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      ):
    '''simple docstring'''







    assert get_writer_batch_size(SCREAMING_SNAKE_CASE      ) == expected







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:    Union[str, Any]           = {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GitForCausalLM',
						    'GitModel',
						    'GitPreTrainedModel',
						    'GitVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
						from .processing_git import GitProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_git import (
												    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GitForCausalLM,
												    GitModel,
												    GitPreTrainedModel,
												    GitVisionModel,
												)

else:
						import sys

						lowerCAmelCase					:    Optional[Any]           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




