

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Union[str, Any] )   ->       Dict:
				assert isinstance(lowerCAmelCase  ,      lowerCAmelCase )
				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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Dict  ,      lowerCAmelCase:  str )   ->       Dict:
				_UpperCAmelCase       :		str 		=    tmp_path / "cache"
				_UpperCAmelCase       :		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():
								_UpperCAmelCase       :		Tuple 		=    ParquetDatasetReader(lowerCAmelCase  ,      cache_dir=lowerCAmelCase  ,      keep_in_memory=lowerCAmelCase ).read()
				_check_parquet_dataset(lowerCAmelCase  ,      lowerCAmelCase )

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

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

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  int=("train",) )   ->       Optional[Any]:
				assert isinstance(lowerCAmelCase  ,      lowerCAmelCase )
				for split in splits:
								_UpperCAmelCase       :		Optional[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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  int )   ->       Tuple:
				_UpperCAmelCase       :		Any 		=    tmp_path / "cache"
				_UpperCAmelCase       :		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(
								    {"train": parquet_path}  ,      cache_dir=lowerCAmelCase  ,      keep_in_memory=lowerCAmelCase ).read()
				_check_parquet_datasetdict(lowerCAmelCase  ,      lowerCAmelCase )

@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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  Tuple )   ->       Union[str, Any]:
				_UpperCAmelCase       :		str 		=    tmp_path / "cache"
				_UpperCAmelCase       :		List[Any] 		=    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
				_UpperCAmelCase       :		int 		=    features.copy() if features else default_expected_features
				_UpperCAmelCase       :		Optional[Any] 		=    (
				    Features({feature: Value(lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
				)
				_UpperCAmelCase       :		str 		=    ParquetDatasetReader({"train": parquet_path}  ,      features=lowerCAmelCase  ,      cache_dir=lowerCAmelCase ).read()
				_check_parquet_datasetdict(lowerCAmelCase  ,      lowerCAmelCase )

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

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

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

				_UpperCAmelCase       :		Tuple 		=    Dataset.from_parquet(str(tmp_path / "foo.parquet" ) )
				assert dataset.features == reloaded_dataset.features

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


