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


def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=("train",)    )					->							Optional[int]:
       assert isinstance(__snake_case	,					__snake_case    )
       for split in splits:
              lowercase       :							List[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     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			List[str]	,					__snake_case							:			str    )					->							Optional[Any]:
       lowercase       :							Optional[int]									=				tmp_path / "cache"
       lowercase       :							str									=				{"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       :							Dict									=				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     __magic_name__      ( __snake_case							:			List[Any]	,					__snake_case							:			Optional[Any]	,					__snake_case							:			str    )					->							Optional[Any]:
       lowercase       :							Tuple									=				tmp_path / "cache"
       lowercase       :							Optional[int]									=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
       lowercase       :							Union[str, Any]									=				features.copy() if features else default_expected_features
       lowercase       :							int									=				(
           Features({feature: Value(__snake_case    ) for feature, dtype in features.items()}    ) if features is not None else None
       )
       lowercase       :							Union[str, Any]									=				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     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			List[Any]	,					__snake_case							:			List[Any]    )					->							str:
       if split:
              lowercase       :							Optional[Any]									=				{split: parquet_path}
       else:
              lowercase       :							int									=				"train"
              lowercase       :							Optional[Any]									=				{"train": parquet_path, "test": parquet_path}
       lowercase       :							Dict									=				tmp_path / "cache"
       lowercase       :							Optional[Any]									=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
       lowercase       :							List[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     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			Dict    )					->							str:
       lowercase       :							int									=				ParquetDatasetWriter(__snake_case	,					tmp_path / "foo.parquet"    )
       assert writer.write() > 0
       lowercase       :							Tuple									=				pq.ParquetFile(tmp_path / "foo.parquet"    )
       lowercase       :							List[Any]									=				pf.read()
       assert dataset.data.table == output_table


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

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

       lowercase       :							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     __magic_name__      ( __snake_case							:			List[Any]	,					__snake_case							:			List[str]    )					->							Union[str, Any]:
       assert get_writer_batch_size(__snake_case    ) == expected




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

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

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


