"""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 					__a   (		A    ,				A      ):






     '''simple docstring'''



     assert isinstance(A    ,				A      )
     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 					__a   (		A    ,				A    ,				A      ):






     '''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(A    ,				cache_dir=A    ,				keep_in_memory=A      ).read()
     _check_parquet_dataset(A    ,				A      )







@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 					__a   (		A    ,				A    ,				A      ):






     '''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(A      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     lowercase__				 =							ParquetDatasetReader(A    ,				features=A    ,				cache_dir=A      ).read()
     _check_parquet_dataset(A    ,				A      )







@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"      ), "train", "test"]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
     lowercase__				 =							ParquetDatasetReader(A    ,				cache_dir=A    ,				split=A      ).read()
     _check_parquet_dataset(A    ,				A      )
     assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"    ,				[str, list]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     if issubclass(A    ,				A      ):
          lowercase__				 =							parquet_path
     elif issubclass(A    ,				A      ):
          lowercase__				 =							[parquet_path]
     lowercase__				 =							tmp_path / "cache"
     lowercase__				 =							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
     lowercase__				 =							ParquetDatasetReader(A    ,				cache_dir=A      ).read()
     _check_parquet_dataset(A    ,				A      )







def 					__a   (		A    ,				A    ,				A=("train",)      ):






     '''simple docstring'''



     assert isinstance(A    ,				A      )
     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 					__a   (		A    ,				A    ,				A      ):






     '''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=A    ,				keep_in_memory=A      ).read()
     _check_parquet_datasetdict(A    ,				A      )







@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 					__a   (		A    ,				A    ,				A      ):






     '''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(A      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     lowercase__				 =							ParquetDatasetReader({"train": parquet_path}    ,				features=A    ,				cache_dir=A      ).read()
     _check_parquet_datasetdict(A    ,				A      )







@pytest.mark.parametrize("split"    ,				[None, NamedSplit("train"      ), "train", "test"]      )
def 					__a   (		A    ,				A    ,				A      ):






     '''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(A    ,				cache_dir=A      ).read()
     _check_parquet_datasetdict(A    ,				A    ,				splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							ParquetDatasetWriter(A    ,				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 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							str(shared_datadir / "test_image_rgb.jpg"      )
     lowercase__				 =							{"image": [image_path]}
     lowercase__				 =							Features({"image": Image()}      )
     lowercase__				 =							Dataset.from_dict(A    ,				features=A      )
     lowercase__				 =							ParquetDatasetWriter(A    ,				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=A      ).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 					__a   (		A    ,				A      ):






     '''simple docstring'''



     assert get_writer_batch_size(A      ) == expected





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




