



"""simple docstring"""






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwiftFormerForImageClassification, SwiftFormerModel
	from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class     A__     :




	'''simple docstring'''





	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	int=13 ,    _SCREAMING_SNAKE_CASE:	int=3 ,    _SCREAMING_SNAKE_CASE:	List[str]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=True ,    _SCREAMING_SNAKE_CASE:	List[Any]=0.1 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.1 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=224 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=1000 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=[3, 3, 6, 4] ,    _SCREAMING_SNAKE_CASE:	Dict=[48, 56, 112, 220] ,    )       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= parent
				__lowerCAmelCase   :						int				= batch_size
				__lowerCAmelCase   :						Optional[Any]				= num_channels
				__lowerCAmelCase   :						Any				= is_training
				__lowerCAmelCase   :						List[str]				= use_labels
				__lowerCAmelCase   :						str				= hidden_dropout_prob
				__lowerCAmelCase   :						List[str]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						List[Any]				= num_labels
				__lowerCAmelCase   :						Optional[Any]				= image_size
				__lowerCAmelCase   :						int				= layer_depths
				__lowerCAmelCase   :						Tuple				= embed_dims

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				__lowerCAmelCase   :						List[Any]				= None
				if self.use_labels:
							__lowerCAmelCase   :						Union[str, Any]				= ids_tensor([self.batch_size] ,    self.num_labels)

				__lowerCAmelCase   :						List[str]				= self.get_config()

				return config, pixel_values, labels

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Optional[int]:

				"""simple docstring"""

				return SwiftFormerConfig(
				    depths=self.layer_depths ,    embed_dims=self.embed_dims ,    mlp_ratio=4 ,    downsamples=[True, True, True, True] ,    hidden_act="gelu" ,    num_labels=self.num_labels ,    down_patch_size=3 ,    down_stride=2 ,    down_pad=1 ,    drop_rate=0.0 ,    drop_path_rate=0.0 ,    use_layer_scale=_SCREAMING_SNAKE_CASE ,    layer_scale_init_value=1e-5 ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= SwiftFormerModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase   :						Any				= model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.embed_dims[-1], 7, 7))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.num_labels
				__lowerCAmelCase   :						int				= SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase   :						Optional[int]				= model(_SCREAMING_SNAKE_CASE ,    labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels))

				__lowerCAmelCase   :						int				= SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()

				__lowerCAmelCase   :						Union[str, Any]				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
				__lowerCAmelCase   :						Union[str, Any]				= model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels))







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Dict:

				"""simple docstring"""

				((__lowerCAmelCase)		,			(__lowerCAmelCase)		,			(__lowerCAmelCase))   :						Dict				= self.prepare_config_and_inputs()
				__lowerCAmelCase   :						Tuple				= {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class     A__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE					   =			(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE					   =			False
	SCREAMING_SNAKE_CASE					   =			False
	SCREAMING_SNAKE_CASE					   =			False
	SCREAMING_SNAKE_CASE					   =			False
	SCREAMING_SNAKE_CASE					   =			False

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= SwiftFormerModelTester(self)
				__lowerCAmelCase   :						Dict				= ConfigTester(
				    self ,    config_class=_SCREAMING_SNAKE_CASE ,    has_text_modality=_SCREAMING_SNAKE_CASE ,    hidden_size=37 ,    num_attention_heads=12 ,    num_hidden_layers=12 ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	@unittest.skip(reason="SwiftFormer does not use inputs_embeds")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Optional[int]:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						int				= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase   :						Union[str, Any]				= model_class(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						List[str]				= model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE ,    nn.Linear))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase   :						Dict				= model_class(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Dict				= inspect.signature(model.forward)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__lowerCAmelCase   :						str				= [*signature.parameters.keys()]

							__lowerCAmelCase   :						List[str]				= ["pixel_values"]
							self.assertListEqual(arg_names[:1] ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)

	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> List[str]:

				"""simple docstring"""

				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase   :						Dict				= SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)

	@unittest.skip(reason="SwiftFormer does not output attentions")
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Dict:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

				"""simple docstring"""

				def check_hidden_states_output(_SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]):
							__lowerCAmelCase   :						Union[str, Any]				= model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
										__lowerCAmelCase   :						Any				= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE))

							__lowerCAmelCase   :						Union[str, Any]				= outputs.hidden_states

							__lowerCAmelCase   :						str				= 8
							self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(_SCREAMING_SNAKE_CASE)):
										self.assertEqual(
										    hidden_states[i].shape ,    torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]) ,    )

				__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase   :						Dict				= True
							check_hidden_states_output(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__lowerCAmelCase   :						Tuple				= True

							check_hidden_states_output(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Tuple:

				"""simple docstring"""

				def _config_zero_init(_SCREAMING_SNAKE_CASE:	int):
							__lowerCAmelCase   :						Tuple				= copy.deepcopy(_SCREAMING_SNAKE_CASE)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    1e-10)
										if isinstance(getattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE):
													__lowerCAmelCase   :						int				= _config_zero_init(getattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE))
													setattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							return configs_no_init

				__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= self.model_tester.prepare_config_and_inputs_for_common()

				__lowerCAmelCase   :						List[str]				= _config_zero_init(_SCREAMING_SNAKE_CASE)
				for model_class in self.all_model_classes:
							__lowerCAmelCase   :						Optional[Any]				= model_class(config=_SCREAMING_SNAKE_CASE)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1e9) / 1e9).round().item() ,    [0.0, 1.0] ,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,    )







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

				"""simple docstring"""

				pass






def     _lowercase       (						)		->     Union[str, Any]:
			__lowerCAmelCase   :						Any				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
			return image





@require_torch
@require_vision
class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	@cached_property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> List[Any]:

				"""simple docstring"""

				return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[Any]				= self.default_image_processor
				__lowerCAmelCase   :						int				= prepare_img()
				__lowerCAmelCase   :						List[str]				= image_processor(images=_SCREAMING_SNAKE_CASE ,    return_tensors="pt").to(_SCREAMING_SNAKE_CASE)

				# forward pass
				with torch.no_grad():
							__lowerCAmelCase   :						Dict				= model(**_SCREAMING_SNAKE_CASE)

				# verify the logits
				__lowerCAmelCase   :						List[str]				= torch.Size((1, 1000))
				self.assertEqual(outputs.logits.shape ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[Any]				= torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]).to(_SCREAMING_SNAKE_CASE)
				self.assertTrue(torch.allclose(outputs.logits[0, :3] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))




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


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


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

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

			__lowerCAmelCase   :						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     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected