





'''simple docstring'''

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"		,							[False, True]							)
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								lowercase         = TextDatasetReader(lowerCAmelCase_		,							cache_dir=lowerCAmelCase_		,							keep_in_memory=lowerCAmelCase_							).read()
				_check_text_dataset(lowerCAmelCase_		,							lowerCAmelCase_							)







@pytest.mark.parametrize(
    "features"		,							[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]		,							)
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				lowercase         = features.copy() if features else default_expected_features
				lowercase         = (
				    Features({feature: Value(lowerCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
				)
				lowercase         = TextDatasetReader(lowerCAmelCase_		,							features=lowerCAmelCase_		,							cache_dir=lowerCAmelCase_							).read()
				_check_text_dataset(lowerCAmelCase_		,							lowerCAmelCase_							)







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

				"""simple docstring"""






				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				lowercase         = TextDatasetReader(lowerCAmelCase_		,							cache_dir=lowerCAmelCase_		,							split=lowerCAmelCase_							).read()
				_check_text_dataset(lowerCAmelCase_		,							lowerCAmelCase_							)
				assert dataset.split == split if split else "train"







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

				"""simple docstring"""






				if issubclass(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = text_path
				elif issubclass(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = [text_path]
				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				lowercase         = TextDatasetReader(lowerCAmelCase_		,							cache_dir=lowerCAmelCase_							).read()
				_check_text_dataset(lowerCAmelCase_		,							lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=("train",)							):

				"""simple docstring"""






				assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							)
				for split in splits:
								lowercase         = dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 1
								assert dataset.column_names == ["text"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"		,							[False, True]							)
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								lowercase         = TextDatasetReader({"train": text_path}		,							cache_dir=lowerCAmelCase_		,							keep_in_memory=lowerCAmelCase_							).read()
				_check_text_datasetdict(lowerCAmelCase_		,							lowerCAmelCase_							)







@pytest.mark.parametrize(
    "features"		,							[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]		,							)
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = tmp_path / "cache"
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				lowercase         = {"text": "string"}
				lowercase         = features.copy() if features else default_expected_features
				lowercase         = (
				    Features({feature: Value(lowerCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
				)
				lowercase         = TextDatasetReader({"train": text_path}		,							features=lowerCAmelCase_		,							cache_dir=lowerCAmelCase_							).read()
				_check_text_datasetdict(lowerCAmelCase_		,							lowerCAmelCase_							)







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

				"""simple docstring"""






				if split:
								lowercase         = {split: text_path}
				else:
								lowercase         = "train"
								lowercase         = {"train": text_path, "test": text_path}
				lowercase         = tmp_path / "cache"
				lowercase         = {"text": "string"}
				lowercase         = TextDatasetReader(lowerCAmelCase_		,							cache_dir=lowerCAmelCase_							).read()
				_check_text_datasetdict(lowerCAmelCase_		,							lowerCAmelCase_		,							splits=list(path.keys()							)							)
				assert all(dataset[split].split == split for split in path.keys()							)











'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




