





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A									=				logging.get_logger(__name__)

__A									=				{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"deberta-v2"

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any]=128_100       , UpperCAmelCase_				:   Optional[int]=1_536       , UpperCAmelCase_				:   Union[str, Any]=24       , UpperCAmelCase_				:   int=24       , UpperCAmelCase_				:   Dict=6_144       , UpperCAmelCase_				:   List[Any]="gelu"       , UpperCAmelCase_				:   Optional[Any]=0.1       , UpperCAmelCase_				:   List[str]=0.1       , UpperCAmelCase_				:   Union[str, Any]=512       , UpperCAmelCase_				:   str=0       , UpperCAmelCase_				:   Tuple=0.02       , UpperCAmelCase_				:   List[Any]=1E-7       , UpperCAmelCase_				:   List[str]=False       , UpperCAmelCase_				:   List[Any]=-1       , UpperCAmelCase_				:   List[Any]=0       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Any=None       , UpperCAmelCase_				:   Union[str, Any]=0       , UpperCAmelCase_				:   List[str]="gelu"       , **UpperCAmelCase_				:   Optional[int]       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =hidden_size
   lowerCamelCase__:      List[str]			  =num_hidden_layers
   lowerCamelCase__:      Dict			  =num_attention_heads
   lowerCamelCase__:      Tuple			  =intermediate_size
   lowerCamelCase__:      Dict			  =hidden_act
   lowerCamelCase__:      int			  =hidden_dropout_prob
   lowerCamelCase__:      Optional[Any]			  =attention_probs_dropout_prob
   lowerCamelCase__:      List[Any]			  =max_position_embeddings
   lowerCamelCase__:      Union[str, Any]			  =type_vocab_size
   lowerCamelCase__:      List[Any]			  =initializer_range
   lowerCamelCase__:      List[Any]			  =relative_attention
   lowerCamelCase__:      List[str]			  =max_relative_positions
   lowerCamelCase__:      str			  =pad_token_id
   lowerCamelCase__:      Optional[Any]			  =position_biased_input

   # Backwards compatibility
   if type(UpperCAmelCase_) == str:
     lowerCamelCase__:      Any			  =[x.strip() for x in pos_att_type.lower().split("|")]

   lowerCamelCase__:      Tuple			  =pos_att_type
   lowerCamelCase__:      Optional[Any]			  =vocab_size
   lowerCamelCase__:      Tuple			  =layer_norm_eps

   lowerCamelCase__:      Union[str, Any]			  =kwargs.get("pooler_hidden_size"       , UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =pooler_dropout
   lowerCamelCase__:      Union[str, Any]			  =pooler_hidden_act



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:      Tuple			  ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:      Dict			  ={0: "batch", 1: "sequence"}
   if self._config.type_vocab_size > 0:
     return OrderedDict(
         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)])
   else:
     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->int:



   '''simple docstring'''



   return 12







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       , UpperCAmelCase_				:   int = -1       , UpperCAmelCase_				:   int = -1       , UpperCAmelCase_				:   int = -1       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   Optional["TensorType"] = None       , UpperCAmelCase_				:   int = 3       , UpperCAmelCase_				:   int = 40       , UpperCAmelCase_				:   int = 40       , UpperCAmelCase_				:   "PreTrainedTokenizerBase" = None       , )       ->Mapping[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =super().generate_dummy_inputs(preprocessor=UpperCAmelCase_       , framework=UpperCAmelCase_)
   if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
     del dummy_inputs["token_type_ids"]
   return dummy_inputs








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      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      Optional[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__:      int			  =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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Tuple			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Union[str, Any]			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Dict			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Dict:







  """simple docstring"""





  if issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =parquet_path
  elif issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =[parquet_path]
  lowerCamelCase__:      Optional[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a=("train",)		)     ->		Union[str, Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  for split in splits:
    lowerCamelCase__:      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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      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():
    lowerCamelCase__:      List[str]			  =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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      int			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      Union[str, Any]			  =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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if split:
    lowerCamelCase__:      Union[str, Any]			  ={split: parquet_path}
  else:
    lowerCamelCase__:      int			  ="train"
    lowerCamelCase__:      Union[str, Any]			  ={"train": parquet_path, "test": parquet_path}
  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Union[str, Any]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[Any]			  =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      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0
  lowerCamelCase__:      Tuple			  =pq.ParquetFile(tmp_path / "foo.parquet"		)
  lowerCamelCase__:      Optional[int]			  =pf.read()
  assert dataset.data.table == output_table





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





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

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

  lowerCamelCase__:      List[str]			  =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      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  assert get_writer_batch_size(__a		) == expected

