






'''simple docstring'''


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


lowerCamelCase_				: List[Any]    	=							logging.get_logger(__name__)

lowerCamelCase_				: Optional[Any]    	=							{
    '''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'''





			__a							:							Any					     =					"deberta-v2"






			def __init__(   self							:						Dict ,      lowercase							:						Optional[Any]=1_2_8_1_0_0 ,      lowercase							:						int=1_5_3_6 ,      lowercase							:						Any=2_4 ,      lowercase							:						Optional[Any]=2_4 ,      lowercase							:						Optional[Any]=6_1_4_4 ,      lowercase							:						str="gelu" ,      lowercase							:						Union[str, Any]=0.1 ,      lowercase							:						List[str]=0.1 ,      lowercase							:						Tuple=5_1_2 ,      lowercase							:						List[str]=0 ,      lowercase							:						Dict=0.0_2 ,      lowercase							:						Union[str, Any]=1e-7 ,      lowercase							:						Union[str, Any]=False ,      lowercase							:						List[Any]=-1 ,      lowercase							:						Any=0 ,      lowercase							:						Tuple=True ,      lowercase							:						Tuple=None ,      lowercase							:						Any=0 ,      lowercase							:						Optional[Any]="gelu" ,      **lowercase							:						Optional[Any] ,      )							->    Dict:

									'''simple docstring'''







									super().__init__(**lowercase			)

									UpperCamelCase__         = hidden_size
									UpperCamelCase__         = num_hidden_layers
									UpperCamelCase__         = num_attention_heads
									UpperCamelCase__         = intermediate_size
									UpperCamelCase__         = hidden_act
									UpperCamelCase__         = hidden_dropout_prob
									UpperCamelCase__         = attention_probs_dropout_prob
									UpperCamelCase__         = max_position_embeddings
									UpperCamelCase__         = type_vocab_size
									UpperCamelCase__         = initializer_range
									UpperCamelCase__         = relative_attention
									UpperCamelCase__         = max_relative_positions
									UpperCamelCase__         = pad_token_id
									UpperCamelCase__         = position_biased_input

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

									UpperCamelCase__         = pos_att_type
									UpperCamelCase__         = vocab_size
									UpperCamelCase__         = layer_norm_eps

									UpperCamelCase__         = kwargs.get("""pooler_hidden_size""" ,      lowercase			)
									UpperCamelCase__         = pooler_dropout
									UpperCamelCase__         = pooler_hidden_act
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





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

									'''simple docstring'''







									if self.task == "multiple-choice":
															UpperCamelCase__         = {0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
															UpperCamelCase__         = {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 				A      (   self							:						Union[str, Any]			)							->    int:

									'''simple docstring'''







									return 1_2







			def 				A      (   self							:						List[str] ,      lowercase							:						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,      lowercase							:						int = -1 ,      lowercase							:						int = -1 ,      lowercase							:						int = -1 ,      lowercase							:						bool = False ,      lowercase							:						Optional["TensorType"] = None ,      lowercase							:						int = 3 ,      lowercase							:						int = 4_0 ,      lowercase							:						int = 4_0 ,      lowercase							:						"PreTrainedTokenizerBase" = None ,      )							->    Mapping[str, Any]:

									'''simple docstring'''







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








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
