


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


snake_case    :   Dict	    =					logging.get_logger(__name__)

snake_case    :   Union[str, 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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'deberta-v2'




 def __init__(		self ,  _lowerCamelCase=12_8100 ,  _lowerCamelCase=1536 ,  _lowerCamelCase=24 ,  _lowerCamelCase=24 ,  _lowerCamelCase=6144 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-7 ,  _lowerCamelCase=False ,  _lowerCamelCase=-1 ,  _lowerCamelCase=0 ,  _lowerCamelCase=True ,  _lowerCamelCase=None ,  _lowerCamelCase=0 ,  _lowerCamelCase="gelu" ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

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

       # Backwards compatibility
       if type(_lowerCamelCase   ) == str:
             a						:Optional[int]										=	[x.strip() for x in pos_att_type.lower().split('''|'''   )]

       a						:str										=	pos_att_type
       a						:Optional[Any]										=	vocab_size
       a						:Any										=	layer_norm_eps

       a						:str										=	kwargs.get('''pooler_hidden_size''' ,  _lowerCamelCase   )
       a						:int										=	pooler_dropout
       a						:Optional[int]										=	pooler_hidden_act







class 		_snake_case  (				_snake_case							):




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.task == "multiple-choice":
             a						:Dict										=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             a						:Union[str, Any]										=	{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   ):
       return 12




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = -1 ,  _lowerCamelCase = -1 ,  _lowerCamelCase = -1 ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  _lowerCamelCase = 3 ,  _lowerCamelCase = 40 ,  _lowerCamelCase = 40 ,  _lowerCamelCase = None ,  ):
       a						:Union[str, Any]										=	super().generate_dummy_inputs(preprocessor=_lowerCamelCase ,  framework=_lowerCamelCase   )
       if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
       return dummy_inputs









from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





