





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 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """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    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''deberta-v2'''
      def __init__(		self      ,       a__=128100      ,       a__=1536      ,       a__=24      ,       a__=24      ,       a__=6144      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=0      ,       a__=0.02      ,       a__=1E-7      ,       a__=False      ,       a__=-1      ,       a__=0      ,       a__=True      ,       a__=None      ,       a__=0      ,       a__="gelu"      ,       **a__      ,       ):
         super().__init__(**a__       )

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

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

         A_     :				int			      =	pos_att_type
         A_     :				List[Any]			      =	vocab_size
         A_     :				Union[str, Any]			      =	layer_norm_eps

         A_     :				Optional[int]			      =	kwargs.get("""pooler_hidden_size"""      ,       a__       )
         A_     :				Optional[int]			      =	pooler_dropout
         A_     :				int			      =	pooler_hidden_act






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      @property
      def 		_lowerCamelCase      (		self       ):
         if self.task == "multiple-choice":
            A_     :				List[Any]			      =	{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
            A_     :				Optional[int]			      =	{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 		_lowerCamelCase      (		self       ):
         return 12






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = -1      ,       a__ = -1      ,       a__ = -1      ,       a__ = False      ,       a__ = None      ,       a__ = 3      ,       a__ = 40      ,       a__ = 40      ,       a__ = None      ,       ):
         A_     :				Tuple			      =	super().generate_dummy_inputs(preprocessor=a__      ,       framework=a__       )
         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,
)






