



"""simple docstring"""

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,
)









"""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


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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""deberta-v2"""







						def __init__(self   ,		lowercase__=12_81_00   ,		lowercase__=15_36   ,		lowercase__=24   ,		lowercase__=24   ,		lowercase__=61_44   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=0   ,		lowercase__=0.02   ,		lowercase__=1e-7   ,		lowercase__=False   ,		lowercase__=-1   ,		lowercase__=0   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=0   ,		lowercase__="gelu"   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

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

											# Backwards compatibility
											if type(lowercase__       ) == str:
																snake_case_					:		Union[str, Any] 					=					[x.strip() for x in pos_att_type.lower().split("""|"""       )]

											snake_case_					:		Optional[int] 					=					pos_att_type
											snake_case_					:		List[str] 					=					vocab_size
											snake_case_					:		Tuple 					=					layer_norm_eps

											snake_case_					:		List[Any] 					=					kwargs.get("""pooler_hidden_size"""   ,		lowercase__       )
											snake_case_					:		List[str] 					=					pooler_dropout
											snake_case_					:		int 					=					pooler_hidden_act







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						@property
						def 						__UpperCamelCase							(self       ):
											if self.task == "multiple-choice":
																snake_case_					:		List[Any] 					=					{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																snake_case_					:		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 						__UpperCamelCase							(self       ):
											return 12







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = False   ,		lowercase__ = None   ,		lowercase__ = 3   ,		lowercase__ = 40   ,		lowercase__ = 40   ,		lowercase__ = None   ,		):
											snake_case_					:		str 					=					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




