




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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    '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     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   'deberta-v2'
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: Union[str, Any]=12_81_00    ,       UpperCamelCase_: Optional[int]=15_36    ,       UpperCamelCase_: str=24    ,       UpperCamelCase_: Optional[Any]=24    ,       UpperCamelCase_: int=61_44    ,       UpperCamelCase_: Dict="gelu"    ,       UpperCamelCase_: Optional[Any]=0.1    ,       UpperCamelCase_: List[Any]=0.1    ,       UpperCamelCase_: List[Any]=5_12    ,       UpperCamelCase_: List[Any]=0    ,       UpperCamelCase_: Any=0.02    ,       UpperCamelCase_: Tuple=1E-7    ,       UpperCamelCase_: List[Any]=False    ,       UpperCamelCase_: Any=-1    ,       UpperCamelCase_: Tuple=0    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: Any=None    ,       UpperCamelCase_: List[Any]=0    ,       UpperCamelCase_: str="gelu"    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

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

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

             __lowerCamelCase   =						pos_att_type
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						layer_norm_eps

             __lowerCamelCase   =						kwargs.get("""pooler_hidden_size"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						pooler_dropout
             __lowerCamelCase   =						pooler_hidden_act





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             if self.task == "multiple-choice":
                    __lowerCamelCase   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    __lowerCamelCase   =						{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: Any   ):
             return 12




      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       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    ,       ):
             __lowerCamelCase   =						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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Any       =   [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



