


"""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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'deberta-v2'







		def __init__(					self      :       Dict    ,   a__      :       Dict=12_81_00    ,   a__      :       Any=15_36    ,   a__      :       List[Any]=24    ,   a__      :       Optional[Any]=24    ,   a__      :       Optional[int]=61_44    ,   a__      :       Union[str, Any]="gelu"    ,   a__      :       Optional[Any]=0.1    ,   a__      :       List[Any]=0.1    ,   a__      :       Optional[Any]=5_12    ,   a__      :       Optional[Any]=0    ,   a__      :       int=0.0_2    ,   a__      :       Optional[int]=1E-7    ,   a__      :       Union[str, Any]=False    ,   a__      :       Union[str, Any]=-1    ,   a__      :       Union[str, Any]=0    ,   a__      :       Tuple=True    ,   a__      :       Union[str, Any]=None    ,   a__      :       Dict=0    ,   a__      :       int="gelu"    ,   **a__      :       Optional[Any]    ,   )       ->						Union[str, Any]:





						'''simple docstring'''







						super().__init__(**a__		)

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

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

						_A					  =			pos_att_type
						_A					  =			vocab_size
						_A					  =			layer_norm_eps

						_A					  =			kwargs.get("pooler_hidden_size"    ,   a__		)
						_A					  =			pooler_dropout
						_A					  =			pooler_hidden_act





class snake_case      (  _UpperCamelCase):







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





						'''simple docstring'''







						if self.task == "multiple-choice":
										_A					  =			{0: "batch", 1: "choice", 2: "sequence"}
						else:
										_A					  =			{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 12







		def  a_ (					self      :       Any    ,   a__      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,   a__      :       int = -1    ,   a__      :       int = -1    ,   a__      :       int = -1    ,   a__      :       bool = False    ,   a__      :       Optional["TensorType"] = None    ,   a__      :       int = 3    ,   a__      :       int = 40    ,   a__      :       int = 40    ,   a__      :       "PreTrainedTokenizerBase" = None    ,   )       ->						Mapping[str, Any]:





						'''simple docstring'''







						_A					  =			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



"""simple docstring"""


from typing import TYPE_CHECKING

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


a_       							=    {
    "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_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "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_       							=    [
		    "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_       							=    [
		    "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_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)