


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			:						Optional[int]        =  logging.get_logger(__name__)

A			:						Optional[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 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''deberta-v2'''

					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Any=12_8100 ,   _UpperCAmelCase       :		List[Any]=1536 ,   _UpperCAmelCase       :		List[Any]=24 ,   _UpperCAmelCase       :		List[Any]=24 ,   _UpperCAmelCase       :		Optional[int]=6144 ,   _UpperCAmelCase       :		Tuple="gelu" ,   _UpperCAmelCase       :		List[Any]=0.1 ,   _UpperCAmelCase       :		Tuple=0.1 ,   _UpperCAmelCase       :		str=512 ,   _UpperCAmelCase       :		List[str]=0 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-7 ,   _UpperCAmelCase       :		Optional[Any]=False ,   _UpperCAmelCase       :		Tuple=-1 ,   _UpperCAmelCase       :		Optional[int]=0 ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		Optional[Any]=None ,   _UpperCAmelCase       :		Optional[int]=0 ,   _UpperCAmelCase       :		Optional[int]="gelu" ,   **_UpperCAmelCase       :		Tuple ,   )		->  List[Any]:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase						)

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

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

							lowercase__	     =						pos_att_type
							lowercase__	     =						vocab_size
							lowercase__	     =						layer_norm_eps

							lowercase__	     =						kwargs.get("""pooler_hidden_size""" ,   _UpperCAmelCase						)
							lowercase__	     =						pooler_dropout
							lowercase__	     =						pooler_hidden_act






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		str						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{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       :		int						)		->  int:




							"""simple docstring"""


							return 12


					def     lowerCamelCase__	(self       :		List[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 ,   )		->  Mapping[str, Any]:




							"""simple docstring"""


							lowercase__	     =						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_tf_available,
    is_torch_available,
    is_vision_available,
)


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

