




'''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


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    'deberta'
				def __init__(self			,  _UpperCAmelCase=5_0_2_6_5			,  _UpperCAmelCase=7_6_8			,  _UpperCAmelCase=1_2			,  _UpperCAmelCase=1_2			,  _UpperCAmelCase=3_0_7_2			,  _UpperCAmelCase="gelu"			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=5_1_2			,  _UpperCAmelCase=0			,  _UpperCAmelCase=0.02			,  _UpperCAmelCase=1E-7			,  _UpperCAmelCase=False			,  _UpperCAmelCase=-1			,  _UpperCAmelCase=0			,  _UpperCAmelCase=True			,  _UpperCAmelCase=None			,  _UpperCAmelCase=0			,  _UpperCAmelCase="gelu"			,  **_UpperCAmelCase			,  )     ->	Optional[Any]:
							super().__init__(**_UpperCAmelCase      )

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

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

							__UpperCamelCase				:      Optional[int]									=       pos_att_type
							__UpperCamelCase				:      List[str]									=       vocab_size
							__UpperCamelCase				:      List[Any]									=       layer_norm_eps

							__UpperCamelCase				:      Union[str, Any]									=       kwargs.get("pooler_hidden_size"			,  _UpperCAmelCase      )
							__UpperCamelCase				:      Dict									=       pooler_dropout
							__UpperCamelCase				:      List[str]									=       pooler_hidden_act







class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





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



							if self.task == "multiple-choice":
										__UpperCamelCase				:      Any									=       {0: "batch", 1: "choice", 2: "sequence"}
							else:
										__UpperCamelCase				:      Any									=       {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      )     ->	int:
							return 1_2





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = -1			,  _UpperCAmelCase = -1			,  _UpperCAmelCase = -1			,  _UpperCAmelCase = False			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 3			,  _UpperCAmelCase = 4_0			,  _UpperCAmelCase = 4_0			,  _UpperCAmelCase = None			,  )     ->	Mapping[str, Any]:
							__UpperCamelCase				:      Tuple									=       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









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase				     = {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase				     = [
				    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VisualBertForMultipleChoice''',
				    '''VisualBertForPreTraining''',
				    '''VisualBertForQuestionAnswering''',
				    '''VisualBertForRegionToPhraseAlignment''',
				    '''VisualBertForVisualReasoning''',
				    '''VisualBertLayer''',
				    '''VisualBertModel''',
				    '''VisualBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase				     = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



