'''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


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''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__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	'''deberta'''



			def __init__(       self	,   __lowerCAmelCase=50265	,   __lowerCAmelCase=768	,   __lowerCAmelCase=12	,   __lowerCAmelCase=12	,   __lowerCAmelCase=3072	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=1E-7	,   __lowerCAmelCase=False	,   __lowerCAmelCase=-1	,   __lowerCAmelCase=0	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=0	,   __lowerCAmelCase="gelu"	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

					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(__lowerCAmelCase) == 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"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  pooler_dropout
					lowerCAmelCase        =  pooler_hidden_act







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					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 							a_	(       self):




					"""simple docstring"""


					return 12







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = 3	,   __lowerCAmelCase = 40	,   __lowerCAmelCase = 40	,   __lowerCAmelCase = None	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super().generate_dummy_inputs(preprocessor=__lowerCAmelCase	,   framework=__lowerCAmelCase)
					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,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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




