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


_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     {
    """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        UpperCAmelCase_					(				_a     ):
    UpperCAmelCase_      :							int    			=				'deberta'




    def __init__(       self	: int   ,   lowerCAmelCase	: Tuple=50265   ,   lowerCAmelCase	: Optional[Any]=768   ,   lowerCAmelCase	: Dict=12   ,   lowerCAmelCase	: Tuple=12   ,   lowerCAmelCase	: Any=3072   ,   lowerCAmelCase	: List[str]="gelu"   ,   lowerCAmelCase	: str=0.1   ,   lowerCAmelCase	: Any=0.1   ,   lowerCAmelCase	: str=512   ,   lowerCAmelCase	: Tuple=0   ,   lowerCAmelCase	: Optional[int]=0.02   ,   lowerCAmelCase	: Optional[int]=1e-7   ,   lowerCAmelCase	: int=False   ,   lowerCAmelCase	: Tuple=-1   ,   lowerCAmelCase	: Tuple=0   ,   lowerCAmelCase	: Union[str, Any]=True   ,   lowerCAmelCase	: Union[str, Any]=None   ,   lowerCAmelCase	: Tuple=0   ,   lowerCAmelCase	: Dict="gelu"   ,   **lowerCAmelCase	: Tuple   ,   )						->			List[str]:
      super().__init__(**__lowercase )

      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(__lowercase ) == 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"   ,   __lowercase )
      a__  		=  pooler_dropout
      a__  		=  pooler_hidden_act







class        UpperCAmelCase_					(				_a     ):




    @property
    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			str:




      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     __lowerCamelCase			(       self	: Tuple )						->			Optional[int]:
      return 12




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: Tuple = -1   ,   lowerCAmelCase	: int = -1   ,   lowerCAmelCase	: str = -1   ,   lowerCAmelCase	: List[Any] = False   ,   lowerCAmelCase	: Dict = None   ,   lowerCAmelCase	: Any = 3   ,   lowerCAmelCase	: Union[str, Any] = 40   ,   lowerCAmelCase	: List[str] = 40   ,   lowerCAmelCase	: Union[str, Any] = None   ,   )						->			List[str]:
      a__  		=  super().generate_dummy_inputs(preprocessor=__lowercase   ,   framework=__lowercase )
      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,
)


UpperCamelCase__						=		{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

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