




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						:   Any		=   logging.get_logger(__name__)

_lowerCAmelCase						:   str		=   {
    "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 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		"""deberta-v2"""
      def __init__(			self   :int ,      lowerCamelCase   :str=12_8100 ,      lowerCamelCase   :Any=1536 ,      lowerCamelCase   :Union[str, Any]=24 ,      lowerCamelCase   :str=24 ,      lowerCamelCase   :str=6144 ,      lowerCamelCase   :Optional[Any]="gelu" ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :str=0.1 ,      lowerCamelCase   :str=512 ,      lowerCamelCase   :List[Any]=0 ,      lowerCamelCase   :int=0.02 ,      lowerCamelCase   :Tuple=1e-7 ,      lowerCamelCase   :Tuple=False ,      lowerCamelCase   :List[Any]=-1 ,      lowerCamelCase   :Tuple=0 ,      lowerCamelCase   :str=True ,      lowerCamelCase   :str=None ,      lowerCamelCase   :Tuple=0 ,      lowerCamelCase   :Dict="gelu" ,      **lowerCamelCase   :str ,      )     ->			int:
       super().__init__(**lowerCamelCase      )

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

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

       UpperCAmelCase__							 =						pos_att_type
       UpperCAmelCase__							 =						vocab_size
       UpperCAmelCase__							 =						layer_norm_eps

       UpperCAmelCase__							 =						kwargs.get("pooler_hidden_size" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						pooler_dropout
       UpperCAmelCase__							 =						pooler_hidden_act



class 			_UpperCamelCase      (				lowerCAmelCase		):
      @property
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
        UpperCAmelCase__							 =						{0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCAmelCase__							 =						{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 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			int:
       return 12







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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   ["MaskFormerFeatureExtractor"]
  _lowerCAmelCase						:   Dict		=   ["MaskFormerImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MaskFormerForInstanceSegmentation",
      "MaskFormerModel",
      "MaskFormerPreTrainedModel",
  ]
  _lowerCAmelCase						:   List[str]		=   [
      "MaskFormerSwinBackbone",
      "MaskFormerSwinModel",
      "MaskFormerSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase						:   str		=   _LazyModule(__name__, globals()["__file__"], _import_structure)





