


"""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-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  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Any     =	"""deberta-v2"""






    def __init__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[int]=128100    ,			__UpperCAmelCase    :   Union[str, Any]=1536    ,			__UpperCAmelCase    :   Tuple=24    ,			__UpperCAmelCase    :   Tuple=24    ,			__UpperCAmelCase    :   Dict=6144    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   str=0.1    ,			__UpperCAmelCase    :   Tuple=0.1    ,			__UpperCAmelCase    :   List[Any]=512    ,			__UpperCAmelCase    :   List[str]=0    ,			__UpperCAmelCase    :   Union[str, Any]=0.02    ,			__UpperCAmelCase    :   int=1e-7    ,			__UpperCAmelCase    :   str=False    ,			__UpperCAmelCase    :   int=-1    ,			__UpperCAmelCase    :   Optional[Any]=0    ,			__UpperCAmelCase    :   Optional[int]=True    ,			__UpperCAmelCase    :   Dict=None    ,			__UpperCAmelCase    :   Tuple=0    ,			__UpperCAmelCase    :   List[Any]="gelu"    ,			**__UpperCAmelCase    :   str    ,			):
      super().__init__(**__UpperCAmelCase)

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

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

      a						:		Tuple											=      pos_att_type
      a						:		Optional[Any]											=      vocab_size
      a						:		List[Any]											=      layer_norm_eps

      a						:		int											=      kwargs.get("pooler_hidden_size"    ,			__UpperCAmelCase)
      a						:		Union[str, Any]											=      pooler_dropout
      a						:		Tuple											=      pooler_hidden_act






class 						_A  (  _a  ):







    """simple docstring"""




    @property
    def      __snake_case							( self    :   Optional[Any]):
      if self.task == "multiple-choice":
        a						:		Dict											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		str											=      {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      __snake_case							( self    :   List[str]):
      return 12






    def      __snake_case							( 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    ,			):
      a						:		Any											=      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"""







__lowercase    =				frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image""", """mask_image"""])

__lowercase    =				frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""example_image""", """image""", """mask_image"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])
__lowercase    =				frozenset(["""input_tokens"""])

__lowercase    =				frozenset(["""input_tokens"""])






