


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


_snake_case					=					logging.get_logger(__name__)

_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_ ( lowerCAmelCase__   ):

			'''simple docstring'''




			__A			:				str							=		'''deberta'''






			def __init__(		self						,    __A=5_0265						,    __A=768						,    __A=12						,    __A=12						,    __A=3072						,    __A="gelu"						,    __A=0.1						,    __A=0.1						,    __A=512						,    __A=0						,    __A=0.02						,    __A=1e-7						,    __A=False						,    __A=-1						,    __A=0						,    __A=True						,    __A=None						,    __A=0						,    __A="gelu"						,    **__A						,    ):






					"""simple docstring"""





					super().__init__(**__lowerCAmelCase			)

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

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

					lowerCamelCase			: List[str]   =    pos_att_type
					lowerCamelCase			: str   =    vocab_size
					lowerCamelCase			: List[str]   =    layer_norm_eps

					lowerCamelCase			: Any   =    kwargs.get("pooler_hidden_size"						,    __lowerCAmelCase			)
					lowerCamelCase			: Union[str, Any]   =    pooler_dropout
					lowerCamelCase			: Dict   =    pooler_hidden_act



class      UpperCAmelCase_ ( lowerCAmelCase__   ):

			'''simple docstring'''




			@property
			def _snake_case   (		self			):






					"""simple docstring"""





					if self.task == "multiple-choice":
							lowerCamelCase			: Union[str, Any]   =    {0: "batch", 1: "choice", 2: "sequence"}
					else:
							lowerCamelCase			: 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 _snake_case   (		self			):






					"""simple docstring"""





					return 12






			def _snake_case   (		self						,    __A						,    __A = -1						,    __A = -1						,    __A = -1						,    __A = False						,    __A = None						,    __A = 3						,    __A = 40						,    __A = 40						,    __A = None						,    ):






					"""simple docstring"""





					lowerCamelCase			: Union[str, Any]   =    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,
)


_snake_case					=					{
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RwkvForCausalLM''',
       '''RwkvModel''',
       '''RwkvPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

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



