

"""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


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			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 		_A       (			__a					):
      __a								=	'deberta-v2'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=128100    ,						SCREAMING_SNAKE_CASE__=1536    ,						SCREAMING_SNAKE_CASE__=24    ,						SCREAMING_SNAKE_CASE__=24    ,						SCREAMING_SNAKE_CASE__=6144    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-7    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=-1    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE__		)

         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(SCREAMING_SNAKE_CASE__		) == 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"    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			pooler_dropout
         lowerCamelCase__						=			pooler_hidden_act






class 		_A       (			__a					):





      @property
      def 					_lowerCamelCase  (  self		)   ->					Mapping[str, Mapping[int, str]]:
         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 					_lowerCamelCase  (  self		)   ->					int:
         return 12





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = -1    ,						SCREAMING_SNAKE_CASE__ = -1    ,						SCREAMING_SNAKE_CASE__ = -1    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 3    ,						SCREAMING_SNAKE_CASE__ = 40    ,						SCREAMING_SNAKE_CASE__ = 40    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					Mapping[str, Any]:
         lowerCamelCase__						=			super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__    ,						framework=SCREAMING_SNAKE_CASE__		)
         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_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




