
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


UpperCAmelCase__				:	Any					=						logging.get_logger(__name__)

UpperCAmelCase__				:	int					=						{
    "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 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Union[str, Any]				       =       'deberta'





	def __init__(   self     :    Optional[int] ,							__magic_name__     :    Union[str, Any]=50265 ,							__magic_name__     :    Optional[int]=768 ,							__magic_name__     :    Optional[Any]=12 ,							__magic_name__     :    str=12 ,							__magic_name__     :    List[str]=3072 ,							__magic_name__     :    Optional[int]="gelu" ,							__magic_name__     :    Union[str, Any]=0.1 ,							__magic_name__     :    int=0.1 ,							__magic_name__     :    Union[str, Any]=512 ,							__magic_name__     :    List[str]=0 ,							__magic_name__     :    Optional[int]=0.02 ,							__magic_name__     :    int=1E-7 ,							__magic_name__     :    Tuple=False ,							__magic_name__     :    Union[str, Any]=-1 ,							__magic_name__     :    List[Any]=0 ,							__magic_name__     :    List[Any]=True ,							__magic_name__     :    Any=None ,							__magic_name__     :    Optional[Any]=0 ,							__magic_name__     :    List[str]="gelu" ,							**__magic_name__     :    str ,							):


				"""simple docstring"""

				super().__init__(**__magic_name__							)

				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(__magic_name__							) == 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" ,							__magic_name__							)
				lowerCAmelCase__									=							pooler_dropout
				lowerCAmelCase__									=							pooler_hidden_act






class 				A       (		SCREAMING_SNAKE_CASE__      ):





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				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 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				return 12







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Any = -1 ,							__magic_name__     :    List[str] = -1 ,							__magic_name__     :    Union[str, Any] = -1 ,							__magic_name__     :    int = False ,							__magic_name__     :    List[str] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    Optional[int] = 40 ,							__magic_name__     :    str = 40 ,							__magic_name__     :    Union[str, Any] = None ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							super().generate_dummy_inputs(preprocessor=__magic_name__ ,							framework=__magic_name__							)
				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'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





