

'''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


lowerCamelCase			:				Dict 			=					logging.get_logger(__name__)

lowerCamelCase			:				List[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__   (	SCREAMING_SNAKE_CASE__  ):
		A__					=    '''deberta-v2'''





		def __init__(		self				:     Tuple							, _a				:     Optional[int]=12_8100							, _a				:     str=1536							, _a				:     List[str]=24							, _a				:     Optional[int]=24							, _a				:     Optional[Any]=6144							, _a				:     Union[str, Any]="gelu"							, _a				:     Dict=0.1							, _a				:     Union[str, Any]=0.1							, _a				:     Optional[Any]=512							, _a				:     List[Any]=0							, _a				:     List[str]=0.02							, _a				:     Optional[Any]=1e-7							, _a				:     List[str]=False							, _a				:     str=-1							, _a				:     Dict=0							, _a				:     Tuple=True							, _a				:     Any=None							, _a				:     List[str]=0							, _a				:     Optional[int]="gelu"							, **_a				:     List[Any]							, )     ->   List[Any]:





							'''simple docstring'''






							super().__init__(**A__			)

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

							# Backwards compatibility
							if type(A__			) == str:
												_SCREAMING_SNAKE_CASE							   =[x.strip() for x in pos_att_type.lower().split('|'			)]

							_SCREAMING_SNAKE_CASE							   =pos_att_type
							_SCREAMING_SNAKE_CASE							   =vocab_size
							_SCREAMING_SNAKE_CASE							   =layer_norm_eps

							_SCREAMING_SNAKE_CASE							   =kwargs.get('pooler_hidden_size'							, A__			)
							_SCREAMING_SNAKE_CASE							   =pooler_dropout
							_SCREAMING_SNAKE_CASE							   =pooler_hidden_act







class 							A__   (	SCREAMING_SNAKE_CASE__  ):





		@property
		def 						A  (		self				:     Tuple			)     ->   Any:





							'''simple docstring'''






							if self.task == "multiple-choice":
												_SCREAMING_SNAKE_CASE							   ={0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
												_SCREAMING_SNAKE_CASE							   ={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 						A  (		self				:     Union[str, Any]			)     ->   Dict:





							'''simple docstring'''






							return 12





		def 						A  (		self				:     Any							, _a				:     List[Any]							, _a				:     int = -1							, _a				:     Tuple = -1							, _a				:     Any = -1							, _a				:     Any = False							, _a				:     str = None							, _a				:     List[Any] = 3							, _a				:     Dict = 40							, _a				:     List[str] = 40							, _a				:     Tuple = None							, )     ->   Union[str, Any]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =super().generate_dummy_inputs(preprocessor=A__							, framework=A__			)
							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 ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				Optional[Any] 			=					"T5Config"







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig
