





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			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      {
    '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       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """deberta-v2"""


      def __init__(		self      ,							a__=12_8100      ,							a__=1536      ,							a__=24      ,							a__=24      ,							a__=6144      ,							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__(**a__)

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

             # Backwards compatibility
             if type(a__) == str:
                    _lowerCamelCase							:     Union[str, Any]	=		[x.strip() for x in pos_att_type.lower().split('''|''')]

             _lowerCamelCase							:     int	=		pos_att_type
             _lowerCamelCase							:     Optional[int]	=		vocab_size
             _lowerCamelCase							:     str	=		layer_norm_eps

             _lowerCamelCase							:     Optional[Any]	=		kwargs.get('''pooler_hidden_size'''      ,							a__)
             _lowerCamelCase							:     Any	=		pooler_dropout
             _lowerCamelCase							:     Union[str, Any]	=		pooler_hidden_act







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             if self.task == "multiple-choice":
                    _lowerCamelCase							:     int	=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                    _lowerCamelCase							:     Union[str, 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							:     Optional[int]	=		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








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

