






"""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						:						str					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						Dict					   =       {
    '''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       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					"""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						)

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

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

					A__							 =       pos_att_type
					A__							 =       vocab_size
					A__							 =       layer_norm_eps

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





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					if self.task == "multiple-choice":
									A__							 =       {0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
									A__							 =       {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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 12






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = -1  ,					__a = -1  ,					__a = -1  ,					__a = False  ,					__a = None  ,					__a = 3  ,					__a = 40  ,					__a = 40  ,					__a = None  ,					):


					"""simple docstring"""

					A__							 =       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 math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


