


'''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  :		int		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		int		     =	{
    '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  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Tuple			      = """deberta-v2"""



	def __init__(					self ,					snake_case__=12_8100 ,					snake_case__=1536 ,					snake_case__=24 ,					snake_case__=24 ,					snake_case__=6144 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=0 ,					snake_case__=0.0_2 ,					snake_case__=1e-7 ,					snake_case__=False ,					snake_case__=-1 ,					snake_case__=0 ,					snake_case__=True ,					snake_case__=None ,					snake_case__=0 ,					snake_case__="gelu" ,					**snake_case__ ,					):
		super().__init__(**snake_case__    )

		lowerCAmelCase					:    str      							= hidden_size
		lowerCAmelCase					:    Tuple      							= num_hidden_layers
		lowerCAmelCase					:    Union[str, Any]      							= num_attention_heads
		lowerCAmelCase					:    Tuple      							= intermediate_size
		lowerCAmelCase					:    Dict      							= hidden_act
		lowerCAmelCase					:    int      							= hidden_dropout_prob
		lowerCAmelCase					:    Any      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Tuple      							= max_position_embeddings
		lowerCAmelCase					:    Tuple      							= type_vocab_size
		lowerCAmelCase					:    str      							= initializer_range
		lowerCAmelCase					:    Any      							= relative_attention
		lowerCAmelCase					:    Dict      							= max_relative_positions
		lowerCAmelCase					:    int      							= pad_token_id
		lowerCAmelCase					:    Union[str, Any]      							= position_biased_input

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

		lowerCAmelCase					:    List[str]      							= pos_att_type
		lowerCAmelCase					:    Optional[Any]      							= vocab_size
		lowerCAmelCase					:    Tuple      							= layer_norm_eps

		lowerCAmelCase					:    Dict      							= kwargs.get('pooler_hidden_size' ,					snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= pooler_dropout
		lowerCAmelCase					:    Optional[Any]      							= pooler_hidden_act



class  lowerCAmelCase				(				a							):



	@property
	def 				lowercase     (					self    ):
		if self.task == "multiple-choice":
			lowerCAmelCase					:    str      							= {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 				lowercase     (					self    ):
		return 12



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = -1 ,					snake_case__ = -1 ,					snake_case__ = -1 ,					snake_case__ = False ,					snake_case__ = None ,					snake_case__ = 3 ,					snake_case__ = 40 ,					snake_case__ = 40 ,					snake_case__ = None ,					):
		lowerCAmelCase					:    Tuple      							= super().generate_dummy_inputs(preprocessor=snake_case__ ,					framework=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'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

_lowerCAmelCase  :		Dict		     =	{
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Any			      = """deformable_detr"""
	_lowerCamelCase   :					List[str]			      = {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}



	def __init__(					self ,					snake_case__=True ,					snake_case__=None ,					snake_case__=3 ,					snake_case__=300 ,					snake_case__=1024 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=0.0 ,					snake_case__=True ,					snake_case__="relu" ,					snake_case__=256 ,					snake_case__=0.1 ,					snake_case__=0.0 ,					snake_case__=0.0 ,					snake_case__=0.0_2 ,					snake_case__=1.0 ,					snake_case__=True ,					snake_case__=False ,					snake_case__="sine" ,					snake_case__="resnet50" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=False ,					snake_case__=300 ,					snake_case__=False ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=1 ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=0.1 ,					snake_case__=0.2_5 ,					snake_case__=False ,					**snake_case__ ,					):
		if backbone_config is not None and use_timm_backbone:
			raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'    )

		if not use_timm_backbone:
			if backbone_config is None:
				logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'    )
				lowerCAmelCase					:    Optional[int]      							= CONFIG_MAPPING['resnet'](out_features=['stage4']    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    List[str]      							= backbone_config.get('model_type'    )
				lowerCAmelCase					:    str      							= CONFIG_MAPPING[backbone_model_type]
				lowerCAmelCase					:    Optional[Any]      							= config_class.from_dict(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= use_timm_backbone
		lowerCAmelCase					:    List[Any]      							= backbone_config
		lowerCAmelCase					:    Any      							= num_channels
		lowerCAmelCase					:    Tuple      							= num_queries
		lowerCAmelCase					:    Dict      							= max_position_embeddings
		lowerCAmelCase					:    int      							= d_model
		lowerCAmelCase					:    List[str]      							= encoder_ffn_dim
		lowerCAmelCase					:    List[str]      							= encoder_layers
		lowerCAmelCase					:    int      							= encoder_attention_heads
		lowerCAmelCase					:    str      							= decoder_ffn_dim
		lowerCAmelCase					:    str      							= decoder_layers
		lowerCAmelCase					:    Dict      							= decoder_attention_heads
		lowerCAmelCase					:    str      							= dropout
		lowerCAmelCase					:    List[str]      							= attention_dropout
		lowerCAmelCase					:    Union[str, Any]      							= activation_dropout
		lowerCAmelCase					:    str      							= activation_function
		lowerCAmelCase					:    Any      							= init_std
		lowerCAmelCase					:    Any      							= init_xavier_std
		lowerCAmelCase					:    Dict      							= encoder_layerdrop
		lowerCAmelCase					:    int      							= auxiliary_loss
		lowerCAmelCase					:    Optional[Any]      							= position_embedding_type
		lowerCAmelCase					:    List[str]      							= backbone
		lowerCAmelCase					:    int      							= use_pretrained_backbone
		lowerCAmelCase					:    int      							= dilation
		# deformable attributes
		lowerCAmelCase					:    List[str]      							= num_feature_levels
		lowerCAmelCase					:    List[str]      							= encoder_n_points
		lowerCAmelCase					:    Union[str, Any]      							= decoder_n_points
		lowerCAmelCase					:    Tuple      							= two_stage
		lowerCAmelCase					:    Dict      							= two_stage_num_proposals
		lowerCAmelCase					:    Union[str, Any]      							= with_box_refine
		if two_stage is True and with_box_refine is False:
			raise ValueError('If two_stage is True, with_box_refine must be True.'    )
		# Hungarian matcher
		lowerCAmelCase					:    Union[str, Any]      							= class_cost
		lowerCAmelCase					:    Dict      							= bbox_cost
		lowerCAmelCase					:    List[Any]      							= giou_cost
		# Loss coefficients
		lowerCAmelCase					:    Dict      							= mask_loss_coefficient
		lowerCAmelCase					:    Any      							= dice_loss_coefficient
		lowerCAmelCase					:    str      							= bbox_loss_coefficient
		lowerCAmelCase					:    Tuple      							= giou_loss_coefficient
		lowerCAmelCase					:    List[str]      							= eos_coefficient
		lowerCAmelCase					:    Any      							= focal_alpha
		lowerCAmelCase					:    Dict      							= disable_custom_kernels
		super().__init__(is_encoder_decoder=snake_case__ ,					**snake_case__    )



	@property
	def 				lowercase     (					self    ):
		return self.encoder_attention_heads



	@property
	def 				lowercase     (					self    ):
		return self.d_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= copy.deepcopy(self.__dict__    )
		if self.backbone_config is not None:
			lowerCAmelCase					:    List[Any]      							= self.backbone_config.to_dict()
		lowerCAmelCase					:    str      							= self.__class__.model_type
		return output
