






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[Any]    =				"""data2vec-vision"""





	def __init__(self     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=3072     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1e-1_2     ,     lowerCAmelCase_=224     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=[3, 5, 7, 11]     ,     lowerCAmelCase_=[1, 2, 3, 6]     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=0.4     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=255     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_							)

		A_		:						Optional[Any]	    =   hidden_size
		A_		:						Dict	    =   num_hidden_layers
		A_		:						Optional[int]	    =   num_attention_heads
		A_		:						str	    =   intermediate_size
		A_		:						List[Any]	    =   hidden_act
		A_		:						int	    =   hidden_dropout_prob
		A_		:						Any	    =   attention_probs_dropout_prob
		A_		:						Tuple	    =   initializer_range
		A_		:						Union[str, Any]	    =   layer_norm_eps

		A_		:						Optional[Any]	    =   image_size
		A_		:						int	    =   patch_size
		A_		:						List[Any]	    =   num_channels
		A_		:						str	    =   use_mask_token
		A_		:						int	    =   use_absolute_position_embeddings
		A_		:						Any	    =   use_relative_position_bias
		A_		:						Union[str, Any]	    =   use_shared_relative_position_bias
		A_		:						str	    =   layer_scale_init_value
		A_		:						Optional[Any]	    =   drop_path_rate
		A_		:						List[str]	    =   use_mean_pooling
		# decode head attributes (semantic segmentation)
		A_		:						List[Any]	    =   out_indices
		A_		:						List[Any]	    =   pool_scales
		# auxiliary head attributes (semantic segmentation)
		A_		:						List[str]	    =   use_auxiliary_head
		A_		:						Dict	    =   auxiliary_loss_weight
		A_		:						List[str]	    =   auxiliary_channels
		A_		:						Optional[int]	    =   auxiliary_num_convs
		A_		:						Any	    =   auxiliary_concat_input
		A_		:						Union[str, Any]	    =   semantic_loss_ignore_index






class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Any    =				version.parse("""1.11"""						)





	@property
	def    lowerCamelCase(self							):
		return OrderedDict(
		    [
		        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
		    ]							)





	@property
	def    lowerCamelCase(self							):
		return 1e-4









"""simple docstring"""
import copy

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		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
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





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





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





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

